Developed Model Research Articles

The Padlet Media Design Based on Bima Local Literature to Improve Student Writing Skills

This research aims to improve students' writing skills through the use of Padlet media which is integrated with local wisdom of Bima regional literature. The background of this research is based on the low writing ability of students caused by monotonous traditional learning methods and the lack of use of interactive media. This research uses the Research and Development (R&D) method with the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) development model. Local literature, such as folklore and poetry, is integrated into Padlet's platform as teaching materials to provide relevant cultural context for students. The participants in this study are 30 students of the Indonesian Language and Literature Education Study Program at STKIP Harapan Bima. The results of the study showed a significant improvement in students' writing skills after the use of Padlet based on local wisdom. The average writing score of students increased by 25%, especially in the aspects of cohesion, coherence, and idea development. In addition, students also reported increased motivation and active involvement in writing learning through Padlet's collaborative features, such as writing sharing and feedback. However, there are several technical obstacles faced, such as limited internet access and lack of digital literacy among students. The results of this study have implications that the integration of digital technology and local wisdom can be an innovative strategy in improving students' writing skills, as well as fostering their cultural awareness.

Teacher competency development model of higher vocational schools in Shandong province, China

Teacher competency is fundamental to successful vocational education reforms and quality enhancement. The rapid economic transformation and industrial upgrading in Shandong Province require highly skilled talents, prompting the implementation of modern vocational education (MVE) reform. However, the current competency of vocational teachers (VTs) often falls short of MVE standards, and there is an urgent need for a development model suitable for regional characteristics as a guide. In this study, we aim to assess the current state of VT competency development in Shandong Province and propose an integrated competency development model tailored to its specific needs. Our research employed a mixed-methods design, incorporating a cross-sectional survey of 280 VTs and 140 administrators and semi-structured interviews with management personnel. We conducted the exploratory factor analysis (EFA) on a comprehensive survey questionnaire and identified six key determinants of VT competency development. Based on these findings and expert interviews, we developed an integrated competency development model, comprising the teacher’s personal qualities and educational philosophy, educational technology competence, digital teaching competence, social policy, professional identity, and school culture and support. This model offers practical implications for designing effective teacher training programs and establishing supportive institutional environments, ultimately aiming to enhance the quality of vocational education through improved teacher competency.

Mitigating anticipated medicine shortages through inter-facility medicine sharing

This study develops an optimisation model to facilitate inter-facility medicine sharing in response to anticipated medicine shortages. These facilities include hospitals and medical representatives. We adopt the concept of collective response proposed in our study literature. The optimisation model is developed according to the real-world practices of inter-facility medicine sharing. We utilise case studies of particular healthcare networks to demonstrate the efficacy of the developed model. The efficacy encompasses the model’s application to real-world case studies, as well as its validity and reliability within a specific system. The results show that the developed model is able to determine which facilities should share the requested amount of medicines; and to reduce total lead times by at least one day compared to the ones obtained in the current practice. The model can be used as a decision-support tool for healthcare practitioners when responding to shortages. The study presents the managerial implications of medicine sharing at the network level and supports the development of collaboration amongst facilities in response to medicine shortages.

Blended Learning for Skill Development in EFL Teacher Preparation

Background: Blended learning, which integrates online and face-to-face instruction, is increasingly essential in teacher education, especially for English as a Foreign Language (EFL). Gap: Research is limited on its specific effectiveness in building EFL pedagogical skills. Aim: This study examines whether blended learning better supports teaching success for EFL prospective teachers than traditional methods. Results: Blended and face-to-face methods equally boost educational success, but the blended approach better prepares future teaching skills. Novelty: This study reveals blended learning's unique value in EFL teacher preparation. Implications: Institutions should adopt blended learning to enhance adaptive teaching skills for modern classrooms. Highlights: Blended learning enhances teaching skills by combining online and face-to-face methods. This approach uniquely prepares EFL teachers for modern, adaptive classrooms. Institutions should integrate blended models for effective teacher development. Keywords: Blended Learning, EFL Teacher Training, Pedagogical Skills, Educational Success, Hybrid Teaching

Neurodevelopmental Process Monitoring of Cytosine Arabinoside-Exposed Neurons Using Raman Spectroscopy.

