Search Results (15,624)

An automated air-conditioner cleaning system was developed as a retrofit solution for conventional split-type units to reduce residual moisture in the evaporator section and suppress post-shutdown microbial accumulation. The system was integrated with an 18,000 BTU h⁻¹ air-conditioner and implemented using an Arduino-based closed-loop control platform with temperature and relative humidity monitoring. After shutdown, the indoor fan was operated under low-, medium-, or high-speed conditions to remove retained moisture from the cooling coil. System performance was evaluated in an 18 m³ test room through measurements of electrical consumption, operating cost, relative humidity, and microbial contamination in room air and on the evaporator coil before and after system installation. Low-speed operation showed the lowest current demand, power consumption, and electricity cost, with corresponding values of 0.36 ± 0.01 A, 79.2 ± 0.8 W, and 0.47 THB per 150 min. Post-shutdown humidity reduction was achieved under all tested conditions, while the high-speed mode provided the fastest drying response, reducing relative humidity to approximately 60% within 120 min. In the room air, the greatest reduction in airborne fungi after shutdown was observed at low speed, whereas the greatest reduction in airborne bacteria was observed at medium speed. On the evaporator coil, the strongest bacterial suppression was obtained at low speed, where the bacterial count after 24 h decreased from 633.33 ± 34.27 CFUs before installation to below the detection limit after installation. These results indicate that the proposed system reduced moisture retention and microbial contamination with minimal energy consumption. Full article

The in-service assessment of marine propulsion engines requires more than nominal rating comparison because operating severity is shaped by propeller demand, resistance growth, air-path response, and thermal state. This study develops a quantitative benchmarking method for the regime-dependent performance assessment of a low-speed two-stroke Wärtsilä 6RT-flex58T-D engine installed on a 31,000 DWT multi-purpose container vessel. The method integrates certified sea-trial measurements, endurance-test records, manufacturer load-diagram constraints, and a 15% service-margin projection within one reference framework. Three representative regimes are evaluated: a measured light-running baseline (SR1), a measured thermally stabilised sustained regime (SR2), and a projected heavy-running regime derived from the baseline using a 15% sea-margin assumption (R2). Comparison is performed using indicators of operating-point position, shaft torque, propeller-law consistency, selected air-path and thermal variables, load-diagram proximity, and corrected specific fuel oil consumption where available. The SR1 baseline followed the fitted propeller law with deviations not exceeding 1.18%, confirming a coherent light-running reference. In SR2, corrected SFOC decreased from 174.4 to 172.0 g/kWh, while the exhaust temperature before turbine increased from 359 °C to 435 °C, and the corresponding thermal margin decreased from 156 °C to 80 °C. Under the +15% service-margin projection, the required shaft power at the 100% trial point increased from 12,046.0 to 13,852.9 kW, exceeding the 13,560 kW installation MCR by 2.2%, with corresponding 15% increases in torque and BMEP. These results demonstrate that measured baseline operation, sustained-load severity, and projected heavy-running demand can be distinguished quantitatively within one installation-specific load-diagram-based benchmarking framework. Full article

Martensitic chromium-molybdenum steels such as 15Kh5M are widely used in high-temperature oil and gas equipment, but their weldability is limited by high hardenability and susceptibility to cold cracking, which usually necessitate energy-intensive preheating. This study evaluates an alternative route based on the combination of root-pass mechanical vibration (50 Hz, ~1 mm amplitude) and post-pass water-air jet cooling during mechanized GMAW. Three welding variants were compared: conventional preheated welding, vibration-assisted welding without preheating, and hybrid thermo-vibrational welding with active cooling. Among the tested conditions, the hybrid route produced the narrowest heat-affected zone, reducing its width from about 7 mm to about 3 mm, which is consistent with a compressed thermal cycle. Microhardness in the heat-affected zone decreased from 380 to 440 HV in the preheated condition to 330–370 HV in the hybrid condition. Optical microscopy further indicated a finer and more homogeneous transformed microstructure in the hybrid case. Results indicate that simultaneous vibro-treatment and controlled cooling effectively mitigate harmful metallurgical effects typically induced by rapid cooling, enabling preheat-free fabrication of thick-walled components. The proposed hybrid approach may offer energy savings, shorter production cycles, and improved automation compatibility in field welding applications. Full article

