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.

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.

Reproductive Toxicity Effect of Isocycloserum 9.2% w/w DC on Earthworm, Eisenia fetida

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A laboratory study was conducted to find out the reproductive toxicity of Isocycloserum 9.2% w/w DC formulation on the inbred earthworm test species Eisenia fetida maintained in the laboratory.  A preliminary range finding experiment was performed with concentrations of 50, 100, 250, 500 and 1000 mg/kg dry artificial soil of Isocycloserum 9.2% w/w DC. Based on the results of the range finding study, a full-fledged main experiment was conducted with concentrations of 50, 100, 250, 500, 750 and 1000 mg/kg dry artificial soil of Isocycloserum 9.2% w/w DC. Results of the main experiment revealed that there were no biomass changes observed in treated groups when compare to control group, indicating no adverse effects on growth or physiological condition. Similarly, cocoon production and juvenile emergence of earthworm were not significantly affected at any tested concentration of Isocycloserum 9.2% w/w DC, demonstrating that the test chemical does not have any potential adverse effect on the reproductive ability of earthworm.

Molecular Docking Insights into the Interaction of Cyclodextrin-Based Systems with Bioactive Monoterpenes of Biotechnological Interest

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Cyclodextrin-based systems are promising carriers for improving the stability and bioavailability of hydrophobic bioactive compounds. Monoterpenes, the major constituents of medicinal plant essential oils, exhibit significant pharmacological potential but are limited by poor aqueous solubility, high volatility, and chemical instability. This study investigated the structural and energetic interactions of sulfobutylether-β-cyclodextrin (SBE-β-CD) with the major bioactive constituents of essential oil of Alpinia zerumbet (EOAz), through molecular docking simulations. OEAz was obtained by hydrodistillation and complexed with SBE-β-CD (1:1 molar ratio) using the co-precipitation method. The resulting host–guest inclusion complexes, formed between SBE-β-CD and terpinen-4-ol or 1,8-cineole, were evaluated by molecular docking simulations. These compounds were selected based on their established status as major constituents of EOAz. Computational analyses were performed using HEX 8.0.0 software, followed by structural inspection in PyMOL. The docking results revealed energetically favorable interaction clusters, indicating high affinity and structural stability between the evaluated molecules. The most stable conformations demonstrated significant spatial complementarity and multiple intermolecular interactions, including hydrophobic contacts and hydrogen-bond-associated stabilization. Energetic analyses showed negative binding energies compatible with stable molecular association, supporting the formation of persistent host–guest complexes. Furthermore, the predicted host–guest interactions indicate that cyclodextrin complexation may favor the preservation of monoterpene integrity and bioavailability, factors that could contribute to enhanced or more consistent biological activity. Overall, these findings demonstrate the usefulness of molecular docking for investigating cyclodextrin-based systems and provide preliminar structural evidence for future studies exploring the therapeutic and biotechnological applications of monoterpene inclusion complexes.

Dietary Supplementation with Spring Onion on Growth, Nutrient Utilization, Economic Analyses and In Vitro Methane Emissions in West African Dwarf Goats

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The study was conducted to determine the effect of dietary supplementation with spring onion on growth, nutrient utilization, economic analyses and in vitro methane emissions in West African dwarf goats. Two hundred and forty West African dwarf male goats aged between 7 to 8 months old with initial average live body weight of 7.00 ± 0.08kg were used for the study. They were randomly allotted to four dietary treatments with sixty goats per treatment in a completely randomized design. The prepared experimental diets that were received by goats contained ED1 (60% guinea grass with no spring onion as the control group), ED2 (59% guinea grass with 1% spring onion), ED3 (58% guinea grass with 2% spring onion) and ED4 (57% guinea grass with 3% spring onion). Firstly, the evaluation comprised in vitro study of methane gas production and then followed by in vivo study of growth, nutrient utilization and economic analysis of goats. In all the diets examined, results showed that in vitro ammonia nitrogen concentration(12.04 mg/100ml), total volatile fatty acids(4.01 mmol), acetate (64.93 mol/100mol), butyrate (12.09 mol/100mol), methane (71.22 mL/gDM), fractional rate of gas production(0.079h-1), average gas production rate(3.04Ml/h), daily feed intake (256.29g/day), feed conversion ratio (6.15), digestibility of ether extract (65.05%), ash (68.01%), faecal with urinary nitrogen output(2.98 and 1.72g/day), total nitrogen output (4.78g/day) and total cost in naira (125,400) were significantly (p<0.05) higher in diet ED1 than diets ED2, ED3 and ED4. Goats on diet ED4 were significantly (p<0.05) improved in propionate ( 25.67 mol/100mol), methane/total volatile fatty acids ratio (22.23), effective dry matter degradability(60.21g/kg), true dry matter degradability(74.52g/kg), final body weight(11.96kg), total with daily weight gain 4.99kg and 71.29g), digestibility of dry matter(72.43%), crude protein (70.86%), crude fibre (71.02%), nitrogen free extract(70.17%), neutral and acid detergent fibre(69.86 and 66.21%), nitrogen intake (11.05g/day), nitrogen balance and retained (8.08 and 0.73g/day), nitrogen retention % intake (73.12%), total revenue in naria (263, 270) and net profit in naria (169,020) than those on other diets. Meanwhile, no significant differences (p>0.05) were found in rumen pH, acetate/propionate ratio, iso-butyrate, valerate, iso-valerate, asymptotic gas production and initial body weight among treatments. It can be concluded that inclusion levels of 3 and 4% spring onion as supplement to 58 and 57% guinea grass with 40% concentrate diet (ED3 and ED4) improved performance of goats and suppressed in-vitro methane gas with cost reduction that resulted in higher net profit.

Residual-Regime Markov Modelling for Predictive Control in Cyber-Physical Systems

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Cyber-physical systems operate through a continuous interaction between physical processes, computational intelligence, communication networks and control mechanisms. Their behaviour is rarely fully deterministic: sensor noise, delayed communication, nonlinear dynamics, regime changes and early-stage failures create uncertainty that cannot be adequately represented by a single forecasting or control method. This paper proposes an original Residual-Regime Markov Forecast–Control Framework for cyber-physical systems. The framework brings together three modelling layers that play different roles. The ARIMA component handles the basic linear time‑series structure and keeps the model interpretable. On top of that, a machine‑learning layer works on the residuals to capture the nonlinear behaviour that ARIMA cannot. The final layer uses a Markov‑style state representation, turning the forecast errors, system signals and operating conditions into probabilistic regimes that describe how the system is likely to evolve. Unlike classical hybrid forecasting models that stop at prediction, the proposed approach links prediction to decision-making by using Markov transition probabilities, hidden-state belief updates and risk-aware policy selection. The main idea is that forecast errors are not treated only as modelling imperfections; instead, they are interpreted as early indicators of changing system regimes. A simulation-oriented evaluation design is presented for an industrial cyber-physical process with normal operation, peak load and degradation conditions. The proposed framework is expected to improve predictive maintenance, anomaly anticipation and control-policy selection by connecting statistical forecasting, data-driven correction and probabilistic decision logic in a single pipeline. The contribution of the paper lies in transforming hybrid forecasting into a regime-aware forecast–control architecture suitable for intelligent CPS monitoring and adaptive technical management.