Raman spectroscopy is used to monitor the development of live neurons exposed to cytosine arabinoside (ara-C). Ara-C is widely used to culture neurons and exclude non-neuronal cells. In this study, Raman spectra obtained from neurons exposed to ara-C were plotted using an analytical model of neuronal development to evaluate the impact of ara-C on neuronal development. After two days of culturing, neurons were exposed to ara-C for 24 h at final concentrations of 0 (control), 5, and 25 μM. Principal component analysis (PCA) was performed to build an analytical model for evaluating neurodevelopmental disorders caused by ara-C treatment. We projected the Raman spectra obtained from ara-C-treated cells onto the control group dataset. The distribution of PC1 scores for neurons exposed to ara-C at a final concentration of 5 μM was not significantly different from that of the control group. In contrast, under a final concentration of 25 μM, the data population at 10 and 15 days of culturing overlapped significantly with that of neurons at 4 days of normal culturing. These results suggest that Raman spectroscopy can detect very small physiological alterations in the neurons even after a short-term exposure (24 h) of ara-C. Our analytical method has high potential to evaluate the developmental stages for living neurons under exposure to chemicals.

Multiscale, mechanistic model of Rheumatoid Arthritis to enable decision making in late stage drug development

Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disease that affects about 0.1% to 2% of the population worldwide. Despite the development of several novel therapies, there is only limited benefit for many patients. Thus, there is room for new approaches to improve response to therapy, including designing better trials e.g., by identifying subpopulations that can benefit from specific classes of therapy and enabling reverse translation by analyzing completed clinical trials. We have developed an open-source, mechanistic multi-scale model of RA, which captures the interactions of key immune cells and mediators in an inflamed joint. The model consists of a treatment-naive Virtual Population (Vpop) that responds appropriately (i.e. as reported in clinical trials) to standard-of-care treatment options—Methotrexate (MTX) and Adalimumab (ADA, anti-TNF-α) and an MTX inadequate responder sub-population that responds appropriately to Tocilizumab (TCZ, anti-IL-6R) therapy. The clinical read-outs of interest are the American College of Rheumatology score (ACR score) and Disease Activity Score (DAS28-CRP), which is modeled to be dependent on the physiological variables in the model. Further, we have validated the Vpop by predicting the therapy response of TCZ on ADA Non-responders. This paper aims to share our approach, equations, and code to enable community evaluation and greater adoption of mechanistic models in drug development for autoimmune diseases.

New Directions and Development Models for College Student Education based on Machine Learning Classification Algorithms

For the contemporary student teaching system, the information-based education management system has played a positive role in the rapid promotion of teaching. In order to get the teaching exploration data of modern information management from the data increment on the Internet, this paper updates the technology of data mining. This paper constructs a quantitative table for teacher positions and conducts ideological and political job evaluations in the database. This paper mainly focuses on the management and construction of ideological informatization based on the [Formula: see text]-means algorithm. By using the [Formula: see text]-means algorithm to analyze the daily behavior data of students, we can discover their behavior patterns and trends, providing decision support for student management. For example, by analyzing data such as students’ internet time, social activities, and library borrowing records, we can understand their interests, hobbies, and learning habits, providing a foundation for personalized education. Cluster analysis of students’ ideological and political performance using the [Formula: see text]-means algorithm can identify student groups with different ideological tendencies. This helps schools to carry out targeted ideological and political education work, and improve the pertinence and effectiveness of ideological and political education. Through quality tracking analysis of educational evaluation, it has carried out educational informatization tracking management using an information model. In the process of judging and analyzing different subjects, it utilized the hot topics of universities for a more comprehensive information intelligent tracking management.

Exploring Investment And Economic Growth In Global Prospective: A Technology Based Development Model

Exploring Investment And Economic Growth In Global Prospective: A Technology Based Development Model

Mathematical Modeling of Biogas Production in Sanitary Landfills

Objective: The objective of this study is to evaluate the biogas production potential in sanitary landfills using the Verhulst mathematical model, which is employed to represent natural phenomena constrained by a saturation point. Theoretical Framework: It was based on references on energy use from waste, possible conversion routes, in addition to mathematical models that estimate the energy potential for biogas generation. Method: The methodology adopted for this research involves the use of data collected in an experimental cell, subsequently applying the De Verhulst model through a regression with minimization of errors using the Least Squares Method (LSM). Results and Discussion: The results indicated that the developed model correlates well with the observed data, demonstrating its suitability as a predictive model for biogas production. Research Implications: The research contributes to the construction of a theoretical framework for the adaptation, elaboration and use of mathematical models to predict the energy potential in the generation of biogas in landfills. Originality/Value: This study contributes to the literature by using a population growth model (Verhulst) to predict landfill biogas production.

