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Driving the change:How do personal factors and socio-economic context influence electric vehicles adoption across Europe?
No full textThis paper investigates the determinants influencing consumer adoption of battery electric vehicles (BEVs) across three European markets characterized by distinct socio-economic contexts and varying levels of EV market maturity. We develop a theoretical model based on Theory of Planned Behavior. A survey was conducted involving 737 consumers in Germany, Italy, and Norway. The data was analyzed using structural equation modeling, multigroup analysis, and Kruskal-Wallis's test. The findings indicate that hedonic motivations, ascription of responsibility, subjective norms, and direct experience significantly enhance consumers' willingness to purchase BEVs. Conversely, range anxiety and environmental concerns negatively affect purchase intentions. Significant differences in consumer perceptions of BEVs and the effect of behavioral determinants across the three countries are highlighted. This research contributes to the literature on sustainable mobility adoption and proposes several avenues for future investigation. The findings can inform the development of marketing strategies and policy interventions to foster EV adoption in Europe.</p
Unsupervised Detection of Postoperative Complications in Home-Monitored Patients: Preliminary Results
No full textWearable sensors enable remote, continuous patient monitoring at home, offering a promising approach for early detection of postoperative complications. However, analyzing continuous long-term physiological data remains challenging, particularly in the absence of precisely labeled deterioration events. Unsupervised change point detection methods can address this issue by identifying physiological deviations without requiring predefined event labels. This study investigates the feasibility of using a Long-Short-Term Memory (LSTM) autoencoder for detecting postoperative complications from continuous heart rate and respiration rate data using a wearable patch sensor while monitoring patients in their homes. The autoencoder was applied to identify physiological deviations that may indicate potential complications after major abdominal oncological surgeries in ten patients. The model was trained on data from seven patients to recognize deviations from normal physiological patterns and evaluated on three patients. The proposed model detected change points preceding the clinically documented complication time in two test patients, identifying these deteriorations an average of 3.25 hours earlier than the standard Remote Early Warning Score (REWS) alarm system. These findings suggest that LSTM autoencoder-based change point detection could be a valuable tool for identifying postoperative complications early in remote patient monitoring settings, to support timely intervention and potentially improving patient outcomes
Comparative analysis of manual slum identification using satellite data:A case study of Medellin, Colombia
No full textThis paper investigates the identification of slums in Medellín, Colombia, conducted by two independent research teams. By comparing two similar methodologies, based on Earth observation (EO) data and utilizing manual visual image interpretation (MVII), this study highlights overlaps and differences in the identified areas. The findings reveal significant congruence in core informal area zones but notable divergences in peripheral and less densely developed regions. These results underscore the necessity for transparent conceptual definitions of the target class ’slum’, for transformation of conceptual approaches in mapping approaches, for the explanation of effects of ambiguities in both, and thus for the development of improved mapping protocols
Mapping Deprived Urban Areas with an Optimized Loss-weight Feature-guided Deep Learning Model
No full textThe growth of deprived urban areas (DUA), often associated with slums or informal settlements, is one of the consequences of rapid urbanization. Earth Observation (EO) data provides valuable information for mapping and monitoring such urban areas to assess the Sustainable Development Goal (SDG) indicator 11.1.1. Previous studies show that building density is one of the most informative morphometric variables to map DUA. However, building density when available are often mono-temporal and lack information about the exact date it was assessed. To address this gap, we present a deep learning-based approach that integrates building density regression from EO data to guide the learning process of a pixel-wise classification network. Our methodology optimizes the combined loss function of a dual-output semantic segmentation model, balancing classification and regression tasks, using Sentinel-1 and Sentinel-2 as input. This balance improves the accuracy of building density predictions, which, in turn, enhances the detection of DUAs and model interpretability. We evaluated our approach in Salvador (Brazil) and Nairobi (Kenya), achieving improvements of 9.75% and 0.60%, respectively, compared to previous studies
Integration of FunKey approach in SysML
No full textThis paper proposes a framework that integrates the previously introduced FunKey approach into a formal system modeling language to enhance early-stage architectural decision-making in systems engineering. FunKey links system functions to key drivers through a coupling matrix, emphasizing structured function allocation to improve system architecting during the conceptual design phase. Up until now, the lack of integration with Model-based systems engineering (MBSE) has limited FunKey’s broader application. This paper shows how budget allocation, trade-off analysis, and architecture configuration exploration of FunKey can be implemented into SysML. This resulting framework provides systems engineers with enhanced capabilities for evaluating complex architectures, improving decision-making and alignment with high-level requirements
Towards Edge-Based Idle State Detection in Construction Machinery Using Surveillance Cameras
