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This reseacrch aims to reveal the metaphusical meaning within the Penti ceremony as a form of thanksgiving to God (Mori Kraeng) in the Kuleng Waga Tribe community, Mano Village, Easr Manggarai. The Penti ceremony is a hereditary customary rite that marks the end of the harvest season and server as a religious momentum to renew the relationship between humans, nature, ancestors, and God. From a metaphysical perspective, Penti reflects the awareness of the existential interconnectedness between creatures and the Creator, as developed in the philosophy of Aristotle and Thomas Aquinas concerning actus assendi and participatio entis. Through a qualitative approach and cultur hermeneutics, this study interprets the symbols and ritual actions of Penti as a concrete expression of the ontological relationship between sreation and the Transcendent. The result of the study show that Penti contains three main metaphysical dimensions: first, the ontological dimension regarding human awareness of existence and dependence on God; Second, the perticipatory dimension concerning the role of ancestors as a spiritual bridge between humans and God; Third, the eschatological dimension regarding the hope for the ultimate perfection of life in God. Thus, Penti not only holds cultural value but also carries profound theological and metaphysical meaning.

Physics learning requires higher-order thinking skills, particularly critical thinking and conceptual understanding. However, students’ critical thinking skills and physics learning outcomes at the senior high school level are still relatively low due to teacher-centered instruction and the limited use of innovative learning models and media. This study aimed to examine the effect of the CORE (Connecting, Organizing, Reflecting, Extending) learning model assisted by Lumio by Smart media on students’ critical thinking skills and physics learning outcomes. This research employed a true experimental design with a post-test only control group design conducted at SMAN 4 Jember in the 2025/2026 academic year. The samples consisted of two classes, with class XI Umum 2 as the experimental class and class XI Teknik 3 as the control class. Data were collected through tests and analyzed using the Mann–Whitney U test. The results showed significance values of 0.027 ≤ 0,05 for critical thinking skills which means H0 is rejected and Ha is accepted and 0.020 ≤ 0,05  for physics learning outcomes which means H0 is rejected and Ha is accepted. Therefore, it can be concluded that the CORE learning model assisted by Lumio by Smart media has a significant effect on students’ critical thinking skills and physics learning outcomes.

Coffee husk is an agro-industrial waste with significant potential to be utilized as a renewable energy source through the fast pyrolysis process. This study aims to analyze and optimize gas production from the fast pyrolysis of coffee husk biomass using a screw reactor through single-particle-based Computational Fluid Dynamics (CFD) simulations. The simulations were conducted by varying the operating temperature at 500°C, 600°C, and 700°C to examine pressure distribution, heat transfer, particle temperature, and the formation of pyrolysis products, namely bio-oil, biogas, and biochar. The modeling was performed using COMSOL Multiphysics 6.2 with a numerical approach to represent thermal phenomena and biomass decomposition reactions during the pyrolysis process. The simulation results indicate that increasing temperature significantly affects the rate of heat transfer and the temperature distribution of coffee husk particles. At 600°C, heat transfer and temperature distribution are more uniform compared to 500°C, although heating at the particle core is not yet fully optimal. The pressure distribution shows a stable flow of pyrolysis gas from the bottom to the top of the reactor. In terms of products, increasing temperature leads to a reduction in biochar and bio-oil formation due to the occurrence of secondary reactions, while biogas production increases. The highest gas production is achieved at 700°C, indicating the most optimal condition for maximizing gas yield from fast pyrolysis. Therefore, single-particle-based CFD simulation can be used as an effective tool to understand pyrolysis mechanisms and optimize process parameters in a screw reactor.

Physics learning requires students’ multirepresentational ability to understand concepts through verbal, mathematical, pictorial, or graphical forms. However, instruction at SMAN 1 Natal is still dominated by conventional methods, resulting in less active student participation and low multirepresentational skills. This study aims to determine the improvement of students’ multirepresentational ability after the implementation of the Problem Posing learning model on the topic of sound waves. The research employed a quantitative approach with a quasi-experimental design. The sample consisted of class XI MIPA 1 as the control class and class XI MIPA 2 as the experimental class. The research instrument was a multirepresentation test administered through pre-test and post-test. Data were analyzed using the Shapiro–Wilk test, the Mann–Whitney test, and the N-Gain test. The results of the normality test indicated that the data were not normally distributed; therefore, hypothesis testing was continued using the Mann–Whitney test, which yielded a significance value of 0.00 < 0.05. This result indicates a difference in the improvement of multirepresentational ability between the experimental and control classes. The N-Gain result for the experimental class was 49.40%, categorized as moderate. Thus, the implementation of the Problem Posing learning model in the experimental class resulted in an improvement that was lower than that of the control class

