Search Results (16,013)

This study examines the potential effect of hypothetical Urban Air Mobility (UAM) demand redistribution on congestion and signalized intersection performance in the urban environment of Topoľčany, Slovakia. Based on a calibrated microsimulation model, scenarios involving the redistribution of a portion of ground traffic demand away from the road network were analyzed at 30% and 50% during the morning peak period. The evaluation focused primarily on travel times and intersection load. The results indicate that a moderate reduction in ground traffic demand leads to a significant reduction in travel times and traffic intensity. The most substantial improvement was observed in the 30% redistribution scenario. In comparison, a further increase to 50% did not yield proportional benefits, suggesting a nonlinear threshold effect in the transport system’s performance. It should be emphasized that the UAM scenarios in this study do not represent a full operational simulation of Urban Air Mobility, including aerial corridors, vertiports, waiting times, intermodal transfers, or airspace capacity. Instead, they represent demand redistribution scenarios used to evaluate the response of the existing signalized road network to reduced ground traffic demand. The study identifies limitations arising from simplified model assumptions and the absence of broader environmental, operational, and social considerations. Nevertheless, the findings show that even a moderate reduction in road traffic demand, potentially associated with future multimodal mobility concepts, can contribute to improved traffic efficiency in congested urban networks. Full article

Air entrainment in self-compacting concrete (SCC) is governed by coupled interactions between chemical admixtures, empirical workability behaviour, aggregate-skeleton geometry and early air-bubble stability. In highly flowable mixtures, the hardened air-void system cannot be assessed reliably from total air content alone because bubble escape, redistribution and coalescence in the fresh state may change the final pore structure. This study evaluates the link between early fresh-air retention and hardened air-void characteristics in 25 SCC mixtures arranged according to a five-level Graeco-Latin square design. The analysed factors were air-entraining admixture (AEA) dosage (0.00–0.20% by mass of cement), binder type, water-to-binder ratio (0.29–0.41) and the volumetric paste-to-aggregate filling parameter φ (1.1–1.5). The aggregate skeleton was kept constant to separate paste-composition and volumetric-filling effects from aggregate grading. Fresh concrete was characterised by slump-flow diameter, T₅₀ flow time, density and air content after 5 and 15 min; these quantities were treated as empirical workability and early-retention indicators, not as direct rheological parameters. Hardened concrete was examined after 28 days according to EN 480-11 using total hardened air content A, spacing factor L, micropore content A₃₀₀ and specific surface α. The slump-flow diameter ranged from 50 to 79 cm, fresh air content after 5 min from 1.6% to 8.6%, air loss between 5 and 15 min from 0.41 to 1.12 percentage points, hardened air content from 1.20% to 8.59%, and spacing factor from 0.13 to 0.44 mm. Strong correlations were obtained between fresh and hardened air contents (A₅ vs. A: r = 0.920, R² = 0.846, p < 0.001, 95% CI for r: 0.824–0.964; A₁₅ vs. A: r = 0.922, R² = 0.849, p < 0.001, 95% CI for r: 0.828–0.965), while hardened air content was strongly and inversely related to spacing factor (A vs. L: r = −0.907, R² = 0.822, p < 0.001, 95% CI for r: −0.958 to −0.797). The recalculated ANOVA showed that statistical significance was response-dependent: w/b was significant for early air loss ΔA (F = 4.190, p = 0.040, partial η² = 0.677) and micropore content A₃₀₀ (F = 4.058, p = 0.044, partial η² = 0.670), whereas binder type showed near-threshold tendencies for fresh and hardened air contents. No single factor was statistically significant for all air-void descriptors. The SDI-based approach is therefore presented as a bounded explanatory framework, not as an externally validated prediction model. Direct durability claims, including freeze–thaw resistance, require separate experimental verification. Full article

