Rotating machinery commonly operates under coupled mechanical and electrical excitations, where closely spaced vibration frequencies can generate complex dynamic responses and interfere with accurate fault diagnosis. The beating phenomenon represents a critical form of amplitude modulation in rotating systems and serves as a valuable diagnostic indicator for identifying resonance interactions, electromechanical coupling, and instability mechanisms in industrial equipment. This study investigates the dynamic characteristics of beating phenomena in industrial rotating machinery through analytical modeling, vibration signal analysis, and industrial case studies. A mathematical formulation based on sinusoidal superposition was developed to describe the interaction between adjacent frequency components and the resulting amplitude modulation behavior. Time-domain and frequency-domain analyses were performed to evaluate the relationship between beat frequency, modulation envelope, and vibration response characteristics. Two industrial case studies involving a centrifugal pump and a variable-frequency-drive-driven induction motor were examined using vibration monitoring data, fast Fourier transform (FFT) analysis, envelope analysis, and MATLAB-based numerical simulations. The results demonstrated that closely spaced frequency components generated periodic amplitude modulation and produced distinct beating patterns in both the time and frequency domains. In the pump system, the interaction between vibration components at 202.875 Hz and 202.785 Hz produced a measurable beat response that was strongly associated with unstable vibration behavior. In the variable-frequency-drive-driven motor, interference between the 2X and 2LF components was identified as the primary source of beating and abnormal vibration amplification. The implemented corrective actions, including the elimination of unintended current paths and the installation of an insulated bearing, significantly reduced vibration severity and restored stable operating conditions. The findings indicate that beating behavior is strongly associated with coupled electromechanical interactions and provides valuable diagnostic information for identifying closely spaced excitation sources, bearing degradation, and modulation-induced instabilities in rotating equipment. Furthermore, the combined application of FFT analysis, envelope analysis, and vibration condition monitoring enables the reliable identification of fault-related modulation effects and enhances diagnostic accuracy in complex industrial machinery. The proposed analytical and monitoring framework offers an effective approach for vibration-based condition monitoring, early fault detection, and reliability enhancement in complex industrial machinery systems.
Airport service quality (ASQ) plays a central role in shaping passenger satisfaction, airport competitiveness, and the long-term performance of aviation infrastructure systems. Existing studies increasingly recognise airports not merely as service environments but as integrated infrastructure systems in which transportation networks, operational processes, information systems, and passenger-facing functions interact dynamically. However, current knowledge remains fragmented across service domains, evaluation methods, passenger characteristics, and industry benchmarking practices. This study investigates the analytical foundations and research evolution of ASQ from an infrastructure systems perspective. A systematic literature review was conducted following the PRISMA protocol using a Scopus-based corpus of 303 peer-reviewed publications published between 1976 and 2024, supplemented by major industry reports and benchmarking frameworks. The review synthesised evidence across airport service domains, service attributes, and key performance indicators (KPIs), passenger heterogeneity, survey methodologies, social media-based assessment approaches, and the interaction between airline and ASQ. The results showed that ASQ research has evolved from isolated service evaluation toward increasingly integrated and multi-dimensional assessment frameworks. Processing and non-processing service domains were found to exert asymmetric effects on passenger satisfaction, while substantial variations were identified across demographic, behavioural, geographic, and travel-related passenger profiles. The review further showed that industry benchmarking systems provide operational comparability but remain only partially aligned with academic analytical approaches. Several research gaps were identified, particularly in landside infrastructure evaluation, arrival-stage service assessment, integrated objective–subjective performance measurement, and system-level understanding of airport operations. The findings indicate that ASQ should be interpreted as an emergent property of interconnected infrastructure subsystems rather than as isolated service encounters. This study provides an integrated conceptual foundation for future research on intelligent, resilient, and evidence-based airport infrastructure management and supports more transparent and analytically grounded decision-making for airport operators, policymakers, and researchers.
