Search Results (3,562)

Accurate, scalable mapping of coral reef habitats is essential for monitoring ecosystem health and detecting change over time. In this study, we introduce a novel mathematically based nonlinear spectral unmixing method for benthic habitat classification, which provides sub-pixel estimates of benthic composition, capturing the mixed benthic composition within individual pixels. We compare its performance against two machine learning approaches: semi-supervised K-Means clustering and AdaBoost decision trees. All models were applied to high-resolution PlanetScope satellite imagery and ICESat-2-derived terrain metrics. Models were trained using a ground truth dataset constructed from benthic photoquadrats collected at Heron Reef, Australia, with additional input features including band ratios, standardized band differences, and derived ICESat-2 metrics such as rugosity and slope. While AdaBoost achieved the highest overall accuracy ($93.3\%$) and benefited most from ICESat-2 features, K-Means performed less well ($85.9\%$) and declined when these metrics were included. The spectral unmixing method uniquely captured sub-pixel habitat abundance, offering a more nuanced and ecologically realistic view of reef composition despite lower discrete classification accuracy ($64.8\%$). These findings highlight nonlinear spectral unmixing as a promising approach for fine-scale, transferable coral reef habitat mapping, especially in complex or heterogeneous reef environments. Full article

Charging electric vehicles (EVs) and integrating renewable energy sources (RESs) are becoming key aspects of residential energy systems. However, the variability of RES generation, combined with uncontrolled EV charging, poses challenges for reliability, power quality, and supply-demand balancing within communities. The challenges only grow when considering other electrified building loads as well. Accurate forecasting of power demand and renewable generation is essential for efficient and sustainable grid operation, optimal use of RESs, and effective energy trading within communities. Deep learning (DL), including supervised, unsupervised, and reinforcement learning (RL), has emerged as a promising solution for predicting consumer demand, renewable generation, and managing energy flows in residential environments. This paper provides a comprehensive review of the development and application of these methods for forecasting and energy management in residential communities. Evaluation metrics across studies indicate that supervised learning can achieve highly accurate forecasting results, especially when integrated with unsupervised K-means clustering and data decomposition. These methods help uncover patterns and relationships within the data while reducing noise, thereby enhancing prediction accuracy. RL shows significant potential in control applications, particularly for charging strategies. Similarly to how V2G-simulators model individual EV usage and simulate large fleets to generate grid-scale predictions, RL can be applied to various aspects of EV fleet management, including vehicle dispatching, smart scheduling, and charging coordination. Traditional methods are also used across different applications and help utilities with planning. However, these methods have limitations and may not always be completely accurate. Our review suggests that integrating hybrid supervised-unsupervised learning methods with RL can significantly improve the sustainability and resilience of energy systems. This approach can improve demand and generation forecasting while enabling smart charging coordination and scheduling for scalable EV fleets integrated with building electrification measures. Furthermore, the review introduces a unifying conceptual framework that links forecasting, optimization, and policy coupling through hierarchical deep learning layers, enabling scalable coordination of EV charging, renewable generation, and building energy management. Despite methodological advances, real-world deployment of hybrid and deep learning frameworks remains constrained by data-privacy restrictions, interoperability issues, and computational demands, highlighting the need for explainable, privacy-preserving, and standardized modeling approaches. To be effective in practice, these methods require robust data acquisition, optimized forecasting and control models, and integrated consideration of transport, building, and grid domains. Furthermore, deployment must account for data privacy regulations, cybersecurity safeguards, model interpretability, and economic feasibility to ensure resilient, scalable, and socially acceptable solutions. Full article

Pasture systems, typically composed of grasses, legumes, and forage crops, are vital livestock nutrition sources. The quality of these pastures depends on various factors, including species composition and growth stage, which directly impact livestock productivity. Remote sensing (RS) technologies offer powerful, non-invasive means for large-scale pasture monitoring and classification, enabling efficient assessment of pasture health across extensive areas. However, traditional supervised classification methods require labelled datasets that are often expensive and labour-intensive to produce, especially over large grasslands. This study explores unsupervised clustering as a cost-effective alternative for identifying pasture types without the need for labelled data. Leveraging spatiotemporal data from the Sentinel-2 mission, we propose a clustering framework that classifies pastures based on their temporal growth dynamics. For this, the pasture segments are first created with quick-shift segmentation, and spectral time series for each segment are grouped into clusters using time-series distance-based clustering techniques. Empirical analysis shows that the dynamic time warping (DTW) distance measure, combined with K-Medoids and hierarchical clustering, delivers promising pasture mapping with normalised mutual information (NMI) of 86.28% and 88.02% for site-1 and site-2 (total area of approx. 2510 ha), respectively, in New South Wales, Australia. This approach offers practical insights for improving pasture management and presents a viable solution for categorising pasture and grazing systems across landscapes. Full article