Classification of vehicle engines using the chemical composition of the exhaust from these engines can be used to identify the engine’s design and verify compliance with environmental regulations through the vehicle’s emissions. This paper describes a method to identify the type of vehicles using machine learning (ML), where low-cost MQ series sensors measure the gases and particle emissions from a vehicle exhaust system, while simultaneously collecting and measuring the vehicle’s temperature and humidity levels. A custom-designed multi-sensor exhaust sensing module is employed to capture real-time exhaust emissions prior to entering the atmosphere. Exhaust samples are collected from vehicles representing three major engine categories: petrol, diesel, and compressed natural gas (CNG). In addition, fresh air samples are collected as a baseline environmental reference for comparison. All exhaust measurements are collected under controlled and consistent engine operating conditions to ensure comparable emission profiling across vehicle classes. To ensure consistent combustion-based emission profiling, this study focuses on conventional fuel-powered vehicles. MQ-series gas sensors are sensitive to combustion by-products emitted during engine operation, such as carbon monoxide (CO), hydrocarbons (HC), while also exhibiting cross-sensitivity to other gaseous components present in exhaust mixtures. Nevertheless, the proposed system performs pattern-based classification using relative sensor response signatures. Standardization of data is achieved through z-score normalization. The best models developed (based on three separate experimental designs) are trained and validated using six supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (RBF), k-Nearest Neighbors, Random Forest, Gradient Boosting Decision Tree, and XGBoost and are compared against one another. Evaluation of the tested algorithms using various evaluation metrics demonstrated that ensemble models outperformed all other algorithms, achieving the highest accuracy of 99.5%. Furthermore, noise analysis confirms that the proposed solution maintains high classification accuracy (more than 89%) even under substantial sensor perturbations mimicking the real-world deployment. The solution proposed below illustrates how using gas sensors and advanced algorithms can provide accurate exhaust identification and identify engines in real-time. Full article

Airblast sprayers remain the dominant pesticide delivery system in California citrus; however, mechanistic characterization of spray transport and off-target fate under realistic field-scale atmospheric variability remains limited. Regulatory airblast drift assessments in the United States (U.S.) currently rely on a sparse, dormant-apple canopy representation, despite substantial structural differences from foliated citrus canopies that may influence drift behavior. To address this gap, this study quantified airblast spray drift in a commercial citrus orchard across multiple downwind distances under varied daytime meteorological conditions and evaluated the influence of distance and weather variables on measured drift. Airborne and sedimentation drift were measured from a conventional axial-fan airblast sprayer operating at 10.3 bar, 5.1 km·h⁻¹, and 935 L·ha⁻¹ in a 4.0 m tall mandarin (Citrus reticulata) orchard using a U.S. Environmental Protection Agency (EPA)-approved, International Organization for Standardization (ISO) standard 22866-aligned protocol. Drift collectors (n = 2688), including flat cards, artificial foliage, and horizontal and vertical string samplers, were deployed from 33 m upwind to 183 m downwind of the orchard edge. Airborne drift measurements showed no significant vertical stratification or near-field decay between 8 m and 23 m downwind (p > 0.05), indicating rapid plume homogenization following canopy exit. In contrast, sedimentation drift declined sharply within 30 m and attenuated logarithmically with distance, governed by progressive droplet depletion and plume dilution. Estimated drift cessation distances were 127.5 m for artificial foliage and 182.1 m for horizontal string samplers. Drift magnitude varied significantly among trials (p < 0.05), reflecting sensitivity to meteorological variability. Multiple linear regression identified wind direction, wind speed, and atmospheric pressure as significant predictors of downwind deposition (p < 0.05), whereas air temperature and relative humidity primarily influenced drift through evaporative control of droplet lifetime. Collectively, these results demonstrate that spray drift from foliated citrus canopies is substantially attenuated relative to dormant-canopy scenarios. Although not intended to define regulatory buffer distances, the high-resolution dataset generated provides mechanistically interpretable parameterization inputs for next-generation airblast drift models, supporting improved representation of canopy interactions, plume evolution, and meteorological modulation in regulatory exposure assessments. Full article