PENGEMBANGAN MICROLEARNING VIDEO 360° PADA FENOMENA EFEK FOTOLISTRIK

This research aims to develop a 360° microlearning video in a modern physics course on the phenomenon of the photoelectric effect. The research uses the R&D method with the ASSURE development model which consists of (1) Analyze Learners, (2) State Standars and Objectives, (3) Select Methods, Media and Materials, (4) Utilize Technology, Media, and Materials, (5) Require Learner Participation, (6) Evaluate and Revise. However, in this study, it was carried out only up to the Utilize Technology, Media, and Materials stage. Data collection was carried out using instruments in the form of a questionnaire of initial abilities, student characteristics, interviews, and documentation to students of physics education and physics study programs at the State University of Jakarta. The results of this development research were carried out product feasibility testing by experts, namely media experts obtained a percentage achievement of 87.5% with a very feasible interpretation and material experts obtained a percentage achievement of 93.8% with a very feasible interpretation. The results of the feasibility test by experts show that the 360° video microlearning developed is very feasible to be tested on students. The final product of this research is a 360° video as a medium in accordance with the predetermined learning design. This product is published through the YouTube platform to be easily accessed by students.

Strategy of Development of Sustainable Red Chili Agribusiness Areas in North Sumatra Province

The development of sustainable red chili agribusiness areas in North Sumatra Province is a challenge that involves various aspects, including cultivation technology, farmer institutions, supply chain efficiency, government policy support, and environmental sustainability. This study aims to identify and formulate a sustainable red chili agribusiness development strategy that can increase farmers' productivity and welfare without sacrificing the environment. Through a qualitative approach, data were collected from in-depth interviews with farmers, agribusiness stakeholders, as well as a review of government policies related to sustainable agriculture. The results show that the application of precision agriculture technology, such as drip irrigation systems and integrated pest management, can significantly increase productivity while preserving natural resources. Strengthening farmer institutions, such as cooperatives, is also very important in coordinating production, improving market access, and stabilizing farmers' income. In addition, supply chain efficiency through the construction of storage and distribution centers will reduce post-harvest losses and price fluctuations, while government policy support is needed in the provision of agricultural infrastructure and stable price regulation. In conclusion, the sustainable development model of red chili agribusiness areas must include synergies between environmentally friendly technologies, strong institutions, efficient supply chains, and proactive policy support. With this model, red chili agribusiness areas in North Sumatra can develop sustainably, contribute to food security, and improve farmers' welfare.

MODEL OF MAXIMAL WEIGHTS INVERSE CHAINS FOR THE ANALYSIS OF THE INFLUENCE FACTORS OF THE SOFTWARE COMPLEXES SUPPORT

Context. The problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of multi-layer perceptron models into the models of subjective perception of the object of software complexes support, as well as the applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode, is considered. The object of research is the influencing factors of support of software complexes. Objective – the goal of the work is to develop a model of inverse chains of maximum weights for the analysis of influencing factors of the software complexes support. Method. A model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support was developed for the analysis of the influencing factors of the software complexes support. The developed model provides possibility to identify and form feedback chains of maximum weights for the identification and further analysis of influencing factors that are reflected into the results of the object perception (the supported software complex or its support processes), by the relevant subjects of interaction which directly or indirectly interact with it. Results. Results of the resolved applied practical problem of primary monitoring of the frequency manifestation of a given influencing factor in the post-real-time mode have been provided as an example of the applied practical use of the developed model. The output results of the developed models functioning – are the reverse chains of maximum weights. In the future, the results obtained by the developed model are used to solve the applied-scientific problem of identification, formation and restoration of the boundaries of influencing factors, lost as a result of the implementation of the appropriate models of multilayer perceptron inside the models of subjective perception of the software complexes support. So the developed model of maximal weights inverse chains for the analysis of the influence factors of the software complexes support resolves this applied-scientific problem, initially caused by the implementation of the corresponding multilayer perceptron models inside the model of the subjective perception of the object of software complexessupport. The developed model provides the possibility of carrying out a qualitative analysis of the transformation of the input characteristics of the object of support into the output resulting characteristics of its subjective perception. Conclusions. Developed model allows to resolve the described problems. At the same time, the developed model improves the classical understanding of multilayer perceptron artificial neural networks, as it introduces an additional value to the neurons of hidden layers, which (starting from now) are able to perform a completely new role of influencing factors markers, while in the classical understanding of multilayer perceptron artificial neural networks they did not perform any functions other than arithmetic to ensure the possibility of correct learning and functioning of a multilayer perceptron artificial neural networks.

