Search Results (160)

The likely increase in the frequency and severity of droughts with climate warming will pose an enormous challenge for the conservation of forest biodiversity. Documenting the response of species to recent droughts can inform future conservation actions. Mammals that breed and mature slowly may be especially vulnerable to drought-induced disruption to breeding. The yellow-bellied glider (Petaurus australis, Shaw) is a threatened low-density, arboreal marsupial of eastern Australia. Following a severe drought in 2019, one population had declined by 48% by 2021. The present study investigated whether this population had recovered 3–4 years (2022 and 2023) after that drought. Audio surveys of this highly vocal species were conducted at 42 sites, sampling > 1000 h per year, and producing recordings of 2038–2856 call sequences. The probability of occupancy varied little across the two survey years (0.92–0.97). Local abundance in 2023 had returned to pre-drought levels (45% of occupied sites had ≥3 individuals compared to 6% in 2021). These findings show a recovery from a drought-induced decline required at least 3 years, in keeping with the slow life history traits of this species. This study highlights the importance of considering a species’ life history strategy when evaluating its sensitivity to drought. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article

In recent decades, the Brazilian Cerrado has faced rapid land conversion, resulting in the loss of approximately half of its original vegetation cover. Most existing conservation units within the biome are increasingly threatened by the expansion of land use around their boundaries. The establishment of buffer zones with land use regulations may protect biodiversity within these protected areas. In this study, we evaluated and ranked the 10 km buffer zones of 15 national parks (NPs) located in the Cerrado biome, identifying their priority for biodiversity conservation and sustainable land use interventions. The analysis considered the following data: land use and land cover change from 2012 to 2020, extent of natural vegetation fragments, presence or absence of state and municipal conservation units within the buffer zones, and drainage density. Two multicriteria analysis methods, the analytic hierarchy process and the weighted linear combination, were applied to classify the buffer zones into five levels of threat: very high, high, moderate, low, and very low. Among the 15 buffer zones analyzed, 11 were classified as having high to very high priority for conservation actions. The buffer zones surrounding the Serra da Bodoquena, Emas, Canastra, and Brasília NPs were ranked as having very high priority. Between 2012 and 2020, the most severe reductions in ecological connectivity were observed in the buffer zones of Grande Sertão Veredas (44.5%), Nascentes do Rio Parnaíba (40.4%), and Serra das Confusões (36.7%). Given the relatively high proportion of natural vegetation in the buffer zones located in the northern Cerrado, we recommend prioritizing conservation efforts in this region. In contrast, in the southern portion of the biome, where land occupation is more intense, strategies should focus on promoting environmentally sustainable land use practices. Full article

Assessing the impact of community-based conservation programs on wildlife biodiversity remains a significant challenge. This pilot study was designed to develop and demonstrate a scalable, spatially explicit workflow using thermal infrared (TIR) imagery and unmanned aerial vehicles (UAVs) for non-invasive biodiversity monitoring. Conducted in a 2-hectare grassland area in Chitwan, Nepal, the study applied TIR-based grid sampling and multi-species occupancy models with thin-plate splines to evaluate how species detection and richness might vary between (1) morning and evening UAV flights, and (2) the Chitwan National Park and Kumroj Community Forest. While the small sample area inherently limits ecological inference, the aim was to test and demonstrate data collection and modeling protocols that could be scaled to larger landscapes with sufficient replication, and not to produce generalizable ecological findings from a small dataset. The pilot study results revealed higher species detection during morning flights, which allowed us to refine our data collection. Additionally, models accounting for spatial autocorrelation using thin plate splines suggested that community-based conservation programs effectively balanced ecosystem service extraction with biodiversity conservation, maintaining richness levels comparable to the national park. Models without splines indicated significantly higher species richness within the national park. This study demonstrates the potential for spatially explicit methods for monitoring grassland mammals using TIR UAV as indicators of anthropogenic impacts and conservation effectiveness. Further data collection over larger spatial and temporal scales is essential to capture the occupancy more generally for species with larger home ranges, as well as any effects of rainfall, flooding, and seasonal variability on biodiversity in alluvial grasslands. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

