Search Results (13,859)

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

Aerosol microphysical and optical properties play a crucial role in cloud microphysics, precipitation physics, and flood formation over areas characterized by complex monsoon regimes. This research presents a multi-source data integration approach to analyzing the spatio-temporal interaction between precipitation, aerosols, and flooding in the state of Kerala, incorporating an air mass trajectory analysis to examine its potential contribution to flooding. The results show that the Aerosol Optical Depth (AOD) values were high in the coastal districts (>0.8) in the La Niña year (2021) but low in the El Niño year (2015). On the precipitation side, 2018 and 2021 were both years with a high degree of anomalies, resulting in heavy rainfall that led to widespread flooding in the Thrissur district, among others. The trajectory analysis revealed that the Indian Ocean controls the precipitation during the southwest monsoon and the pre-monsoon. The post-monsoon precipitation is mainly sourced from the Arabian Peninsula and Arabian Sea, transferring marine aerosols along with desert aerosols. The overall study shows that the variability in aerosols and precipitation is more subject to change by the meteorological dynamics, as well as influenced by the regional changes in land use and land cover, causing fluxes in the land–atmosphere interactions. In conclusion, the present study highlights the possible interactive functions of atmospheric dynamics and anthropogenic land use modifications in generating a flood hazard. It provides essential information for land management policies and disaster risk reduction. Full article

This study presents a robust framework for optimizing the site selection of Electric Vehicle Charging Stations (EVCS) in Qatar by integrating a Geographic Information System (GIS) with a Multi-Criteria Decision-Making (MCDM) model. The core innovation lies in the enhancement of the conventional Analytic Hierarchy Process (AHP) with a Removal Sensitivity Analysis (RSA). This unique integration moves beyond traditional, subjective expert-based weighting by introducing a transparent, data-driven methodology to quantify the influence of each criterion and generate objective weights. The Analytic Hierarchy Process (AHP) was used to evaluate fourteen criteria related to accessibility, economic and environmental factors that influence EVCS site suitability. To enhance robustness and minimize subjectivity, a Removal Sensitivity Analysis (RSA) was applied to quantify the influence of each criterion and generate objective, data-driven weights. The results reveal that accessibility factors, particularly proximity to road networks and parking areas exert the highest influence, while environmental variables such as slope, CO concentration, and green areas have moderate but spatially significant impacts. The integration of AHP and RSA produced a more balanced and environmentally credible suitability map, reducing overestimation of urban sites and promoting sustainable spatial planning. Environmentally, the proposed framework supports Qatar’s transition toward low-carbon mobility by encouraging the expansion of clean electric transport infrastructure, reducing greenhouse gas emissions, and improving urban air quality. The findings contribute to achieving the objectives of Qatar National Vision 2030 and align with global efforts to mitigate climate change through sustainable transportation development. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Normal shock pressure ratios in equilibrium air for Mach numbers up to 30 and altitudes to 300,000 feet are shown to be correlated by a simple power law which provides an accuracy of ±2%, thereby permitting direct calculation of corresponding enthalpy ratios accurate to ±1% without iteration; a slight change in power-law coefficients extends this capability to Mach 65. Temperature, density, and compressibility may be then found directly from tables for high temperature air. For Mach numbers up to at least 6, a linear approximation for specific heat provides direct solutions for post-shock state variables, while a complementary logarithmic model of the equation of state enables direct solutions for Mach numbers up to about 12. This approach, which provides accuracy within ±3% for all relevant variables in the practical flight corridor of vehicles at these low to moderate hypersonic Mach numbers, should prove useful in design and analysis because the algebraic solutions obtained need neither iteration or interpolation. Full article

