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This research aims to develop and evaluate the performance of a steam plant prototype designed as an alternative source of electrical energy to support the Vessel to Grid (V2G) concept. Utilization of backup energy on ships is becoming important as electricity demand increases and demands for a more sustainable electrical system. This system relies on ESP32 microcontroller technology as a control center that functions to monitor and control several key parameters, including steam pressure, combustion temperature, boiler water level, and the generated electrical voltage. The research method used is an experiment with a static and dynamic testing approach. Static testing is carried out to measure the performance of main components such as the boiler, turbine, and generator separately, while dynamic testing focuses on evaluating the overall system by involving the integration of sensors and supporting actuators. The test data is then analyzed quantitatively to determine the system's response to variations in steam pressure, temperature, and other operational conditions. The results show that the steam produced by the boiler is able to rotate the turbine, thereby driving the generator to produce electricity. The maximum voltage achieved is 25.7 volts at a steam pressure of 50 psi. The highest energy conversion efficiency was recorded at 4%, while the lowest efficiency was 0.9%. These findings demonstrate that, despite its relatively low efficiency, the prototype can function as an alternative energy source and emergency backup solution. Thus, this research provides an initial contribution to supporting the implementation of the V2G concept through the development of a small-scale steam plant-based energy conversion system.

Stock returns represent a crucial parameter that serves as a reference for investors in evaluating company performance. A decline in returns has occurred in several mining companies listed on the IDX, despite the sector’s vital role in the national economy. This study aims to examine the effect of Corporate Social Responsibility (CSR), Return on Assets (ROA), Return on Equity (ROE), Debt to Equity Ratio (DER), and Firm Size on the stock returns of mining companies listed on the IDX during the 2022–2024 period. The sample was determined using purposive sampling, resulting in 56 observational data after outliers were removed. To meet the assumptions of classical tests, several variables were transformed using natural logarithms, and data were analyzed using multiple linear regression. The results indicate that CSR, ROE, and Firm Size have no significant effect on stock returns, whereas ROA and DER show a significant positive effect. These findings suggest that investors tend to emphasize financial fundamentals, particularly profitability and capital structure, rather than non-financial aspects such as CSR activities. The implication for companies is the need to enhance operational efficiency and optimize financial structures to attract investors and improve returns. Future researchers are encouraged to incorporate external variables such as global commodity prices, market risk, and macroeconomic indicators, as well as expand the observation period and apply more diverse methodological approaches to provide a more comprehensive understanding of stock return dynamics in the mining sector.

Reservoir heterogeneity has long been recognized as a critical factor influencing the efficiency of enhanced oil recovery (EOR) methods. Among the techniques applied, cyclic waterflooding is considered one of the promising approaches due to its relatively simple operational design and potential to improve sweep efficiency. This method involves alternating water injection in specific cycles to mobilize trapped oil and redistribute reservoir pressure. However, the variation in geological properties such as porosity, permeability, and fluid saturation creates challenges in achieving uniform displacement, especially in reservoirs with high heterogeneity. Understanding the role of heterogeneity is therefore crucial for optimizing cyclic waterflooding applications. This study applies a literature review approach by synthesizing findings from previous experimental and field studies that evaluated cyclic waterflooding under different reservoir conditions. The analysis compares the performance of cyclic water injection periods across reservoirs characterized by varying levels of heterogeneity. Parameters such as injection rate, water breakthrough time, and oil recovery factor were considered in evaluating the effectiveness of this method. The results highlight that reservoirs with high heterogeneity often experience uneven fluid distribution, leading to early water breakthrough and reduced oil recovery. In contrast, reservoirs with relatively low heterogeneity tend to respond better to cyclic waterflooding, resulting in improved sweep efficiency and higher incremental recovery. Moreover, the optimization of cycle timing and water injection intervals appears to significantly mitigate the negative effects of heterogeneity. In conclusion, the study emphasizes that reservoir heterogeneity plays a decisive role in determining the success of cyclic waterflooding. Tailoring injection strategies based on geological variability is essential to maximize recovery efficiency. Future research should focus on integrating advanced reservoir characterization techniques with adaptive cyclic flooding models to further enhance oil production outcomes.

