Search Results (1,439)

Coastal green tides may alter nearshore bioaerosols through coupled marine, atmospheric, and meteorological processes, yet their effects on airborne microbial communities remain poorly resolved. Atmospheric samples were collected in Aoshan Bay, Qingdao, China, during five phases of the Ulva prolifera green tide in 2019 (pre-bloom, 19 April; early bloom, 15 June; middle bloom, 15 July; late bloom, 6 August; post-bloom, 30 August); seawater samples were collected at one nearshore site on each of the five sampling dates, with microbial sequencing performed for the middle-bloom (15 July) and late-bloom (6 August) phases. Bacterial and fungal communities were characterized; although bioaerosols may also contain microalgae and viruses, this study profiled only the bacterial and fungal fractions, using bacterial 16S rRNA gene (V3-V4 region) and fungal internal transcribed spacer (ITS2) amplicon sequencing and evaluated together with meteorological variables, air-pollutant concentrations, and 72-h backward air-mass trajectories. Proteobacteria dominated the airborne bacterial assemblages (81.28–97.83%), with Sphingomonas as the most abundant genus (47.85–89.84%). Basidiomycota and Ascomycota dominated the fungal assemblages, whereas Cryptococcus and Alternaria were the major fungal genera. Community richness and composition varied across bloom phases. Chytridiomycota was undetected before the bloom (0%), appeared after bloom onset, and reached its highest relative abundance during the middle phase (8.19%). Spatial patterns indicated joint terrestrial and marine influences, although bacterial communities in seawater and air remained highly dissimilar. Temperature, relative humidity, particulate matter, ozone, and air-mass origin were associated with changes in microbial diversity and composition. These findings provide an observational baseline for coastal bioaerosol dynamics during a macroalgal green tide, extending the HAB–bioaerosol literature—which has focused predominantly on cyanobacterial blooms—to a large green macroalga. Bacteria and fungi showed contrasting environmental responses: bacterial richness increased with temperature, whereas fungal diversity declined. Greater compositional similarity between seawater and air for fungi than for bacteria suggests differential environmental filtering at the air–sea interface and implies that multiple source pathways—direct aerosolization, sea-surface release, and in-situ atmospheric production—may differentially shape the two domains. Given the single-date-per-phase sampling design, the absence of sequenced laboratory contamination controls, and the lack of absolute abundance data, these results should be regarded as preliminary and hypothesis-generating, underscoring the need for ASV-level source tracking, controlled chamber experiments, and replicated multi-year designs in future assessments of bloom–atmosphere interactions. Full article

Ozone (O₃) is a prevalent environmental pollutant that can induce oxidative stress and respiratory epithelial injury. Dysregulation of pulmonary microbiota homeostasis plays an important role in the progression of lung damage and inflammatory responses. However, there remains a lack of systematic research on the effects of O₃ exposure on pulmonary microecology and its potential association with pulmonary inflammation. In this study, twenty-three SPF-grade C57BL/6N male mice (aged 6–7 weeks) were randomly divided into a filtered air control group (n = 11) and an O₃ exposure group (n = 12). Mice in the O₃ group underwent whole-body inhalation exposure to 1 ppm O₃ for 4 h daily over 8 consecutive weeks, while control mice were maintained under identical conditions with filtered air exposure. The 1 ppm concentration (equivalent to 0.2–0.3 ppm in humans) and 8-week daily 4 h exposure regimen was selected to model the 2-month summer high-O₃ season with typical afternoon O₃ peaks. The results showed that the O₃-exposed group exhibited marked inflammatory infiltrates in the lung, as well as significantly elevated macrophage inflammatory protein-2 (MIP-2) and tumor necrosis factor-alpha (TNF-α) in bronchoalveolar lavage fluids compared with controls. Pulmonary microbiota sequencing revealed that O₃ exposure significantly elevated the Shannon index of pulmonary microbiota in mice, and four differential genera including Deinococcus, Aerococcus, Enterococcus and Proteiniphilum were identified. Correlation analysis revealed that MIP-2 was significantly correlated with Aerococcus. In conclusion, the findings of this study suggest that exposure to O₃ induces inflammation and significant changes in the microbial composition of the lungs, which provides an experimental basis for further investigating mechanisms linking microbiota dysbiosis to lung injury triggered by ambient air pollutants. Full article

