Search Results (47)

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

Ailanthus altissima (AA) is an invasive tree species rapidly spreading worldwide, colonizing both urban and agricultural or forestry environments. This three-year study aimed to assess its effects on the growth and yield traits of the Koroneiki olive cultivar under co-cultivation in pots, combined with two irrigation regimes, full and deficit irrigation (60% of full). Within each irrigation regime, olive trees were grown either in the presence or absence (control) of AA. The trial evaluated several parameters, including vegetative growth, yield traits, and oil quality characteristics. Co-cultivation with AA had no significant impact on tree growth after three years, though it significantly reduced oil content per fruit. Antioxidant capacity of the oil improved under deficit irrigation, while AA presence did not significantly affect it, except for an increase in o-diphenol concentration. Neither the fatty acid profile nor squalene levels were significantly influenced by either treatment. Fruit weight and color were primarily affected by deficit irrigation. During storage, olive oil quality declined significantly, with pre-harvest treatments (presence or absence of AA and full or deficit irrigation regime) playing a critical role in modulating several quality parameters. In conclusion, the presence of AA near olive trees did not substantially affect the key quality indices of the olive oil, which remained within the criteria for classification as extra virgin. Full article

Smart buildings’ role is crucial for advancing smart cities’ performance in achieving environmental sustainability, resiliency, and efficiency. The integration barriers continue due to technology, infrastructure, and operations misalignments and are escalated due to inadequate assessment frameworks and classification systems. The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. Six optimised machine learning algorithms (K-Nearest Neighbours (KNNs), Support Vector Regression (SVR), Random Forest, Adaptive Boosting (AdaBoost), Decision Tree (DT), and Extra Tree (ET)) were employed to train the model and perform feature engineering and permutation importance analysis. The SVR-trained model substantially outperformed other models, achieving an R-squared of 0.81, Root Mean Square Error (RMSE) of 0.33 and Mean Absolute Error (MAE) of 0.27, enabling precise integration prediction. Case studies revealed that low-integration buildings gain significant benefits from progressive target upgrades, whilst those buildings that have already implemented some integrated systems tend to experience diminishing marginal benefits with further, potentially disruptive upgrades. The conclusion of this study states that by utilising the developed machine learning model, owners and policymakers are capable of significantly improving the integration of smart buildings to build better, more sustainable, and resilient urban environments. Full article

With the transformation of urban development patterns and profound changes in population structure in China, outdoor activity spaces in residential areas are facing common issues such as obsolete infrastructure, insufficient barrier-free facilities, and intergenerational conflicts, which severely impact residents’ quality of life and hinder high-quality urban development. Guided by the principles of all-age friendly and inclusive design, this study innovatively integrates eye-tracking and multi-modal physiological monitoring technologies to collect both subjective and objective perception data of different age groups regarding outdoor activity spaces in residential areas through human factor experiments and empirical interviews. Machine learning methods are utilized to analyze the data, uncovering the differentiated response mechanisms among diverse groups and clarifying the inclusive characteristics of these spaces. The findings reveal that: (1) Common Demands: All groups prioritize spatial features such as unobstructed views, adequate space, diverse landscapes, proximity accessibility, and smooth pavement surfaces, with similar levels of concern. (2) Differentiated Characteristics: Children place greater emphasis on environmental familiarity and children’s play facilities, while middle-aged and elderly groups show heightened concern for adequate space, efficient parking management, and barrier-free facilities. (3) Technical Validation: Heart Rate Variability (HRV) was identified as the core perception indicator for spatial inclusivity through dimensionality reduction using Self-Organizing Maps (SOM), and the Extra Trees model demonstrated superior performance in spatial inclusivity prediction. By integrating multi-group perception data, standardizing experimental environments, and applying intelligent data mining, this study achieves multi-modal data fusion and in-depth analysis, providing theoretical and methodological support for precisely optimizing outdoor activity spaces in residential areas and advancing the development of all-age friendly communities. Full article