Heating, ventilation, and air conditioning (HVAC) systems struggle to maintain optimal thermal comfort because perception is subjective and varies significantly across individuals. Traditional uniform cooling strategies often overlook demographic diversity, leading to inequitable comfort outcomes and inefficient building operations. To address this limitation, this study analyzed a web-based survey of 366 university occupants using a partial proportional odds model with multiple imputation and inverse-frequency weighting. Interaction terms, specifically Age–Activity, Gender–Clothing, and Age–Clothing, were included to assess combined effects that reflect demographic disparities in adaptive capacity. The results show that clothing insulation, activity, age, gender, race/ethnicity, and space type significantly influence thermal responses. Notably, male occupants were more than three times as likely to report feeling too warm (odds ratio [OR] = 3.24), whereas older adults exhibited significantly lower odds of reporting feeling too warm (OR = 0.42). Substantial variation was observed across racial and ethnic groups (ORs ranging from 2.4 to 6.5). These findings highlight the limitations of traditional population-average comfort approaches and provide valuable scientific insights for demand-response-ready HVAC strategies that adjust temperature setpoints dynamically without sacrificing comfort. By offering accurate, real-time estimates across diverse thermal ranges, these occupant-centric models reduce HVAC energy use and associated emissions at the building scale while supporting ancillary services for flexible load shifting and smarter coordination within low-carbon electric grids. Ultimately, incorporating demographic and contextual diversity into building controls reduces unnecessary cooling waste while promoting thermal equity, establishing a human-centric foundation for sustainable built environments. Full article

As the heat flux of electronic devices continues to increase, conventional air cooling and single-phase liquid cooling technologies are increasingly constrained by heat transfer limits and pumping power consumption. However, systematic investigations on the coupling between microchannel evaporators and the overall dynamic response of MPTL systems remain limited. To address this issue, a visualization experimental platform for the microchannel MPTL was developed, and flow boiling experiments were conducted under varying heat fluxes and circulating flow rates. Key parameters including wall temperature, fluid temperature, pressure drop, and flow patterns were measured to characterize the thermal–hydraulic behavior of the system. The results show that the wall temperature increases stepwise with increasing heat flux, reaching a critical heat flux of 814.2 W/cm² at a mass flux of 105.6 kg/(m²·s), where heat transfer deterioration occurs. During this transition, inlet temperature oscillations with an average amplitude of 8 °C were observed due to vapor backflow. With decreasing circulating flow rate, the flow pattern evolved sequentially from single-phase flow to bubbly, slug, churn, annular, and reverse annular flow, accompanied by a shift in the dominant heat transfer mechanism from forced convection to nucleate boiling and convective evaporation. The best heat transfer performance occurred under annular flow conditions at an outlet vapor quality of 0.4–0.5. These findings provide useful guidance for the design and operation optimization of microchannel MPTL systems in high-heat-flux electronic cooling applications. Full article

Recent studies have shown that common household activities produce characteristic patterns in indoor air pollutants, enabling activity inference using environmental measurements alone. However, pollutant-based approaches are usually formulated as flat multi-class classification problems, even though indoor environments are dominated by long baseline periods with no emission-generating activity, leading to false alarms and unstable predictions. This work proposes a gated hierarchical inference framework for recognizing activities from indoor air quality data. A first-stage gate detects whether a time window contains activity-induced pollutant dynamics, while a second-stage classifier conditionally identifies the specific activity only when activity relevance is detected. Multivariate time-series measurements of particulate matter, volatile organic compounds, nitrogen oxides, carbon dioxide, temperature and relative humidity were collected using a portable monitoring system during controlled household cooking and cleaning experiments. Temporal windows were processed using recurrent neural network models in both stages. By separating activity detection from activity identification, the proposed method aligns inference with the physical generation of indoor pollutant signals and improves robustness in baseline-dominated monitoring scenarios while maintaining reliable discrimination among activities. The framework supports unobtrusive activity recognition and enables applications in exposure-aware monitoring and intelligent indoor environmental management. Full article