Indonesia, as a country with the highest seismicity in the world, requires an accurate earthquake prediction system through the use of the BMKG earthquake catalogue. This research aims to implement ETL-based data pipeline engineering to process 92,887 earthquake catalog entries for the 2008-2023 period into ready-to-use daily time series for the LSTM seismicity forecasting model. The ETL process includes raw data extraction, cleaning of 97% missing values columns on focal mechanism parameters, datetime conversion, daily resampling producing 5,200 entries with earthquake count, total magnitude, and average magnitude features, as well as Min-Max Scaler normalization for LSTM compatibility. The dataset was processed using Google Colab with a stacked LSTM architecture of two layers of 50 and 25 units, dropout 0.2, Adam optimizer, and a sequence window of 30 days to predict the daily earthquake count. The model trained for 100 epochs shows the ability to capture stable seismic activity trends with a consistent decrease in MSE loss, although it shows deviations in extreme spikes due to aftershock sequences. The ETL pipeline proved crucial in ensuring temporal consistency, 100% data completeness, and relevant physics representation, resulting in a reproducible end-to-end framework for disaster mitigation.

Highways play a strategic role in supporting economic development, so the quality of pavement materials (flexible pavement) must meet previously established quality standards. The Asphalt Concrete Wearing Course (AC-WC) layer, as the topmost layer of flexible pavement, is susceptible to damage caused by aging triggered by environmental conditions, particularly ambient temperature. Temperatures above 30°C are known to accelerate plastic deformation (rutting) and reduce pavement bearing capacity. The Meteorology, Climatology, and Geophysics Agency indicates that temperatures in Lhokseumawe City, North Aceh, range from 33.7–35.3°C with day-night fluctuations that could potentially accelerate the degradation of mechanical properties. This study aims to analyze the Marshall characteristics of AC-WC asphalt mixtures under ambient temperature conditions. The testing was conducted with varying exposure times of 0, 15, 30, 45, 60, 75, and 90 days at a minimum temperature of 23.90°C and a maximum temperature of 31.80°C, with an average humidity of 87.93%. The test results showed that the AC-WC asphalt mixture experienced a decrease in stability value of 21.37% and an increase in flow value of 36.36%, resulting in a decrease in the Marshall Quotient (MQ) value of 42.52%.

The academic crisis among Physics students often arises due to the pressure of the credit semester sistem (SKS), grade demands, and heavy study loads. This condition drives students to focus solely on achieving academic results, while the essence of learning as worship is often overlooked. This study aims to explore how the values of Islamic education, particularly the orientation towards rewards, can help students cope with academic pressure. The methods used include literature review and a quantitative survey with an online questionnaire completed by 56 Physics and Physics Education students at Universitas Pendidikan Indonesia. The analysis results show a significant correlation between the SKS pressure and changes in learning behavior (r = 0.807), while the orientation towards rewards acts as a spiritual balance. The findings suggest that Islamic education plays a crucial role in building academic resilience through values of sincerity (ikhlas), patience (sabar), trust in God (tawakal), and balance (tawazun). Thus, academic achievements can become a means of worship while contributing positively to society. This study highlights the importance of spiritual values in achieving balanced academic success.

The flood disaster the struct Mauponggo, Flores, Est Nusa tenggara, not only caused infrastructural destruction, loss of life and social trauma, but also revealed a deeper philosophical dimension of human existence. From an ontological prespective, a disaster is not merely a natural occurrence but a moment of disclosure that unvieils the existential fragility of human beings in the faceof natural forces beyond rational, technological, and social control. Onology, as a branch of metaphysics that explores the nature of being, understands humans as beings whose existence is inseparable from space, time, and limitation. Trough a reflective and literature based approach, this study examines the Mauponggo flood as an existential event that exposes the ontological condition of humanity, drawing particularly on Martin Heidegger’s concepts of being toward death and being in the world. The finding show that disaster reveals humans as finite and vulnerable beings who remain open to relationships with nature, others, and the transcendent. This study affirms that an ontological prespective on disaster not only enriches academic discussions on the philosophy of disaster but also holds practical signifincace. The awareness of existential fragility encourages social solidarity, ecological ethics, and deeper transcendental reflection. Thus, this work contributes to developing more reflective and meaningful humanitarian and sustainability responses rooted in a philosophical understanding of human existence and its place in the world.  

