Theory Of Technology Research Articles

Graph theory approaches for molecular dynamics simulations.

Graph theory, a branch of mathematics that focuses on the study of graphs (networks of nodes and edges), provides a robust framework for analysing the structural and functional properties of biomolecules. By leveraging molecular dynamics (MD) simulations, atoms or groups of atoms can be represented as nodes, while their dynamic interactions are depicted as edges. This network-based approach facilitates the characterization of properties such as connectivity, centrality, and modularity, which are essential for understanding the behaviour of molecular systems. This review details the application and development of graph theory-based models in studying biomolecular systems. We introduce key concepts in graph theory and demonstrate their practical applications, illustrating how innovative graph theory approaches can be employed to design biomolecular systems with enhanced functionality. Specifically, we explore the integration of graph theoretical methods with MD simulations to gain deeper insights into complex biological phenomena, such as allosteric regulation, conformational dynamics, and catalytic functions. Ultimately, graph theory has proven to be a powerful tool in the field of molecular dynamics, offering valuable insights into the structural properties, dynamics, and interactions of molecular systems. This review establishes a foundation for using graph theory in molecular design and engineering, highlighting its potential to transform the field and drive advancements in the understanding and manipulation of biomolecular systems.

Study on progressive collapse of overall structure based on numerical simulation method and prediction of structural collapse

This study investigated the structural progressive collapse using finite element (FE) and theoretical methods. Firstly, an FE model was established based on the component method and layered shell element method. The reliability of modeling approaches was evaluated by comparing them with the previous experimental tests by the author. The NIST building was taken as an example to investigate the structural collapse behavior and the failure modes after different component failure scenarios including single column, double columns, multiple columns, and horizontal components based on the alternative path method. The results indicated that the failure of gravity columns and joints more easily triggered the progressive collapse of the structure. After the failure of double moment-resisting frame columns, the structure still demonstrated considerable robustness. The impact of floor fragments played a controlling role in the process of collapse propagation. A method for achieving rapid collapse of structures was proposed based on the structural collapse characteristics. The study found that a demolition strategy based on gravity columns can facilitate the rapid collapse of structures. After comparing the different simplified modeling methods, it was revealed that the 2D models underestimated the anti-collapse performance of structures compared to the 3D models. Under the same scenarios, the predicted structural collapse response of the 2D model was several times greater than that of the 3D model (up to 7 times). Meanwhile, the structural collapse occurred earlier in the 2D model. The anti-collapse performance of structures adopting different design codes also was compared. It was revealed that GSA 2016 has put forward requirements for higher collapse resistance of buildings. In this study, a new calculation method was proposed, namely the Collapse Probability Prediction Method Based on the Randomness of Structural Load (CPPM-RSL). In the CPPM-RSL, load reliability theory was employed to ensure that the structural collapse resistance design fell within the realm of structural engineering theory. Meanwhile, the CPPM-RSL offered greater engineering interpretability compared to previous methods. Additionally, a new simplified mechanical model was proposed to predict the collapse resistance of structures. The new simplified model did not depend on the computational results obtained from the finite element method (FEM) unlike the existing model. At the same time, the simplified method also exhibited high reliability as revealed by comparisons with the FEM. To solve highly nonlinear problems, machine learning (ML) algorithms were established to predict the anti-collapse performance and the collapse probability of structures based on the proposed theoretical model. Meanwhile, the applicability and performance of the multiple ML algorithms in estimating the structural collapse behavior were compared and evaluated. The results indicated that the LightGBM model exhibited stronger robustness and predictive ability.

Optimizing LLM Strategies for Playing Mendikot using Prompt Engineering

This study investigates the integration of a large language model (LLM) enhanced by prompt engineering and game theory to effectively engage in the strategic card game Mendikot. By refining complex prompts and leveraging a tailored visual understanding of game dynamics, we significantly bolster the decision-making prowess of the LLM. Our methodology involved the systematic simplification of game prompts to facilitate deeper learning and faster response times, coupled with the implementation of a visual recognition system to interpret and react to game states dynamically. The results illustrate that the adapted LLM outperforms traditional AI approaches in strategic decision-making tasks, underscoring a substantial improvement in both the accuracy and efficiency of game-play. This research not only demonstrates a viable model for enhancing AI interaction in recreational gaming but also opens avenues for deploying advanced AI strategies in complex strategic environments, offering insights into the broader application of AI in leisure and competitive arenas. The findings suggest that AI can transcend conventional gaming roles, potentially transforming strategic gameplay in digital and physical platforms.