Pengembangan Media Pembelajaran Berbasis Whatsapp dengan Fitur Chatbot untuk Meningkatkan Pemahaman Siswa pada Materi Kebijakan Moneter dan Fiskal Kelas XI SMAS Islam Nurul Amanah

The purpose of this development research is to produce a whatsapp-based learning media product with a chatbot feature to improve students' understanding of monetary and fiscal policy materials. SMAS Islam Nurul Amanah Class XI is the subject of this study. In this study, a type of development research (Development Research) is used. The development model in this study uses the ADDIE development model. This model consists of five main phases or stages, namely Analysis, Design, Development, Implementation, and Evaluation. In this study, quantitative and qualitative methods are used. The results of the study showed that the whatsapp-based learning media with the chatbot feature was considered quite valid based on the results of a questionnaire filled out by material validators with a score of 70%, media validators with a score of 78%, and obtained a very valid assessment from economic learning practitioners with a score of 87%. Based on the results of validation and field trials, whatsapp-based learning media with a chatbot feature that has been developed by researchers is feasible to be used in the learning process in economics lessons, especially in monetary and fiscal policy material for grade XI of SMAS Islam Nurul Amanah.

IDENTIFICATION AND LOCALIZATION OF VULNERABILITIES IN SMART CONTRACTS USING ATTENTION VECTORS ANALYSIS IN A BERT-BASED MODEL

Context. With the development of blockchain technology and the increasing use of smart contracts, which are automatically executed in blockchain networks, the significance of securing these contracts has become extremely relevant. Traditional code auditing methods often prove ineffective in identifying complex vulnerabilities, which can lead to significant financial losses. For example, the reentrancy vulnerability that led to the DAO attack in 2016 resulted in the loss of 3.6 million ethers and the split of the Ethereum blockchain network. This underscores the necessity for early detection of vulnerabilities. Objective. The objective of this work is to develop and test an innovative approach for identifying and localizing vulnerabilities in smart contracts based on the analysis of attention vectors in a model using BERT architecture. Method. The methodology described includes data preparation and training a transformer-based model for analyzing smart contract code. The proposed attention vector analysis method allows for the precise identification of vulnerable code segments. The use of the CodeBERT model significantly improves the accuracy of vulnerability identification compared to traditional methods. Specifically, three types of vulnerabilities are considered: reentrancy, timestamp dependence, and tx.origin vulnerability. The data is preprocessed, which includes the standardization of variables and the simplification of functions. Results. The developed model demonstrated a high F-score of 95.51%, which significantly exceeds the results of contemporary approaches, such as the BGRU-ATT model with an F-score of 91.41%. The accuracy of the method in the task of localizing reentrancy vulnerabilities was 82%. Conclusions. The experiments conducted confirmed the effectiveness of the proposed solution. Prospects for further research include the integration of more advanced deep learning models, such as GPT-4 or T5, to improve the accuracy and reliability of vulnerability detection, as well as expanding the dataset to cover other smart contract languages, such as Vyper or LLL, to enhance the applicability and efficiency of the model across various blockchain platforms. Thus, the developed CodeBERT-based model demonstrates high results in detecting and localizing vulnerabilities in smart contracts, which opens new opportunities for research in the field of blockchain platform security.