Human innovation has continually expanded the boundaries of knowledge, from mastering atomic science to reaching the Moon and now into the era of Industry 4.0, where artificial intelligence, the Internet, and advanced additive manufacturing turn imagination into reality. Among these achievements, hypersonic vehicles represent a pinnacle of technological advancement. Modern vehicles reach speeds exceeding Mach 27 (approximately 9300 m/s), where the air at the leading edges transforms into a chemically reactive, thermally ionized plasma. At such velocities, stagnation temperatures climb to 9000–12,000 K (8726.85–11,726.85 °C), creating one of the most extreme environments encountered by any human-made system—conditions under which conventional materials cannot survive without advanced cooling strategies. To address this challenge, researchers worldwide have developed and experimentally validated a range of thermal protection and leading-edge cooling techniques. This review presents the historical evolution of hypersonic vehicles, highlights recent advancements, examines the key challenges posed by sustained hypersonic flight, and surveys state-of-the-art cooling strategies. The discussion emphasizes methods that combine passive, active, adaptive, and hybrid approaches to protect vehicle integrity under extreme thermal loads, providing insight into the current and future capabilities of hypersonic thermal manageme nt. Full article

Single-photon detection is a promising approach for low–slow–small Unmanned Aerial Vehicle (UAV) detection, holding great value in urban air defense and security monitoring. In complex urban environments, intense non-uniform building clutter and multi-echo aliasing easily submerge weak target signals, severely limiting traditional single-photon systems under low signal-to-background ratios. To address this, this paper proposes an urban-oriented detection strategy based on a free-running single-photon array, and designs a dual-optimized pulse position modulation laser detection and range image enhancement algorithm. By establishing temporal correlations via pulse sequence convolution, the algorithm effectively isolates weak UAV echoes from strong background clutter to break through detection limitations. Compared with the popular Markov correction method that often suppresses overlapping weak targets under strong reflections, the proposed method significantly improves small-target feature retention, successfully balancing background elimination and detection sensitivity. Field tests and quantitative evaluations demonstrate that the system reliably eliminates building clutter and achieves stable continuous tracking of weak UAV signals within 1.5 km, providing a highly robust and effective technical solution for urban low-altitude surveillance. Full article

This study explores the selection of a heat recovery system for cogeneration units based on gas engines supplying the district heating system in Opole in order to enhance the efficiency and sustainability of the system. The proposed modifications focus on utilizing low-temperature (LT) waste heat from engine cooling circuits and improving exhaust heat recovery. The research examines retrofitting three cogeneration engines (total thermal capacity of 7.6 MW) by integrating water-to-water heat pumps to upgrade low-temperature waste heat (55–45 °C up to 700 kW), enhancing heat supply to the district heating network. Additionally, a second stage of economizers is evaluated to maximize condensation-based exhaust heat recovery from the existing 95–135 °C system. These system modifications increase the overall thermal capacity up to 9–9.1 MW. To maintain heat supply during cogeneration unit shutdowns (due to failures or electricity price fluctuations), an auxiliary air-to-water cascade heat pump provides an additional 0.8–1 MW. With increasing electricity price volatility, these system modifications provide crucial operational flexibility. Computational simulations confirm that the hybrid configuration successfully upgrades waste heat while strictly maintaining the existing engine return water safety limit. The evaluation demonstrates high economic profitability alongside stable emission reductions. This research presents a case study in optimizing heat recovery in cogeneration-based district heating networks, demonstrating practical and scalable applications for sustainable energy systems. Full article

Waste lubricating oils (WLOs) represent a major stream of hazardous petroleum-based residues, with global generation exceeding 24 million tons annually. Improper disposal of WLOs poses risks to soil, water, and air quality, while their chemical composition makes them a potential secondary resource within circular economy frameworks. This review summarizes conventional, advanced, and emerging technologies reported for the recycling and valorization of WLOs into high-value petrochemicals and carbon-based materials. Established processes such as acid–clay treatment, solvent extraction, and vacuum distillation are discussed together with more recent approaches, including catalytic upgrading, hydrotreatment, membrane separation, and thermochemical conversion methods such as pyrolysis and catalytic cracking. Reported data on process performance, environmental considerations, techno-economic indicators, and life cycle assessment outcomes are comparatively analyzed to outline current trends, technical challenges, and future development directions in WLO recycling. Particular attention is given to thermochemical pathways capable of generating carbonaceous materials, including carbon black, porous carbons, and functional carbon nanostructures with potential applications in adsorption, catalysis, electrochemical systems, and tribological formulations. Hybrid and integrated process configurations described in the literature are highlighted for their potential to improve recovery efficiency, enhance product quality, and reduce environmental burdens. In addition, recent life cycle assessment (LCA) and techno-economic analysis (TEA) studies are reviewed to provide insight into the environmental and economic implications of advanced re-refining systems. Overall, the reviewed literature indicates that WLO recycling represents not only an important element of sustainable lubricant management but also a promising waste-to-carbon strategy for the production of value-added carbon-based materials and petrochemical products. Full article