A Critical Analysis of Social Engineering Theory in the Context of Legal Development in Indonesia

The theory of Social Engineering is a legal theory that views law as a tool for social engineering. However, the concept of Social Engineering in the context of legal development in Indonesia faces gaps in its implementation, often clashing with social, economic, political, and cultural realities. This research aims to analyze the concept of Social Engineering, as proposed by Roscoe Pound, within the framework of modern law. It also seeks to evaluate the relevance of this theory to legal development in Indonesia. This study adopts a normative juridical method with a conceptual approach. The data used is secondary in nature, derived from primary, secondary, and tertiary legal sources. Data collection is conducted through a literature review, while data analysis is performed qualitatively. Roscoe Pound's Social Engineering theory views law as a tool to create a balance of interests within society, aiming to meet its ever-evolving needs. In a modern context, this theory aligns with an approach that considers the impact of legal policies on social welfare. In Indonesia, the application of this theory is evident in legal reforms focused on social justice and public welfare. However, challenges such as weak law enforcement and discrepancies between policies and social realities need to be addressed in order to achieve effective legal development. Keywords: Theory; Law; Social Engineering.

Hyers-Ulam Stability of Fifth Order Linear Differential Equations

In this paper, we study the Hyers-Ulam stability for the fifth-order linear differential equation. In particular, we treat $\varsigma$ as an arrangement of differential equation and in the form%\begin{equation*}$$\varsigma^{v}(x)+\eta_1\varsigma^{iv}(x)+\eta_2\varsigma^{'''}(x)+\eta_3\varsigma^{''}(x)+\eta_4\varsigma^{'}(x)+\eta_5\varsigma(x)=\Omega(x)$$%\end{equation*}where $\varsigma \in c^{5} [k, l]$, $\Omega \in [k, l]$. We demonstrate that$\varsigma^{v}(x)+\eta_1\varsigma^{iv}(x)+\eta_2\varsigma^{'''}(x)+\eta_3\varsigma^{''}(x)+\eta_4\varsigma^{'}(x)+\eta_5\varsigma(x)=\Omega(x)$ has the Hyers-Ulam stability. Two illustrative examples are given to represent the effectiveness of the proposed method. Fifth-order linear differential equations find applications in a wide range of fields, from engineering and control theory to physics, biology, and beyond. These equations are powerful tools for modeling systems with complex dynamics that involve multiple interacting forces or rates of change. Understanding and analyzing their stability and behavior can lead to significant advancements in the design, control, and optimization of these systems.

Research of the distribution symmetrical components in an electrical system with a diesel generator at an unsymmetrical load

Purpose. Investigation symmetrical components of current and voltage in microgrid elements with an autonomous synchronous generator of limited power, which works in parallel with a network transformer under the condition of non-metric load. Methodology. During the research, visual programming methods were used in the Simulink/Matlab package, methods of electrical engineering theory in terms of determining symmetrical component currents and voltages in observation branches, methods of synthesizing schemes for replacing elements of electric power systems for symmetrical component currents, methods of data processing and construction diagrams using specialized software tools.. Findings. As a result the analysis known studies, it was established that under the condition of researching low-power generating systems in conditions of asymmetry, insufficient attention is paid to the nature of the asymmetric load. Using the well-known structure of a microgrid, which includes a synchronous generator, a machine load, and an external network transformer, its model was built, in which, by introducing asymmetric loads of various types, a series of experiments was implemented with the registration of voltages and currents of forward, reverse, and zero sequences in the branches of the network. The analysis of the results obtained during the research established that the positive sequence voltage in the case of a change in the active load does not change its level, under the same circumstances, in the case of an active-capacitive and active-inductive load, the voltage change has the opposite character. In the case of increasing asymmetry of an active-inductive nature, the positive sequence voltage decreases, and in the case of an active-capacitive one, it increases. The specified changes in some cases exceed 3%. Positive sequence currents in the case of active load imbalance differ slightly. In the case of a positive sequence current of the transformer, its continuous growth is observed, which in the case of active-capacitive and active-inductive loads exceeds the active one by an average of 1.5 times. Negative sequence currents in the studied model, which are formed by an asymmetrical load, are completely closed to the transformer, causing zero-sequence voltages, and the nature asymmetrical load significantly affects the value of these currents, a 61% increase was noted. Originality. It was established that in a microgrid, which includes a synchronous generator, a machine load, a symmetrical active load of comparable power to the generator, an external network transformer, in addition to the fact of asymmetry caused by an asymmetrical load, the character of an asymmetrical load (active, active-inductive or active-capacitive). Practical value. In the case of using in microgrids devices that provide an increase in the quality of electrical energy, for example, symmetrical devices, it is necessary, in addition to the asymmetry factor, to take into account the natural load that causes the latter.