Cassava tapai is a traditional Indonesian food product produced through the fermentation of cassava (Manihot esculenta) with the help of microorganisms, particularly the yeast Saccharomyces cerevisiae. This product not only has cultural and economic value but also contains bioactive compounds with health benefits, such as probiotics and fermentation metabolites. However, the quality of cassava tapai is significantly influenced by process factors, particularly incubation time. This study aimed to analyze microbial growth and changes in the chemical properties of cassava tapai with varying fermentation times. The study was conducted using an experimental design with fermentation times of 24, 48, and 72 hours at room temperature. The main parameters observed included the number of microbial colonies (cfu/g), pH changes, and alcohol content produced during the fermentation process. The results showed that microbial growth increased significantly, peaking at 48 hours, with the highest colony count compared to other treatments. After 72 hours, the number of colonies decreased, likely due to ethanol accumulation and decreased substrate availability, which reduced microbial activity. The pH value tended to decrease with increasing fermentation time, reflecting the formation of organic acids during the process. Meanwhile, the alcohol content showed an increasing trend from the beginning to the end of fermentation, although the growth rate was relatively slower at 72 hours. These findings confirm that varying incubation length significantly influences microbial dynamics and chemical changes in cassava tapai. The optimal fermentation time is around 48 hours, as this is the phase where the balance between microbial growth, alcohol formation, and sensory characteristics is maintained. The results of this study can serve as a basis for developing standards for cassava tapai production at both household and industrial scales, while also strengthening efforts to preserve traditional foods with a modern scientific approach.

Accurate battery modeling is crucial for the development of battery-based energy storage systems, especially for real-time control and energy management applications. This study proposes a dynamic parameter modeling approach for LiFePO₄ batteries using a first-order Thevenin equivalent circuit model. Parameter estimation is performed to obtain the internal battery parameters based on the Thevenin model, and the parameter dynamics are derived using the Euler numerical method to represent battery behavior during charging and discharging processes. Model validation is conducted by comparing the predicted terminal voltage with actual measurements using root mean square error (RMSE) and mean absolute error (MAE) as evaluation metrics. The results show that the model accurately captures the battery dynamics, with an RMSE of 0.233 and an MAE of 0.047. Therefore, the proposed model is suitable for real-world applications that require accurate and dynamic estimation of internal battery parameters.

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.  

Improvement amount Distributed Denial of Service (DDoS) attacks in cloud infrastructure and edge computing demands solution adaptive, distributed, and efficient detection in a way computing. Research This propose an optimized Federated Learning (FL) based DDoS detection model using Centroid Opposition-Based Bacterial Colony Optimization (COBCO) to training the Elman Neural Network (ENN). The proposed architecture consists of of two components Main: on the edge node side, a hybrid Convolutional Neural Network–Gated Recurrent Unit (CNN–GRU) model is used to extraction feature local from traffic data network, while on the server side, model parameters from each node are collected and used for training an optimized ENN with COBCO. Approach This aim increase accuracy detection at a time maintain efficiency local data communication and privacy. In progress experimental, model tested use three benchmark datasets: NSL-KDD, CICIDS2017, and CICDDoS2019. The preprocessing process includes feature encoding categorical, normalization numeric, class balancing using SMOTE, as well as validation cross (k-fold). Initial results show that combination of FL, CNN–GRU, and COBCO–ENN produces improvement significant in accuracy and time convergence compared to approach conventional such as PSO, GA, and non- federative models. In addition, the proposed model capable maintain performance detection tall although executed in edge environment with limitations source Power.  Study This give contribution important in development system scalable, privacy-preserving, and adaptive intelligent DDoS detection to dynamics Then cross modern network. Integration of FL and COBCO in ENN training shows potential big for used in implementation real in cloud-edge infrastructure. In addition, the proposed model demonstrates strong scalability and adaptability, making it highly suitable for dynamic and evolving network environments.