Air pollution negatively impacts human health and environmental sustainability, particularly in areas with high industrial activity. This study comparatively evaluated deep learning-based models for estimating PM₁₀ and SO₂ pollutants in Dilovası and Ereğli (Turkey), industrial areas with high pollutant loads. The study utilized Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), an RNN–GRU stacked hybrid model, an attention-based hybrid model, and the proposed GRU–LSTM dual-path hybrid model. The proposed method consists of four main stages: data conversion into a time-series format, data preprocessing and feature generation, model architecture development, and model training and performance evaluation. The dataset consisted of 365 daily PM₁₀ and SO₂ observations obtained from the Air Monitoring Center for the Dilovası and Ereğli monitoring stations. Model performance was evaluated using the coefficient of determination (R²), training time, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) metrics. The findings showed that the hybrid models provided higher accuracy compared to the single-track models. Specifically, the proposed GRU–LSTM dual-path hybrid model produced the highest R² and lowest error values for both pollutant parameters in both the Dilovası and Ereğli regions. In Dilovası, this model achieved R² = 0.97 for SO₂ and R² = 0.96 for PM₁₀; in Ereğli, it reached R² = 0.92 for SO₂ and R² = 0.98 for PM₁₀. Thus, it has been shown that the GRU–LSTM dual-path hybrid model, which models short-term and long-term temporal dependencies in parallel, is an effective and reliable method for air pollutant forecasting in industrial areas. These findings demonstrate the potential of the proposed model to support air quality monitoring, early warning systems, and environmental decision-making in industrial regions. Full article

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

To tackle the intermittency of renewable energy and realize the repurposing of abandoned oil and gas wells, this study proposes a compressed air energy storage (CAES)-based cogeneration system integrated with geothermal energy and abandoned oil and gas wells, and conducts a five-dimensional comprehensive analysis covering exergy, exergoeconomic, exergoenvironmental, economic and environmental performance. The optimal operating parameters are determined as air compressed to 200 bar, an ORC turbine inlet pressure of 16 bar and an inlet temperature of 110 °C. The system’s annual total power generation is 2,971,416.5 kWh during low-power daytime operation, and 20,131,785 kWh during high-power nighttime operation. Compared with conventional CAES systems, the proposed system reduces total exergy destruction by 4121.35 kW and increases exergy efficiency from 48.49% to 63.38%. Coolers, geothermal heat exchangers and compressors are the main sources of exergy destruction cost and capital investment, while COM1, HE1 and HOT1 are the key components causing environmental impacts. The system realizes cogeneration of power, hydrogen and pure water, with a static payback period of about 5.4 years and significantly reduced TEWI value at elevated turbine inlet pressure. This system achieves multi-objective synergies in energy efficiency, economy and environment, providing a feasible scheme for the green repurposing of abandoned oil and gas wells and cascaded utilization of renewable energy. Full article

Supercooled large droplets (SLDs), typically defined as droplets with diameters exceeding 100 μm, represent a significant meteorological hazard to aviation safety. Unlike conventional cloud-sized droplets, SLDs have higher inertia and can follow more ballistic trajectories, leading to impingement well aft of leading-edge ice protection systems. SLD icing is further complicated by high-speed splashing, secondary-droplet re-impingement, delayed solidification, and surface water runback. This paper reviews recent progress in understanding SLD impact, splashing, surface water transport, and ice accretion. The review discusses droplet impact on dry and wet surfaces, oblique impingement, ambient-air effects, non-instantaneous solidification, runback dynamics, and downstream ice growth. Emerging ice protection technologies, including superhydrophobic, lubricant-infused, and compliant surfaces, are also evaluated. By synthesizing these developments, this review connects fundamental droplet-impact physics with practical aviation icing challenges and mitigation strategies. Full article

Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning. Full article

The transport sector plays a significant role in air pollution, and real-world emissions measurements are becoming increasingly important. In this study, emissions from a turbocharged, direct-injection gasoline internal combustion engine (ICE) vehicle and a port fuel injection (PFI) hybrid electric vehicle (HEV) were compared using a portable emissions measurement system (PEMS) under real-world driving conditions. The CO₂, CO, NO_x, and PN emissions of the two vehicles were measured in urban, rural, and motorway sections. HEV CO₂ emissions were ~20% lower than ICE emissions in the entire Real Driving Emissions (RDE) cycle, while in urban operation, they were almost 50% lower. PN emissions were lower for HEV in rural and motorway sections than for ICE, but significant PN peaks occurred during the early urban phase, attributable to the slower engine warm-up of the HEV. Machine learning analysis (Random Forest and Extra Trees Regressor) indicated that coolant temperature was the dominant driver of HEV PN emissions. The results indicate that powertrain characteristics and thermal management strongly influence real-world driving emissions, highlighting their importance for the further development of hybrid vehicles. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

With the expansion of electromagnetic wave communication frequency bands and the improvement of integrated circuit integration, electromagnetic waves emitted by on-chip antennas are easily scattered by electronic components, causing radiation pattern distortion, which limits the improvement of integration and communication stability. Traditional cloaks can reduce electromagnetic scattering, but they cannot achieve broadband and omnidirectional performance simultaneously, and are mostly designed for external sources, making it difficult to protect on-chip antenna radiation patterns. In this work, a broadband air-impedance-matched metamaterial (AIMM) with characteristic impedance matched to free space is proposed in 2–8 GHz, with geometry-tunable phase delay and transmittance higher than 93%. Based on AIMM, a broadband source-surrounded cloak (SSC) is designed, which can guide electromagnetic waves from the surrounded source to bypass obstacles in any direction and restore the original wavefront outside the cloak, so as to protect the radiation pattern from scattering distortion. Numerical simulations show that the SSC works well in the whole bandwidth and remains effective when the source is offset. This work has important potential for improving the integration of integrated circuits and the stability of communication systems. Full article

Indoor Air Quality (IAQ) monitoring, especially in multi-zone smart buildings, is typically limited by the high computational and energy requirements of continuous sensor processing, which makes event-driven methods desirable for efficiency. Energy proportionality, in this context, refers to a system whose computational cost scales with the significance of detected environmental changes rather than with the fixed sampling rate. This paper presents a spike-driven neuromorphic sensing framework for decentralized IAQ monitoring that combines adaptive Kalman filter preprocessing, dynamic threshold-based asynchronous spike encoding, and a Leaky Integrate-and-Fire neural network with Spike-Timing-Dependent Plasticity (STDP) learning. Multiple-parameter IAQ data including PM₁, PM_2.5, PM₁₀, CO₂, CO, TVOCs, and O₃ were sampled from nine functionally differing zones of an educational building in Metro Manila, Philippines. The neuromorphic model yielded a mean Sparse Firing Ratio of 10.94%, a Mean Response Time of 10.62 timesteps, and an energy efficiency proxy score of 9.28. Neuron population scaling and parameter robustness analyses revealed that the four neurons per parameter were enough to saturate the performance, and FLOP-based estimation indicated an 8.9-fold computational reduction (approximately 89% fewer FLOPs) compared to LSTM inference. In addition, the revised Performance Efficiency Index and composite efficiency score corroborated the stable and energy-proportional nature of behavior in all zones. These results illustrate that spike-based neuromorphic computation is an energy-efficient and scalable way for decentralized smart-building IAQ monitoring, though hardware-level validation on dedicated neuromorphic processors remains necessary for absolute power saving verification. Multi-seed validation (five seeds) with expanded baselines including GRU, Temporal CNN, XGBoost, and Logistic Regression confirmed the robustness and repeatability of reported metrics. Full article