Predictive Pontryagin Optimal Control of Nonlinear Fully Distributed DCS and CPS under Reliability and Uncertainty Constraints

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The article provides an overview of the optimal control prediction framework for non-linear CPS and DCS in automation. The core issue is the fact that the present architecture of modern automation systems does not behave like a closed loop control system anymore. The design of these systems combines sensors, actuators, edge controllers, communication channels, digital twin, software-as-a-service, human involvement, and states of cyber-security. Thus, control design must not only address accuracy but should be a multicriteria design problem that accounts for reliability, uncertainty, probability mass, delay in communication, and energy costs. Four levels are considered in the proposed framework. The first level refers to forecasting based on hybrid ARIMA-ML model for a short horizon. The second level is concerned with estimating of risk states using Markov model/HMM.  The contribution is a simulation-ready mathematical architecture in which each node solves a local Hamiltonian problem using predicted states, neighbour information and reliability constraints, while the global CPS behavior emerges through networked local decisions. The paper formulates the nonlinear dynamics, cost functional, Hamiltonian conditions, reliability constraints and evaluation protocol for smart factories, smart grids, intelligent buildings and smart campuses. The framework is positioned as a bridge between predictive maintenance and optimal distributed automation control.

Developing Van Hiele-Based Project Learning Tools through Plastic Waste Paving Block Design on Similarity Materials

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This study aimed to develop Project Based Learning (PjBL) instructional tools through plastic-waste paving block design based on Van Hiele theory that are valid and practical for learning similarity topics. The study employed a Research and Development (R&D) approach using the 4D model developed by Thiagarajan, consisting of the define, design, develop, and disseminate stages. The participants were seventh-grade students of SMP Science Quran Al Irsyad Al Islamiyyah Jember in the 2025/2026 academic year, consisting of an experimental class and a control class. Data were collected using validation sheets, observation sheets, student response questionnaires, and readability instruments. The results indicated that the developed instructional tools met the validity criteria, with validity scores of 3.76 for the teaching module, 3.67 for the student worksheet, and 3.72 for the user guide. The practicality criteria were also achieved, as indicated by a learning implementation score of 3.5 (high category), student activity of 90% (very good category), and student responses of 92% (very positive category). These findings indicate that Project Based Learning instructional tools integrating Van Hiele theory and plastic waste paving block design are valid and practical for supporting geometry learning on similarity topics through authentic project activities involving plastic-waste paving block design.

Implementing the 5S Program to Reduce Waste: A Lean Maintenance Approach

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This study aims to evaluate the implementation of the 5S program as a strategy to enhance maintenance effectiveness, reduce waste, and improve productivity and operational quality in the workplace. The research uses a case study approach with descriptive analysis to identify activities that contribute to inefficiencies in the maintenance process. Big Picture Mapping is applied to visualize the current value stream and identify potential sources of waste, while a work area audit assesses the initial condition of the operational environment. The study focuses on companies implementing lean principles and requiring continuous improvement in maintenance practices. The findings indicate that the application of the 5S methodology—Seiri (Sort), Seiton (Set in Order), Seiso (Shine), Seiketsu (Standardize), and Shitsuke (Sustain)—creates a more organized and efficient workplace. This structured system improves workflow, enabling tasks to be completed more quickly and accurately. Furthermore, it enhances productivity, stabilizes quality, and supports better alignment with customer expectations. The program also promotes a culture of discipline and continuous improvement, contributing to sustainable operational performance and operational excellence.

