Search Results (304)

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

Pedestrian safety at urban crosswalks remains a major public concern, as both vehicle speeds and roadway characteristics strongly influence drivers’ behaviour when approaching these locations. This study investigates driver behaviour patterns when approaching pedestrian crossings by integrating operating speed with key road-layout features derived from a naturalistic driving experiment conducted in Florence. A dataset of 401 observations was analysed using an unsupervised clustering framework specifically designed to handle mixed numerical and categorical variables. After preprocessing, the optimal number of clusters was identified using an elbow-based model selection applied to the K-Prototypes algorithm. The analysis produced four distinct clusters, primarily differentiated by operating speed and secondarily by contextual variables such as lane number, lane width, and acceleration behaviour. Lower-speed clusters were associated with single narrow-lane configurations, whereas higher-speed clusters were characterised by wider or multilane segments and more frequent acceleration near crossings. Information Gain analysis confirmed the dominant role of lane-related attributes, while the presence of crosswalks alone did not systematically reduce speeds. Complementary clustering excluding speed resulted in fewer clusters, indicating that speed adds essential granularity to behavioural segmentation. These findings highlight the interplay between road design and driver behaviour and provide evidence-based insights to support crosswalk configurations that mitigate high-speed conflicts in urban settings. Full article

This paper investigates the stability of telehandlers operating on inclined terrain through a sequential methodological approach. In a first stage, stability is assessed using quasi-static methods based on force and moment equilibrium, including the load transfer matrix and the stability pyramid. These approaches account for gravitational and inertial effects through equivalent external forces and moments applied at the global centre of gravity, enabling efficient evaluation of load redistribution and proximity to rollover thresholds under generalized quasi-static conditions. The application of these methods highlights intrinsic limitations when addressing structurally complex machines such as telehandlers equipped with a pivoting rear axle and evolving mass distribution due to boom motion. In particular, quasi-static approaches require a priori assumptions regarding the effective rollover axis and cannot fully capture the coupled geometric and contact interactions between rear axle articulation limits, centre of gravity migration, tyre–ground interface behaviour, and support polygon evolution. To overcome these limitations, a nonlinear dynamic multibody model based on the three-dimensional Bond Graph (3D Bond Graph) methodology is introduced. The model is implemented within a virtual tilt–rotation test platform and validated against experimental results obtained from ISO 22915-14 stability tests. The comparison confirms compliance with normative requirements and demonstrates that the dynamic framework captures condition-dependent rollover mechanisms and transitions between distinct virtual rollover axes that cannot be fully explained by quasi-static formulations. Unlike most previous studies, which focus on fixed configurations or forward-driving scenarios, the proposed framework analyzes stability evolution under spatial inclination while accounting for structural articulation constraints. The explicit identification of rollover axis transitions induced by rear axle articulation provides a deeper mechanistic interpretation of telehandler stability and supports the use of high-fidelity dynamic simulation as a complementary tool for test interpretation, experimental planning, and the development of predictive stability and operator assistance systems. Full article

The growing complexity of maritime navigation creates safety challenges that drive the shift toward autonomous systems. Maritime vessel behaviour modelling is critical for safe and efficient autonomous operations. Representation learning offers a systematic approach to learn feature embeddings encoding vessel behaviour for improved situational awareness and decision-making. We introduce a three-stage representation learning pipeline evaluating six architectures on real-world AIS trajectories. Grouped Masked Autoencoder (GMAE)-Risk Extrapolation (REx) combines group-wise masked autoencoding at the semantic feature level with risk extrapolation regularisation, forcing encoders to learn cross-group dependencies between temporal, kinematic, spatial, and interaction features. DAE and EAE provide robust and uncertainty-aware baselines. Evaluation uses a dual-pipeline framework on two years of Kiel Fjord AIS data (176,787 trajectories, 527,225 segments). Pipeline 1 applies three-stage representation learning using vessel-type classification as encoder selection probe. GMAE-REx achieves 86.03% validation accuracy, outperforming DAE (85.63%), EAE (85.56%), and baselines Transformer (84.93%), TCN (76.27%), LiST (85.12%). Pipeline 2 applies unsupervised clustering to discover intrinsic behavioural structure. Learnt representations consistently outperform expert features on DBCV, conductance, and modularity metrics, organising trajectories by operational context rather than vessel type. This behaviour-oriented organisation enables cross-vessel knowledge transfer for autonomous navigation, VTS monitoring, and safety analysis. Full article