Pengembangan Media Ajar Interaktif Berbantuan Microsoft Powerpoint Menggunakan Add-In Classpoint Pada Mata Pelajaran Komputer Akuntansi Kelas XI AKL

This development research aims to produce an interactive teaching media product assisted by Microsoft Powerpoint using the Classpoint add-in in the Accounting Computer subject class XI AKL. Another goal is to find out the process of developing teaching media, the feasibility of teaching media, and the response of students to the developed teaching media. This study uses a type of R&D (Research and Development) research with the ADDIE development model which consists of 5 stages, namely Analyse, Design, Develop, Implement, and Evaluate. The test subjects of this study are grade XI AKL students from SMK YPM 11 Wonoayu. The results of this study show the results of the material expert test by the Lecturer of the Faculty of Economics and Business and the Teacher of the Accounting Computer subject of SMK YPM 11 Wonoayu which is said to be very feasible with an average percentage of 88.13%, while the results of the media expert test by the Lecturer of the Faculty of Economics and Business are said to be very feasible with an average presentation of 88.89%. The response from students of class XI AKL SMK YPM 11 Wonoayu to the interactive teaching media developed received an average of 97.99% which was included in the category of very understanding. From the average results of the above percentage, it can be said that interactive teaching media assisted by Microsoft Powerpoint using the Classpoint add-in in the Accounting Computer subject class XI AKL is very feasible and can be applied to learning in the classroom

Modelling project activities for future teachers within the innovative infrastructure of pedagogical universities: integrative solutions

Introduction. In the quest to enhance the training of future teachers, particular emphasis is placed on identifying the educational potential of new learning environments within pedagogical universities under the Ministry of Education of Russia. These institutions have undergone infrastructure updates as part of federal programs designed to support education. Aim. The aim of this study is to develop an original model for organising the project activities of student teachers, taking into account the contemporary infrastructural capabilities of pedagogical universities under the Ministry of Education of Russia. Methodology, methods and techniques. The methodological foundation of this research is grounded in the principles of community development pedagogy and the design of evolving educational systems. The integration of integrative, synergetic, environmental, and system-activity approaches facilitated the identification and development of components within the author’s model for organising project activities for future teachers in the context of pedagogical technoparks, such as “Quantorium”, and technoparks focused on universal pedagogical competencies in higher education institutions under the Ministry of Education of the Russian Federation. The validation of the developed model was conducted through expert assessments. Results. The authors developed the model for organising project activities for future teachers within the innovative infrastructure of pedagogical universities. The main components of the model are outlined, including integrative objectives and anticipated outcomes of students’ project activities, interdisciplinary project content, and a technological component that facilitates the transformation of interdisciplinary project content into personally meaningful experiences through the technology of collaborative distributed project activities. The study identifies and justifies the conditions necessary for the effective implementation of the developed model, focusing on the management of processes, resources, and participants involved in interdisciplinary project activities. Scientific novelty. Integrative processes, within the context of this study, are regarded as a pedagogical phenomenon that reflects the interconnections among the objects, processes, and subjects involved in project activities. Practical significance. The research materials can be utilised in the development of programmes aimed at enhancing the infrastructure of pedagogical universities. They can also assist in the refinement of educational curricula for training teaching staff, particularly in disciplines that prepare future educators to effectively organise project-based activities within contemporary educational environments.

ЛЮДСЬКИЙ КАПІТАЛ В КОНЦЕПЦІЇ ВІДКРИТОЇ НАУКИ

Introduction. Any economic system's development is only possible with capital participation. Over several centuries, a stable concept has emerged that considers human capital as an essential source of investment for innovation, scientific and technological progress, and the stable development of the state. At the same time, the practical realization of this concept is associated with such categories as education, science, healthcare, competencies, skills, professional abilities of employees, intellectual output, etc. The purpose of the article: The aim is to comprehensively investigate the innovative potential of human capital from the perspective of a driving force for progress in the knowledge age. Methods: Modern science is based on openness and accessibility as tools of scientific search and research. Human capital is an essential element of this search, expressed in intellectual abilities, self-development, and theoretical and practical generalizations. The basis for more profound research and generalizations is the study of internal and external influences on the formation of human capital and the definition of its role in the scientific sense. Results: Analyzing human capital through the prism of its structural formation, it is worth noting that its meaning has changed in different periods. In the twentieth century, when the economy was formed in the context of industrial development, human capital was focused on technological excellence and ingenuity. In the 21st century, economic development is dominated by the intellectual and scientific component, which is the basis for the functioning of the national system. Therefore, human capital in this period is gaining new meaning as a knowledge economy. In forming the country's national economy, the intangible form of capital is becoming increasingly important, where human resource is the basis of socio-economic development. Conclusions. As a result of the theoretical analysis of existing approaches and new proposals, the innovative economic development model is developing dynamically. Human (intellectual) capital is prioritized in this model, stimulating informatization and introducing modern scientific and technological innovations. Human capital is a complex phenomenon that adapts to modern innovative and technological changes and acts as an organizational motivator for achieving a high level of industrialization and a civilized environment.