In the context of smart cities with advanced Internet of Things (IoT) systems, ensuring the sustainability and safety of freshwater resources is pivotal for public health and urban resilience. This study introduces EWAIS (Ensemble Learning and Explainable AI System), a novel framework designed for the smart monitoring and assessment of water quality. Leveraging the strengths of Ensemble Learning models and Explainable Artificial Intelligence (XAI), EWAIS not only enhances the prediction accuracy of water quality but also provides transparent insights into the factors influencing these predictions. EWAIS integrates multiple Ensemble Learning models—Extra Trees Classifier (ETC), K-Nearest Neighbors (KNN), AdaBoost Classifier, decision tree (DT), Stacked Ensemble, and Voting Ensemble Learning (VEL)—to classify water as drinkable or non-drinkable. The system incorporates advanced techniques for handling missing data and statistical analysis, ensuring robust performance even in complex urban datasets. To address the opacity of traditional Machine Learning models, EWAIS employs XAI methods such as SHAP and LIME, generating intuitive visual explanations like force plots, summary plots, dependency plots, and decision plots. The system achieves high predictive performance, with the VEL model reaching an accuracy of 0.89 and an F1-Score of 0.85, alongside precision and recall scores of 0.85 and 0.86, respectively. These results demonstrate the proposed framework’s capability to deliver both accurate water quality predictions and actionable insights for decision-makers. By providing a transparent and interpretable monitoring system, EWAIS supports informed water management strategies, contributing to the sustainability and well-being of urban populations. This framework has been validated using controlled datasets, with IoT implementation suggested to enhance water quality monitoring in smart city environments. Full article

Rising food demands are increasingly threatened by declining crop yields in urbanizing riverine regions of Southern Asia, exacerbated by erratic weather patterns. Optimizing agricultural land suitability (AgLS) offers a viable solution for sustainable agricultural productivity in such challenging environments. This study integrates remote sensing and field-based geospatial data with five machine learning (ML) algorithms—Naïve Bayes (NB), extra trees classifier (ETC), random forest (RF), K-nearest neighbors (KNN), and support vector machines (SVM)—alongside land-use/land-cover (LULC) considerations in the food-insecure Dharmapuri district, India. A grid searches optimized hyperparameters using factors such as slope, rainfall, temperature, texture, pH, electrical conductivity, organic carbon, available nitrogen, phosphorus, potassium, and calcium carbonate. The tuned ETC model showed the lowest root mean squared error (RMSE = 0.15), outperforming RF (RMSE = 0.18), NB (RMSE = 0.20), SVM (RMSE = 0.22), and KNN (RMSE = 0.23). The AgLS-ETC map identified 29.09% of the area as highly suitable (S1), 19.06% as moderately suitable (S2), 16.11% as marginally suitable (S3), 15.93% as currently unsuitable (N1), and 19.21% as permanently unsuitable (N2). By incorporating Landsat-8 derived LULC data to exclude forests, water bodies, and settlements, these suitability estimates were adjusted to 19.08% (S1), 14.45% (S2), 11.40% (S3), 10.48% (N1), and 9.58% (N2). Focusing on the ETC model, followed by land-use analysis, provides a robust framework for optimizing sustainable agricultural planning, ensuring the protection of ecological and social factors in developing countries. Full article

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involvement of TCs in the air quality index (AQI), focusing on aspects related to the air quality before, during and after cyclones. This research employs multimodal methods, which include meteorological data and different satellite observations. Deep learning approaches, i.e., ConvLSTM, CNN and Real-ESRGAN models, are combined with a regression model to analyze the temporal variability in the air quality associated with tropical cyclones. Deep learning models are deployed to uncover complex patterns and non-linear interdependencies between cyclones’ features and the AQI to give predictive insights into the air quality fluctuations throughout the different stages of tropical cyclones. Furthermore, this study explores the aftermaths of TCs in terms of the air quality with respect to post-cyclone recovery. The findings offer an enhanced view of the role of TCs in the regional or global air quality, which will be useful for policymakers, meteorologists and environmental researchers. Utilizing a CNN for tropical cyclone (TC) classification and the extra trees regressor (ETR) for AQI prediction results in accuracy of 92.02% for the CNN and an $R^2$ of 83.33% for the ETR. Hence, this work adds to our knowledge and enlightens us on the complex interactions between TCs and the air quality, highlighting wider public health concerns regarding climate adaptation and urban renewal. Full article

China, as the world’s largest producer, trader, and consumer of aquatic foods, lacks comprehensive research on consumption patterns and willingness to pay for sustainable aquatic food. This study addressed this gap through an online survey of 3403 participants across Chinese provinces. A majority of consumers (34.7% of the participants) consume aquatic food twice or more per week, mainly from traditional markets (26%). Most prefer fresh or live products (76%), with 42% seeing no difference between farmed and wild options. Consumption is higher among older, affluent, urban, and coastal residents. Crustaceans, especially shrimp, are frequently consumed species, with growing interest in luxury species like salmon and abalone. Taste and quality emerge as the primary factors motivating consumer choices in aquatic food purchases. Food safety is the primary concern, followed by environmental impact. Notably, 92.4% of participants would pay extra for certified products. Factors influencing a higher willingness to pay include higher income, inland residence, price sensitivity, origin consciousness, and concerns about food safety and the environment. The findings highlight that China’s aquatic food industry and consumption can become more sustainable by aligning with consumer preferences for high-quality and diverse aquatic food through both production and import, while also addressing concerns related to food safety and environmental impact. This research provides valuable insights into China’s rapidly transforming aquatic food market landscape, offering implications for industry innovation and the promotion of sustainable consumption patterns. Full article