Digital deception: generative artificial intelligence in social engineering and phishing

The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.

An integrated systematic method for interlaced unmanned spatial systems (IUSS) design process

Unmanned Aerial Systems (UAS) have garnered significant interest in recent years. These systems, commonly consisting of one or more Unmanned Aerial Vehicles (UAV) and satellite systems, have been extensively used to enhance the effectiveness of various SOSs, such as disaster management and relief efforts. In addition, the inaugural Mars unmanned helicopter, Ingenuity (Ginny), successfully took flight in April 2021, marking the beginning of the utilization of these systems on Mars. This research aims to build an integrated approach for developing Interlaced Unmanned Spatial Systems, considering the high level of precision required for space systems. This study aims to set up and optimize all parts of the proposed architecture for the design of IUSS, using Model-Based Systems Engineering theories and Dependency Structural Matrix foundations. This research introduces a comprehensive coherence architecture that considers all design domains, including the design process, design office, products, and requirements. Additionally, a design workflow model is provided.

Integrating theory and practice in value engineering within Egypt’s construction industry

Egypt’s economy is unstable, marked by high unemployment, low incomes, and safety concerns, which impede its rapid development. One crucial sector that faces difficulties is construction, dealing with issues like funding shortages, payment disputes, and ineffective project management. Conventional project success criteria strongly emphasize efficient resource use and waste minimization. These criteria are symbolized by the iron triangle of cost, time, and quality. A key instrument for obtaining value for money in building projects is value engineering (VE). Even though VE is acknowledged in theory, its application is still limited in most developing countries, including the Egyptian construction industry. Therefore, to bridge the gap between theory and practice, this paper attempts to investigate the current state and application of VE in Egypt. The study further outlines strategies for integrating VE into construction practices and identifies obstacles to their implementation. This study employed both qualitative and quantitative research approaches to attain the research aim, beginning with a thorough literature review to assess the application of value engineering (VE) in both local and international construction industries. Then, a web-based survey was conducted to gather diverse perspectives on VE’s role and its current practice in Egyptian construction projects. The gathered data were analyzed mainly using descriptive and thematic analysis approaches. The paper’s findings revealed that there is a large gap between VE in theory and practice in Egypt. Reasons include lack of awareness and understanding, time and financial constraints, and resistance to change. Bridging the gap to enhance VE in Egypt includes a focus on the role of educational institutions and training, public awareness and recognition, and early project lifecycle integration. This research has gained insights about VE environment, process, duration, types of projects applied, and effectiveness, aiming to get a comprehensive overview of VE in Egypt. Furthermore, this research has identified a significant gap between VE theory and practice in Egypt. To bridge this gap and enhance the implementation of VE in Egypt, strategies such as focusing on the role of educational institutions, increasing public awareness and recognition, and integrating VE approach early in the project lifecycle are suggested. These findings will guide construction industry practitioners to deeply understand the current state of VE in Egypt and practically integrate VE in the construction industry to minimize unnecessary costs, enhance functionality requirements, and achieve value for client’s money.

Analytical calculation method for development dynamics of water-flooding reservoir considering rock and fluid compressibility

The existing development dynamics calculation methods for water-flooding reservoir are difficult to comprehensively consider factors such as injection-production imbalance, irregular well pattern, reservoir heterogeneity and compressibility. Therefore, leveraging reservoir engineering and material balance theory, and considering the influence of reservoir compressibility on reservoir development dynamics, a set of analytical calculation method for development dynamics of water-flooding reservoir considering rock and fluid compressibility water-flooding with clear theories and high computational efficiency were derived and constructed. Based on the oil-water two-phase Darcy law, the reservoir compression coefficient is introduced, and the development dynamics calculation method of a single injection-production unit considering compressibility is derived and established by combining the material balance theory, which can quantitatively describe the relationship between the oil and water production rates per unit. On this basis, the injection-production splitting strategy is introduced, which is extended to the calculation of development dynamics for multiple well groups in the whole reservoir. In this study, seven working conditions with various injection-production ratios were designed to validate the reliability of the new method. Subsequently, the method was applied to actual block for further verification, and the prediction accuracies of oil and water production rates were improved by 67.48% and 19.07%, respectively.