Feature selection is a critical step in regression problems where a large number of continuous explanatory variables explain the same target through different dependency structures. Classical filters may remain sensitive to a single form of dependence, a single scale, or a specific discretization scheme; generalized entropy measures, on the other hand, typically require the parameters to be fixed at a single point. This study proposes a framework that evaluates the Sharma–Mittal entropy volumetrically across a two-dimensional parameter region rather than for a single parameter pair. For the continuous target and explanatory variables, the marginal, joint, and conditional densities are obtained using a Gaussian kernel density estimation; the conditional entropy and information gain surfaces are integrated across the region Ω = [0.05, 0.95]² in the α-β plane to define three indices: PICSME, which measures the conditional uncertainty volume; PIGSME, which measures the gain volume; and NIGSME, which is the ratio of this gain to the total entropy volume of the target. The method is supported by bandwidth consistency and the renormalization of conditional densities; thus, the issue of negative gain that can occur in the continuous variables is resolved, yielding positive and interpretable scores across all six datasets. It is formally demonstrated that the fact that the three indices produce the same ranking is not an empirical observation but rather the result of a monotonicity relationship valid under a fixed target entropy volume. The method is compared with Pearson and Spearman correlations, the Shannon information gain, mutual information, and random forest variable importance across six regression datasets (Airfoil Self-Noise, AirQualityUCI, BodyFat, Meteorology, Concrete, and WineQualityWhite) that differ in their sample size, dimensions, and application domain. The evaluation is not limited to ranking consistency; the out-of-sample prediction performance is measured using least-squares models on the top-k subsets, with rankings calculated from the training partition. The findings show that NIGSME exhibits a performance comparable to that of built-in filters, outperforms them on the Concrete and Meteorology datasets, and never ranks as the weakest method on any dataset. The results demonstrate that volumetric entropy metrics defined across the entire parameter space provide a feature-ranking tool that is independent of parameter selection for continuous variables. Full article

Electrospun poly(vinylidene fluoride) (PVDF) nanofiber filters are attractive for particulate air filtration because they combine high filtration efficiency with low airflow resistance. Here, three inorganic additives, namely aluminum chloride (AlCl₃), potassium nitrate (KNO₃), and silicon nitride (Si₃N₄), were investigated using formulation-specific electrospinning conditions selected to achieve stable fiber formation. Although all filters exhibited high initial filtration efficiencies above 96%, clear differences were observed in fiber morphology, pressure drop, electrostatic potential decay, and long-term filtration stability. Among the tested samples, the Si₃N₄-containing filter showed the best long-term performance, retaining approximately 94% filtration efficiency after 30 days, whereas neat PVDF decreased to about 85%. These results suggest that differences in long-term filtration stability are closely associated with charge-retention behavior, and that the incorporation of inorganic additives can influence the durability of PVDF-based electrospun nanofiber air filters. Full article