This study investigates how positive and negative framing affect sustainable investment behaviour, emphasising the mediating role of investor confidence and the moderating role of intention. An experimental design with 301 participants was employed, comparing control, positive, and negative framing conditions. Participants allocated both simulated and real monetary endowments to a green investment (recycling) project, and the PROCESS macro for SPSS 29 was used to test mediation and moderation models. The results show that positive framing directly increases allocation to sustainable investment, while negative framing operates indirectly by enhancing investor confidence, which in turn drives greater investment. Moderation analysis further demonstrates that negative framing strengthens the link between intention and real monetary commitment, even though the direct effect of framing on actual financial behaviour remains weak. This paper contributes to behavioural finance by clarifying the differential mechanisms of positive and negative framing in investment decisions and highlighting confidence as a key psychological pathway in sustainable finance behaviour. It also differentiates short-term and long-term behaviour to capture the complexity of sustainable investment. Full article

The manufacturing sector drives industrialisation and contributes substantially to economic growth and employment creation. Despite this, it faces the challenges of diminishing size and lack of competitiveness, mainly due to operational uncertainty. The study developed an approach to managing operational uncertainty using Industry 4.0 and 5.0 technologies. It employed a multimethod quantitative design based on the post-positivist paradigm, with data collected from 22 experts and 262 responses from a manufacturing firms’ survey. The study employed an integrated fuzzy decision-making trial and evaluation laboratory (DEMATEL) with partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The fuzzy DEMATEL results reveal that growing geopolitical tension, cost-of-living-driven consumer behavioural change, pandemic turbulence, lack of energy stability and security, and the entrenched power of large firms are causal dimensions of operational uncertainty. Industry 4.0 and 5.0 technologies, with capabilities for scenario planning and supply chain integration, flexible production and mass customisation, real-time system and process monitoring and response, root cause analysis, and sustainable solutions, can manage operational uncertainty. These technologies include artificial intelligence (AI), the Internet of Things (IoT), big data analytics, and, to a lesser extent, advanced robotics, blockchain, and augmented and virtual reality (AR/VR). This study advanced configuration theory and a new integrated methodology (fuzzy-DEMATEL-PLS-SEM-fsQCA) to develop solutions for sustained performance during operational uncertainty in manufacturing. This research offers valuable information to advance the subject, make meaningful changes in day-to-day manufacturing operations, and promote practical real-world problem solving. Full article

Land-use and land-cover change plays a critical role in shaping urban expansion patterns and modifying near-surface soil conditions, hydrological behaviour, and geomorphological stability in rapidly developing regions. This study presents a comparative modelling framework to analyze long-term land-use change and its implications for regional-scale geotechnical risk screening by integrating historical land-use classification, Markov transition analysis, and machine learning–based spatial simulation. Landsat imagery from 1985 and 2024 was classified using a Support Vector Machine approach, and future land-use projections for 2063 were generated using both the TerrSet Land Change Modeler (LCM) and the GeoFLUS model under identical transition demands. Spatial driving variables included topographic, hydrological, and accessibility-related factors that influence soil behaviour and urban suitability. The results reveal sustained urban expansion primarily driven by the systematic conversion of agricultural land into built-up surfaces, while forested areas and water bodies exhibit high class persistence, as indicated by dominant diagonal values in the Markov transition matrix. Although both models reproduce consistent directional trends, they generate distinct spatial allocation patterns, with LCM producing compact and centralized growth and GeoFLUS generating more spatially dispersed expansion. These differences lead to contrasting implications for potential settlement, flooding, and slope instability zones. By treating future land-use maps as alternative geotechnical screening scenarios rather than fixed predictions, this study demonstrates how model uncertainty can be incorporated into hazard-sensitive planning. The proposed framework supports preliminary geotechnical zoning and infrastructure planning by identifying robust development corridors and spatial uncertainty zones where detailed site investigations may be prioritized. The methodology is transferable to other rapidly urbanizing regions facing complex soil and geomorphological constraints. Full article

This study investigates the fatigue behaviour of carbon fibre–aluminium adhesively bonded tubular joints, representative of the suspension arm of a Formula SAE racing car, under both constant- and variable-amplitude fatigue loading. A linear elastic stress analysis was conducted using two-dimensional axisymmetric finite element models to determine the singular stress field parameters—specifically the Generalised Stress Intensity Factor (H₀) and the stress singularity exponent (s)—at critical adhesive–adherend interfaces. Experimental tests under quasi-static loading and constant amplitude, as well as variable-amplitude fatigue conditions, were performed. The constant-amplitude fatigue data were reanalysed in terms of both nominal maximum shear stress and H₀. The results show that the scatter index of the fatigue data was reduced by a factor of 1.46 when H₀ was used as the fatigue-driving parameter, indicating an improved correlation of the experimental results. Variable-amplitude fatigue tests were interpreted using Miner’s cumulative damage rule, confirming the suitability of H₀-based life estimation models even under realistic, variable-amplitude loading conditions. The results demonstrate that H₀ is an effective parameter for rationalising fatigue performance of tubular bonded joints and highlight its potential for fatigue design in composite–metal structural applications. Full article