Employee shift scheduling in the hospitality industry remains a critical yet complex task due to fluctuating operational demands, fairness requirements, and labour regulations. Many hotels still rely on manual scheduling methods, which are time-consuming and prone to biases, particularly in ensuring fair workload distribution across employees. Despite numerous studies on workforce scheduling, limited attention has been given to integer linear programming (ILP) models that address gender-based restrictions and operational fairness simultaneously in real-world hotel contexts, especially in developing regions such as Central Java. This study proposes an Integer Linear Programming (ILP) model to generate optimal shift schedules for hotel staff over a 31-day planning horizon. The model incorporates operational constraints, including one shift per day, gender-based restrictions (which prevent female staff from working night shifts), availability, minimum staffing levels, and fairness in workload distribution. Key parameters and binary decision variables were defined to ensure compliance with the hotel's specific requirements. Empirical data were collected from a hotel in Central Java involving 20 employees, and the model was implemented using Python with a Gurobi solver. The ILP model successfully generated optimal schedules in under 10 seconds, significantly outperforming the manual method, which required over 4 hours. While the manual schedule resulted in an imbalance where some employees worked over 27 days and others only 22, the ILP approach enforced a strict maximum of 26 working days for all staff. Furthermore, the fairness index (FI) improved from 19.2% in the manual method to 0% in the ILP-generated schedule, indicating complete equity in workload allocation. The proposed ILP model demonstrates its effectiveness in improving scheduling fairness, operational efficiency, and compliance with labour policies. This work not only addresses a critical research gap in hospitality scheduling practices in Indonesia but also offers a replicable framework for other labour-intensive service sectors. Future research may explore multi-objective extensions incorporating employee preferences, satisfaction, and dynamic demand fluctuations.

This research explores the effectiveness of heuristic techniques for solving combinatorial optimization problems, with a particular focus on the Traveling Salesman Problem (TSP). Combinatorial optimization is a critical area of study, especially in fields like computer science, engineering, and economics, where finding optimal solutions from a finite set of possibilities is crucial. However, the NP-hard nature of many combinatorial problems, such as the TSP, makes traditional exact methods like Branch-and-Bound and Dynamic Programming computationally expensive and inefficient for larger problem sizes. The primary objective of this research is to evaluate the performance of heuristic methods, including Simulated Annealing (SA), Genetic Algorithms (GA), and Iterative Computation techniques, such as Tabu Search (TS) and Particle Swarm Optimization (PSO). These methods are tested for their ability to provide approximate solutions efficiently. The findings reveal that while ACO provided the best solution quality, it had the longest runtime. TS was the fastest, though with slightly lower solution quality. SA and GA demonstrated a balance between solution quality and computational efficiency, but their performance heavily depended on parameter tuning. The hybridization of SA and GA showed potential for improving solution quality but introduced additional complexity. The research concludes that heuristic methods, especially when combined, offer viable solutions for large-scale combinatorial optimization problems, though the trade-off between solution quality and computational time must be considered when selecting an algorithm.

This study focuses on the development of an interactive web-based learning platform for Proportional-Integral-Derivative (PID) control systems, aimed at addressing the conceptual challenges faced by electrical engineering students when learning PID through conventional teaching methods. Despite its foundational role in control theory, PID remains difficult to grasp without practical visualization and hands-on experimentation. To bridge this gap, the research introduces a practical and accessible platform that enhances conceptual understanding through real-time simulations and physical interaction. The proposed system integrates key hardware components including an ESP-32 microcontroller, DC motor, rotary encoder, BTS 7960 motor driver, and I2C LCD. The platform’s web interface is built using HTML, Tailwind CSS, and JavaScript, enabling intuitive user interaction. Motor response data is captured via the ESP-32 and transmitted to the web interface using the WebSocket protocol, allowing users to instantly visualize system behavior as PID parameters (Kp, Ki, Kd) are adjusted. This dynamic feedback mechanism enables students to observe changes in system characteristics such as rise time, overshoot, and settling time in real time. To evaluate the platform’s feasibility, practicality, and educational effectiveness, beta testing was conducted among electrical engineering students using Likert-scale questionnaires. The results demonstrated that users were able to successfully interpret the impact of PID tuning on system performance. The average evaluation score reached 75.13%, indicating strong agreement regarding the platform’s educational value and its effectiveness in enhancing learning outcomes. In conclusion, the study affirms that the developed web-based platform offers a feasible, engaging, and pedagogically effective alternative to traditional learning approaches. By combining interactive simulations with physical experimentation, the platform significantly improves students’ understanding of PID control systems.

