Modeling biological systems with Boolean networks (BNs) is a well-established approach that enables qualitative reasoning about their dynamics, including gene and signaling networks. Several semantics for BNs, i.e., scheduling of component updates, have been proposed that can significantly affect the predicted dynamic behaviors. The synchronous and asynchronous ones are the most popular, but they fail to capture some of the behaviors observed in reality and accounted for by quantitative models. Recently, the most permissive semantics has been introduced, guaranteed not to miss any behavior achievable by a quantitative model following the same logic as the BN, and, in addition, significantly reducing the computational complexity of dynamical analysis. But this time, it appears too permissive and tolerates spurious behaviors in many real situations. After clarifying the relationships among existing semantics, we define the threshold semantics whose dynamic behaviors are all those of a single-threshold network, a subclass of multivalued networks, for any possible threshold map. The spurious behaviors are excluded by the threshold semantics, whose qualitative behaviors constitute a proper abstraction of real biological processes, grounded in activation and inhibition influences regulated by single thresholds whose values are unknown, as is generally the case. We show that threshold semantics is stricter (for reachability between Boolean configurations) than the cuttable extended semantics (called here linear semantics) and stricter than a given constrained version of the most permissive semantics. We then seek to operationalize this threshold semantics. For this, we equip the constrained version of the most permissive semantics with a system of symbolic constraints, attached to any given trajectory and verifiable by a satisfiability solver. The satisfiability of this set of constraints ensures the consistency of the formal threshold conditions associated with the transitions along the trajectory. It defines a new operational semantics at the trajectory level. Then we prove that this semantics is equivalent to the threshold semantics. Finally, we prove that the computational complexity of this semantics is the same as that of classical semantics, i.e., PSPACE, and we state several conjectures for future work.

Accurate assessment of plant-available potassium (K) in soils is crucial for optimizing crop nutrition and enhancing the efficiency of fertilizer use. This study systematically benchmarked ten widely used soil K extractants, Calcium Chloride (CaCl 2 ), Ammonium Acetate (NH 4 OAc), Ammonium Bicarbonate-Diethylenetriaminepentaacetic Acid (AB-DTPA), Morgan's extractant (Morgan), Calcium acetate lactate extractant (Ca-AL), Kelowna extractant (Kelowna), Olsen extractant (Olsen), Modified Kelowna extractant (Kelowna-2), Nitric Acid (HNO₃), and Sodium Tetraphenylborate (NaTPB) to identify the most effective method for quantifying available K and defining critical thresholds for wheat production in Inceptisols. Pot trials were conducted on soils from twenty Inceptisol series in the Gangetic alluvial plains of Eastern India using five K fertilizer rates that simulates the wide K variability in real field situations. Among the tested methods, NaTPB emerged as the most reliable extractant, showing the strongest correlation (R 2 = 0.83, P < 0.05) with Bray’s percent yield (BPY) and a critical K threshold of 1110.3 kg ha −1 . CaCl 2 also demonstrated high accuracy (R² = 0.82). Multivariate analysis revealed that NaTPB-extractable K was significantly influenced by soil clay content and electrical conductivity, which together explained 76.9 % of its variability. Furthermore, NaTPB effectively captured K from multiple pools, including water-soluble, exchangeable, and non-exchangeable pools, providing a more comprehensive index of plant-available K. A critical K concentration of 0.35 % in wheat grain was identified as the threshold for optimal yield, offering a practical benchmark for site-specific K management. By integrating chemical extraction, crop response modeling, and soil property analysis, this research presents a novel and scientifically robust framework for assessing K fertility. With the successful implementation in Eastern India, the findings have benchmarked broader applicability to Inceptisols in other agroecological regions, providing a scalable diagnostic approach for sustainable nutrient management. This study makes a significant contribution to precision agriculture and global efforts to optimize fertilizer recommendations through the development of improved soil testing methodologies. • Soil potassium assessment is vital for optimizing wheat nutrition and fertilizer use. • Lack of clarity exists on which K extractant best predicts wheat yield in Inceptisols. • NaTPB showed the strongest yield correlation (R 2 = 0.83, P < 0.05) and defined critical K thresholds (1110.3 kg ha −1 ). • NaTPB uniquely captures K from all pools, improving extraction reliability. • Future studies can adapt this approach to other Inceptisols for soil nutrient planning.