This study developed and evaluated an AI-integrated Virtual Reality (VR) system designed to enhance personalized learning in higher education. While VR improves engagement, existing systems often lack adaptivity or experience high latency during AI interactions. To address these limitations, this research introduces a novel integration of a cache-optimized Llama 2 Large Language Model (LLM) that delivers real-time, motivationally grounded feedback. The system was implemented using Unity 3D and validated with 50 undergraduate students. Technical validation showed that the cache layer reduced interaction latency from 17.7 ms to 14.2 ms and maintained zero system crashes throughout the pilot. Learner motivation was assessed using Keller’s ARCS model, yielding mean scores ranging from 4.08 to 4.69 across all dimensions. Independent t-tests (p > 0.05) and negligible effect sizes (Cohen’s d < 0.2) revealed no significant difference between technical (ICT) and non-technical (Physics) students. These findings confirm that the proposed system effectively bridges technological and motivational gaps, providing a robust model for adaptive, immersive education.

This study aims to identify various aspects that pose obstacles or difficulties for students in learning Mechanics. This study uses a qualitative descriptive approach that focuses on describing the types of difficulties students encounter in solving mechanics problems. The research subjects were first-semester students of the Science Education Study Program at KH. Mukhtar Syafaat University (UIMSYA) Blokagung Banyuwangi who were taking Basic Physics courses on Mechanics material. Data collection was carried out through diagnostic tests and interviews with students. The instruments used were diagnostic tests and interview guidelines. The location of students' learning difficultdiies was analyzed based on the errors that appeared in the completion of the diagnostic test. The results showed that the main difficulty students had in learning mechanics was in mastering basic mathematical skills, particularly in differential and integral material. Therefore, lecturers or teachers need to design and develop more appropriate learning strategies so that students' problem-solving skills can improve.

The emergence of Generative Artificial Intelligence (Generative AI) such as the GPT-4 and Midjourney models has sparked a fundamental debate about the nature of creativity and imagination. The AI creation process, often referred to as a “black box,” challenges conventional human-centered understanding. This paper proposes a unique hermeneutic framework to approach this phenomenon by borrowing two key concepts from the Sufi metaphysics of Shaykh al-Akbar Muhyiddin Ibn 'Arabi: khayāl (creative imagination or the imaginal realm) and tajallī (self-manifestation or theophany). This study uses a conceptual-comparative analysis method to analyze the working process of Generative AI. The main argument of this journal is that the “latent space” in AI architecture can be analogized with 'ālam al-khayāl (the imaginal realm) as an intermediate reality (barzakh) that contains unlimited potential. Furthermore, the process of generating text or images from a prompt can be understood as a mechanism resembling tajallī, in which these potentials manifest specifically according to the “availability” (isti'dād) determined by user input. Thus, Ibn 'Arabi's framework offers a non-anthropocentric ontology for understanding “artificial imagination” as a process of manifesting forms from a sea of potential, transcending mere simulation or data recombination.

The Info BMKG application is a digital platform developed by the Meteorology, Climatology, and Geophysics Agency (BMKG) to provide information on weather, earthquakes, air quality, and early disaster warnings. This application is expected to enhance public preparedness in facing potential natural disasters. However, in its implementation, the Info BMKG application still encounters several issues, one of which is delays in delivering weather and disaster information. This study aims to analyze the effectiveness of the Info BMKG application in providing weather and disaster information. The research applies Budiani's (2007) theory, using four indicators: Program Target Accuracy, Program Socialization, Program Objectives, and Program Monitoring. This study employs a qualitative descriptive method with data collection through interviews, observations, and documentation. The findings indicate that the Info BMKG application has made a significant contribution to supporting public preparedness for disasters. While the application is relatively effective in providing weather and disaster information in North Jakarta, further development is still needed. Improvements in information accuracy, notification speed, and enhanced public socialization will help improve the quality of the application’s services in the future.