The digital transformation in education has encouraged the adoption of computer-based tests (CBT) using video content, which demands stable and efficient network performance. This study aims to evaluate the performance of two queue management algorithms, namely Random Early Detection (RED) and Per Connection Queue (PCQ), in maintaining the quality of service (QoS) of school networks during online video-based examinations. A case study approach was applied using a real network topology in a school environment, and QoS parameters such as throughput, delay, packet loss, and jitter were measured. The implementation was conducted using a MikroTik RB450Gx4 router configured with simple queue settings for each algorithm. The results show that PCQ provides more consistent performance under high user loads, achieving an average throughput of 56,482 bps and lower delay compared to RED. Conversely, RED performs better in scenarios with a small number of users. The study recommends using PCQ for networks with dynamic and dense user environments, while RED is more suitable for low-traffic conditions where latency stability is crucial. These findings offer practical guidance for managing bandwidth and improving the quality of CBT delivery in educational settings.

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.

The increasing number of devices connected to the network and the rapid growth of data traffic have posed challenges in managing conventional networks. Traditional networks often experience inefficiencies in handling high traffic loads due to static traffic management, leading to congestion in some network paths while others remain underutilized. Software-Defined Networking (SDN) has emerged as an innovative solution by separating the control plane and data plane, enabling more flexible, centralized, and programmable network management. This study evaluates the performance of OpenFlow-based SDN using the ONOS Controller and compares it with conventional home Wi-Fi networks based on the TIPHON standard. The simulation was conducted using Virtual Machines (VMWare) and Mininet, which were connected to the ONOS Controller via the OpenFlow protocol, while the conventional network testing was performed using the Command Prompt. The testing included three data transmission scenarios: 1 host to 1 host, 2 hosts to 1 host, and 2 hosts to 2 hosts simultaneously, with measured parameters including delay and packet loss. The results indicate that in the 1 host to 1 host scenario, both SDN and conventional networks had a delay of less than 150 ms and 0% packet loss. However, in the 2 hosts to 2 hosts scenario, the conventional network experienced an increase in delay and packet loss of up to 17%, whereas SDN remained stable with a delay below 150 ms and 0% packet loss. This confirms SDN's superior efficiency in handling high network traffic through more dynamic traffic distribution. Additionally, this study confirms that SDN's centralized control allows for more adaptive traffic management, reduces congestion, and improves overall network stability. These research demonstrate SDN's efficiency in handling high traffic loads, making it a superior solution for modern network demands.

Mining roads play an important role in supporting the smooth running of mining activities, especially in the process of transporting materials. The bearing capacity of the soil as a road subgrade greatly affects the stability and resistance of the road to heavy equipment loads. This study aims to analyze the effect of the plasticity index and compaction parameters on the California Bearing Ratio (CBR) value, as well as to determine the thickness of the road layer based on the laboratory CBR value. Testing was carried out at the South Pit of PT. Bina Sarana Sukses site PT. Baramulti Suksessarana with a field test method using the Dynamic Cone Penetrometer (DCP) and laboratory tests such as proctor tests, Atterberg limit tests, and CBR tests on various variations of clay and sand soil mixtures. The results showed that increasing the plasticity index decreased the CBR value, while increasing the maximum dry density and decreasing the optimum water content increased the CBR value. Based on the laboratory CBR value, recommendations were obtained for the appropriate road layer thickness to ensure optimal bearing capacity for heavy equipment passing through the research area.