In this study we have evaluated three advanced water treatment technologies in laboratory conditions, electrochemical (EC), fluidized bed (FB) and nanocomposite-based systems. The performance of the three technologies were evaluated based on several characteristics, such as pollutant removal efficiency, operating cost (USD/m$^3$), specific energy consumption (kWh/kg), throughput (kg/h), space-time yield (STY, kg/m$^3$$\cdot$h) and energy utilization efficiency (kg/kWh). The results show that the nanocomposite system offers the best treatment efficiency (93.17% removal efficiency and very low variability (standard deviation = 0.78%), showing good stability and reliability of the process. We found that nanocomposite system had moderate operating cost of 0.109−0.116 USD/m$^3$ and specific energy consumption of 3.60−6.52 kWh/kg, with an average value of 4.70 kWh/kg. Also, it has the highest STY (0.94 kg/m$^3$$\cdot$h) and high energy utilization efficiency (0.2776 kg/kWh). In contrast, the FB system has the lowest average operating cost (0.1016 USD/m$^3$), lowest average specific energy consumption (4.20 kWh/kg) and the best energy utilization efficiency (0.2493 kg/kWh) and is the most economical option even with the lowest pollutant removal efficiency. The EC system provided the best removal efficiency (91.32%), but the highest operating cost (0.1242 USD/m$^3$) and energy consumption (6.50 kWh/kg) of the other technologies. In analysis of variance (ANOVA) and Tukey’s Honestly Significant Difference (HSD) tests, there was significant difference between all the technologies ($p$ $<$ 0.05). The nanocomposite system achieved 5.39% removal efficiency and the FB system was able to have better energy utilization than the EC and nanocomposite technology. In general, the nanocomposite technology was the best in terms of treatment efficiency, energy efficiency, and operational cost optimization and the FB system is the best choice for large-scale applications.
University students, as a key youth consumer demographic, will play a vital role in shaping sustainable purchasing behavior in the future. This study aims to uncover the factors influencing students’ intention to minimize food waste at universities using the extended norm activation theory. An online survey of 664 students examined intentions to reduce food waste on campus. Of these, 245 students used online food delivery (OFD), while 419 engaged in in-canteen dining (IC). To evaluate the empirical data, this study utilized a partial least squares structural equation model and executed measurement invariance testing within the composite model. The empirical results demonstrate that the activation of personal norms is driven by awareness of consequence and the ascription of responsibility, which consequently has a direct impact on the intention to reduce food waste. Personal norms also indirectly influence the intention to minimize food waste. Students who purchased meals only reported weaker personal norms and lower intention to reduce food waste than those who ate in the canteen. However, the OFD group showed greater awareness of consequence, which supported their efforts to reduce food waste, compared with the IC group. Overall, this study provides further insight into the psychological mechanisms underlying sustainable food consumption among university students.
Advanced driver assistance systems (ADAS) rely heavily on robust object tracking to ensure safe and autonomous navigation, especially in complex outdoor environments. Traditional Kalman filter (KF)-based methods, while effective in ideal conditions, often fall short in scenarios with high noise, asynchronous sensor data, occlusions, and varying environmental conditions. The existing tracking techniques do not adequately address the challenges of multi-object tracking under low Signal-to-Noise Ratio (SNR) or nonlinear dynamics. To bridge this gap, this work proposes Radar and Sensor-Based Tracking with Adaptive Spatial-Temporal Analysis (RASTA), a modified KF-based architecture designed to enhance multi-object tracking using mmWave radar in ADAS. The primary objective of this work was to improve tracking accuracy, handle sensor uncertainty, and enable robust performance in dynamic and noisy conditions. The methodology involved simulating ADAS motion using a discrete Langevin process with bistable dynamics, converting Cartesian trajectories to polar coordinates, and introducing noise to emulate real-world radar behavior. Experimental validation using a mmWave dataset showed that RASTA achieved up to 12.4% improvement in azimuth estimation and 10.7% in radial distance accuracy over baseline methods. The results show RASTA’s effectiveness in delivering reliable, accurate tracking.