The use of LUZs in urban environments is a critical factor for ensuring efficient vehicle mobility in cities. Poor utilisation of these zones can generate negative externalities, such as double parking or illegal occupation of pedestrian crossings or garage doors. The purpose of the study is to provide city governance with a methodology based on the OEE model to evaluate the efficiency of individual zones or sets of zones and to inform decisions that improve their use without disrupting the coexistence with other city users. To validate the methodology, all deliveries made in selected areas of the city of Zaragoza over the course of one month were studied. The results of the study reveal a considerable loss of efficiency and some recommendations are proposed achieve a better use: only 51.44% of deliveries used the LUZs correctly, and the total OEE ratio was just 0.37. This low level of efficiency is due to the incorrect use by delivery drivers, who often use LUZs as parking spaces, and the illegal occupation of the zones by unauthorised private vehicles. Full article

The acceleration of industrialization and urbanization worldwide has dramatically improved living standards but has also introduced serious environmental and public health challenges. One of the most critical challenges is air pollution, particularly indoors, where individuals typically spend over 90% of their time. Ensuring good Indoor Air Quality (IAQ) is essential, especially in heavily frequented public spaces such as shopping malls. This study focuses on assessing IAQ in a large shopping mall located in Tabuk, Saudi Arabia, covering retail zones as well as an attached underground parking area. Monitoring is conducted over a continuous two-month period using calibrated instruments placed at representative locations to capture variations in pollutant levels. The investigation targets key contaminants, including carbon monoxide (CO), carbon dioxide (CO₂), fine particulate matter (PM_2.5), total volatile organic compounds (TVOCs), and formaldehyde (HCHO). The data are analyzed and compared against international and national guidelines, including World Health Organization (WHO) standards and Saudi environmental regulations. The results show that concentrations of CO, CO₂, and PM_2.5 in the shopping mall are generally within acceptable limits, with values ranging from approximately 7 to 15 ppm, suggesting that ventilation systems are effective in most areas. However, the study identifies high levels of TVOCs and HCHO, particularly in zones characterized by poor ventilation and high human occupancy. Peak concentrations reach 1.48 mg/m³ for TVOCs and 1.43 mg/m³ for HCHO, exceeding recommended exposure thresholds. These findings emphasize the urgent need for enhancing ventilation designs, prioritizing the use of low-emission materials, and establishing continuous air quality monitoring protocols within commercial buildings. Improving IAQ is not only crucial for protecting public health but also for enhancing occupant comfort, satisfaction, and overall building sustainability. This study offers practical recommendations to policymakers, building managers, and designers striving to create healthier indoor environments in rapidly expanding urban centers. Full article

The Greek islands have been blessed with excellent wind potential, with hundreds of sites featuring annual average wind velocity higher than 8–10 m/s. Due to specific regulations in the legal framework, some GWs of wind parks have been submitted since the late 2000s by a small number of large investors in the Greek islands, favoring the creation of energy monopolies and imposing serious impacts on natural ecosystems and existing human activities. These projects have caused serious public reactions against renewables, considerably decelerating the energy transition. This article aims to summarize the legal points in the Greek framework that caused this distorted approach and present the imposed potential social and environmental impacts. Energy monopolies distort the electricity wholesale market and lead to energy poverty and a low standard of living by imposing higher electricity procurement prices on the final users. The occupation of entire insular geographical territories by large wind park projects causes important deterioration of the natural environment, which, in turn, leads to loss of local occupations, urbanization, and migration by affecting negatively the countryside life. Serious concerns from the local population are clearly revealed through an accomplished statistical survey as well as a clear intention to be engaged in future wind park projects initiated by local stakeholders. The article is integrated with specific proposed measures and actions toward the rational development of renewable energy projects. These refer mainly on the formulation of a truly supportive and just legal framework aiming at remedying the currently formulated situation and the strengthening of the energy communities’ role, such as through licensing priorities, funding mechanisms, and tools, as well as additional initiatives such as capacity-building activities, pilot projects, and extensive activation of local citizens. Energy communities and local stakeholders should be involved in the overall process, from the planning to the construction and operation phase. Full article