The parameter optimization design of a power-cycling hydrodynamic mechanical transmission (PCHMT) is an important approach to improving the fuel economy of wheel loaders. First, the output capacity characteristics of the PCHMT were analyzed, revealing the qualitative relationships among structural parameters, efficiency, and the output capacity coefficient. Second, 400 sets of V-cycle operation condition tests were conducted on loaders using five different materials, and a representative loading–hauling cycle was synthesized with the K-means clustering algorithm. Third, a parameter optimization model for the PCHMT was developed based on its output capacity characteristics, and the optimal structural parameters were determined using a genetic algorithm. Finally, a simulation model of the loader powertrain was established to compare fuel consumption under optimal and non-optimal parameters. The results show that although transmission efficiency at the same speed ratio is higher with non-optimal parameters, fuel consumption with optimal parameters is 2.6% lower, confirming the effectiveness of this optimization design method. Full article

Planetary exploration missions have acquired a growing amount of remote sensing data, offering a reliable basis for studying the geological evolution of planetary bodies such as Mars. In recent years, machine learning models have emerged as powerful tools for remote sensing by providing scalable and adaptive solutions for planetary science. We present a machine learning approach to map the spatial distribution of minerals on Mars, representing a step toward large-scale automated mineral mapping. Although existing CRISM dimensionality reduction methods are useful, the feature space remains high-dimensional, and relying on RGB overlays limits the ability to preserve and detect complex relationships, increasing the risk of missing important spectral patterns. Our framework utilises the Self-Organising Map (SOM) model and k-means clustering to identify clusters of spectral signatures, which may correspond to distinct minerals. It reduces dimensionality to a two-dimensional grid while preserving key high-dimensional patterns and relationships, providing a more reliable and interpretable basis for semi-automated analysis than RGB overlays. Although the clusters can be labelled by referencing a spectral library, our framework does not require labelled data and can operate in an unsupervised manner. The framework retains full spectral dimensionality of input features. The results indicate that our framework can identify the spatial distribution of minerals on Mars, even in complex spectral environments with overlapping features. Moreover, the SOM model output is interpretable rather than a black box, providing intuitive guidance for mineral exploration when applied in a semi-automated workflow. Full article

Effective and sustainable planning for future smart transportation systems is hindered by outdated traffic management models that fail to capture real-world dynamics, leading to congestion and significant environmental impact. To address this, advanced machine learning models are required to provide high-fidelity insights into urban mobility. In this work, we propose an adaptive machine learning approach to traffic pattern recognition that synergizes the HDBSCAN and k-means clustering algorithms. By employing a data-driven weighted voting mechanism, our solution provides a robust analytical foundation for sustainable planning, integrating structural analysis with precise cluster refinement. The crafted model was validated using a high-fidelity simulation of the Khmelnytskyi, Ukraine, transport network, where it demonstrated a superior ability to identify distinct traffic modes, achieving a V-measure of 0.79–0.82 and improving cluster compactness by 10–14% over standalone algorithms. It also attained a scenario identification accuracy of 92.8–95.0% with a temporal coherence of 0.94. These findings confirm that our adaptive approach is a foundational technology for intelligent transport systems, enabling the planning and deployment of more responsive, efficient, and sustainable urban mobility solutions. Full article

The dynamic development of autonomous vehicle (AV) technologies has intensified the need to understand the factors influencing their acceptance. This study aims to develop user profiles reflecting different levels of enthusiasm toward autonomous buses in Warsaw. A quantitative research design was employed, using a survey of 385 residents collected via CAPI, CATI, and CAWI methods. Cluster analysis (k-means method) identified distinct user profiles based on attitudes toward autonomous buses and general trust in technology: Cluster 1—Enthusiastic Adopters, Cluster 2—Sceptical Opponents, and Cluster 3—Cautious Optimists. The study confirmed that demographic characteristics (age, gender, education level, occupational status) significantly influence the level of enthusiasm for autonomous buses. Younger, highly educated, and professionally active individuals showed highest levels of acceptance. Furthermore, a higher level of general trust in technology was positively associated with greater acceptance of autonomous buses. The research highlights important implications and recommends focusing on districts with a higher concentration of Enthusiastic Adopters and targeted communication strategies for Sceptical Opponents and Cautious Optimists. However, study limitations include the geographic restriction to Warsaw and the absence of data capturing changes in attitudes over time. Future research should be expanded to other cities, exploring ongoing dynamics of trust and acceptance. Despite limiting the research to one specific city, the research tool used and the research itself can be applied to similar cities regardless of their geographical location or size. Full article