This systematic literature review examines queue system simulation in hospitals across four key service areas: emergency departments, outpatient clinics, laboratories, and pharmacies. Following PRISMA methodology, 72 relevant studies (2015-2020) were analyzed to identify simulation models, software tools, performance parameters, and emerging trends. Findings reveal Discrete-Event Simulation dominance (59.7%), with increasing hybrid model adoption integrating System Dynamics and Agent-Based approaches. Emergency departments remain the primary application focus (52.8%), while Arena and AnyLogic emerged as predominant simulation platforms. Patient waiting time (91.7%) and resource utilization (77.8%) constitute the most evaluated performance metrics. Technological convergence trends demonstrate integration of real-time data analytics, machine learning, and digital twin concepts into traditional simulation frameworks. This review contributes methodological insights for optimizing hospital queueing systems while identifying research gaps in cross-departmental model interoperability and comprehensive value-based performance evaluation within contemporary healthcare systems.  

Nonlinear dynamical systems represent a fundamental area of study in applied mathematics due to their relevance across various disciplines, including physics, biology, and engineering. Their inherent complexity, characterized by phenomena such as bifurcation, chaos, and sensitivity to parameter variations, often limits the effectiveness of traditional manual analysis, particularly when addressing high-dimensional or computationally intensive models. This study aims to address these challenges by applying computational modeling and numerical simulation techniques to analyze the stability of nonlinear dynamical systems. The research employs analytical methods, including equilibrium point identification and linearization, which are then validated and extended through the fourth-order Runge-Kutta numerical method. Simulations were conducted to visualize equilibrium points, phase portraits, and parameter-driven bifurcation phenomena. The findings demonstrate a strong correspondence between analytical and numerical approaches, with minimal error margins (≤1%) observed in equilibrium point estimation, thus confirming the reliability of computational methods. Moreover, the bifurcation analysis revealed critical transitions such as pitchfork and Hopf bifurcations, which indicate sudden shifts from stability to instability behaviors that are difficult to capture through manual calculations alone. The integration of computational approaches provides clear advantages, offering systematic exploration of parameter spaces and detailed visualizations of system dynamics, thereby expanding the scope of stability analysis. In conclusion, this study emphasizes that computational modeling is not only an effective complement to analytical methods but also a necessary strategy for advancing the understanding of nonlinear dynamical systems in applied mathematics.  

Introduction: Achieving carbon neutrality in industrial systems is essential for mitigating climate change and promoting sustainability. The increasing demand for energy optimization and carbon emission reduction has driven the development of advanced technologies, particularly hybrid machine learning (ML) models. These models, combining ensemble learning and reinforcement learning (RL), offer significant promise in optimizing industrial processes, reducing energy consumption, and improving environmental performance. This study explores the application of hybrid ML models in achieving carbon neutral goals through dynamic process optimization and energy control in industrial settings. Literature Review: Hybrid ML models integrate different machine learning techniques to handle complex and dynamic environments effectively. Ensemble learning methods, such as boosting, bagging, and stacking, combine multiple algorithms to improve predictive performance and robustness. Reinforcement learning (RL), on the other hand, enables real time decision making and adaptation based on trial and error interactions with the environment. In energy optimization, these models are used to reduce energy intensity and carbon emissions, enhancing overall operational efficiency. Previous studies have demonstrated the effectiveness of ML models in energy management, but challenges such as data quality, model integration, and computational complexity remain. Materials and Method: The study applies hybrid ML models combining ensemble learning and RL to optimize energy consumption and minimize carbon emissions in industrial processes. Data from real time sensors and operational parameters are used to train the models. The ensemble learning component improves the accuracy of energy predictions, while RL ensures dynamic process adjustments in response to fluctuating energy demand. The models were tested in various industrial settings, including manufacturing processes, smart grids, and microgrid systems. Performance metrics such as energy efficiency, carbon emissions reduction, and operational costs were evaluated to assess the effectiveness of the models.  Results and Discussion: The hybrid ML models achieved significant reductions in energy intensity (15-20%) and carbon emissions (18-25%). The real time adaptability of the RL component allowed the models to adjust energy consumption patterns dynamically, improving energy efficiency and reducing waste. The models demonstrated their ability to adapt to varying operational conditions, ensuring optimal energy use. A cost-benefit analysis showed that the hybrid models provided substantial energy savings and reduced operational costs, with a return on investment (ROI) of 30-35% within the first year of deployment. However, challenges such as computational complexity and data quality issues were identified, highlighting the need for further refinement in model development.