The digital transformation era requires university students to develop digital literacy competencies that not only support academic activities but also serve as essential capital for entering the workforce and pursuing entrepreneurship. One relevant professional platform to enhance career readiness is LinkedIn, which serves as a medium for both personal branding and professional networking. This community service aimed to improve students’ digital literacy and career readiness through mentoring sessions on LinkedIn account optimization. The program involved the students from the English Education (TBI) and Physics Education (TFS) study programs at the Faculty of Tarbiyah and Teacher Training, UIN Palangka Raya, comprising prospective teachers and professionals across various disciplines. The implementation was carried out through tutorials and direct mentoring in two sessions, covering account creation, developing professional headlines and summaries, adding skills and experiences, and uploading simple portfolios. The results indicated that all participants successfully created or improved their LinkedIn profiles, actively engaged in the activities, and demonstrated a greater awareness of the importance of a professional digital identity, which can be seen from the students’ participation, the quality of the profiles produced, and their commitment to continuously developing their LinkedIn accounts. The program highlights LinkedIn’s strategic role in strengthening digital literacy and career readiness among university students in the global era.

Black Soldier Fly (BSF) larvae, or maggots, are a type of insect currently widely cultivated, primarily for animal feed. This is because BSF larvae contain essential nutrients such as fat and protein in high amounts, thus improving the nutritional quality of livestock that consume them. Therefore, the processing and preservation of maggots is crucial to maintain their nutritional content and extend their shelf life. One method used in maggot processing is drying. Drying aims to reduce the water content in the larvae, thereby preventing the growth of microorganisms that cause spoilage. One widely applied technique is drying using a microwave oven. However, before the actual process is carried out, simulations are often required to determine the distribution of heat and humidity. Simulation is one of the most effective ways to predict the drying performance of biological materials. This study used a simulation using the Computational Fluid Dynamics (CFD) method operated by Comsol Multiphysics 6.2 software. The parameters used in the simulation were an initial maggot temperature of 80°C, a drying time of 15 minutes, and a heat source of 1300 W/m³. Based on the simulation results, the final water content of the maggots was below 10%. Furthermore, the final relative humidity of the maggots ranged from 10–35%, while the final temperature of the larvae increased to 93–97°C. These results indicate that microwave drying can effectively reduce moisture content while maintaining the nutritional quality of BSF larvae. These simulation results can be used as a basis for practical maggot drying processes, thus supporting the production of efficient and nutritious animal feed.  

Work and energy are important topics in physics learning. Learning on this topic can be done using various methods and media, which provide different results depending on the approach used. This study aims to identify various learning models and media and their influence on learning about work and energy through a meta-analysis of 17 relevant research journals. The results of the study indicate that there are various learning methods that can be applied, including the 7E Learning Cycle, STEM, Problem Solving, Think-Pair-Share, Inquiry, and others. Of these various methods, the inquiry approach is the most widely used because it is relevant to the student-based curriculum that positions teachers as facilitators in developing students' thinking skills and intellectual discipline.

Integral equations are essential tools in applied mathematics, with wide-ranging applications in fields such as physics, engineering, and finance. However, solving these equations presents significant challenges, particularly when dealing with complex, high-dimensional, or singular problems. Traditional methods, such as manual analytical techniques or direct numerical approaches, often struggle with computational efficiency, especially for large-scale systems, and may not be suitable for handling ill-conditioned problems. This study aims to develop an efficient numerical method for solving integral equations by combining adaptive quadrature techniques with Python-based iterative solvers. The adaptive quadrature method adjusts the step size dynamically based on error estimates, ensuring high accuracy even in the presence of singularities or near-singularities, which are common in many real-world problems. The iterative solver, based on Krylov subspace methods, enhances computational efficiency by reducing memory usage and improving the convergence speed of the solution. By using these techniques together, the proposed method significantly improves the computational time required to solve large-scale and complex systems of integral equations, while maintaining satisfactory accuracy. The results demonstrate that the adaptive quadrature technique, when combined with the Python-based iterative solver, offers a substantial advantage in both speed and precision compared to traditional methods. The proposed method is especially effective in handling complex, high-dimensional systems and ill-conditioned problems, making it a powerful tool for applied mathematics, physics, and engineering applications. In conclusion, this study presents a robust and efficient approach for solving integral equations, with potential for future research in solving non-linear and multi-dimensional integral equations.