Survey campaigns in non-residential buildings show that occupants are often dissatisfied with the indoor environmental quality (IEQ), including the indoor air quality (IAQ) conditions. Occupant-centric controls (OCCs) have the potential to improve occupants’ satisfaction with IAQ and thermal comfort. Currently, applications of OCC systems with IAQ perceptions are limited due to a lack of a suitable modelling approach to predict occupants’ subjective IAQ assessment. In addition, a comprehensive overview of possible confounding variables for subjective IAQ in non-residential buildings is missing. This paper presents a systematic review of 46 papers on subjective IAQ assessments during field investigations in non-residential buildings. The following characteristics of the studies are examined: (1) the study context, (2) study and survey type, (3) dataset and sample size, (4) subjective IAQ assessment scales, (5) analysis and modelling techniques, and (6) associated variables. The review identified 46 different assessment scales and 20 different analysis techniques, respectively, indicating a lack of uniformity across the studies. The vast majority of studies were conducted in classrooms or offices. Other non-residential buildings, such as hospitals and sports halls, were underrepresented. Moreover, most of the studies failed to elaborate on the choice of a statistical technique and to report on the required sample size, compromising the validity of the statistical results. Furthermore, the review highlighted the limited scope of the subjective IAQ assessment analysis, with half of the reviewed studies investigating no more than four different variables. Lastly, only three of the reviewed papers focused on determining an accurate predictive model for subjective IAQ assessment. Full article

The composition of air pollutants in industrial complexes differs from that of general urban areas, often containing more hazardous substances that pose significant health risks to both workers and residents nearby. In this study, PM_2.5 and its 29 chemical components (eight ions, two carbon species, and 19 trace elements) were measured and analyzed at five monitoring sites adjacent to the Yeosu and Gwangyang industrial complexes from August 2020 to December 2024. Chemical characterization and source identification were conducted. The average PM_2.5 concentration was 18.63 ± 9.71 μg/m³, with notably higher levels observed during winter and spring. A low correlation (R = 0.56) between elemental carbon (EC) and organic carbon (OC) suggests a dominance of secondary aerosols. The charge balance analysis of [NH₄⁺] with [SO₄²⁻], [NO₃⁻], and [Cl⁻] showed slopes below the 1:1 line, indicating that NH₄⁺ is capable of neutralizing these anions. Positive matrix factorization (PMF) identified eight contributing sources—biomass burning (10.4%), sea salt (11.8%), suspended particles (7.1%), industrial sources (4.6%), Asian dust (5.2%), steel industry (21.8%), secondary nitrate (16.4%), and secondary sulfate (22.7%). These findings provide valuable insights for the development of targeted mitigation strategies and the establishment of effective emission control policies in industrial regions. Full article

Shipping is a high-energy-intensive sector and a major source of climate-relevant and harmful air pollutant emissions. In response to growing environmental concerns, the sector has been subject to increasingly stringent regulations, promoting the uptake of alternative fuels and emission control technologies. Accurate and diverse emission factors (EFs) are critical for quantifying shipping’s contribution to current emission inventories and projecting future developments under different policy scenarios. This study advances the development of load-dependent EFs for ships by incorporating alternative fuels, biofuels and emission control technologies. The methodology combines statistical analysis of data from an extensive literature review with newly acquired on-board emission measurements, including two-stroke propulsion engines and four-stroke auxiliary units. To ensure broad applicability, the updated EFs are expressed as functions of engine load and are categorized by engine and fuel type, covering conventional marine fuels, liquified natural gas, methanol, ammonia and biofuels. The results provide improved resolution of shipping emissions and insights into the role of emission control technologies, supporting robust, up-to-date emission models and inventories. This work contributes to the development of effective strategies for sustainable maritime transport and supports both policymakers and industry stakeholders in their decarbonization efforts. Full article

