The Philosophy of “Ichigo Ichie” in Japanese and “Urip Iku Urub” in Javanese (A Cognitive Linguistics Study)

Japanese and Javanese are languages that are full of philosophy. One of the well-known philosophies in Japanese​ is “Ichigo ichie” whereas in Javanese there is also a famous philosophy a which is called “Urip” iku urub”. This research aims to understand both philosophies based on cognitive linguistics study. The study sees that language reflects the speaker’s ways of thinking, conceptualizing, and viewing the world. Therefore, a language is not only a means of communication but also a reflection of the user’s life philosophy. In this study, the researchers try to study both philosophies as reflections of both Japanese and Javanese languages’ people.  In doing the research, a qualitative method is used. The researchers get the data through library sources in the form of articles, books, and references ​ related with the “Ichigo ichie” and “Urip iku urub” philosophies. Furthermore, the data were also obtained through interviews with informants regarding the implementation of both philosophies in real life. The research has finally found that both philosophies have one similarity. The similarity lies in the form of emphasis on the importance of undergoing a meaningful life.  Both philosophies see life as not only a matter of physical existence but also as a precious value to actualize. The research has found that there is a difference between them. The difference is that the Japanese “Ichigo ichie” philosophy focuses more on internal goals that emphasize self -awareness and mental attitude to appreciate every moment, whereas the Javanese philosophy of “Urip iku urub” focuses on the external goal of realizing social welfare by giving benefits to other people. In daily life, “Ichigo ichie” philosophy is realized   in the practice of the self-discipline in all aspects of life, a characteristic of Japanese culture. In business field, the “Ichigo ichie” philosophy is manifested in the Japanese’s special characteristic style of hospitality. On the other hand, the implementation of "Urip iku urub" philosophy is focused on the importance of giving benefits to others. This can be seen in various kinds of life activities in Javanese society. Although both philosophies come from two different languages and two long-distance countries, both contain universal values that  can be applied by anyone without considering cultural boundaries

Enhanced ELM Model Approach to Mitigate Multicollinearity of Lagged Independent Variables of ARMA Process

This paper presents a regularized Extreme Learning Machine (ELM) framework for identifying nonlinear dynamic systems affected by multicollinearity, with application to a Hammerstein-structured model of a Continuous Stirred Tank Reactor (CSTR). The model architecture employs a single-hidden-layer feedforward network (SLFN) for the static nonlinear block, and an autoregressive linear dynamic block whose order is determined using a Lipschitz quotient criterion. Traditional ELM models are known to suffer from instability when lagged input variables are highly correlated, a common occurrence in block-oriented system identification. To address this, the study investigates enhanced variants of ELM incorporating regularization, namely Ridge-ELM and Liu-ELM, which introduce biasing parameters to improve numerical stability and generalization. The proposed regularized ELM variants are evaluated against traditional ELM using performance metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). Results show that Ridge-ELM and Liu-ELM significantly reduce parameter variance and improve predictive performance on datasets. Additionally, confidence intervals and condition number analysis demonstrate improved robustness in the presence of multicollinearity. Cross-validation is used to tune hyperparameters, and the Diebold-Mariano test confirms that the improvements are statistically significant. This approach offers a computationally efficient, scalable solution for robust nonlinear system identification in multivariate chemical processes and beyond

Implementation of Adaptive Hysteresis-Band Current Control for Bidirectional H-Bridge DC-DC Converter

The substantial adoption of electric vehicles (EVs) has the potential to disrupt the power grid due to uncontrolled charging. A promising solution is Vehicle-to-Grid (V2G) technology, which allows EVs to return surplus energy to the grid to maintain reliability. However, a key challenge is developing a bidirectional converter with a rapid response that can also maintain a constant switching frequency to avoid harmonic disturbances. This study introduces an innovative adaptive hysteresis bandwidth current control method for H-bridge converters. This method provides a rapid and precise response to dynamic current and voltage fluctuations while maintaining a constant switching frequency for the MOSFET. The adaptive bandwidth values are mathematically derived from converter principles and implemented via a lookup table in a microcontroller. The simulation results showed a response time of less than 1 millisecond without overshoot. Experimental validation demonstrated the system’s efficacy in maintaining a constant switching frequency under dynamic changes. In charging mode, the average frequency was 14.88 kHz (with a range of 0.79 kHz), and the efficiency was 85.74%, while the fixed hysteresis band has a frequency variation of 14.26 kHz to 20.66 kHz (with a range of 6.37 kHz). In discharge mode, the average frequency was 16.62 kHz (with a range of 0.33 kHz), the efficiency was 77.40%, and the fixed bandwidth is approximately 2.3 kHz on frequency from 15.82 kHz to 18.12 kHz. With adaptive control, the frequency change range is successfully constant with a small range

Impact of Steady-State Error Minimization on the Performance of Numerical Optimization Techniques in Linear Automatic Control Systems