This study addresses target-to-ROV relative localization in an unmanned surface platform-assisted UAV–ROV search-and-recovery system. Because the submerged ROV is not assumed to be visible from the air, the UAV observes the floating target and a GNSS-equipped ROV-associated surface buoy in the same image. The buoy position and target-to-buoy image displacement are combined to construct a world-frame target-position measurement, whose covariance accounts for buoy GNSS uncertainty and correlated image-projection errors. An upward-looking ROV imaging sonar provides range–bearing measurements. A delay-aware extended Kalman filter fuses the asynchronous observations using sea-state- and confidence-dependent covariance adaptation and normalized-innovation gating. ROV acoustic/inertial navigation uncertainty is propagated into the sonar measurement covariance and the reported relative-state covariance, avoiding duplication of the same navigation error in the aerial channel. The method is evaluated using a JONSWAP-based temporal disturbance model, Monte Carlo simulations, and single-factor and joint sea-state–occlusion–delay sensitivity tests. Under the nominal sea-state-5 condition, the proposed method achieves a mean ROV-frame relative RMSE of 0.992 m, compared with 1.083 m for ROV-only localization and 1.054 m for fixed-covariance fusion, with no run exceeding the 5 m divergence threshold. The results demonstrate improved relative-localization robustness within the simulated environment. Full article

Run-flat tires improve vehicle safety by maintaining mobility under air pressure loss, yet their higher vertical stiffness is widely perceived to reduce ride comfort. This work proposes a simulation-based approach for assessing the impact of tire construction, size, and inflation pressure on vertical comfort using standardized random road excitations and frequency-weighted acceleration metrics. Vehicle vertical dynamics were captured using a two-degree-of-freedom quarter-vehicle formulation, built in MATLAB/Simulink and driven by stochastic road inputs representative of typical paved surfaces and realistic driving conditions. The vertical acceleration of the sprung mass was evaluated through an ISO-compliant frequency-weighting filter, enabling a comfort assessment directly linked to whole-body vibration criteria. Simulations over multiple tires and operating configurations showed that run-flat tires consistently produce higher weighted acceleration levels than conventional tires at matched conditions, and that increased inflation pressure further amplifies this comfort deficit. The results provided a quantitative characterization of the impact of run-flat technology on ride comfort and highlight the key role of tire-related parameters in shaping the vertical vibration environment experienced by vehicle occupants. All results are reported at the tire-suspension-sprung mass subsystem level and should be interpreted as indicative comparative benchmarks rather than absolute occupant comfort predictions. These insights can further inform the development of vibration-based monitoring strategies for tire and suspension performance. Full article

Air-to-underwater extremely low frequency (ELF) signal recovery requires a receiver that accounts for both cross-medium attenuation and impulsive interference. Seawater weakens the desired electromagnetic waveform while altering the tail behavior that remains observable within the receiver bandwidth. Conventional recursive robust filters normally choose their linearity parameters and output scale from empirical or data-driven rules and therefore do not explicitly incorporate this depth-dependent physical–statistical coupling. This work formulates the propagation-guided scaled recursive weighted myriad (PG-SRWMy) filter as a mathematically structured framework that links a propagation-evolving stochastic process model with robust nonlinear recursive estimation for reference-normalized underwater ELF recovery. The propagation state determines a depth-evolving effective stable-like description through a characteristic exponent and a dispersion parameter, and these stochastic descriptors are transformed into branch-specific initial linearity parameters of the recursive myriad estimator. The same propagation model yields a positive and bounded normalization transform for the reconstruction scale, while first-order sensitivity relations characterize the local effect of environmental-state uncertainty. The original SRWMy sample-wise recursion is retained for data-driven adaptation. Numerical experiments show that PG-SRWMy accelerates bilinear-parameter stabilization, lowers waveform-reconstruction error, and improves end-to-end reference-normalized signal-to-noise ratio (SNR) gain across changes in receiver depth and surface-side impulsiveness. The results support propagation-aware initialization as a mathematically structured and statistically interpretable route to depth-adaptive underwater ELF reception. Full article