Newborns, particularly in the first few weeks of life are vulnerable to life-threatening diseases that may progress rapidly when not identified in a timely manner. An early diagnosis and prediction of neonatal diseases are necessary to increase the survival percentages and make correct treatment. However, limited clinical records and class imbalance are major obstacles for disease prediction. In this paper, we propose machine learning-based multi-class classification framework for neonate Diseases prediction. Due to limited availability of real clinical data, a synthetic dataset with 100,000 records including 63 neonatal and maternal parameters was created to mimic real life situations. The dataset was pre-processed in a systematic manner with feature engineering, class balancing, and feature selection based on ANOVA F-test. Different types of machine learning and deep learning models were implemented and compared. Among all models, LightGBM achieved the best accuracy of 79.60% and F1-Score of 78.47% with consistent performance across all disease categories. The study demonstrates that, with careful feature selection, class-imbalance treatment and ensemble modeling, neonatal disease forecasting may significantly improve. This framework provides a strong foundation in the direction of well performing clinical decision support systems to improve neonatal care.

Clinical attachment is a crucial component in the training of health professionals, particularly nursing, midwifery and medical students. It accords students an opportunity to put into practice what they have learned in theory; thus, it involves the integration of theoretical knowledge and practical skills. Having been taught the theory in class enables a student to learn, assimilate and store knowledge for future use and to apply it in the care of patients and clients. It therefore, requires critical thinking, problem-solving abilities, specialised psychomotor, and technological skills, and a professional value system. In other words, classroom teaching goes hand-in-hand with clinical teaching. The aim of clinical teaching in the training of nursing, midwifery and medical students is to produce competent professionals capable of providing expert patient care, which is based on sound knowledge and practiced skills. Clinical teaching is defined as teaching and learning focused on, and usually directly involving patients and their problems. It occurs in real life situations, translates theory into reality and centres around patient care. Hence, it is an integral part of providing patient care that accords a studentwith opportunities for observations and decision making. Patient care becomes a reality, and a student can observe the responses ofpatients to illness and care provided. Since it is a small group activity in which a student is an active participant, each student is given an opportunity to develop self-confidence by performing under expert guidance. Therefore, there is need to have qualified personnel to guide, support, stimulate and facilitate student learning by designing appropriate activities in appropriate settings that allow learning. One of the clinical sites where clinical teaching and learning takes place in Zambia is the Neonatal Unit which consists of the Intensive Care Unit (NICU), Kangaroo Mother Care (KMC), High Care (HC), Special Care Baby Unit (SCBU) also called Standard in-patient care (SIC) unit, where newborns who require special attention are nursed. The NICU is a highly specialised place that caters for the complex needs of small and sick newborns.

Elective courses play an important role in specialized informatics education. Among their features, it is possible to note the opportunity to organize student research activities within the course, for example, for students in socio-economics classes as they become familiar with economic professions. The article describes in detail the elective course "Spreadsheet processor in solving economic problems" which focuses on mastering a range of spreadsheet functions in the "Financial" category. The goals and objectives of the course and its thematic planning are presented. The course content is examined in detail, with an emphasis on practical assignments. Through these assignments, students not only master the spreadsheet's new built-in functions but also learn to effectively solve economic problems related to real-life situations. Methodological developments for several practical assignments are provided, outlining possible research processes. The practical assignments include research-based tasks, which enhance students' understanding of the course material.

The role of professional teachers becomes a key element in creating active and meaningful learning environments amid the dynamic demands of 21st-century education. The shift from teacher-centered to student-centered learning requires teachers not only to deliver content but also to facilitate engaging and participatory learning experiences. This study aims to analyze how professional teachers contribute to fostering student engagement and constructing meaningful learning processes. A qualitative approach with a library research design was employed, utilizing relevant and credible sources such as academic journals, books, and official documents. Data were collected through documentation techniques and analyzed using content analysis, including data reduction, data display, and integrative conclusion drawing. The findings indicate that professional teachers perform multiple roles, including as educators, facilitators, motivators, and learning managers, which directly influence students’ level of participation and depth of understanding. Learning environments that are designed to be active and contextual are shown to enhance critical thinking skills, social interaction, and the ability to connect knowledge with real-life situations. Thus, the effectiveness of learning is not solely determined by the methods applied, but also by the teacher’s ability to integrate professional competencies into classroom practices in a comprehensive and meaningful way.