Islamizing Environmental Law in Indonesia; Rethinking the Green Fatwa

The enforcement of environmental law in Indonesia is inseparable from the existence of laws regulating the environment. However, the said law and its enforcement fail on a large scale to ask people to participate positively in environmental protection and conservation. The failure to apply and enforce the law has inspired many activisms to look at Islamic principles as the potential value to help the protection of the environment. In Indonesia, green fatwas have also been enacted to help address the issues. While many articles and academic works begin to propose Islamic environmentalism, this article goes even further by proposing the Islamization of environmental legislation. As a doctrinal study, this article relies on legal material related to the topic of green fatwas and environmental law. To sharpen the analysis, this article also employs social engineering theory. This article demonstrates dualism between the green fatwas and environmental law has resulted in the failure to attract people’s compliance. Therefore, the article recommends that assimilation between the green fatwas and the environmental law be made in the future politics of law concerning environmental law.

Design and implementation of a novel didactic strategy using learning styles for teaching control theory

This paper explains the design and implementation of a novel didactic strategy based on learning styles for teaching automatic control theory in engineering. Traditional research on engineering education has worked to develop teaching methodologies, educational resources, and assessment tasks without considering the learning styles of the students. However, it is important to consider the learning styles of students to increase the number of useful tools that aid students in understanding abstract ideas and difficult concepts. To demonstrate that it is possible to design excellent courses of automatic control by using class activities concerning the learning styles of students. The proposal includes knowing the learning styles of students that enable professors to design appropriate didactic strategies based on the student needs. The proposed didactic strategy is appropriate and enhances the understanding of difficult concepts related to automatic control.

Architecture Preliminary Design and Trade-Off Optimization of Stratospheric Airship Based on MBSE

System architecture design is crucial for forward design in aerostat system engineering, yet a comprehensive research framework has been lacking. This paper presents a new method for stratospheric airship architecture preliminary design and optimization trade-off, grounded in Model-Based Systems Engineering (MBSE) theory. Firstly, a requirement analysis for a stratospheric airship is conducted using SysML, leading to the analysis and acquisition of the airship’s mission architecture design. Additionally, a multidisciplinary coupling simulation platform is developed with MATLAB, and the architecture preliminary design’s Pareto front is derived using the NSGA-II algorithm. Finally, based on the Pareto optimization set, the TOPSIS algorithm is applied to derive the optimal architecture preliminary design scheme for the airship. The optimization results validate the accuracy of the architecture preliminary design obtained from the requirement analysis, the reliability of the multidisciplinary coupling simulation platform, and the feasibility of the optimization algorithms. This comprehensive study spans the requirement analysis to the optimal architecture scheme, providing theoretical reference and design guidance for the forward design of airship systems engineering.

Maximum pitting corrosion depth prediction of buried pipeline based on theory-guided machine learning

Buried pipelines are crucial for the transportation of oil and natural gas resources. However, pipeline failure accidents have frequently occurred due to corrosion. Therefore, an accurate corrosion depth prediction model is necessary for the reliable supply of energy. In this paper, a theory-guided machine learning (ML) model is developed for maximum pitting corrosion depth prediction, the engineering theory and domain knowledge are integrated into feature space to improve the model interpretability. Firstly, several new feature variables are constructed based on the interactions between independent variables. Then, feature importance of all feature variables is obtained using random forest (RF). A hybrid multi-objective grey wolf optimization (HMOGWO) is proposed to optimize the hyper-parameters of RF model, considering feature number, prediction accuracy, and stability simultaneously. Finally, a comprehensive pitting corrosion dataset is utilized for performance evaluation. The results indicate that the proposed theory-guided model can achieve high prediction accuracy and stability, the optimal feature subset can be determined using multi-objective optimization method simultaneously, which solves the problems of model interpretability and feature selection for traditional ML models with the single-objective optimizer. This study is of great significance to the transportation safety of buried pipelines.