To address the limitations of conventional point-based sampling methods (e.g., water-sensitive paper and filter paper) in characterizing the three-dimensional deposition distribution on curved fruit surfaces, this study proposed a fruit-surface deposition quantification approach validated through a coupled workflow comprising multi-view 3D reconstruction, target point cloud segmentation, and chemical wash-off quantification. A carmine solution was used as a tracer to simulate pesticide deposition. High-accuracy colored point clouds of apples were generated via multi-view 3D reconstruction, and the target deposition point cloud was automatically extracted using a combined strategy integrating K-means-based coarse color clustering with HSV threshold refinement. The target point cloud count was regressed against the measured single-fruit deposition volume. The results showed a significant linear relationship between the target point cloud count and deposition under both laboratory and real orchard air-assisted spraying conditions (laboratory: R² = 0.827, r = 0.909, n = 13, p < 0.01; orchard: R² = 0.885, r = 0.941, n = 17, p < 0.01). This method can characterize the differences in pesticide deposition among fruits while preserving the three-dimensional location information of the tracer-related region, thus providing a methodological basis for characterizing pesticide deposition on the fruit surface. Full article

Electronic attack (EA) using jamming signals is an essential component of electronic warfare (EW), consisting of the use of electromagnetic energy to disrupt, block or reduce the effectiveness of enemy communications, radar and navigation systems. In current conflicts, jamming EA is also successfully used against drones (air/ground), which have become an important component of modern warfare. In this context, the article proposes a method of protection against jamming (anti-jamming) for drone communications, thus achieving their resilience to electronic attack. The proposed method combines the wavelet transform with threshold SURE, adaptive filtering (LMS) and Bidirectional Long Short-Term Memory for anti-jamming resilience of 64-QAM (quadrature amplitude modulation) communication used by drones, evaluates the performance of the results obtained against electronic warfare systems and presents the open challenges for future research. Full article

Autonomous flight planners consume sensor-derived estimated states rather than simulator ground truth. Learned dynamics surrogates are queried over a planning horizon through architecture-native multi-step interfaces, so strong short-horizon accuracy can still yield unreliable long-horizon trajectory scores. We address this with a JSBSim sensor-interface evaluation protocol that assesses surrogate predictions from onboard sensing and lightweight estimation—inertial measurement unit (IMU), global navigation satellite system (GNSS)/air-data, barometric, and vertical-speed channels with delay, dropout, asynchronous refresh, and estimator filtering—and guides surrogate selection by a planning task. This paper contributes an evaluation-and-selection methodology for learned flight-dynamics surrogates used by autonomous planners; it is not a new flight-dynamics, guidance, or control method, and it does not propose a new neural architecture. The contribution is a transparent and auditable sensor-interface and estimator-conditioned protocol, instantiated on five surrogate families over a 20 s prediction horizon. We emphasize at the outset that under strict native-unit physical tolerances, all evaluated surrogates diverge on 90–100% of validation windows and none is field-ready; all results are comparative JSBSim stress-test evidence, not field-readiness evidence. Results reveal criterion-dependent ordering: long short-term memory (LSTM) networks are strongest on root-mean-square error (RMSE@1s/RMSE@20s), shared-threshold failure, and absolute fidelity, whereas the Transformer is favored under self-scaled divergence and capped-risk criteria. These aggregate scores combine heterogeneous simulator-coordinate units and are benchmark diagnostics rather than physical safety margins. Because different threshold families select different winners, our central conclusion is not that one surrogate is best, but that the choice of metric and threshold family is itself part of the benchmark assessment. In separately generated JSBSim candidate-screening episodes, Transformer has approximately 8% lower mean cost under abstract sensing corruption, whereas estimator-loop screening shifts toward LSTM; feedback-rich tracking remains exploratory at 40 episodes per scenario and has mixed cost and success endpoints. Cross-aircraft transfer degrades substantially. Surrogates should be assessed using sensing-aware, estimator-conditioned, task-specific long-horizon criteria, not short-horizon accuracy alone. Full article