The article is devoted to modern approaches to teaching applied mathematics in engineering education. Applied mathematics is a fundamental discipline for future engineers, forming essential skills in analysis, modeling, and the solving of complex technical problems. The study aims to analyze contemporary approaches to teaching applied mathematics in engineering education with a professional orientation and to identify methods and technologies that ensure the integration of theoretical mathematical knowledge with practical engineering tasks. The research involved the study of scientific literature and methodological materials on teaching applied mathematics and project-based learning. Project-based and case-study methods were applied to organize student learning through practical tasks and engineering projects. The article examines the principles of professionally oriented education, emphasizing the integration of theoretical knowledge with practical engineering problems. Special attention is given to curriculum design, teaching methods, and the use of project-based learning to enhance student engagement and develop professional competencies. Approaches to organizing lectures, laboratory work, and computational exercises that reflect real engineering situations are described. The study demonstrates that linking mathematical education with professional practice contributes to improving the practical readiness of graduates and supports the continuous improvement of engineering education. The findings can be useful for educators, curriculum developers, and policymakers in organizing the teaching of applied mathematics in higher education institutions with an engineering profile.

Introduction: Preceptorship is a clinical teaching model in nursing that serves as a strategic approach to enhance nursing students' competencies. This model aims to develop students into professional nurses who are knowledgeable, responsible, and capable of improving the quality of nursing services in clinical settings. Objectives: The study aimed to describe the experiences of nursing students during clinical practice using the preceptorship model at a hospital in Surabaya, Indonesia. Methods: This descriptive study involved nursing students in their final academic year. A total population of 81 students was included using probability sampling with a simple random sampling technique. Data were collected through a structured questionnaire and analyzed descriptively. Results: The study found that 77 students (95.1%) actively participated in clinical practice, 62 students (76.5%) were able to manage cases independently, 74 students (91.4%) demonstrated the ability to apply clinical knowledge and skills, and 71 students (87.7%) were able to interact effectively with their preceptors. Conclusions: The preceptorship model enabled students to be fully engaged in learning and healthcare service delivery, increased their confidence in case management, and allowed them to integrate theoretical knowledge into real clinical situations. Positive interaction with preceptors significantly supported students’ development of clinical skills and professional competence.

Abstract: Kenya’s education system has shifted from a content-based curriculum to Competency-Based Education (CBE), previously referred to as CBC. This transition marks a major change in how teaching and learning are approached in schools. Unlike the former system, which mainly emphasized examination performance and content coverage, CBE focuses on what learners are able to do with the knowledge and skills they acquire. It prioritizes the development of competencies such as critical thinking, creativity, communication, collaboration, self-efficacy, and problem-solving. Learning is therefore more practical, learner-centered, and connected to real-life situations. Under CBE, the role of the teacher has also changed significantly. Teachers are no longer viewed only as transmitters of knowledge, but as guides, mentors, and facilitators of learning. They are expected to observe learners closely, support individual growth, and create opportunities for learners to discover and develop their abilities. This approach recognizes that learners are different and progress at different rates, and therefore require guidance that is supportive rather than judgmental. One of the most important responsibilities of teachers—particularly at upper primary level (Grades 4–6)—is guiding learners to identify their interests, strengths, and talents. At this stage, learners begin to show clear preferences in different learning areas such as languages, mathematics, sciences, creative arts, sports, and social activities. Proper guidance at this level helps learners build confidence, develop a positive attitude toward learning, and prepare for informed subject selection at Junior Secondary School (JSS) and future career pathways. Teachers must ensure that learners are exposed to a wide range of learning experiences without prematurely limiting or labeling them. This policy guide is designed to support schools and teachers in fulfilling this critical role effectively. It aims to support teachers during TPD and CEMASTEA training by providing clear guidance aligned with national CBE expectations. It also seeks to simplify CBE guidance for primary school teachers by presenting practical, easy-to-understand strategies that can be applied in everyday classroom situations. In addition, the guide provides a clear, school-based framework that promotes consistency and fairness in learner guidance. Ultimately, it seeks to standardize how subject and pathway guidance is conducted in schools, ensuring that all learners receive equal support and opportunities to discover their potential.