Mediterranean peri-urban forests play a crucial role in urban sustainability, yet their ecosystem services remain underexplored. This study quantifies and maps six regulating ecosystem services—carbon sequestration, air pollutant removal, surface runoff retention, precipitation interception, soil water regulation, and wildlife refuge—in a representative Mediterranean peri-urban forest, Monte de El Pardo (Spain). The analysis integrates cartographic and environmental data, biophysical modelling (i-Tree), and field surveys to provide a spatially explicit assessment. The results reveal that riparian formations and mixed stone pine–broadleaved woodlands provide the highest values across most services, while holm oak forests and dehesas contribute substantially due to their extensive coverage. Total annual carbon sequestration was estimated at 27,917,803 kg C yr⁻¹, equivalent to 102,329,511 kg CO₂e yr⁻¹. Hydrological regulation was also significant, with 94.5% of the area showing medium soil permeability and over half the territory presenting complex, multi-layered vegetation structure. Overall, Mediterranean peri-urban forests function as major carbon sinks, hydrological regulators, and biodiversity cores, reinforcing their importance as ecological and climatic stabilisers in metropolitan regions. Full article

Wildfires are one of the main natural hazards around the world, and are becoming increasingly important in the current context of climate change. To limit the impacts of fires, policies are implemented following various phases of risk management. These concern prevention (risk communication and information, forest monitoring, fuel management, the installation of firewalls, etc.) and suppression (firefighting interventions) measures. This article presents a systematic literature review analyzed through the prism of climate change and policy. It is carried out using a textometric approach. The corpus is composed of 720 articles published from 1997. A marked increase is evident from 2021. The analysis enables the clustering of the main issues. Six main themes were revealed by Reinert Clustering: Health issues, Disaster risk management, Natural environment, Management of the natural environment, Fire characteristics, and Fire modeling. These themes are composed of 36 sub-themes. In addition, the article shows that some issues (anthropogenic health and management/governance issues, and natural environment issues around fire and natural environment characterization) remain constant over time while others increase/decrease in importance (air quality, carbon storage and CO₂ emissions, ecosystems and biodiversity, and the effects of fires on the natural environment at the expense of anthropogenic issues). Full article

The main objective of this research was to evaluate the seasonal variability of PM₁₀-bound polycyclic aromatic hydrocarbons (PAHs), their sources, and analyse their health impacts We confirmduring the COVID-19 pandemic period. The chemical composition of PM₁₀ in terms of PAH content was carried out using the gas chromatography-mass spectrometry (GC-MS) technique. PM₁₀ samples were collected in Krakow from 2020 to 2021. A total of 92 samples of particulate matter (PM₁₀ fraction) were analysed. The analyses contained 16 basic PAHs identified by the United States Environmental Protection Agency (U.S. EPA) as the most harmful. The information obtained on the concentrations of PAHs was used to determine the profiles of pollution sources, exposure profiles, and the values of toxic equivalency factors recommended by the EPA: mutagenic equivalent to B[a]P (ang. mutagenic equivalent, MEQ), toxic equivalent to B[a]P (ang. toxic equivalent, TEQ), and carcinogenic equivalent to 2,3,7,8-tetrachlorodibenzo-p-dioxin (ang. carcinogenic equivalent, CEQ). In Kraków, heavy PAHs accounted for over 90% of the total PAHs detected in the PM₁₀ samples. In addition, air trajectory frequency analysis was performed to obtain information on the possibility of transporting pollutants from selected areas in the vicinity of the studied site. Interpreting the trajectory results provided information on the nature of air pollution sources. Analysis of Kraków’s air mass trajectory showed that the highest daily concentration of PM₁₀ in the air flow was from the southwest and east for days. Full article