In the digital era, home security has become a crucial aspect with the increasing risk of theft due to the weaknesses of traditional lock systems. This study develops an automatic door security system based on the Internet of Things (IoT) by integrating Radio Frequency Identification (RFID) technology and mobile applications. This system allows users to lock and unlock the door and monitor the door condition and battery power in real-time from a distance. To increase flexibility and security, this system also utilizes the Mamdani Fuzzy logic method in decision making, based on parameters such as battery power, user distance from the door, and environmental security level. With this approach, the system can dynamically adjust access according to the situation. The test results show that the developed system is able to provide a more efficient, secure, and adaptive security solution compared to conventional locks, as well as providing better convenience and control for users in managing home security intelligently and integratedly.  

This study aims to analyze the effect of water quality on the productivity of bamboo lobsters (Panulirus versicolor) cultivated in a floating net cage sistem (KJA) in the waters of Sinabang Bay, Simeulue Regency. Water quality is one of the main environmental factors that determines the success of marine cultivation, especially for high-value species such as bamboo lobsters. Water quality parameters observed in this study included temperature, salinity, pH, dissolved oxygen (DO), and ammonia levels. Measurements were carried out periodically during the 90-day cultivation period to capture environmental dynamics that affect the biological performance of lobsters. This study used a Completely Randomized Design (CRD) experimental design with three treatments based on different cultivation locations that have variations in natural water quality, each with three replications. The results showed that optimal water quality significantly affected the increase in bamboo lobster productivity, as indicated by the parameters of the specific growth rate (SGR), feed conversion efficiency (FCR), and survival rate (SR). The location with the best water quality (P1), which had high DO and low ammonia levels, recorded a daily SGR of 1.82%, a FCR of 1.6, and a SR of 90%. Conversely, the location with low water quality (P3) showed significantly decreased biological performance, with suboptimal SGR, FCR, and SR, primarily due to low dissolved oxygen and high levels of toxic ammonia. These findings emphasize the importance of continuous monitoring and management of water quality in marine cage sistems to support optimal lobster growth and survival. Therefore, managing aquatic environmental quality is key to increasing mariculture productivity and ensuring the sustainability of fishery resources in coastal areas.

Mining with an open pit system is carried out by excavating and removing the overburden to obtain coal. However, before mining, it is important to obtain geotechnical data information. As well as the lithology of the rocks below the surface, it is necessary to carry out geotechnical drilling (full coring). This study can determine the value of slope geometry safety factors and plan safe slope geometry, both individual slopes and overall slopes. Therefore, this was done to determine the influence of GSI geotechnical parameters on the value of static and dynamic slope safety factors. The method used in determining the safety factor and the probability of an avalanche is the Morgenstern-Price boundary equilibrium method with the Generalized Hoek-Brown collapse criterion in static and dynamic slope conditions. The input parameters used in the analysis were natural density, compressive strength value (UCS), geological srength index (GSI), disturbance factor (D), intact rock constant (mi), as well as seismic load factor, and groundwater level. The optimal geometry on the Highwall slope is the configuration of the Highwall slope with a height of 74 m and an angle of 23°, supported by a single slope of 5-10 meters, a berm of 7 meters with an angle of 40°. Seam D Claystone lithology with GSI 50, Siltstone with GSI 70, Sandstone with GSI 70, and Seam E Claystone lithology with GSI 50, Siltstone with GSI 40, Sandstone with GSI 75.Can be applied within a safe limit where FK Static 1.7 PK Static 0% and FK Dynamic 1.4 PK Dynamic 6%.