The present paper examines the theoretical foundations for developing the communicative competence of future teachers on the basis of communicative grammar in higher pedagogical education. The relevance of the study is conditioned by the growing need to prepare teachers who are able not only to master grammatical knowledge, but also to use language effectively in pedagogical interaction, academic discourse, and professional communication. The paper argues that grammar should be interpreted not as a closed system of rules to be memorized, but as a functional resource for creating meaning in concrete communicative situations. Within this perspective, communicative competence is understood as an integrative quality combining grammatical accuracy, discourse coherence, sociolinguistic appropriateness, strategic flexibility, and pedagogical sensitivity. The study is based on theoretical analysis, comparison, and synthesis of linguistic, pedagogical, and methodological sources devoted to communicative competence, communicative language teaching, functional grammar, and teacher education. The results demonstrate that communicative grammar provides an effective theoretical basis for the formation of future teachers’ communicative competence because it connects language form with meaning, context, intention, and interaction. The paper identifies the most important conceptual principles of this process, including the competence-based approach, the communicative-activity approach, contextualization of grammatical material, integration of receptive and productive speech activity, and reflection-oriented pedagogical practice. It is concluded that the development of communicative competence in future teachers becomes more effective when grammar instruction is organized as purposeful speech activity linked with professional communication, classroom discourse, and real pedagogical situations.

The problem of analyzing crime has become crucial to law enforcement agencies because of growing urbanization, advanced criminal activities, and the weaknesses of the conventional manualized approaches. Traditional methods are likely to fail when dealing with large volumes of data, slow reaction time, and offensive patterns or risky spots. This paper intends to test and build intelligent crime pattern and hotspots analysis system to facilitate proactive policing and decision-making based on data. The proposed system is an approach based on simulation that involves the integration of machine-learning algorithms and spatial analysis as ways of identifying, classifying, and visualizing crime patterns. The artificial data sets were created to imitate a real-life crime situation and controlled experimentation was possible. The main methods used are clustering which detects patterns (K-Means and DBSCAN), classification models (Random Forest, which predicts types of crime) and Kernel Density Estimation (KDE) (identifies hotspots). The results of the study proved that the system was effectively used to group crime incidences into significant clusters, detect high-density crime localities and offer predictive data that are highly accurate, precise, and recall. Spatial visualizations and heat maps were used to identify the common hotspots of crime, allowing resources to be focused. Conclusively, the smart system is enhanced when compared to the general crime analysis systems since it improves efficiency, accuracy, and actionable data to law enforcement agencies. Combining machine learning and spatial analytics gives it a scalable, data-driven platform that can be used to support proactive policing, optimized resource allocation, and more effective crime prevention strategies, which lead to better community safety.

Currently, valuable information requires security because cyber threats have escalated. The CIA Triad is one of the core concepts in information security which defines three main objectives of any security program: Confidentiality, Integrity, and Availability. Each component of the triad covers a different aspect to ensure proper protection and management of information. Confidentiality ensures that sensitive details are only obtained by authorized personnel or corporations. Maintaining integrity involves ensuring data accuracy and reliability by preventing unauthorized changes. Availability guarantees that information and related systems can be used as needed. This paper presents an innovative hybrid form of security system aiming at selecting the best cryptographic and steganographic techniques. Additionally, the Huffman encoding scheme is employed to increase the embedding capacity of the proposed mechanism. Thus, cryptography and steganography are taken as measures in the field of communication and information security. Cryptography and steganography are two different but connected areas within the broader information security domain. Both methods share some common features in securing information although they differ in their functions and performance characteristics. In this paper, an integrated method that combines Cryptography, compression, steganography, and the InterPlanetary File System has been presented as a basis for information security. Consequently, this system is implemented by using a Python Tkinter module that makes it possible to be used in real-life situations without much difficulty. This application enables its users to encrypt messages, compress them, and hide them inside other files like they were of no significance at all. The interface design ensures that users can move around different functions easily without demanding technological expertise or knowledge. A flexible framework is offered through which sensitive data can be secured against various digital landscapes subjecting to current threats in data security. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records. The novelty of this work lies in the unified implementation of ECC-based key exchange, AES-GCM encryption, Huffman compression, LSB steganography, and IPFS decentralized storage in a real-world deployable GUI framework. Unlike prior studies that address these methods separately, this system integrates them into a streamlined pipeline that enhances embedding efficiency, encryption robustness, and secure data retrieval over decentralized platforms. The holistic approach and practical usability make it distinct from existing security models. In general, this paper demonstrates how cryptography, compression, LSB steganography, and IPFS may be combined, thus showing the practicality and benefits of such a unified approach for safeguarding valuable digital records.