Wetlands in dryland regions are rapidly degrading under the combined effects of climate change and human regulation, yet long-term, seasonally resolved assessments of surface water extent (SWE) and its dynamics remain scarce. Here, we map and analyze seasonal surface water extent (SWE) over the period 2000–2024 in the Ile River Delta (IRD), south-eastern Kazakhstan, using Landsat TM/ETM+/OLI data within the Google Earth Engine (GEE) framework. We integrate multiple indices using the modified Normalized Difference Water Index (mNDWI), Automated Water Extraction Index (AWEI) variants, Water Index 2015 (WI2015), and Multi-Band Water Index (MBWI) with dynamic Otsu thresholding. The resulting index-wise binary water maps are merged via ensemble agreement (intersection, majority, union) to delineate three SWE regimes: stable (persists most of the time), periodic (appears regularly but not in every season), and ephemeral (appears only occasionally). Validation against Sentinel-2 imagery showed high accuracy F1-Score/Overall accuracy (F1/OA ≈ 0.85/85%), confirming our workflow to be robust. Hydroclimatic drivers were evaluated through modified Mann–Kendall (MMK) and Spearman’s (r) correlations between SWE, discharge (D), water level (WL), precipitation (P), and air temperature (AT), while a hybrid ensemble–occurrence framework was applied to identify degradation and transition patterns. Trend analysis revealed significant long–term declines, most pronounced during summer and fall. Discharge is predominantly controlled by stable spring SWE, while discharge and temperature jointly influence periodic SWE in summer–fall, with warming reducing the delta surface water. Ephemeral SWE responds episodically to flow pulses, whereas precipitation played a limited role in this semi–arid region. Spatially, area(s) of interest (AOI)-II/III (the main distributary system) support the most extensive yet dynamic wetlands. In contrast, AOI-I and AOI-IV host smaller, more constrained wetland mosaics. AOI-I shows persistence under steady low flows, while AOI-IV reflects a stressed system with sporadic high-water levels. Overall, the results highlight the dominant influence of flow regulation and distributary allocation on IRD hydrology and the need for ecologically timed releases, targeted restoration, and transboundary cooperation to sustain delta resilience. Full article

Collision avoidance between unmanned aerial vehicles (UAVs) and non-cooperative targets (e.g., off-nominal operations or birds) presents significant challenges in urban air mobility (UAM). This difficulty arises due to the highly dynamic and unpredictable flight intentions of these targets. Traditional collision-avoidance methods primarily focus on cooperative targets or non-cooperative ones with fixed behavior, rendering them ineffective when dealing with highly unpredictable flight patterns. To address this, we introduce a deep reinforcement learning-based collision-avoidance approach leveraging global and local intent prediction. Specifically, we propose a Global and Local Perception Prediction Module (GLPPM) that combines a state-space-based global intent association mechanism with a local feature extraction module, enabling accurate prediction of short- and long-term flight intents. Additionally, we propose a Fusion Sector Flight Control Module (FSFCM) that is trained with a Dueling Double Deep Q-Network (D3QN). The module integrates both predicted future and current intents into the state space and employs a specifically designed reward function, thereby ensuring safe UAV operations. Experimental results demonstrate that the proposed method significantly improves mission success rates in high-density environments, with up to 80 non-cooperative targets per square kilometer. In 1000 flight tests, the mission success rate is 15.2 percentage points higher than that of the baseline D3QN. Furthermore, the approach retains an 88.1% success rate even under extreme target densities of 120 targets per square kilometer. Finally, interpretability analysis via Deep SHAP further verifies the decision-making rationality of the algorithm. Full article

Over-the-Air updates have emerged as a critical competitive frontier in the Software-Defined Vehicle era. While offering value creation opportunities, automakers face strategic uncertainty regarding pricing models (e.g., subscription vs. one-time purchase). To clarify these dynamics, this study develops an evolutionary game model of duopolistic pricing competition. Unlike traditional studies with exogenous payoff assumptions, we innovatively employ the Hotelling model to endogenously derive firm profit functions based on consumer utility maximization. The highlights of this study include: (1) We establish an integrated “static–dynamic” framework connecting micro-level consumer choice with macro-level strategy evolution; (2) We identify that product differentiation is the decisive variable governing market stability; (3) We demonstrate that under moderate differentiation, the market exhibits a robust self-correcting tendency towards “Tacit Collusion” (mutual high pricing). However, simulation results also warn that an asymmetric disruptive strategy by a market leader can override this robustness, forcing the market into a low-profit equilibrium. These findings provide theoretical guidance for automakers to optimize pricing strategies and avoid value-destroying price wars. Full article