Predicting the removal efficiency of titanium dioxide nanoparticles in an activated sludge system using fate modeling depending on the operating conditions

Wastewater treatment plants (WWTPs) are a major discharge pathway for engineered nanoparticles (ENPs) that can potentially impact aquatic systems, making it crucial to understand ENP behavior in these facilities. In this study, we developed a fate model for titanium dioxide (TiO2) nanoparticles (NPs) to predict their removal efficiency (RE) in activated sludge (AS) systems. The model, based on material balance equations for free and sludge-attached TiO2 NPs, incorporated a linear-adsorption partition coefficient (Kd) and pseudo-second-order kinetics for TiO2 NP attachment to the activated sludge. Validated with data from three WWTPs, the model investigated RE correlations with inflow TiO2 NP concentration, sludge retention time (SRT), hydraulic retention time (HRT), mixed liquor suspended solid (MLSS) concentration, and water temperature. The model reasonably predicted the comparative performance of the WWTPs but underestimated the RE by up to ∼29 %, probably due to system simplification, limited Kd data, the presence of other Ti-containing NPs besides TiO2 NPs, and unincorporated removal mechanisms such as sweeping in the secondary clarifier. The model revealed favorable design/operating conditions for increasing the TiO2 NP RE of the AS system: shortening the SRT, increasing the MLSS concentration, and increasing the water temperature. The developed model could be useful for estimating and comparing the expected TiO2 NP REs across different WWTPs under varying operating conditions or for tracing the material flow and fate of TiO2 NPs in environmental compartments involving WWTPs, both regionally and country-wide.

Development and validation of prediction models for prehospital triage of military trauma patients.

The introduction of wireless sensors will enable military care providers to continuously and remotely assess/monitor vital signs. Prediction models are needed to use such data adequately and aid military care providers in their on-scene decision-making to optimise prehospital triage and improve patient outcomes. A prospective cohort comprising data from eight Emergency Medical Services and seven inclusive trauma regions was used to develop and validate prediction models that could aid military care providers in their prehospital triage decisions. Healthy (American Society of Anesthesiologists physical status classification 1 or 2) admitted adult trauma patients (aged ≥16 and ≤50 years), who suffered from a trauma mechanism that could occur to military personnel and were transported by ambulance from the scene of injury to a hospital, were included. A full model strategy was used, including prehospital predictors that are expected to be automaticly collectible by wireless sensors or to be incorporated in a personalised device that could run the models. Models were developed to predict early critical-resource use (ECRU), severe head injury (Abbreviated Injury Scale (AIS) ≥4), serious thoracic injury (AIS ≥3) and severe internal bleeding (>20% blood loss). Model performance was evaluated in terms of discrimination and calibration. Prediction models were developed with data from 4625 patients (80.0%) and validated with data from 1157 patients (20.0%). The models had good to excellent discriminative performance for the predicted outcomes in the validation cohort, with an area under the curve of 0.80 (95% CI 0.76 to 0.84) for ECRU, 0.83 (0.76 to 0.91) for severe head injury, 0.75 (0.70 to 0.80) for serious thoracic injury and 0.85 (0.78 to 0.93) for severe internal bleeding. All models showed satisfactory calibration in the validation cohort. The developed models could reliably predict outcomes in a simulated military trauma population and potentially support prehospital care providers in their triage decisions.

Development of Particulate Matter Concentration Estimation Models for Road Sections Based on Micro-Data

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.