Highway rest areas are relevant components of road infrastructure, providing drivers with essential opportunities to rest and mitigate fatigue-related crash risks. Despite their acknowledged importance, little is known about the factors that influence their actual utilization. This study addresses this gap by applying supervised machine learning algorithms to predict hourly occupancy levels of truck parking lots at highway rest areas using a dataset collected from digital monitoring systems in Poland. The dataset includes 10,740 observations and 33 features describing infrastructural, administrative, and locational characteristics of selected rest areas in Poland. Eight classification models—Gradient Boosting, XGBoost, Random Forest, k-NN, Decision Tree, Logistic Regression, SVM, and Naive Bayes—were implemented and compared using standard performance metrics. Gradient Boosting emerged as the best-performing model, achieving the highest prediction accuracy and identifying key features such as the presence of fuel stations, rest area category, and facility amenities as significant predictors of occupancy. The findings highlight the potential of interpretable machine learning methods for supporting infrastructure planning, particularly in identifying underutilized or overburdened facilities. This research demonstrates a data-driven approach for analyzing rest area usage and provides practical insights for optimizing facility distribution, enhancing road safety, and informing future investments in transport infrastructure. Full article

Urban traffic management faces significant challenges in balancing parking supply with user demand. This study introduces a novel parking demand model that integrates simulation-based pricing with elasticity functions derived from revealed preference data, segmented across predefined user categories, such as short-term visitors (e.g., shoppers) and monthly subscribers (e.g., commuters). Unlike previous models, this approach does not rely on survey-based inputs and explicitly accounts for both natural and chaotic demand behaviors, thereby improving forecasting accuracy under oversaturated conditions. The model supports sustainable parking management by optimizing space availability, while simultaneously increasing occupancy and enhancing revenue generation. Full article

Parking availability prediction is a critical component of intelligent transportation systems, aiming to reduce congestion and improve urban mobility. While traditional deep learning models such as Long Short-Term Memory (LSTM) networks have been widely applied, they lack mechanisms to quantify uncertainty, limiting their robustness in real-world deployments. This paper proposes a Bayesian Neural Network (BNN)-based framework for parking occupancy prediction that explicitly models both epistemic and aleatoric uncertainty. Although BNNs have shown promise in other domains, they remain underutilised in parking prediction—likely due to the computational complexity and the absence of real-time context integration in earlier approaches. Our approach leverages contextual features, including temporal and environmental factors, to enhance uncertainty-aware predictions. The framework is evaluated under varying data conditions, including data scarcity (90%, 50%, and 10% of training data) and synthetic noise injection to simulate aleatoric uncertainty. Results demonstrate that BNNs outperform other methods, achieving an average accuracy improvement of 27.4% in baseline conditions, with consistent gains under limited and noisy data. Applying uncertainty thresholds at 20% and 30% further improves reliability by enabling selective, confidence-based decision making. This research shows that modelling both types of uncertainty leads to significantly improved predictive performance in intelligent transportation systems and highlights the potential of uncertainty-aware approaches as a foundation for future work on integrating BNNs with hybrid neuro-symbolic reasoning to enhance decision making under uncertainty. Full article

Smart cities are urban areas that use contemporary technology to improve citizens’ overall quality of life. These modern digital civil hubs aim to manage environmental conditions, traffic flow, and infrastructure through interconnected and data-driven decision-making systems. Today, many applications employ intelligent sensors for real-time data acquisition, leveraging visualization to derive actionable insights. However, despite the proliferation of such platforms, challenges like high data volume, noise, and incompleteness continue to hinder practical visual analysis. As missing data is a frequent issue in visualizing those urban sensing systems, our approach prioritizes their correction as a fundamental step. We deploy a hybrid imputation strategy combining SARIMAX, k-nearest neighbors, and random forest regression to address this. Building on this foundation, we propose an interactive web-based pipeline that processes, analyzes, and presents the sensor data provided by Basel’s “Smarte Strasse”. Our platform receives and projects environmental measurements, i.e., NO₂, O₃, PM_2.5, and traffic noise, as well as mobility indicators such as vehicle speed and type, parking occupancy, and electric vehicle charging behavior. By resolving gaps in the data, we provide a solid foundation for high-fidelity and quality visual analytics. Built on the Flask web framework, the platform incorporates performance optimizations through Flask-Caching. Concerning the user’s dashboard, it supports interactive exploration via dynamic charts and spatial maps. This way, we demonstrate how future internet technologies permit the accessibility of complex urban sensor data for research, planning, and public engagement. Lastly, our open-source web-based application keeps reproducible, privacy-aware urban analytics. Full article