The complexity of modern vehicle control systems, the increasing diversity of powertrain and exhaust aftertreatment applications, and the need for shortened development times require innovative approaches towards calibration. This paper presents an experimental, analytical, and modeling study of particulate filter (commonly called DPF—diesel particulate filter) in a diesel hybrid vehicle where models have been developed to simulate test data, replacing the requirement of numerous tests on testbed or on the road with system simulations and offline parameter optimisation techniques. A soot estimation model has been developed based on the operation of the engine including its transient response, and the thermal–chemical behaviour of the DPF. A methodology has been developed to optimize the calibratable maps and parameters within this model. The results show that the proposed method improves the accuracy of soot estimation in the engine transient operation and avoids a large number of experimental tests required in traditional calibration methods. Modern automotive manufacturers face regulatory compliance requirements ensuring emission standards across diverse real driving emission (RDE) boundary conditions encompassing route characteristics, driving dynamics, and ambient environmental variables throughout vehicles’ operational lifetime. The soot load in the DPF and the DPF regeneration frequency can massively impact the tailpipe NO_x emissions and overall fuel consumption, so it is key to accurately estimate the soot accumulation in all operating conditions. This means testing and validating calibration in each possible scenario and so needs an enormous number of tests on testbed and on the road. These tests, however, can be replaced with system simulations and offline calibration if we have a robust model for the system, as described in the following parts of this paper. Full article

This paper presents a numerical model of the discharge cycle of a subsea open-cycle hydro-pneumatic energy storage system intended for offshore long-duration energy storage. During discharge at high pressure ratios, air expansion can lead to significant cooling, penalising system performance. The modelled system comprises a subsea pipeline which stores compressed air coupled to a pair of reciprocating liquid pistons that expand the air to drive a hydraulic motor. The study focuses on the transient thermal behaviour of the system during air expansion at high pressure ratios, starting from an initial pressure of 200 bar in the subsea air receiver pipeline down to a target pre-charge pressure of 80 bar. A parametric study investigates the influence of the output hydraulic power and the convective heat transfer coefficients, assessing the ability of the system to approach ideal isothermal expansion. The results indicate that for the high pressure ratios considered and using currently available heat transfer coefficient correlations, significant cooling occurs within the subsea liquid piston pipeline. For a baseline output hydraulic power of 500 kW, a polytropic index of 1.23 and a work ratio just below 64% were obtained. However, the results also show that by reducing the output hydraulic power and integrating internal heat transfer mechanisms, this cooling can be substantially mitigated, resulting in quasi-isothermal conditions with work ratios higher than 86%. Full article

As the world grows more and more out of kilter with wars and fake news, the climate crisis is being ignored, leaving eco-media scholars striving to uncover new ways of keeping it firmly in the spotlight. This paper draws on extensive scholarship across eco-film studies, using narratives I have not analysed before—The Age of Stupid and Zone of Interest—to speak to contrasting ways of representing and communicating the crisis. While in the academy and within particular strands of environmental communications (EC), eco-textual analysis is often sidelined and replaced with a focus on empirical audience and behavioural research, together with more political economy types of investigations. Nevertheless, there remains a central place for understanding and appreciating how stories and images function, both in stylistic and thematic terms, while deploying new creative imaginaries to represent the climate crisis and provoke debate over future, more sustainable models of living. These readings will be analysed through an ethics of care lens while being underpinned by environmental media literacy, which can be argued to drive pro-active engagement and consideration. Full article

Near-critical and supercritical carbon dioxide (SC-CO₂) is extensively utilized in high-pressure separation, extraction, polymer processing, and carbon capture and utilization (CCU) technologies owing to its tunable density, low viscosity, high diffusivity, and environmentally benign nature. Reliable phase equilibrium data are indispensable for process design and optimization, especially in the near-critical region characterized by pronounced non-idealities, high compressibility, and density fluctuations. This review synthesizes experimental phase behaviour studies for binary mixtures of CO₂ with diverse components, including hydrocarbons, alcohols, ethers, esters, ketones, water, monomers/polymers, ionic liquids (ILs), and deep eutectic solvents (DESs), compiling extensive vapour–liquid equilibrium (VLE), liquid–liquid equilibrium (LLE), and critical data across industrially relevant pressure (up to 40 MPa) and temperature (up to 400 K) ranges. It critically evaluates analytical (sampling and non-sampling) and synthetic methodologies, addressing challenges in CO₂-rich phase handling, depressurization artefacts, and near-critical phenomena, while assessing data consistency against established reliability criteria. Key trends emerge, such as enhanced solubility with increasing pressure and CO₂ density, chain-length dependencies in hydrocarbons and alcohols, and Lewis acid–base interactions driving solvation in polar systems. The review highlights gaps in multicomponent data and proposes integrating high-quality experiments with advanced thermodynamic modelling to enhance predictive accuracy. Future directions emphasize high-precision in situ techniques, expanded datasets for complex mixtures, and novel CO₂-philic solvents to advance sustainable SC-CO₂ applications. Full article