Background/Objectives: Alzheimer’s disease (AD) exhibits clinical and biological variability. This study aimed to identify MRI-defined subtypes reflecting distinct biological pathways of neurodegeneration and cognitive decline. Methods: We applied principal component analysis followed by k-means clustering (k = 3) on volumetric MRI data from 924 participants and validated clusters using cerebrospinal fluid (CSF) biomarkers (Aβ₄₂, total tau, p-tau, CTRED, MAPres, glucose, CTWHITE). Results: Three major phenotypes emerged: (1) a tau/vascular limbic subtype with pronounced hippocampal and amygdala atrophy and elevated tau and CTRED levels; (2) a volume-preserved, low-amyloid subtype consistent with early-stage or cognitively resilient AD; and (3) a diffuse-atrophy subtype with high amyloid and tau burden and ventriculomegaly. Comparative analysis revealed progressive biological shifts from amyloid accumulation to tau aggregation and vascular compromise across these clusters. Conclusions: MRI-based clustering validated by CSF biomarkers delineates biologically meaningful AD endophenotypes. The results suggest a gradual cognitive decline driven by amyloid–tau–vascular interactions, supporting multimodal phenotyping as a practical approach for precision staging and intervention. Full article

The accuracy of Precise Orbit Determination (POD) for Global Navigation Satellite Systems (GNSS) critically depends on optimal tracking station selection. This study proposed and validates a novel framework that integrates a hybrid-weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model with spherical k-means clustering, effectively resolving the challenge of balancing station data quality with uniform spatial distribution. The framework generates by first a comprehensive quality score for each station based on 40 indicators and then selects the top-scoring station from distinct geographical clusters to construct a well-distributed, high-quality network. To validate the methodology, we performed multi-GNSS POD using networks of 30, 60, and 90 stations selected by the proposed framework. The accuracy was assessed via two independent methods: orbit comparisons (Root Mean Square, RMS) against final Analysis Center (AC) orbits and Satellite Laser Ranging (SLR) validation. The results demonstrate that the optimized 60-station network (e.g., RMS of ~2.5, 5.3, 2.1, and 5.4 cm for GPS, GLONASS, Galileo, and BDS, respectively) achieves an accuracy comparable to that of a 90-station network. Moreover, a 30-station globally uniform network outperforms a 90-station network of high-quality but spatially clustered stations. This study provides an objective and quantitative solution for establishing efficient and reliable GNSS tracking networks, directly benefiting ACs and other high-precision applications. Full article

From 2020 to 2021, crop production increased by 54% globally, and the popularity of commercial agriculture to increase profitability is gradually increasing. However, global warming and climate issues make it difficult to maintain stable crop production. To improve crop production efficiency, techniques for efficiently managing large-scale commercial farmland are needed. This study proposes a satellite image-based soil moisture and onion yield prediction technique as a methodology for managing large-scale farmland. This preemptive soil moisture management technique effectively manages increased soil pressure, resulting in soil drying due to rising temperatures. To remotely identify agricultural land, vegetation indices were extracted from satellite image data, and K-means clustering was applied. Ensemble machine learning is performed on soil images collected from satellite images. This model combines soil physical properties with soil environmental factor information to develop a model. The results show that soil color information obtained from satellite images is highly correlated with soil organic matter content. The proposed model is validated using crop yield data and environmental factor data obtained from actual crop production experiments. Consequently, the proposed methodology can be effectively applied to manage large-scale farmland and enables decision-making to improve profitability. Full article

Global electric vehicle sales are growing exponentially, with the European Union actively promoting the adoption of electric vehicles to significantly reduce mobility-related emissions. Concurrently, research efforts are increasingly directed toward optimizing vehicle charging strategies for the effective integration of renewable energy sources. Nevertheless, despite extensive theoretical studies, few practical implementations have been carried out. In response, this paper presents a digital twin of a microgrid designed specifically for optimizing the charging schedules of an electric vehicle fleet, with the goal of maximizing photovoltaic self-consumption. Machine learning algorithms are utilized to forecast vehicle energy consumption, and various heuristic optimization methods are applied to determine optimal charging schedules. The system incorporates an interactive dashboard, enabling users to input specific preferences or delegate charging decisions to a real-time optimizer. Additionally, a user-centric decision support system was developed to provide recommendations on optimal vehicle connection timings and heat pump setpoints. Certain algorithms failed to converge on a feasible optimal solution, even after 340 s and over 500 generations, particularly within high-production scenarios. Conversely, using the GWO-WOA algorithm, optimal charging schedules are computed in less than 25 s, balancing photovoltaic power exports under varying weather conditions. Furthermore, K-Means was identified as the most effective clustering technique, achieving a Silhouette Score of up to 0.57 with four clusters. This configuration resulted in four distinct velocity ranges, within which energy consumption varied by up to 5.8 kWh/100 km, depending on the vehicle’s velocity. Finally, the facility managers positively assessed the usability of the DT dashboard and the effectiveness of the decision support system. Full article

