This article analyses the energy transition trajectories of 25 African countries, combining machine learning and econometric methods. Initially, a Dynamic Time Warping (DTW)-based partitioning approach is used to divide countries into three groups with distinct socio-economic and energy profiles. The Light Gradient Boosting Machine (LightGBM) model is then used to evaluate the significance of macroeconomic and structural variables. A Pooled Autoregressive Distributed Lag (Panel ARDL) model is then applied to each group to examine the short- and long-term relationships between macroeconomic and structural variables and renewable energy consumption. The results demonstrate consistency in the importance of variables identified by a Long Short-Term Memory (LSTM) model and their long-term effects within the Panel ARDL framework, thereby showcasing the robustness of the approach. The analysis reveals different dynamics: the first group is hindered by macroeconomic vulnerabilities such as financial instability and high debt, whereas the second group enjoys more favourable conditions. These results provide a basis for developing policies tailored to the specific contexts of each group to accelerate the energy transition in Africa. Thus, the study contributes to a better understanding of the key factors and helps to guide sustainable development strategies on the continent.
The fourth industrial revolution, or Industry 4.0, is fundamentally transforming manufacturing through the integration of cyber-physical systems, the Internet of Things (IoT), big data analytics, artificial intelligence, and intelligent automation. Despite its potential benefits, digital transformation remains challenging because it requires substantial investment, workforce capability development, and organizational change. Existing Industry 4.0 maturity models inadequately address systematic criteria weighting and uncertainty in digital maturity assessment, limiting their ability to provide comprehensive and decision-oriented evaluations. This study develops a seven-dimensional Industry 4.0 digital maturity framework by integrating the Analytic Hierarchy Process (AHP) and the Fuzzy Inference System (FIS). AHP is employed to derive expert-based priority weights among maturity dimensions, while FIS accommodates uncertainty and subjectivity in qualitative assessments through fuzzy reasoning. The research methodology comprises model conceptualization, criteria weighting using AHP, maturity evaluation using FIS, and validation through a case study of an automotive manufacturing company. The findings indicate that the Strategy, Culture and Expertise, and Organization and Change Management dimensions receive the highest priority weights, while Intelligent Manufacturing achieves the highest maturity score. The case organization obtained an overall maturity index of 0.73, corresponding to Stage 4, which indicates a high level of digitalization. The proposed AHP–FIS framework provides a structured, adaptive, and data-driven approach for evaluating Industry 4.0 maturity and offers decision support for prioritizing digital transformation initiatives and planning continuous improvement. The findings demonstrate the practical feasibility of the framework within the investigated automotive manufacturing context and provide methodological insights for future development of Industry 4.0 maturity assessment models.
Graph-based representations provide a useful systems-level framework for modelling interactions among structure, dynamics, and behaviour. This paper proposes a dual-graph framework for modelling indoor movements and activities. The first layer is a location graph that represents feasible movement through the spatial connectivity of an indoor environment. The second layer is a mixed causal/contextual activity graph that combines directed activity dependencies with undirected contextual associations. The two layers are coupled through an activity-to-location mapping, yielding a probability-preserving dynamical model in which spatial occupancy is jointly influenced by graph-constrained movement and activity-driven spatial expectations. Two features distinguish the proposed framework from conventional dual-graph models. First, the activity layer is explicitly constructed as a mixed directed/undirected network and second, a cross layer coupled mismatch residual framework is proposed to detect inconsistencies between semantic activity evolution and observed movement. The paper also establishes the probabilistic properties of the movement operator, discusses manual and data-driven construction of the interlayer mapping and introduces an optional reverse-coupling extension. Simulations in a six-location living environment examine the effects of the activity-mixture parameter, the mapping matrix, and the coupling gain. The results support the framework as an interpretable basis for indoor behaviour modelling and also highlight some of its limitations for future studies.