To improve the indoor thermal comfort of embedded atriums and corridors in office buildings during summer, this study aims to optimize air conditioning airflow organization in atriums using computational fluid dynamics (CFD) simulations. Field measurements were carried out to collect air parameters, which were subsequently used to validate the established CFD model. Taking a six-story office building in Xi’an as the research subject and stratified air conditioning as the baseline case, this study investigated the effects of air inlet layout, air inlet type, and air volume distribution on the indoor thermal environment. The results revealed significant vertical temperature stratification within the atrium, with average temperatures ranging from 23.5 °C to 46.1 °C. Based on comparative analysis of multiple optimization scenarios, the following conclusions are drawn: adopting swirl diffusers in the corridors with an air inlet quantity ratio of 1:1:1:1:2 from the first to fifth floors, combined with uniform air supply volume across the first to fourth floors, can maintain the average Predicted Mean Vote (PMV) of each floor within the range of −0.1 to 0.3. Conversely, excessive air supply volume on upper floors and insufficient air supply volume on lower floors significantly degrade the corridor thermal comfort. Full article

The residual mechanical properties after fire exposure form the basis for evaluating the structural performance of aluminium alloy components subjected to fire without collapse. This research investigated the impact of low cooling rates on the residual mechanical properties of 6063-T5 aluminium alloy after various cooling methods were utilized. A total of 48 tensile specimens were subjected to controlled elevated temperatures (ETs) ranging from 200 to 500 °C for 30 min soaking, followed by two cooling regimes: cooling in air (CIA) and cooling in furnace (CIF). For both CIA and CIF conditions, an increase in ETs led to a gradual increase in ductility, particularly elongation at fracture. Moreover, the effects of ETs on the fracture performance were discussed. Key mechanical parameters—namely nominal yield strength, ultimate tensile strength, elastic modulus, and strain at ultimate strength—were quantified across ETs and cooling methods, which were compared among different aluminium alloys. Empirical predictive equations were developed to capture the temperature-dependent degradation trends of mechanical properties, and a plasticity Ramberg–Osgood model was proposed and validated against test data. The metallographic microstructure of 6063-T5 aluminium alloy after different ETs revealed that the evolution of precipitate was the primary contributor to strength degradation. Finally, finite element simulations of aluminium plate girders after various ETs were conducted, which incorporated the proposed constitutive model and replicated the degradation trends observed in tensile tests. These findings provide a reliable foundation for implementing the proposed model into finite element simulations and structural assessment tools for post-fire aluminium alloy structures. Full article

With the progressive decline of tailpipe emissions, non-exhaust sources such as brake wear are becoming an increasingly important contributor to traffic-related particulate matter in urban environments. In this context, improving real-world characterization of brake wear particles is essential for air-pollution assessment, source apportionment, and the development of cleaner and more sustainable road transport systems. Here, we investigated the emissions levels, particle size distribution and elemental composition of particles released during harsh real-world braking events by a single light-duty vehicle braking system equipped with an original manufacturer (OEM) non-asbestos organic (NAO) pad formulation. Using a direct on-vehicle sampling system combined with real-time particle sizing and high-resolution microscopy, we observed that particle emissions remained close to background levels at speeds up to 100 km/h, but rose sharply at 120 km/h, reaching 3.7 × 10⁷ #/cm³ in the 8–10 nm size range. This increase suggests that higher speeds are associated with elevated particle emissions, likely due to the higher braking temperatures reached at increased vehicle speeds. The emitted particles were mainly spherical agglomerates rich in iron, titanium, barium, zirconium, and sulphur, consistent with NAO pad formulations. Our results show that the investigated NAO pad system can deteriorate under thermal stress, potentially leading to higher levels of nanoparticle emissions compared to low-metallic or semi-metallic pads investigated under similar conditions. These findings provide real-world evidence relevant to urban air quality research, support the refinement of non-exhaust emissions inventories, and highlight the importance of thermally resilient friction-material formulations for mitigating residual particulate emissions in increasingly cleaner transport systems. Full article