This paper investigates the impact of steady-state error minimization on the performance of numerical optimization techniques in linear automatic control systems, introducing a novel framework that integrates advanced genetic algorithms and machine learning to enhance controller tuning It highlights the significance of selecting appropriate test signals to generate quality system responses, which directly affects stability and reliability. Various optimization techniques are discussed, including classical methods and modern algorithms such as genetic algorithms and machine learning. Special attention is given to astatic control, which minimizes static errors and enhances controller reliability. Experimental results reveal that optimizing for one signal type can significantly diminish performance for another type. The paper introduces trade-offs that facilitate simultaneous consideration of performance responses to various stimuli. The conclusions underscore the importance of carefully selecting test signals and provide recommendations for automatic control practitioners, ultimately leading to improved reliability and efficiency in systems under dynamic conditions

H-infinity Robust Controller for Precise Temperature Regulation in an Agricultural Growth Chamber Prototype

Temperature regulation is crucial for crop yield optimization in controlled environment agriculture, yet achieving such accuracy is challenging due to system nonlinearities and external disturbances. Since H_(∞ )control is an established theory, its experimental validation on low-cost hardware for agricultural systems remains limited. This paper presents robust control for a nonlinear system, targeting an internal temperature of growth chamber agriculture. Moreover, the primary contribution is the demonstration of a systematic and practical methodology for designing, implementing, and validating an H_(∞ ) controller on an Arduino-based growth chamber prototype, bridging the gap between complex control theory and accessible implementation. A simplified linearized thermal model was derived from a lumped parameter approach using energy balance equations. A second-order weighting function was systematically designed using loop-shaping principles to guarantee robust performance against unmodeled dynamics and sensor noise. The resulting controller was synthesized in MATLAB and deployed on an Arduino Mega microcontroller for experimental testing. Simulations predicted high-precision tracking with a Root Mean Square Error (RMSE) of 0.037 °C and an Integral Absolute Error (IAE) of 0.70. Subsequent experimental validation under real-world conditions confirmed the controller's efficacy, achieving stable temperature regulation within ±2 °C of the set point. The experimental validation yielded an RMSE of 1.04 °C and an IAE of 0.924, highlighting a notable but analyzed performance gap between the idealized simulation and the physical implementation. The results of this work were also compared with MPC and PID controllers, showing the proposed approach demonstrated satisfactory performance and confirming the robustness and stability of the control strategy in practical conditions. This work concludes that the H_(∞ ) framework provides a computationally efficient pathway to achieving robust temperature control on accessible hardware, making advanced control techniques more feasible for distributed agricultural applications

Hybrid Administrative Accountability Model: Testing Indonesian-Brazilian Mining Discretion on Scientific General Principles

The recently established strategic partnership between Indonesia and Brazil, entailing a total investment of US$5 billion in the energy, mining, and sanitary/phytosanitary sectors, necessitates accountable governance in public administration to mitigate significant policy risks. Prior study has partially demonstrated the ineffectiveness of the State Administrative Law (HAN) tool. This dysfunction is apparent in the overlapping permits and inadequate compliance sanctions within the mining and coal industry, further intensified by the susceptibility of State-Owned Enterprise (SOE) hybrid governance in foreign collaborations. This discrepancy engenders a substantial accountability deficit. This essay seeks to evaluate and refine the Hybrid Administrative Accountability Model within the framework of state authority discretion. This work employs a normative legal methodology alongside a comparative administrative law approach to operationalize the Scientific Evidence Principle, which is robust within the SPS system, as a requisite General Principle of Good Governance (GPGG) applicable to all natural resource regimes. The findings indicate that the implementation of this scientific GPGG is essential for addressing the deficiency of legal clarity and regulating the discretion of administrative officials. The suggested paradigm ensures accountability, legal clarity, and transparency, so substantially mitigating the risk of failure in executing bilateral agreements

A Dual-Model Spatiotemporal Flood Prediction System Using Sentinel-1 SAR Imagery and Meteorological Data: A Case Study in Palembang, Indonesia

Floods remain one of the most destructive and frequent natural hazards, especially in urban river basins like Palembang, Indonesia. Improving early warning systems through the integration of radar and meteorological data has become increasingly feasible with advances in remote sensing and machine learning. The research contribution is the development of a dual-model spatiotemporal prediction framework that combines Sentinel-1 SAR imagery and meteorological data using eXtreme Gradient Boosting (XGBoost) for spatial classification and Long Short-Term Memory (LSTM) for temporal forecasting. Radar backscatter values were extracted from Sentinel-1 Single Look Complex (SLC) data and processed into one-kilometer resolution grids using SNAP and QGIS. These were merged with weather variables precipitation, humidity, wind speed, and solar radiation sourced from local meteorological stations. The XGBoost model achieved a precision of 98.3%, accuracy of 99.94%, recall of 97.5%, and F1-score of 97.9%, with SHAP analysis identifying rainfall and wind speed as dominant flood predictors. Spatial predictions aligned closely with historically flood-prone areas along the Musi River. In contrast, the LSTM model, despite forecasting floods up to 12 days in advance with average accuracy of 91.6%, suffered from class imbalance, resulting in a recall of only 22.9% and precision of 36.3%, limiting its applicability for real-time early warning. These findings demonstrate that while spatial classification using radar and weather data is highly effective, temporal forecasting remains challenged by data imbalance and uneven class distribution. Future research should explore cost-sensitive learning, uncertainty quantification, and real-time validation to enhance the system’s operational reliability

