The adoption of intelligent transportation systems (ITS) in developing urban environments is shaped by interrelated institutional, technological, infrastructural, and socioeconomic factors. Before structural relationships among these factors can be interpreted with confidence, the psychometric properties of the measurement model must be established. This study validated an integrated ITS adoption framework for Yogyakarta, Indonesia, comprising eight latent constructs: funding/budget, government policy, infrastructure, technology, social economy, social affordability, smart readiness, and the ITS adoption framework. Data from 300 respondents were analyzed using partial least squares structural equation modeling in SmartPLS 4. The measurement model was evaluated for internal consistency reliability, convergent validity, and discriminant validity. Cronbach’s alpha and composite reliability values exceeded 0.70 across all constructs, and average variance extracted values exceeded 0.50. The Fornell–Larcker criterion and heterotrait–monotrait ratio (HTMT) further supported discriminant validity. These findings indicate that the proposed measurement model is reliable and valid, providing a methodological foundation for subsequent structural-model evaluation and hypothesis testing. The validated instrument can support rigorous investigation of ITS adoption determinants and evidence-based transportation planning in developing urban settings.
This study explored the association between sensory marketing measures and client experience in an Algerian beauty clinic. This study adopted a descriptive-analytical approach, based on a questionnaire administered to a convenience sample of 30 female clients of the clinic. Both descriptive and inferential statistical analyses were conducted to test the research hypotheses. The findings revealed that the overall application of sensory marketing in the clinic was positively associated with client experience. The pre–post comparison showed a statistically significant difference in overall client experience between the two measurement phases. Following the intervention, significant associations were observed between the sensory stimuli (sight, sound, smell, taste, and touch) and client experience. Among these five dimensions, sound showed the strongest association, followed by sight and taste. This research offered practical insights into how sensory marketing could be leveraged as a tool for enhancing client experience. Understanding changes in clients’ perceptions could help inform service improvements and sensory-marketing decisions.
Achieving sustainable development requires institutional arrangements capable of integrating social equity, economic inclusion, and environmental stewardship within coherent governance frameworks. Although Islamic social finance (ISF)—particularly almsgiving (Zakat) and charitable endowments (Waqf)—has been extensively discussed in relation to poverty alleviation and welfare governance, its role within sustainability transitions and institutional governance innovation remains insufficiently theorized. This study develops a conceptual framework that integrates institutional theory, the multi-level perspective (MLP) on sustainability transitions, and social-ecological systems (SES) analysis to examine how ISF may function as a transition-oriented institutional configuration. Drawing on a comprehensive review of 278 peer-reviewed and institutional sources, the analysis identifies four recurring institutional mechanisms: regulatory embedding, strategic resource reallocation, hybrid governance experimentation, and legitimacy reframing. These mechanisms operate across landscape, regime, and niche levels and are illustrated through documented governance models, including integrated Zakat-Waqf-microfinance systems and pilot-scale initiatives reflecting emerging environmental orientation within ISF practice and literature. The study further highlights structural constraints, including regulatory fragmentation and nascent environmental measurement approaches, that condition the extent to which these instruments may support sustainability-oriented institutional adaptation. The findings suggest that Zakat and Waqf possess institutional characteristics compatible with sustainability transitions when embedded within coherent policy and governance frameworks. Rather than assuming automatic transformative potential, the study positions ISF as a context-dependent institutional pathway that may contribute to inclusive development and, under specific regulatory and institutional conditions, to governance frameworks where environmental priorities are institutionally integrated.
Financial Inclusion (FI) can have a significant role in Renewable Energy Transition (RET) in any region. This study investigates this nexus in the Environmental Kuznets Curve (EKC) framework of the resource-rich and geographically closed Gulf Cooperation Council (GCC) economies from 2000–2024. The Spatial Autoregressive (SAR) model is applied to this relationship due to economic, geographic, and policy interdependencies in the GCC region. RET is captured by Renewable Energy Output (REO) and consumption to analyze both demand- and supply-side proxies of the RET. The results show that RET in one economy has positive spillovers in the neighboring economies. However, FI negatively influences the RET in both proxies of the REO and Renewable Energy Consumption (REC). Thus, FI could not support the RET in the GCC region. Income per capita has a U-shaped effect on the RET. Therefore, economic growth can support the RET after a threshold point. Moreover, Foreign Direct Investment (FDI), Trade Openness (TO), and Human Capital (HC) promote the RET. The findings have significant implications for sustainable development in the GCC region. The results suggest that the GCC governments should support the financial sector in financing the renewable energy sector. Moreover, supporting globalization and HC development can enhance the RET. The positive spillovers also suggest regional coordination on sustainable energy policies.