The expansion of the shipbuilding industry in coastal areas contributes substantially to economic development while simultaneously posing significant risks to air quality and community health. This study analyzed the concentration and spatial distribution of air pollutants and assessed non-carcinogenic health risks in the shipyard industrial area of Batam City. Air quality measurements were conducted for PM$_{2.5}$, SO$_2$, NO$_2$, CO, and Pb parameters at multiple receptor points at different distances from the emission source. The field measurement data were then integrated with dispersion modeling using the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD) based on local meteorological conditions. Health risk was evaluated using the Hazard Quotient (HQ) approach with reference to national air quality standards. Pollutant concentrations decreased consistently with increasing distance from emission sources, with PM$_{2.5}$ exhibiting the widest and most persistent spatial distribution. Although most pollutant concentrations remained below regulatory thresholds, PM$_{2.5}$ yielded HQ values exceeding 1.0 across all receptor distances up to 2000 m, indicating significant non-carcinogenic health risk at all observed distances. Model validation demonstrated strong spatial agreement between measured and simulated concentrations ($R^2$ $>$ 0.84), with a consistent tendency toward underestimation of absolute values. The integration of spatial dispersion modeling with health risk assessment offers a comprehensive analytical framework for air quality management and public health protection in coastal industrial settings.
The increasing pressure on maritime ports to reduce greenhouse gas emissions has accelerated the adoption of artificial intelligence to support decarbonization strategies. However, existing research remains fragmented across operational, environmental, and energy domains. This study provides a structured analysis of artificial intelligence applications in port decarbonization by integrating a systematic review with bibliometric analysis. A total of 165 records were identified from the Scopus database, and after screening and eligibility assessment, 62 peer-reviewed articles published between 2021 and 2025 were included in the final analysis. The systematic review identifies four major thematic areas: energy management, emission monitoring and prediction, operational optimization, and renewable and alternative energy integration. The bibliometric analysis complements these findings by revealing dominant research clusters and the intellectual structure of the field. The results indicate that operational optimization represents the most mature application area, delivering efficiency gains that contribute to indirect emission reduction. Emission monitoring and prediction provide accurate environmental diagnostics but remain limited in decision support integration. Energy management demonstrates growing application with varying impact on emission reduction, while renewable and alternative energy integration remains an emerging field with strong long-term potential. Despite these advances, several gaps persist, including limited real-world validation, fragmented data environments, and weak integration between predictive models and operational decision-making. The study contributes by providing an integrated perspective that links artificial intelligence techniques with port operations and decarbonization outcomes. The findings offer insights for researchers, port authorities, and policymakers seeking to advance the implementation of artificial intelligence in sustainable port development.
Industry 4.0 transforms modern manufacturing systems through the integration of cyber-physical systems, the Industrial Internet of Things, artificial intelligence (AI), machine learning (ML), and digital twin (DT) technologies. Autonomous industrial control remains a critical challenge in complex engineering environments because conventional control architectures often struggle to handle nonlinear dynamics, distributed decision-making, system uncertainties, and real-time operational variability. This review investigates the role of AI-, ML-, and DT-enabled autonomous control systems in improving adaptive intelligence, predictive capability, operational optimization, and resilient decision-making within smart industrial environments. A comprehensive technical review was conducted to examine recent developments in intelligent system modeling, predictive analytics, adaptive and self-learning control, real-time anomaly detection, multi-objective optimization, quality control, and energy-efficient industrial operations. The architectures and operational mechanisms of the AI–ML–DT-integrated control frameworks were analyzed from the perspective of complex cyber-physical industrial systems. The interrelationships among distributed sensing, intelligent data processing, virtual simulation, and autonomous control layers were also evaluated to identify current technological capabilities and implementation limitations. The analysis showed that the integration of AI, ML, and DT technologies significantly improved predictive maintenance performance, adaptive process control, fault diagnosis accuracy, operational flexibility, and energy optimization in Industry 4.0 environments. The reviewed studies demonstrated that DT-assisted virtual environments enabled safe real-time optimization and intelligent decision validation before physical deployment. The results also revealed that autonomous control architectures enhanced the resilience and self-adaptive capability of industrial systems operating under dynamic and uncertain conditions. However, several limitations were identified, including interoperability constraints, model synchronization challenges, computational complexity, cybersecurity risks, and scalability issues in distributed industrial networks. This study demonstrates that the convergence of AI, ML, and DT technologies establishes an important foundation for next-generation autonomous cyber-physical industrial systems. The proposed review provides a comprehensive engineering perspective for understanding intelligent industrial control architectures and offers valuable insights into the development of scalable, adaptive, and energy-efficient autonomous manufacturing systems for future Industry 4.0 applications.
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