No full textThe construction industry faces significant challenges in optimizing equipment utilization, as underused machinery leads to increased operational costs and project delays. Accurate and timely monitoring of equipment activity is therefore key to identifying idle periods and improving overall efficiency. This paper presents the Edge-IMI framework for detecting idle construction machinery, specifically designed for integration with surveillance camera systems. The proposed solution consists of three components: object detection, tracking, and idle state identification, which are tailored for execution on resource-constrained, CPU-based edge computing devices. The performance of Edge-IMI is evaluated using a combined dataset derived from the ACID and MOCS benchmarks. Experimental results confirm that the object detector achieves an F1 score of 71.75%, indicating robust real-world detection capabilities. The logistic regression-based idle identification module reliably distinguishes between active and idle machinery with minimal false positives. Integrating all three modules, Edge-IMI enables efficient on-site inference, reducing reliance on high-bandwidth cloud services and costly hardware accelerators. We also evaluate the performance of object detection models on Raspberry Pi 5 and an Intel NUC platforms, as example edge computing platforms. We assess the feasibility of real-time processing and the impact of model optimization techniques
A hierarchical cross-departmental agent-based approach to explore the impacts of policy interplay on land use dynamics
No full textUnderstanding the interactions between competing land policies is crucial for identifying governance challenges and assisting urban planners and policy analysts in making informed decisions. However, a methodology for incorporating land use patterns and the policy implementation processes within the framework of hierarchical land management remains underexplored. Here, we employ an agent-based model (ABM) to investigate how land use change occurs as policies intersect across different hierarchical levels and branches of government in Wuhan, China. Changes in land use arise from the interplay between five agents—the central level, the local level that incorporates three departments, and the village collective level—in the decisions on land acquisition, conversion, and reclamation. Four parameters characterize the enforcement levels of relevant policies, and multi-objective optimization with genetic algorithms was applied to calibrate them. The results show that: (1) Our ABM exhibits a figure of merit value of 0.3 at the city level and 0.58 in the larger urban area, indicating its capability to simulate real land use dynamics. (2) Policy implementation gaps led to high land conversion and low farmland reclamation. (3) The dynamic enforcement scenarios provide a viable pathway for negotiated governance, enabling demand-responsive rate attenuation and conflict mitigation, which is distinct from the exacerbated land use conflicts observed under the other scenarios. (4) Policy should incorporate adaptive mechanisms to maintain a buffer between competing land demands rather than binary constraints. This ABM introduces a novel hierarchical framework to decode policy interplay and implementation tensions, advancing sustainable land governance and urban planning insights.</p
Fine Sediment Dynamics Affected by Large-Scale Interventions:A 3D Modelling Study of An Engineered Estuary
No full textEstuaries around the world are extensively modified to support dense human populations. They trap both fluvial and marine sediments, creating regions with high suspended sediment concentrations (SSCs), called estuarine turbidity maxima (ETM), which affect port navigability and ecological values. The impact of human-induced narrowing and deepening on sediment trapping and flushing is still subject to debate, particularly in strongly stratified estuaries. This study evaluates the effects of channel deepening and intertidal wetland reclamation on sediment trapping and flushing in a highly engineered and stratified estuary. A schematized 3D numerical model, inspired by the Rotterdam Waterway, was developed and validated under present-day conditions. Subsequently, human interventions were simulated in combination with high discharge events which occur every 1, 10 and 100 years. Results indicate that the amount of sediment flushed during high discharges is almost entirely dependent on river discharge and channel depth. Net sediment flushing occurred at Freshwater Froude numbers of 0.2 or higher. Sediment trapping decreased with shallower channels and larger intertidal area: on average, a 1.5m shallower channel reduced trapping by 13%, while a 0.5km wider intertidal area reduced it by 35%. Namely, while both interventions reduced trapping due to tidal flow, they enhanced trapping due to estuarine circulation flow, the dominant trapping mechanism in the system. Finally, a conceptual model is presented, explaining the impact of intertidal wetlands and channel depth on the key processes relevant for fine sediment dynamics in stratified estuaries
Quality Observations of Solar Home Systems for Rural Electrification
No full textIncreasing the expected life time of energy systems for rural electrification is a crucial step towards sustainable electrification. While there are reports of solar home systems failing prematurely, the durability considerations made for these systems have not yet received much attention. In this paper, two solar home systems have been dissected, and the durability aspects of their design and compliance to their respective standard have been assessed. It has been shown that the life-time of the systems can be significantly enhanced. It has furthermore been argued that the metrics with which advancements in universal energy access is currently measured are incomplete and should incorporate the durability of such energy systems.</p
Thulium-Doped Al<sub>2</sub>O<sub>3</sub>Waveguide Amplifiers Fabricated via Radio Frequency Reactive Co-Sputtering
No full textAl2O3 is a promising material in integrated optics due to its extensive transparency window, spanning from ultraviolet (200 nm) to infrared (5500 nm), and its low optical propagation loss across this spectrum. Its high solubility for rare earth elements enables the incorporation of various dopants, making it a particularly suitable host material for integrated optical rare earth amplifiers and lasers. In this work, we report the deposition of thulium-doped Al2O3 films using radio frequency reactive co-sputtering, which is a cost-effective and scalable technique to produce thick and high quality films. These films are fabricated into optical waveguides and their spectral characteristics, including emission cross sections and lifetime, are measured. Thulium-doped Al2O3 waveguide amplifiers are successfully fabricated, demonstrating signal enhancements of 21 dB at 1818 nm using a 1609 nm pump in a 10 cm-long waveguide, and 27 dB at 1850 nm using a 790 nm pump in a 20 cm-long waveguide. Net on-chip gains of 9 dB in a 10 cm-long waveguide and 1.5 dB in a 3 cm-long waveguide are achieved with the 1609 nm and 790 nm pumps, respectively.</p