Antioxidant Properties and Storage Behavior of Malayan Cherry Fruit Water Extracts

The fruits of the Malayan cherry (Muntingia calabura L.) have been used for diabetes management in Banyumas due to their potential antioxidant properties. This study aims to optimize water extraction for antioxidant properties and determine the organoleptic and physicochemical properties of the selected extracts during 18 days of storage. The effects of the extraction method (infusion, 15-min decoction, 30-min decoction) and the drug-to-solvent ratio (1:10, 1:20, 1:100) on total flavonoid content (TFC), total phenolic content (TPC), and antioxidant activity, measured by DPPH scavenging activity and FRAP methods, were evaluated using their respective standard assays. The selected extract (from infusion at a 1:20 ratio) was stored in airtight bottles at 4±2 °C for 18 days. The organoleptic, pH, and antioxidant properties stability was evaluated accordingly. The crude drug-to-solvent ratio significantly affected TFC, with the 1:100 ratio generating the highest value (6.43 mg QE/g DW). Both the extraction method and the solvent ratio significantly defined TPC, DPPH scavenging activity, and FRAP, with the infusion at a 1:20 ratio showing the highest TPC (4.15 mg GAE/g DW), DPPH scavenging activity (25.2 mmol TE/100 g DW), and FRAP (93.93 mmol TE/100 g DW). Infusion at a 1:20 ratio showed significant decreases in pH, TFC, TPC, and DPPH scavenging activity on day 6 of storage, whereas changes in aroma and taste were observed on day 12. The infusion method at a 1:20 ratio is optimal for extracting antioxidants from Malayan cherry fruit extract. However, its application is limited by significant bioactive degradation during storage, which needs to be addressed with the development of a suitable dosage form

Stability Optimization of Variable Frequency Drives Using Sliding Mode Control with Linear Matrix Inequalities for Multi-Agent Systems

Variable Frequency Drives (VFDs) serve as essential elements for modern industrial operations which focus on enhancing energy efficiency and decreasing operational expenses. These systems encounter major stability issues when they operate under adverse conditions which include sudden load changes and power disturbances and delays in signal processing and mechanical system responses. Sliding mode control (SMC) has proven to be an effective solution because it provides adaptable monitoring techniques which also maintain system stability. The research study delivers its main contribution through the implementation of linear matrix inequality (LMI) method within the SMC framework to enhance stability in multi-agent VFD systems. The proposed technique operates to direct the switching functions of the DC link DC-DC CUK converter in the subject system. The subject system exists in mathematical form which allows researchers to study its behavior when exposed to standard input testing signals and its stability characteristics. The system maintains its stability during quick load variations which proves that the control method produces better results for systems that manage speed and torque in motor groups. The system stability during fast load variations proves that the control method produces better results for motor group speed and torque control systems which makes the technology suitable for transportation systems and renewable energy systems and other applications. Standard VFDs present challenges because they require specific motor types and operate with various communication protocols and expensive components. The authors indicate that it should be investigated to gauge the feasibility of having more advanced control algorithms incorporated with the suggested control system; enhancing its adaptability and performance control

Hambatan Pemenuhan Hak-Hak Anak sebagai Pelaku Tindak Pidana Pelecehan Seksual di LPKA Kelas II Yogyakarta

Currently, children are not only victims of sexual abuse but also perpetrators, so they must be held accountable for the consequences of their actions at Special Child Guidance Institutions (LPKA), one of which is the LPKA in Yogyakarta. At the LPKA, children, as perpetrators, still receive their full rights as children, even though there are limitations and differences in their implementation. Based on this, this paper will further discuss the obstacles that affect the fulfillment of children's rights at the LPKA. The research method used is normative empirical, with data collection techniques through interviews, observation, questionnaires, and documentation studies. The results of the study show that the LPKA in Yogyakarta strives to create an environment that supports the rehabilitation and reintegration of child offenders, but the main obstacles affecting the fulfillment of children's rights in an education, health and recreation include limited facilities, a lack of competent professionals, and constraints in the existing system. This paper recommends the need for improvements in regulations, facilities, and existing systems to optimize the fulfillment of children's rights at the LPKA in Yogyakart