Parking management systems play a crucial role in addressing parking shortages and operational challenges; however, high initial costs and infrastructure requirements often hinder their implementation. Edge computing offers a promising solution by reducing latency and network traffic, thus optimizing operational costs. Nonetheless, the limited computational resources of edge devices remain a significant challenge. This study developed a real-time vehicle occupancy detection system utilizing SSD-MobileNetv2 on edge devices to process video streams from multiple IP cameras. The system incorporates a dual-trigger mechanism, combining periodic triggers and parking space mask triggers, to optimize computational efficiency and resource usage while maintaining high accuracy and reliability. Experimental results demonstrated that the parking space mask trigger significantly reduced unnecessary AI model executions compared to periodic triggers, while the dual-trigger mechanism ensured consistent updates even under unstable network conditions. The SSD-MobileNetv2 model achieved a frame processing time of 0.32 s and maintained robust detection performance with an F1-score of 0.9848 during a four-month field validation. These findings validate the suitability of the system for real-time parking management in resource-constrained environments. Thus, the proposed smart parking system offers an economical, viable, and practical solution that can significantly contribute to developing smart cities. Full article

Local residents’ satisfaction plays a crucial role in the successful management of national parks. However, limited attention has been paid to residents’ preferences in the management of national parks, which hinders the sustainable development and optimization of management systems. To address this gap, we focused on the Dongning area of Northeast China Tiger and Leopard National Park (NCTLNP) as a case study and employed the importance–performance analysis (IPA) framework to assess residents’ perceptions and cognitive rankings of current ecosystem services. Additionally, we examined how demographic and socio-economic factors influence these perceptions. Our findings reveal that local residents prioritize ecosystem services that directly impact their livelihoods and that their material, social, spiritual, and cultural needs are not fully met. Satisfaction and importance ratings varied across regions, with significant influences occurring from the residents’ sex, occupations, and livelihoods. Based on these results, we recommend strengthening the institutional framework for national park management and enhancing the scientific effectiveness of management policies by incorporating residents’ perspectives into decision-making processes. Full article

With the rising adoption of electric vehicles (EVs), fire-related issues have garnered significant attention, prompting extensive research efforts. This study investigates the dispersion of toxic gases generated during EV fires in confined spaces, such as underground parking garages, to enhance fire safety protocols. Using the fire dynamics simulator (FDS), simulations were conducted for 24 kWh, 53 kWh, and 99.8 kWh battery scenarios to assess the impact of increasing battery capacities on toxic gas emissions. The results indicate that hydrogen fluoride (HF) concentrations in poorly ventilated areas peaked at 488.2 ppm, significantly exceeding the Acute Exposure Guideline Level (AEGL-2) threshold of 12 ppm. The exposure time exceeding AEGL-2 (30 min) was recorded as 53 min and 49 s for the 99.8 kWh scenario, highlighting a substantial risk to occupants and emergency responders. Additionally, the fractional effective dose (FED) for asphyxiant gases and the fractional effective concentration (FEC) for irritant gases were analyzed, revealing that larger battery capacities and proximity to the fire source reduced tenability time by up to 47% compared to smaller batteries. These findings provide critical insights into fire safety measures, emphasizing the necessity of early fire detection systems, enhanced ventilation strategies, and battery-specific fire suppression technologies in confined environments. Full article