Wrong-way driving (WWD) is one of the most dangerous traffic behaviors, which mainly causes head-on collisions resulting in death. Traditional methods of detection, such as loop detectors and manual surveillance, are often inadequate due to high costs, limited coverage, and delayed response times. In this paper, we present a new real-time computer vision-based framework for automatic detection of WWD instances. The system uses the latest You Only Look Once version 9 (YOLOv9) object detection model for strong and fast vehicle identification. Additionally, a multi-object tracking algorithm is used, which allows the system to keep track of the vehicles’ identities across the video frames. The fundamental part of our approach is the arrangement of consecutive virtual detection zones on the road; a vehicle is accused of a WWD violation if it moves through these zones in the wrong order. Our experimental results show that the framework can be a practical and efficient method for obtaining high accuracy and real-time performance. This system presents great promise for practical use after conducting additional experiments with longitudinal and multi-camera models. Besides, it is a cheap and handy method of making roads safer on highways and city streets.
This paper presented a computational investigation of micromagnetorotation (MMR) and thermal transport characteristics in steady two-dimensional Williamson nanofluid flow over a stretching sheet. The mathematical model incorporated the non-Newtonian behavior of the Williamson fluid together with micromagnetorotational effects, magnetic field influence, viscous dissipation, and heat transfer mechanisms. By employing appropriate similarity transformations, the governing partial differential equations were reduced to a system of coupled nonlinear ordinary differential equations (ODEs). The resulting boundary-value problem was solved numerically using a shooting technique combined with the Runge–Kutta method. The effects of key physical parameters—including the Williamson parameter, magnetic parameter, MMR parameter, micropolar parameter, Prandtl number, and Eckert number—on the velocity, microrotation, and temperature distributions were examined in detail. The numerical results revealed that increasing the Williamson parameter suppressed the fluid velocity and enhanced non-Newtonian resistance within the boundary layer. Higher magnetic field strength reduced the momentum boundary-layer thickness due to the Lorentz force, while MMR significantly altered the rotational dynamics of fluid microelements. Furthermore, thermal transport was enhanced by viscous dissipation, leading to higher temperature distributions and reduced heat transfer rates at the surface. Variations in the skin-friction coefficient and local Nusselt number were reported. The current findings provided useful insights into the design and optimization of thermal systems involving non-Newtonian nanofluids subjected to micromagnetic rotational effects.
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 Panel 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.
As a feeder port for the Eastern Indonesia region, Anggrek Port is expected to reduce logistics frictions and stimulate regional growth in Gorontalo Province. This study examines how port infrastructure performance influences logistics performance and economic growth by employing a cross-sectional survey ($n$ = 150) involving managers, service providers, and users, analyzed using PLS-SEM (SmartPLS 4.0). The reflective measurement model meets conventional reliability and validity thresholds, and the structural relationships were assessed through bootstrapping. The findings indicate a strong and significant direct effect of port infrastructure performance on economic growth ($\beta$ = 0.679, $p <$ 0.00; $R^2$ = 0.574), whereas no significant effects were identified between infrastructure and logistics performance ($\beta$ = 0.236, $p$ = 0.256) or between logistics performance and economic growth ($\beta$ = 0.192, $p$ = 0.375). These results underscore that the dynamics linking port infrastructure, logistics performance, and economic growth cannot be fully understood through a purely linear structural approach. Based on the observed relational patterns, the policy implications highlight that enhancements to Anggrek Port’s physical infrastructure currently generate more immediate and substantial economic impacts than improvements to its logistical systems.
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.
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