Knowledge Management: Any changes in the Research Agenda?

The concept of knowledge management formed at the end of the 20th century. At the same time, the vector of this concept is evolving due to the emergence of new business models, technologies, and the recognition of the “soft” components of success or the so-called “soft skills”, and changes in the strategies of organizations.The article is a systematic scientific review, the purpose of which is to identify the mainstream in the field of economics and knowledge management. The methodology of the article is based on a set of approaches, including the knowledgebased theory of the firm, the concepts of knowledge engineering, organizational learning, dynamic capabilities and knowledge transformation, etc., and the research methodology is based on taxonomic and cluster analysis of the most cited English-language publications using tools embedded in the VOSviewer software product. The information base consisted of articles for 2018–2024, selected according to the research topic and keywords “Knowledge Management”, and placed in the Dimensions database. As a result, we identified 6 clusters and formalized the directions of modern research in the field of knowledge management. In each cluster, a content analysis of the most cited articles was carried out. The research agenda is currently focused on two main issues: the impact of digitalization and digital technologies on the choice, generation and dissemination of knowledge in an organization; the impact of knowledge on innovation as a mediator of business performance, including its environmental and social aspects. In fact, a consensus has been found in understanding the basic postulates of the concept of knowledge management. However, the problem of finding the “good” metrics for assessing the knowledge of an organization and the impact of their level on business performance remains relevant. It is shown that among the theoretical frameworks in knowledge management, the most popular are knowledge engineering and knowledge-based theory of the firm. The results of the study will be useful to managers-practitioners and researchers in the development of effective knowledge management systems.

СИНТЕЗ ЭЛЕКТРОПРИВОДА КОМПРЕССОРНОЙ УСТАНОВКИ ЭЛЕКТРОВОЗА

The study objective is to improve the reliability and energy efficiency of the electric locomotive compressor drive. To achieve this, the required power of the electric motor was calculated the performance of the compressor unit, the volume of the main tanks and the selected electric motor were checked to start and adjust the capacity at low supply voltage. The task to which the paper is devoted is to design an electric drive for a compressor motor. A vector model of an asynchronous motor was made. The process of direct start-up and starting an asynchronous electric motor with the use of a scalar control system and IR compensation were modeled. Research methods: systematic approach; provisions of the theory of automatic control and electrical engineering; numerical PC simulation using Matlab software package. The novelty of the work is in the use of a frequency-controlled electric drive to start the asynchronous electric motor of the compressor unit instead of the normally installed starting capacitors. This design solution will significantly reduce the amplitude of the starting currents, which in turn will reduce the wear of the winding insulation. The study results are presented, based on the results of the constructed models, as graphs of the dependencies of speed and current during direct start of the electric motor and with the control system. Conclusions: the scalar control system, thanks to the introduction of an intensity setter, as well as IR compensation diagram, allow extending the transition time and controlling the value of the inrush current in acceptable values. Such an approach to make an electric drive compressor motor is able to significantly increase its reliability and service life, as well as to increase the overall energy efficiency of the system.

Circularity in product engineering – towards a forward-looking approach across product generations

AbstractTo enable a truly circular economy in product engineering, the development of products in generations must be considered. Thus, we strive to enable a forward-looking circularity approach, proposing the integration of system generation engineering (SGE) with circular economy principles. By analysing the qualitative interrelations among product generations driven by distinct value preservation strategies on various value creation tiers ("R-strategies"), we extend prior SGE research to advance model theory and support practical application of circular product engineering.