Reliable prediction of energy consumption for electric motorcycles in sub-Saharan Africa requires models that reflect local riding conditions and measured component behaviour. This paper presents a validated, physics-based simulator for the Roam Air electric motorcycle that combines longitudinal dynamics with empirically derived motor and inverter efficiency maps obtained from dynamometer testing. The model ingests measured drive cycles and elevation-derived gradients to compute tractive effort and battery power flow and is validated against six real-world city and highway trips in Nairobi. The simulator reproduces temporal battery-power profiles with strong correlations between 0.87 and 0.91 and predicts energy per distance with small positive bias, achieving errors between 0.4% and 11.3%, where the measured energy consumption per distance ranges between 30.2 and 51.7 Wh/km. A sensitivity analysis quantifies the influence of key design parameters, and a scenario analysis assesses the impact of representative African driving conditions, including terrain, posture, payload, and surface type. The resulting framework is compact, transparent, and potentially adaptable to a wide range of electric two-wheelers, supporting design optimisation and electrification planning in the region. Full article

This paper develops a framework for monitoring and forecasting episodes of systemic financial stress using a combination of market information, macro-financial indicators, and measures derived from time-varying correlation networks, embedded in a sequential machine-learning setting. The contribution is not tied to a single modelling innovation, but rather to the way these ingredients are brought together under an evaluation protocol designed to mimic real-time supervisory use, and to an interpretability layer that makes the resulting predictions easier to inspect. Monthly data covering the period from 2006 to 2025 are used to construct evolving correlation structures and summary indicators of market co-movement. These features are combined with standard predictors and fed into logistic regression, random forest, and gradient boosting models, all estimated in expanding windows and assessed strictly on future observations. Predictive accuracy remains limited, which is consistent with the difficulty of anticipating stress regimes several months ahead at monthly frequency, although gradient boosting attains the highest average AUC across evaluation folds and displays noticeable variation over time. Inspection of SHAP values points to instability in correlation networks, volatility conditions, and short-horizon return behaviour as recurring drivers of the predicted stress probabilities, suggesting that the models draw on information that goes beyond individual market series. Taken together, the results indicate that recurrent statistical regularities and changes in market structure can be exploited for monitoring purposes when models are trained and tested in a sequential fashion. The overall design is intended to be usable in practice and to support supervisory analysis, while remaining transparent enough to allow scrutiny of the signals driving the forecasts. Full article

The rule of law, a foundational value of the European Union as enshrined in Article 2 of the Treaty on European Union, faces challenges in implementation due to historical and political factors that have evolved over the past decade, particularly within Member States in the administrative domain. While institutional backsliding in countries like Hungary and Poland has drawn significant political attention, less emphasis has been placed on the role of public administrations in upholding or undermining the rule of law on a day-to-day basis. This paper argues that the sustainability of the rule of law in the EU requires more than legal compliance mechanisms. These alone do not address the underlying administrative and cultural factors necessary for effective implementation. Instead, it requires closer attention to how rule-of-law principles are embedded in the everyday functioning of public administrations. This argument is informed by the authors’ systematic examination of recent EU monitoring practices and administrative reform instruments. Adopting a mixed conceptual-empirical methodology, the paper draws on primary data from EU Rule of Law Reports (2020–2024), the EU Justice Scoreboard, the Recovery and Resilience Facility (RRF), and the Technical Support Instrument (TSI), complemented by relevant OECD/SIGMA indicators. Several structural obstacles emerge from the analysis. These include symbolic compliance, whereby organisations adopt formal structures without corresponding behavioural change; weak institutional leadership that fails to drive reform momentum; and the absence of integrated performance metrics, which hampers meaningful accountability. Fragmented ownership of reform agendas, in turn, breeds inconsistency in implementation. These challenges point to the limitations of a technocratic or legalistic approach to rule-of-law governance. Strategic leadership and organisational flexibility emerge from the evidence as preconditions—not merely facilitators—of genuine internalisation, though the relationship is context-dependent. Digitalisation can reinforce these dynamics, yet its contribution depends on whether it is embedded within broader integrity-oriented reforms. The paper advocates for a shift from externalized compliance mechanisms to a model that emphasizes administrative ownership through specific strategies such as developing integrity-based leadership programs and embedding governance practices that prioritize transparency and accountability. It proposes concrete institutional reforms, including performance-linked conditionalities that tie funding to measurable outcomes, ethical leadership academies to train future leaders, integrity audits to ensure accountability, and administrative benchmarking to set clear standards, as tools to foster autonomous, value-driven public institutions capable of adapting to evolving governance challenges while maintaining core democratic values. Full article