Groundwater contamination by fluoride (F⁻) and arsenic (As) is a serious environmental issue and poses significant human health risks in many developing countries, including Pakistan, particularly in Sindh Province. To investigate a realistic situation of groundwater contamination, a total of 170 groundwater samples were collected and analyzed concerning F- and (As) along with other physicochemical parameters. The concentrations of fluoride (F⁻) and arsenic (As) in groundwater samples ranged from 0.5 to 6.35mg/L and 0.5 to 22µg/L, with mean values of 1.82mg/L and 5.78µg/L, respectively. Hydrochemical facies result show that water type of the groundwater resources of the study area belong to mixed CaNaHCO3 followed by CaHCO3 type while few samples fall into NaCl type. Gibbs diagrams indicate that rock-water interaction controls groundwater hydrochemistry, with saturation indices showing calcite, dolomite, fluorite, and goethite are saturated in the study area. Machine Learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN) and Logistic Regression (LR), were applied and the target variable was F- due to its higher concentration in groundwater samples as compared to As. In ML, models the permutation feature, as well as the mean decrease in impurity (MDI), was used to identify the variables affecting F- in the research region. Among the models, RF achieved the highest accuracy (0.94) and sensitivity (0.97), along with a relatively low error rate (0.06). ANN showed strong performance with an accuracy of 0.92 and sensitivity of 0.88, while LR demonstrated comparatively lower sensitivity (0.81) despite achieving an accuracy of 0.90. The study shows that groundwater in the Dadu Canal Command area is affected by dual contamination, and that integrating hydrogeochemical analysis with machine learning helps identify sources and spatial risk, supporting groundwater management in Sindh.

The development of digital technology has transformed the patterns of music consumption and distribution globally, including in Indonesia. Short-video–based social media platforms such as TikTok have become spaces for creative expression while simultaneously posing serious challenges to the protection of music copyright. The widespread use of unlicensed song clips, remixes, and viral cover versions on TikTok reflects the weakness of digital law enforcement, even though Indonesia normatively possesses a comprehensive legal framework through Law Number 28 of 2014 on Copyright, Law Number 1 of 2024 concerning the Second Amendment to Law Number 11 of 2008 on Electronic Information and Transactions, and other implementing regulations. This research intends to examine Indonesia's regulations regarding music copyright protection on the TikTok platform and to evaluate how well these rules are applied in real situations. The study uses a standard legal approach that includes statutory, conceptual, and literary methods. It involves qualitatively examining primary and secondary legal resources through legal interpretation. The findings indicate that music copyright protection in Indonesia is normatively established and covers moral rights, economic rights, as well as civil, criminal, and administrative sanctions. However, its practical effectiveness remains limited due to reliance on manual reporting mechanisms (notice and takedown), the absence of regulations on automatic detection systems, and weak inter-agency coordination among the Directorate General of Intellectual Property, the Ministry of Communication and Digital Affairs, and the National Collective Management Organization. This study concludes that adaptive legal policy reforms are necessary through the implementation of technological enforcement, integration of national digital systems, and the enhancement of public legal literacy to strengthen copyright protection in the digital era.

Thermodynamics and Statistical Physics is a basic foundational course in undergraduate physics education; however, it is also widely considered as one of the most difficult study contents. Students frequently meet difficulties with abstract concepts, mathematical formalism, and the connection between macroscopic laws and microscopic explanations. Traditional teaching methods usually pay heavy focus on derivation works and regular exercise tasks, which can restrict students’ conceptual comprehension and reduce the link between theoretical knowledge and physical real situations. To solve these existing problems, this research puts forward a problem-based reform model for this course. The reform takes meaningful physical problems as starting points for learning activities, and it combines classroom discussion, concentrated mini-lectures, and case analysis works. Its target is to help students build a more coherent understanding about key concepts like entropy, equilibrium, and thermodynamic processes. This study discusses the theoretical basis, design details, classroom practice process, and wider educational importance of the reform. It argues that problem-based teaching can make Thermodynamics and Statistical Physics have higher engagement degree, more easy to understand, and more related to the cultivation of scientific reasoning ability in higher education.