The Effect of Digital Payment on MSME Performance: Mediating Role of Transaction Velocity and Security

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Within an Accounting Information System (AIS) framework, digital payments function as automated data-capture tools integrated into the revenue cycle. By using digital payments or payment gateways, organizations can record transactions accurately and in real time, thereby eliminating manual processes that are susceptible to human error. A robust AIS ensures that all cash inflows are authorized and recorded, which supports reliable financial reporting and objective measurement of micro, small, and medium enterprise (MSME) performance. Digital payments further enhance financial and operational outcomes for MSMEs by increasing transaction velocity and security. The present study investigates and empirically validates (a) the causal relationships among digital payment, transaction velocity, transaction security, and MSME performance, and (b) the mediating roles of transaction velocity and security in the relationship between digital payment and MSME performance. Data were collected through a survey using questionnaires distributed to respondents selected via purposive sampling. The study uses primary data and focuses on four constructs: digital payment, transaction velocity, transaction security, and MSME performance, each measured by 21 reflective indicators. Respondents included MSME entrepreneurs at the managerial level (owners or managers) operating in the wholesale and retail, culinary, fashion, agribusiness, and service sectors. The survey targeted MSMEs on the islands of Java, Bali, and Sumatra, yielding 145 valid data points. Structural equation modeling with partial least squares was employed for data analysis, using SmartPLS version 3.29 for data processing and hypothesis testing. All hypotheses concerning the direct effects of exogenous constructs on endogenous constructs were significantly supported. Furthermore, empirical findings indicate that transaction velocity and security mediate the causal relationship between digital payment and MSME performance.

Cross-Country Transfer Learning for FDI Forecasting in Small Macroeconomic Datasets

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This study examines whether transfer learning can improve machine learning performance under data-scarce forecasting conditions. The empirical application focuses on forecasting foreign direct investment (FDI) inflows using CatBoostRegressor. The source domain is represented by a broad international panel of countries, while the target domain consists of Central Asian economies.

CatBoost is evaluated in two settings: without transfer learning, where the model is trained only on target-region data, and with transfer learning, where the model is first pretrained on the source domain and then fine-tuned on the target domain. The results are compared with simple baseline models, including a lagged-target naive model and a regional mean benchmark.

The findings show that transfer learning substantially improves predictive accuracy. Compared with the target-only CatBoost model, the transfer learning model reduces RMSE from 4.0421 to 3.0426 and MAE from 3.3504 to 2.3369. also improves from − 5.8205 to − 0.0761. These results suggest that transfer learning can help stabilize machine learning forecasts when target-domain observations are limited.

The Effectiveness of the Duolingo Platform in Vocabulary Retention and EFL Young Learners’ Attitudes: Evidence from a Private English Center in Vietnam

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This article investigates the effectiveness of Duolingo in supporting vocabulary retention among young EFL learners and examines their attitudes toward its use. Employing a sequential explanatory mixed-methods design with a quasi-experimental pre-test/ post-test control group framework, the study was conducted over a 12-week intervention period with 60 learners aged 10 to 11 (CEFR Level A2) at a private English center in Ho Chi Minh City, Vietnam. The experimental group (n = 30) used Duolingo as a supplementary after-school vocabulary tool for approximately 30 minutes per day while the control group (n = 30) received conventional classroom-based instruction only. Quantitative data were collected through researcher-designed parallel-form vocabulary tests and a 25-item Likert-scale questionnaire grounded in the Affective-Behavioral-Cognitive (ABC) attitude model. Qualitative data were gathered through semi-structured interviews with ten selected participants. Paired-samples t-tests revealed that the experimental group achieved a statistically significant mean gain of 0.97 points (t = -13.760, p < .001), approximately double that of the control group (0.50 points; t = -6.412, p < .001), despite equivalent baselines. Questionnaire findings indicated strongly positive attitudes across all three ABC dimensions, with particularly high agreement on enjoyment (90%), motivation (88%) and vocabulary transfer to communicative contexts (93.6%). Interview data corroborated these findings with experimental group learners describing spaced repetition, multimodal input and gamified rewards as key factors supporting retention and engagement. These findings suggest that Duolingo, when implemented as a structured and monitored supplementary tool, can meaningfully enhance vocabulary retention and foster positive learning attitudes among young EFL learners.