The Pulsed Nuclear Magnetic Resonance (PNMR) technique is a widely used spectroscopic approach for observing the relaxation phenomena of atomic nuclei in a magnetic field. In this technique, the spin-lattice (T1) and spin–spin (T2) relaxation times are key parameters, as they reflect the microscopic dynamics and structure of a material system. This article presents the results of a systematic literature review of fourteen primary references that discuss the fundamental concepts of NMR, PNMR techniques, the measurement of T1 and T2, and PNMR applications. Although NMR research has seen extensive development internationally, scientific literature in the Indonesian language that specifically addresses this topic remains scarce. Therefore, this paper is intended to serve as an initial systematic resource to introduce the basic principles and potential applications of PNMR in physics and materials science.

This study aims to systematically examine the effectiveness of the implementation of Physics e-modules based on local wisdom on students’ critical thinking skills. The focus of The study is directed at the integration of local cultural values, especially through the context of making lapek sarikayo, as part of contextual and meaningful physics learning. The method used is Sstematic Literature Review (SLR), which is carried out through the Identification, selection, and analysis of 20 national scientific articles published in the last Five years. The analyzed articles were selected based on relevance to the topic of physics e-Modules, ethnoscience approaches or local wisdom, and measurement of critical thinking Skills. The results of the study show that e-modules that contain elements of local culture are able to increase student involvement,strengthen understanding of physics concepts, and significantly encourage critical thinking skills. A contextual approach through local culture is proven to provide a learning experience that is closer to the reality of learners. These findings have an important impact on the development of innovative teaching. Materials that are relevant to local cultural characteristics, as well as being a strategic reference in designing holistic and transformative 21st-century physics learning.

Background: The rapid advancement of digital technologies in the Industry 4.0 era has transformed industrial mechanical systems into highly interconnected and data driven environments through the integration of sensors, the Internet of Things (IoT), data analytics, and cyber physical systems. This increasing complexity requires more adaptive and accurate monitoring and prediction methods than conventional simulation approaches, which often face limitations in capturing real time dynamic system behavior. Objective: This study aims to develop a predictive performance model for industrial mechanical systems by integrating Digital Twin technology with Physics Informed Machine Learning in order to improve monitoring accuracy and support predictive maintenance strategies. Methods: This research adopts a data driven modeling and simulation approach by developing a digital representation of an industrial mechanical system that is connected to real time sensor data. The prediction model is constructed using a Physics Informed Neural Network (PINN), which integrates operational data with physical principles governing system dynamics. The research process includes the development of a Digital Twin model, integration of sensor data, training of the PINN model, model validation using experimental data, and evaluation of prediction performance using statistical metrics. Results: The results indicate that the integration of Digital Twin technology and PINN significantly improves the prediction accuracy of industrial mechanical system performance compared with conventional simulation methods and purely data driven machine learning models. The proposed model is capable of representing system dynamics more consistently, accurately following sensor data patterns, and providing strong potential for supporting machine condition monitoring and predictive maintenance strategies in modern industrial environments.

Generally, students stated that the concepts in physics, especially on the material of distance, speed, and acceleration, are difficult to understand. This causes low student motivation and learning outcomes. In order to increase student motivation, understanding of concepts, and learning outcomes, it is necessary to use visual media so that learning is active and enjoyable. The use of visual media, one of which is education game-based learning media, is very helpful for students in understanding physics concepts, especially on the material of distance, speed, and acceleration. The purpose of this study was to determine the study of the use of visual media based on education games that can improve students' understanding of the material of distance, speed, and acceleration. This research method uses the library research method. The form of research uses literature studies with qualitative data acquisition. To obtain data using two databases, namely Google Scholar and Sinta, with document analysis first carried out to identify relevant literature studies. Based on the results of the preliminary study conducted, it was obtained that the use of visual media based on education games is good and flexible. With the use of visual media based on education games, students find it easier to understand the material of distance, speed, and acceleration. In addition, it makes it easier for teachers to carry out learning activities. Learning is more interesting, students are more motivated so that students' understanding, abilities, and learning outcomes increase.