Sport events generate economic, social, and environmental impacts that shape residents’ perceptions and levels of support. In the context of sustainable urban development, understanding how residents evaluate these impacts provides valuable knowledge about community responses to tourism and event-led growth. Drawing on the Triple Bottom Line (TBL), Social Exchange Theory (SET), and Social Representations Theory (SRT), this study examines residents’ evaluations of the Rally Sierra Morena (RSM), a large-scale international motorsport event recently incorporated into the European Rally Championship (ERC). Data were collected shortly before the event using a self-administered questionnaire (n = 1529). An exploratory factor analysis (EFA) identified a multidimensional structure of perception, and a non-hierarchical k-means cluster analysis identified three clusters: Skeptics, who perceived stronger negative than positive impacts in economic and environmental dimensions; Pragmatists, who emphasized positive economic benefits while acknowledging environmental costs; and Enthusiasts, who consistently rated positive impacts higher across all dimensions and expressed the strongest support for the event. By integrating perceptual and sustainability-based approaches, this study connects residents’ evaluations of a motorsport event with broader discussions on urban resilience and sustainable community development. Full article

Circulation type classification (CTC) is an important method in atmospheric sciences, which reveals the relationship between atmospheric circulation and regional weather and climate. Accurate circulation classification helps to improve weather forecasting accuracy and supports climate change research. China has complex topography and significant spatiotemporal variability in its circulation patterns, making the study of circulation type classification in this region highly significant. This study aims to evaluate the applicability of several mainstream objective CTC methods in the China region. We applied methods including T-mode principal component analysis (PCT), Ward linkage, K-means, and Self-Organizing Maps (SOM) to classify the sea-level pressure daily mean fields from 1993 to 2023 in the study area, and compared the classification results in terms of internal metrics, continuity, seasonal variation, separability of related meteorological variables (e.g., temperature, precipitation), and stability to spatiotemporal resolution. The results show that each method has its advantages in different contexts, with the K-means method showing the best overall performance. Additionally, an optimized approach combining PCT and K-means is proposed. Full article

The objective of this research endeavor is to present engineering data pertaining to the size and shape characteristics of strawberries, which have a wide range of applications in industry, and to obtain the data necessary for the development and design of product processing systems. In this study, standard strawberry varieties were utilized, and analyses were conducted by means of an image-processing method. The projection area (601.5–762.0 mm²), length (34.0 mm), width (28.6 mm) and surface area (28.6 cm²) of the strawberry samples were measured in the horizontal and vertical orientation, in order to ascertain their size characteristics. Furthermore, the sphericity (86.1%) and roundness (1.039–1.087) parameters were calculated for the shape characteristics, accordingly. The findings of the correlation analysis suggested that the size parameters of the fruits exerted no influence on fruit shape characteristics. In the elliptic Fourier analysis performed to reveal the shape differences in the fruit, the contour geometry of each fruit sample was extracted, the principal component (PC) scores describing the shape were obtained and the shape categories of the fruit were determined. Following the analysis of the PCs, it was determined that 90.77% of the total shape variance was explained by the first seven components. Consequently, the shape of the strawberry fruit was defined as a spherical cone. Following the implementation of a discriminant analysis in conjunction with a clustering process, which categorized the samples into seven distinct shape categories employing the k-means algorithm, an accuracy rate of 94.1% was achieved. Full article

This study proposes and empirically assesses a bio-inspired conceptual framework, termed Digital DNA, for modeling Industry 5.0 transformation as a complementary extension of established Industry 4.0 principles with an explicit focus on human-centricity, sustainability, and resilience. Rather than positing a new industrial revolution, our positioning follows the European Commission’s view that Industry 5.0 complements Industry 4.0 by emphasizing stakeholder value and human-technology symbiosis. We encode organizational capabilities (genotype) into four gene groups, Adaptability, Technology, Governance, and Culture, and link them to five human-centric outcomes (phenotype). Twenty capability genes and ten outcome measures were scored, normalized (0–100 scale), and analyzed using correlations, K-means clustering, and mutation/drift tracking to capture both static maturity levels and dynamic change patterns. Results show that high Industry 5.0 readiness is consistently associated with elevated Governance and Culture scores. Three transformation archetypes were identified: Alpha, representing holistic socio-technical integration; Beta, with strong technical capacity but weaker cultural alignment; and Gamma, with fragmented capabilities and elevated vulnerability. The Digital DNA framework offers a replicable diagnostic tool for linking socio-technical capabilities to human-centric outcomes, enabling readiness assessment and guiding adaptive, ethical manufacturing strategies. Full article