This paper aimed to provide a new insight into the application of home vertical gardens in Kigali, the capital of Rwanda, through a pre-assessment of the inhabitants’ perceptions. There are several studies that indicated the awareness of the way residents think about the potential benefits and challenges of home gardens could make a considerable difference in designing and implementing these gardens. The Likert-scaled questionnaire (n = 558) was employed to evaluate how residents perceive vertical gardens, and what issues concern them most. The findings revealed that dwellers are almost familiar with the vertical garden concept and its possible effects on urban environments. The respondents mostly regarded vertical gardens as nice spots to socialize, relax, and interact with nature, and an opportunity for beautification, and recreation by growing ornamental and edible plants. However, they were rather apprehensive about some issues, more importantly, the extra expenses, the complicated operation and maintenance, and the type of structure installed on walls. In conclusion, small-scale and low-cost vertical gardens with lightweight structures and easy-to-use technologies are more likely to encourage householders to embrace home gardens. It is recommended that the vertical garden projects should be integrated into the urban green network strategy, leading to facilitating the processes of decision-making and financing. Full article

The notion of the smart city involves embedding Industry 4.0 technologies to improve the lives of inhabitants in urban environments. Within this context, smart city data layers (SCDLs) concern the integration of extra tiers of information for the purposes of improving communication potential. Under the Industry 4.0 technology grouping, advanced communication technologies, such as virtual reality (VR), further the opportunities to model, recreate, evaluate and communicate scenarios that potentially improve citizens lives at multiple SCDL scales in a smart cities context. The use of added contextual information in SCDLs is of special interest for emergency planning situations at the building scale. In this research, a literature review to understand the current approaches for the use, development and evaluation of VR applications in the context of emergency planning was conducted. The results indicated four main categories of relevant challenges for these types of applications, for which recommendations and a roadmap for VR development are presented. In total, the study identified 10 commonly occurring challenges (e.g., optimization and discomfort) and 19 solution directions (e.g., model construction and spatial directions) in related articles when considering the development of VR for spatial planning and emergency situations. Full article

The large and necessary diffusion of huge solar plants in extra urban areas implies the adoption of maintenance strategies especially where human intervention would require high costs and logistic problems. Animal dejections like bird droppings and agricultural sprays are environmental agents able to significantly decrease light absorption and, in some cases, cause serious damage to the electric conversion systems in a photovoltaic panel. In this work, the performance of a superhydrophobic (SH) coating in terms of durable self-cleaning properties and transparency has been studied in the presence of commercial and simulated contaminants on glass reference and solar panel surfaces. Wettability studies have been carried out both in static and dynamic conditions in order to compare the compositional effect of commercial liquids used as fertilizers or pesticides and molecules like pancreatin as model substances simulating bird droppings. From these studies, it can be observed that the superhydrophobic coating, independently from the surface where it is applied, is able to repel water and substances used such as fertilizers or pesticides and substances simulating bird droppings, maintaining its properties and transparency. This kind of approach can provide information to design suitable spray formulations without the above-mentioned drawbacks to be used in natural environment areas and agrosolar plants. Full article

The increasing number of unmanned aerial vehicles (UAVs) in low-altitude airspace is seriously threatening the safety of the urban environment. This paper proposes an adaptive collision avoidance method for multiple UAVs (mUAVs), aiming to provide a safe guidance for UAVs at risk of collision. The proposed method is formulated as a two−layer resolution framework with the considerations of speed adjustment and rerouting strategies. The first layer is established as a deep reinforcement learning (DRL) model with a continuous state space and action space that adaptively selects the most suitable resolution strategy for UAV pairs. The second layer is developed as a collaborative mUAV collision avoidance model, which combines a three-dimensional conflict detection and conflict resolution pool to perform resolution. To train the DRL model, in this paper, a deep deterministic policy gradient (DDPG) algorithm is introduced and improved upon. The results demonstrate that the average time required to calculate a strategy is 0.096 s, the success rate reaches 95.03%, and the extra flight distance is 26.8 m, which meets the real-time requirements and provides a reliable reference for human intervention. The proposed method can adapt to various scenarios, e.g., different numbers and positions of UAVs, with interference from random factors. The improved DDPG algorithm can also significantly improve convergence speed and save training time. Full article