Аналіз точності самоналаштування динамічної моделі газотурбінного двигуна

One of the most promising directions in the development of aircraft engines is related to the introduction of adaptive automatic control systems (ACS). The defining element of these systems are dynamic mathematical models of engines capable of self-adjustment based on engine operating parameters measured in flight. A number of leading researchers have developed a concept of using such models called STORM (Self-Tuned On-board Real-time Model). However, in the corresponding works, clearly insufficient attention is paid to solving the problem of checking the sufficiency of the information used to ensure the necessary accuracy of the models. This check must be performed a priori (to predict the composition of engine operating modes, and the volume of registered information), as well as posteriori. The subject of this study is the process of forming dynamic mathematical models (MM) of gas turbine engines using real data for the subsequent use of these models to solve problems related to the control and diagnostics of on-board systems. The goal of this study is to determine the dependence of estimation errors of dynamic parameters of mathematical models on influencing factors. Tasks considered in the work: forming the structure of a mathematical model, dividing the identification process into stages according to the structure of the model (estimating the parameters that determine the static and dynamic properties of the object), forming the least-square functional for the assessment task, determining the errors in estimating dynamic coefficients, analyzing influencing factors, and determining dependencies between factors and errors. For this purpose, the methods of the theory of air-jet engines, the theory of linear dynamic systems, and statistical evaluation are used. The following results were obtained: a mathematical model of a turboshaft engine with a reciprocating gas generator was formed, and ratios were obtained that allow determining the errors in estimating the time constant of a reciprocating engine or gas generator. Scientific and practical innovation: for the first time, a ratio was obtained that determines the errors in estimating the time constant based on the specified values of the measurement errors, the intensity of the jump-like change in fuel consumption, and the frequency and duration of observation. These relations are presented in dimensionless coordinates, which makes them universal and able to be applied to any single-stroke turbojet engine or single-stroke gas generator during a priori or a posteriori analysis of results, as well as planning experiments and debugging on-board self-tuning algorithms of models.

A strong hydrogen bond bridging interface based on tannic acid for improving the performance of high-filled bamboo fibers/poly (butylene succinate-co-butylene adipate) (PBSA)biocomposites

Natural plant fiber-reinforced bio-based polymer composites are widely attracting attention because of their economical, readily available, low carbon, and biodegradable, and showing promise in gradually replacing petroleum-based composites. Nevertheless, the fragile interfacial bonding between fiber and substrate hinders the progression of low-cost and abundant sustainable high-performance biocomposites. In this paper, a novel high-performance sustainable biocomposite was built by introducing a high density strong hydrogen-bonded bridging interface based on tannic acid (TA) between bamboo fibers (BFs) and PBSA. Through comprehensive analysis, this strategy endowed the biocomposites with better mechanical properties, thermal stability, dynamic thermo-mechanical properties and water resistance. The optimum performance of the composites was achieved when the TA concentration was 2 g/L. Tensile strength as well as modulus, flexural strength as well as modulus, and impact strength improved by 22 %, 10 %, 15 %, 35 %, and 25 % respectively. Additionally, the initial degradation temperature(Tonset) and maximum degradation temperature(Tmax) increased by 12.07 °C and 14.8 °C respectively. The maximum storage modulus(E'), room temperature E', and loss modulus(E")elevated by 199 %, 75 %, and 181 % respectively. Moreover, the water absorption rate decreased by 59 %. The strong hydrogen-bonded bridging interface serves as a novel model and theory for biocomposite interface engineering. At the same time, it offers a promising future for the development of high performance sustainable biocomposites with low cost and abundant biomass resources and contributes to their wide application in aerospace, automotive, biomedical and other field.

Application of Machine Learning for Productivity Prediction in Tight Gas Reservoirs

Accurate well productivity prediction plays a significant role in formulating reservoir development plans. However, traditional well productivity prediction methods lack accuracy in tight gas reservoirs; therefore, this paper quantitatively evaluates the correlations between absolute open flow and the critical parameters for Linxing tight gas reservoirs through statistical analysis. Dominant control factors are obtained by considering reservoir engineering theories, and a novel machine learning-based well productivity prediction method is proposed for tight gas reservoirs. The adaptability of the productivity prediction model is assessed through machine learning and field data analysis. Combined with the typical decline curve analysis, the estimated ultimate recovery (EUR) of a single well in the tight gas reservoir is forecasted in an appropriate range. The results of the study include 10 parameters (such as gas saturation) identified as the dominant controlling factors for well productivity and geological factors that impact the productivity in this area compared to fracturing parameters. According to the prediction results of the three models, the R2 of Support Vector Regression (SVR), Back Propagation (BP), and Random Forest (RF) models are 0.72, 0.87, and 0.91, respectively. The results indicate that RF has a more accurate prediction. In addition, the RF model is more suitable for medium and high-production wells based on the actual field data. Based on this model, it is verified that the productivity of low-producing wells is affected by water production. This study confirms the model’s reliability and application value by predicting recoverable reserves for a single well.