This study suggests a hybrid model of prediction and anomaly detection of dynamic network based on graph density time series. The main issue that is being tackled is that traditional linear models cannot explain non-linear structural shocks and volatility clustering that are facts in cyber network data. The methodology proposed implies turning network flows of the UNSW-NB15 dataset into dynamic graph snapshots, deriving graph density as a scalar measure, and stabilizing the series by converting it to log-returns. The existence of the “fat tails” and non-Gaussian shocks which cannot be detected using traditional statistical tools was verified by the use of advanced diagnostic tests, like Kurtosis and Jarque-Bera test. As a result, a hybrid model that was a combination of the autoregressive moving average (ARMA) and exponential generalized autoregressive conditional heteroscedasticity (EGARCH) was applied. This research used the selection of the ARMA ($p$, $q$)-EGARCH ($u$, $v$) model as the best specification in terms of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The result of the hybrid model had an accuracy with running time spent in predictive and anomaly detection. Compared with two different methods, the methodology of ARMA ($p$, $q$)-EGARCH ($u$, $v$) has demonstrated the highest level of anomaly detection with a decrease in time processing in prediction and detection processes. This paper shows that structural graph analysis with modeling can be used to increase the resilience and sensitivity of intrusion detection systems.
Engineering systems based on integrated circuits (IC) and microsystem technologies (MST) increasingly rely on materials whose behaviour is governed by coupled thermal, mechanical, and transport processes. Among these materials, borophosphosilicate glass (BPSG) thin films deposited by chemical vapor deposition (CVD) exhibit distinctive low-temperature flow characteristics that critically influence device-level performance. This study aims to provide a physically grounded synthesis of the mechanisms governing the flow behaviour of low-temperature BPSG thin films and to examine their functional roles across IC, MST, and optical device technologies. The analysis integrates reported experimental observations and process data to interpret BPSG behaviour in terms of thermo-viscous flow, compositional dependence, and surface evolution under thermal treatment. The results show that the reduced glass transition temperature induced by boron and phosphorus incorporation enables controlled viscous flow at temperatures as low as approximately 700–800 ℃, leading to effective surface planarization, void elimination, and geometry reconfiguration in complex device reliefs. The interaction between thermal activation, film composition, and structural constraints governs key performance outcomes, including planarization efficiency, gap-filling capability, and stress evolution. In MST and optical applications, the same flow mechanisms enable the formation of sealed cavities, microfluidic channels, and optically functional structures such as microlenses and waveguide cladding layers. It is concluded that the engineering functionality of BPSG films arises from the coupled interaction between thermal processes, material composition, and geometrical confinement. This work provides a unified interpretation of these mechanisms and highlights their implications for process optimisation and device design in integrated and multiphysics engineering systems.
The sustainability of traditional cultural products relies on the capacity of producers to transform consumers' preferences into product design and marketing approaches. In the batik micro, small, and medium enterprises (MSME) context, Generation Z (Gen Z) is one of the key emerging market segments whose preferences depend not only on cultural meanings but also on symbolic value, aesthetics, usability, and relevance of the product. Despite the fact that the Theory of Planned Behaviour (TPB) is extensively applied in explaining purchase intention, only few researchers have attempted to apply this theory to help make product design and management decisions in heritage-based MSMEs. Thus, this study is intended to investigate the impact of symbolic value and aesthetic value on Gen Z’s purchase intention towards traditional batik using an extended TPB model. A quantitative research approach was chosen and 208 participants from Gen Z were surveyed in three cities, namely Yogyakarta, Palembang, and Makassar. Structural equation modelling (SEM) was applied to assess the relationships. The results reveal that symbolic value has significant positive effects on attitude (Estimate = 3.077, $p$ = 0.018), subjective norms (Estimate = 2.046, $p$ = 0.017), and perceived behavioural control (Estimate = 1.677, $p$ $<$ 0.001). On the other hand, aesthetic value don’t significantly affect subjective norms (Estimate = -1.348, $p$ = 0.104) and perceived behavioural control (Estimate = -0.994, $p$ = 0.011), but it have a significant negative effect on attitude (Estimate = -2.312, $p$ = 0.068). Attitude and behavioural control significantly influence purchase intention, whereas subjective norms do not. Purchase intention has a significant positive effect on behaviour. The model demonstrates explanatory power, with $R^2$ values of 0.72 for purchase intention and 0.70 for attitude. These findings contribute to engineering management by showing how SEM-based consumer insights as a decision-support can guide batik MSMEs in product design, product-line segmentation, pricing accessibility, and youth-oriented market adoption..