Salient Object Detection (SOD) aims to identify and segment the most prominent objects in an image. In real open environments, intelligent systems often encounter complex and challenging scenes, such as low-light, rain, snow, etc., which we call constrained conditions. These real situations pose more severe challenges to existing SOD models. However, there is no comprehensive and in-depth exploration of this field at both the data and model levels, and most of them focus on ideal situations or a single condition. To bridge this gap, we launch a new task, Condition-Constrained Salient Object Detection (CSOD), aimed at robustly and accurately locating salient objects in constrained environments. On the one hand, to compensate for the lack of datasets, we construct the first large-scale condition-constrained salient object detection dataset CSOD10 K, comprising 10,000 pixel-level annotated images and over 100 categories of salient objects. This dataset is oriented towards the real environment and includes 8 real-world constrained scenes under 3 main constraint types, making it extremely challenging. On the other hand, we abandon the paradigm of "restoration before detection" and instead introduce a unified end-to-end framework CSSAM that fully explores scene attributes, eliminating the need for additional ground-truth restored images and reducing computational overhead. Specifically, we design a Scene Prior-Guided Adapter (SPGA), which injects scene priors to enable the foundation model to better adapt to downstream constrained scenes. To automatically decode salient objects, we propose a Hybrid Prompt Decoding Strategy (HPDS), which can effectively integrate multiple types of prompts to achieve adaptation to the SOD task. Extensive experiments show that our model significantly outperforms state-of-the-art methods on both the CSOD10 K dataset and existing standard SOD benchmarks.

This study systematically compared social studies items—specifically politics and law, Economics, and Society and Culture in Korea's College Scholastic Ability Test (CSAT), and contemporary society and politics/economics in Japan's Common Test for University Admissions—using Bereday's comparative education methodology and Bloom's taxonomy of cognitive domains. Analysis of sixty Korean items and sixty-one Japanese items revealed distinct assessment priorities between the two countries. Korea placed the highest emphasis on knowledge application, prioritizing the ability to apply concepts to real situations, while Japan emphasized knowledge recall most heavily, prioritizing accurate understanding of fundamental concepts. Despite this difference in priorities, both countries showed a common orientation toward higher-order thinking assessment, with the combined proportion of application and analysis domains exceeding that of knowledge recall in both systems. Regarding resource utilization, Korea demonstrated strong subject-specific differentiation, particularly in economics with extensive use of tables and graphs, while Japan emphasized complex thinking processes through higher use of mixed-format items combining multiple resource types. Korea maintained assessment consistency through a standardized five-option format and elective subject system, whereas Japan pursued mandatory subjects with diverse item structures ranging from four to nine options. This study aims to provide empirical comparative data that may inform policy discussions on Korea's upcoming 2028 university admission reform process.

Enhanced emotion perception for faces behind the observer.

The article describes the process of managing land use. One of the problems in management is identifi ed as the set of cost factors, which does not always correspond to the real economic situation of the population. Additionally, municipalities face complications due to discrepancies between data and the actual soil conditions, as well as slow digitization. Among the proposed measures are the introduction of expert methods (hierarchy analysis, geological ranking) for accurate cadastral valuation and enhanced monitoring with forecasting of changes and integration of GIS technologies for real-time data.

The main aim of this study is to promote awareness among pre-service teachers regarding peace education in Myanmar. It seeks to explore whether the peace education intervention has any effect on the knowledge and attitudes of pre-service teachers. This study was conducted using the quasi-experimental design and a semi-structured interview as a qualitative approach to determine pre-service teachers’ knowledge and attitudes toward promoting peace education. The peace education intervention program was conducted during the 2nd and 3rd weeks of June 2025. The participants included 35 pre-service teachers from the selected education degree college, of whom 6 participants were chosen for the semi-structured interviews. Data analysis was performed using thematic analysis, and the results indicated that pre-service teachers’ definitions of peace reflect their real-life situations based on their experiences. Moreover, pre-service teachers believed that peace education is an essential element for the country’s development and serves as a tool for resolving conflicts that affect children’s learning opportunities. Most pre-service teachers demonstrated an enhanced understanding of peace education and its values, along with a more positive mindset and greater self-awareness. It was found that they experienced significant changes in both their knowledge and attitudes, achieving all the intended objectives through the intervention within three days, indicating that it was effective to some extent. This study suggests that integrating peace education into teacher training programs can effectively promote peace education.