Travel time, or, more generally, level of service, has always been considered the main parameter with which to design roads, particularly in extra-urban areas where geometries and policies, such as speed limits, play a key role in the performance achieved. Unfortunately, this type of approach does not consider the impact on emissions that is obtained when only performance-based goals are pursued. The paper deals with the analysis of the impact on emissions and fuel consumption under different traffic conditions, and we present a new methodology for emission estimation based on the stochastic formulation of the fundamental diagram in a highway environment. The proposed methodology estimates the emissions using a stochastic adaptation of the CORINAIR methodology based on COPERT software on both specific vehicle types and the average Italian vehicle fleet. As expected, due to the convexity of the emission function, accounting for speed dispersion leads to an increase in energy consumption and emissions. Tests show that the stochastic component can lead to an increase in the emission estimation up to 5.5% and, therefore, it should be considered. The methodology has been applied by means of real trajectories, and the results of the application show that performance optimization strategies can contrast with sustainability and emission reduction policies. Results show that for some vehicular classes, emissions or fuel consumption are highly dependent on speed, with different proportionalities. In all cases, the minimum consumption is obtained at speeds ranging from 70 to 90 km/h. The analysis of the curves shows that an increase in speeds, even to reach low speeds, generally leads to an increase in energy consumption and emissions per kilometer traveled and, therefore, is independent of the decrease in travel time. Full article

Time- and precision-critical applications, such as autonomous driving, have extremely rigorous standards for continuous high-precision positioning. The importance of precise point positioning (PPP) technology for self-navigation equipment is self-evident by delivering decimeter-/centimeter-level absolute position accuracy in a global coordinate framework. Nevertheless, the prolonged initialization period renders PPP almost too difficult to be widely used for time-critical applications in real transportation scenarios, for example, in city canyons and overpasses. We proposed a method to accomplish continuous decimeter-level positioning by leveraging triple-frequency GNSS observations with an Inertial Navigation System (INS) aiding in urban environments. In the proposed method, the inertial measurements and the original tri-frequency pseudorange and carrier phase measurements are tightly fused in an extended Kalman filter to obtain the optimal state estimate. Afterwards, with precise extra-wide-lane (EWL) and wide-lane (WL) Uncalibrated Phase Delay (UPD) products, the EWL and WL ambiguities can be resolved sequentially to implement instantaneous decimeter-level positioning. Exploiting the short-term high-precision characteristic of the INS, the continuity of the PPP solution can be ameliorated noticeably, and the ability of decimeter-level positioning can be maintained effortlessly throughout the navigation process. With land vehicle data collected, several experiments are undertaken to comprehensively assess the capability of the proposed system in urban scenarios, taking the solution obtained by a commercial post-processing software as the reference. The single-epoch decimeter-level position estimation can be captured instantaneously for WL ambiguity-fixed PPP solutions. Furthermore, for WL ambiguity-fixed PPP/INS-integrated solutions, the position estimation is better than 0.2 m for horizontal components and improved by 40–90% compared with that of the WL ambiguity-fixed PPP solutions. More importantly, the availability rate of positioning accuracy is better than 0.3 m in the horizontal direction and 0.5 m in the up direction reaching 93.91%, whereas it is only 53.78% for WL ambiguity-fixed PPP solutions. Overall, the WL ambiguity-fixed PPP/INS-integrated solutions have the favorable performance to maintain continuous decimeter-level positioning for self-navigation equipment, even if full GNSS outages are encountered. Full article

Automatic obstacle detection is of great significance for improving the safety of train operation. However, the existing autonomous operation of trains mainly depends on the signaling control system and lacks the extra equipment to perceive the environment. To further enhance the efficiency and safety of the widely deployed fully automatic operation (FAO) systems of the train, this study proposes an intelligent obstacle detection system based on deep learning. It collects perceptual information from industrial cameras and light detection and ranging (LiDAR), and mainly implements the functionality including rail region detection, obstacle detection, and visual–LiDAR fusion. Specifically, the first two parts adopt deep convolutional neural network (CNN) algorithms for semantic segmentation and object detection to pixel-wisely identify the rail track area ahead and detect the potential obstacles on the rail track, respectively. The visual–LiDAR fusion part integrates the visual data with the LiDAR data to achieve environmental perception for all weather conditions. It can also determine the geometric relationship between the rail track and obstacles to decide whether to trigger a warning alarm. Experimental results show that the system proposed in this study has strong performance and robustness. The system perception rate (precision) is 99.994% and the recall rate reaches 100%. The system, applied to the metro Hong Kong Tsuen Wan line, effectively improves the safety of urban rail train operation. Full article