This study investigates strategies to reduce mould growth on steamed beech wood by evaluating drying-based and fungicide-based protection approaches. The drying-based approach focused on optimising the temperature of warm-air drying parameters to control moisture content and limit mould development. The fungicide-based approach involved testing selected agents, including 3-iodo-2-propynyl butyl carbamate, boric acid, quaternary ammonium compounds, and nano-ZnO, for their effectiveness in preventing mould formation. Mould growth was assessed by macroscopic observation and classified according to standardised intensity levels. The results indicate that adjusting drying parameters alone is insufficient to prevent mould growth, whereas specific fungicide treatments provide effective surface protection. These findings offer practical guidance for minimising mould development on beech wood during drying and storage. Full article

Electronic nose platforms based on metal-oxide (MOX) sensors offer potential for low-power gas classification under dynamic operating conditions. This study evaluates a BME688-based digital nose configured with a temperature-modulated heater profile (HP-354) and reduced duty cycle (RDC-5-10) for binary ethylene presence classification in fruit headspace. Seven climacteric fruit types were sealed in bags to allow natural ethylene accumulation and were sampled across multiple sessions over a two-week period. A structured alternating protocol between fruit headspace (Class A) and neutral air (Class B) generated 21 ethylene sessions and 23 neutral-air sessions, comprising 38,882 individual thermal scan cycles (~10 s per cycle). Each full heater cycle was treated as a training instance within BME AI-Studio. A supervised neural-network classifier trained on 70% of cycle-level data achieved 92.9% overall accuracy with a macro F1 score of 91.9% on validation data. Results demonstrate that temperature-modulated MOX signatures enable robust discrimination of biologically generated ethylene from baseline air under realistic headspace variability. This study demonstrated classification feasibility under naturally accumulated fruit emissions while highlighting the need for future concentration-resolved calibration studies. Full article

Platinum group elements (PGEs), especially platinum (Pt), palladium (Pd), and rhodium (Rh), are analyzed as emerging airborne contaminants in urban environments. This study aimed to monitor the spatial and temporal distribution of PGEs in urban air and to evaluate their potential as indicators of traffic-related emissions. The paper presents a five-year monitoring of Pt, Pd, and Rh mass concentrations in airborne particulate matter collected from three urban locations (North, Center, and South) with different traffic loads in Zagreb, Croatia. Weekly samples were digested in acid under high temperature and high pressure, and analyzed using inductively coupled plasma mass spectrometry (ICP-MS). At the monitoring location South, mass concentrations of all PGEs were generally 20–40% higher than at other locations, consistent with its higher traffic density. The PGEs showed seasonal variability, with 40–60% higher mass concentrations in winter and autumn than in spring and summer. The spatial and temporal distribution of PGE mass concentrations across urban locations demonstrates their potential as indicators of traffic-related activity. Palladium mass concentrations were consistently the highest, as a result of its increased use in modern catalytic converters. These findings underscore the relevance of long-term PGE monitoring for understanding urban atmospheric pollution dynamics within changing environmental conditions. Full article

Urban heat islands present a significant obstacle to climate adaptation strategies, yet the interplay between surface and atmospheric thermal elements is not fully understood. This research investigates the spatial relationship between land surface temperature (LST) and near-surface air temperature (TAIR) across Zagreb’s 218 local councils during the summer of 2024, assessing the premise that these constitute separate thermal dimensions with varying land cover correlations. Landsat 8/9-derived LST and CERRA-derived TAIR, temporally aligned to the Landsat overpass slot (09:00 UTC), were examined through spatial autocorrelation (Moran’s I, Getis–Ord Gi*), correlation analysis, and Fisher’s z-tests to compare the effects of the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). The findings indicated partial coupling (r = 0.537, R² = 0.288), with 71.2% of the variance remaining unexplained, suggesting considerable surface-atmospheric decoupling. Furthermore, hot spot overlap analysis revealed limited convergence (11.9% of neighborhoods), while 44.5% displayed divergent thermal extremes. Land cover showed much stronger connections with LST (NDVI: r = −0.970, R² = 0.941; NDBI: r = +0.973, R² = 0.947) than with TAIR (NDVI: r = −0.478; NDBI: r = +0.496), representing reductions in explained variance of 63–64% (p < 0.001). These findings suggest that surface and atmospheric urban heat are related but distinct thermal aspects. Full article