This work developed a machine learning (ML) model for predicting the minimum film thickness ($h_0$) in plain journal bearings. A reference dataset comprising 376,349 datapoints was generated from the approximated pressure journal design method developed by Reason and Narang. The inputs in the dataset included geometric and operational features: Society of Automative Engineers (SAE) oil grades, speed (500–10,000 rpm), bearing load (1,500–9,500 N), bearing length/diameter (0.01–0.08 m), clearance (0.00001–0.00006 m), and initial oil temperature (40–70 ℃). Four supervised learning models, i.e., Artificial Neural Network ($h_0$_ANN), Gaussian Process Regression ($h_0$_GPR), Ordinary Least Squares Regression ($h_0$_OLS), and Ridge Regression ($h_0$_Ridge) were developed and evaluated. The results indicated that Artificial Neural Network (ANN) provided the most accurate predictions via achieving an $R^2$ value of 0.99. The GPR model obtained an $R^2$ value of 0.98 while those of the OLS and Ridge Regression achieved $R^2$ of 0.95 and 0.93, respectively. Having shown superior predictive capabilities, the ANN model was selected for further statistical evaluation. Analysis from Local Interpretable Model-agnostic Explanations (LIME), Shapley Additive exPlanations (SHAP), and radar plots all proposed that the ANN model offered stable predictions within the considered parametric space. The model was presented in an explicit mathematical form in an Excel spreadsheet; its computational efficiency was recommended for the design of plain journal bearings.
The transition toward sustainable urban mobility in emerging economies requires not only technological innovation but also a deeper understanding of market adoption dynamics. This study examines the behavioral determinants of the adoption of solar-assisted micro-mobility among micro, small, and medium enterprises (MSMEs) in urban Indonesia. Positioned at the intersection of renewable energy integration and sustainable transportation systems, the research investigates how environmental concern, performance expectancy, operational cost, and price shape users’ attitudes and subsequently influence adoption intention. A quantitative approach was employed using survey data collected from 300 MSME operators, and the relationships among constructs were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that attitude serves as a central mediating mechanism through which environmental, technological, and economic factors are significantly associated with adoption intention. Among the predictors, performance expectancy emerges as the most influential driver, followed by environmental concern, while operational cost and price play supporting but significant roles within a value-based evaluation framework. The findings highlight that adoption of solar-assisted mobility is primarily driven by perceived functional benefits and sustainability value rather than cost considerations alone. This study contributes to the sustainability literature by advancing a holistic behavioral adoption model for renewable energy-based mobility in emerging urban contexts. The results provide practical implications for policymakers and industry stakeholders in designing strategies to accelerate the diffusion of low-carbon mobility solutions, particularly among resource-constrained MSMEs.
The catastrophic 2021 eruption of Mount Semeru severely degraded local ecosystems, necessitating rapid pioneer vegetation establishment to restore landscape stability. While conventional satellite imagery often obscures critical micro-scale ecological interactions, ultra-high-resolution unmanned aerial vehicles (UAVs) provide an unprecedented tool for assessing post-disaster resilience. This study aimed to quantify the multitemporal recovery rate and model the micro-topographic constraints dictating the spatial heterogeneity of pioneer colonization. A 127.08-ha primary lahar corridor within Volcanic Hazard Zone III was mapped using UAVs (2.7 cm/pixel) over a three-year period (2022, 2023, 2025). Vegetation cover was extracted using the Excess Green (ExG) index and automated Otsu thresholding. The methodology was rigorously validated using 300 multitemporal ground-truth points, yielding an overall accuracy of 93.00%, an F1-score of 0.933, and a Cohen’s Kappa coefficient of 0.860, confirming exceptional classification reliability. Post-classification spatial analysis revealed a net positive vegetation expansion rate of +1.88 ha/year, indicating a successful transition from initial colonization to ecological stabilization. However, recovery was profoundly heterogeneous, governed entirely by geomorphological constraints rather than uniform macro-climatic factors. Population-based zonal statistics identified significant ecological preferences for higher peripheral elevations (mean 3,081.0 m), gentler slopes acting as natural seed traps (45.5°), and Southwest-facing aspects that provide essential micro-climatic buffering against equatorial desiccation. These findings offer a strategic paradigm shift for environmental management, recommending that artificial restoration and erosion control interventions in active stratovolcanoes be precisely targeted at these highly resilient micro-topographical hotspots to accelerate the formation of a natural biological shield.
For Authors
For Reviewers
For Editors
Open Access