Legal Review of Allowances and Calculation of Income Tax (PPh) for Members of the House of Representatives from A Justice Perspective

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Income is one of the tax objects used as the main income for the state. Income tax (PPh) is imposed on tax subjects based on income in the form of salaries, wages, honorariums, allowances, or other forms of payment received by domestic tax subjects as a consequence of the work, position, or services performed, received or obtained during the tax year. The obligation to pay PPh is inherent in individuals according to the principle of personal responsibility. This principle is in line with the provisions of the Law on Income Tax Number 7 of 1983 in conjunction with Law Number 36 of 2008 concerning the Fourth Amendment to Law Number 7 of 1983 concerning Income Tax. However, according to Government Regulation (PP) Number 80 of 2010 and Minister of Finance Regulation (PMK) Number 262/PMK.03/2010, Income tax Article 21 for members of the House of Representatives (DPR) are borne by the Government, not borne by members of the House of Representatives as taxpayers. This paper studied whether the income tax of article 21 members of the House of Representatives borne by the State is in accordance with the principle of justice as a personal responsibility and how the mechanism of imposition of income tax article 21 for members of the House of Representatives is concerned.

Overview of Neuroplasticity in the Application of the Reverse Whole Brain Model in College Students

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Modifications applied when using Whole Brain, from the initial application of the left quadrant to the right, to the change from the right quadrant to the left. The formulation of research questions; What is the modified form of the Whole Brain model?, What are the learning outcomes obtained from the use of the modified Whole Brain model? Can the results obtained from the use of modified Whole Brain models be used as tangible evidence of neuroplasticity?. The researcher used a quantitative descriptive design. This design illustrates how the Reverse Whole Brain Model was used based on the respondents. The researcher uses a questionnaire to collect research data. The questionnaire was developed by the researcher using Slavin’s (1995) Theory of Effectiveness. Researchers also used the Trail Making Test as a data collection instrument. TCR (Respondent Achievement Level/Tingkat Capaian Responden) was used to analyze the questionnaire data. TMT-A was analyzed using parametric tests, and TMT-B was analyzed using nonparametric tests. The Reverse Whole Brain model is considered effective in teaching and learning. The results show that the application of this model in the Listening and Speaking II class is considered an appropriate learning model. It also concluded that the students experience neuroplasticity.

Role of Artificial Intelligence for Modern Organizations Based on Tacit, Explicit, And Reusable Knowledge Resources: A Discussion

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Organizational knowledge management, including human resource management, is the most important mechanism for increasing organizational performance and hence the performance of business. Employees are the most valuable resource, determining a company’s success and growth and enabling its competitiveness in the international market. This article theoretically examines the essential characteristics of organizational knowledge management and the relationship between business performance and human resource management, incorporating artificial intelligence and explicit, tacit, and reusable knowledge. The study demonstrates how organization-wide management of tacit, explicit, and reusable knowledge, including human resource management, can help companies leverage the know-how, skills, competencies, and valuable knowledge of their employees for the company’s development and success. Effective organizational knowledge management is essential for achieving company goals. Organizations should focus on managing explicit and tacit knowledge, as well as ensuring that this knowledge can be reused efficiently. Doing so enhances employee effectiveness by providing access to relevant knowledge and skills, which expands competencies and, in turn, improves overall business performance.

Analog Vs Digital Signals: A Comparative Analysis of Their Roles in Modern Electronics

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Modern electronic and communication systems rely fundamentally on signal processing techniques, where information is represented in either analog or digital form. This study provides a structured comparison between these two signal categories, focusing on their operational behavior, performance characteristics, and practical relevance in contemporary technology.

Analog signals describe physical quantities in a continuously varying manner, offering high fidelity representation of natural phenomena. However, their susceptibility to noise and distortion limits their efficiency in complex systems. Conversely, digital signals represent information in discrete levels, typically binary form, which enhances stability, accuracy, and compatibility with computational systems.

The paper discusses the strengths and limitations of both signal types and highlights their combined use in modern hybrid electronic architectures.