Airborne microplastics (AMPs) are recognized as emerging pollutants, exerting widespread impacts on both the ecological environment and human health. While existing research has predominantly focused on the occurrence characteristics, studies addressing human exposure patterns remain limited. In this study, a typical campus in Zhenjiang, a city in East China, was selected as the research site. By integrating students’ daily activity trajectories, active sampling methods were employed to estimate 24 h inhaled AMP intake, alongside simultaneous monitoring of environmental parameters such as temperature, relative humidity, and air pressure. Each participant used one filter throughout the 24 h monitoring period. The results revealed average AMPs abundances of 0.49 items/m³ and 0.82 items/m³ during the sampling period, with fibers being the most prevalent morphology (63%), followed by fragments (27%) and pellets (10%). μ-Raman spectroscopy analysis identified polypropylene (PP), polystyrene (PS), and polymethyl methacrylate (PMMA) as the primary polymer types, constituting 70% of the total. PP and PS were the dominant polymer types in the two trajectory-integrated samples; however, polymer identity alone did not permit definitive source attribution. A large proportion of the μ-Raman-identified particles fell within the 1–5 μm range; however, this fine-size fraction should be interpreted cautiously because representative spectra and particle-specific spectral matching records were not available for further verification. Based on the activity trajectories, the estimated daily inhalation intake of AMPs by students in campus micro-environments was approximately 91.5 items/day per person. PMMA was identified in Sample A, whose trajectory included the volleyball gym; however, the integrated sampling design did not permit location-specific source attribution or comparison of individual activity locations. This study provides preliminary information on AMP occurrence characteristics and estimated inhaled intake. This study is a preliminary pilot study, with 24 h personal exposure monitoring conducted on only two participants. We will increase the sample size in follow-up experiments to draw broader conclusions regarding airborne microplastic exposure across the campus. Full article

Nano- and pico-bubble water (NPB), containing hydrogen or ozone, is widely used as a cleaning agent due to its bactericidal and antiviral properties. However, some products—such as certain hydrogen waters—contain only large bubbles or have an extremely low bubble count, making them sometimes indistinguishable from ordinary drinking water. To accurately evaluate NPB activity, we developed a method for producing ultra-nano–pico-bubble water (NanoGAS water [NGW]), an ultra-fine bubble water that is stable and non-volatile over extended periods. By combining a mixed gas–liquid fluid rotary mixer and a shear filter, we produced ultra-fine bubbles that could be sealed in water. This method produced bubbles that remained stable in water even after 10 years since production. NGW has been clinically evaluated as a solvent for fecal microbiota transplantation (FMT) and has been demonstrated to be effective at improving bacterial engraftment in the intestinal tract in patients with autism spectrum disorder (ASD). Furthermore, encapsulating specific gases (hydrogen and ozone) can achieve more diverse effects. In this study, we evaluated the aroma-encapsulating effects, as well as the strength and persistence of the antimicrobial activity, of novel NGW formulations (Air-NGW, H₂-NGW, S-O₃-NGW, and L-O₃-NGW). Both H₂-NGW and O₃-NGW generated in this study demonstrated slight inclusion activity with volatile aromatic compounds (citral). Furthermore, both H₂-NGW (at ≥10% dilution) and O₃-NGW (even at a 1% dilution) exhibited sustained antibacterial efficacy against general viable bacteria for 24 weeks. Moreover, additive effects were observed when combined with antibacterial and antiviral compounds (polyoxometalates) developed by the authors. While further consideration, including cost-effectiveness, is needed to translate these findings into practical applications, they provide a fundamental framework for future research. Full article

Indoor air pollution (IAP) is an important public health issue in urban households, particularly in hot climates where residents spend substantial time indoors. While the health risks of indoor air pollution are increasingly recognised, gender differences in exposure-related behaviours, risk perception, and health communication preferences remain underexplored in Gulf cities. This study examined gender differences in awareness, household practices, and preferred information channels related to IAP among English-speaking adults in Dubai. A cross-sectional survey was conducted among 1014 adults using a structured questionnaire. Descriptive, bivariate, and multivariable regression analyses were used to examine gender-based patterns. Overall, participants showed moderate awareness of IAP; gender differences emerged across each of the four behavioural domains examined, with women more likely to use chemical-based cleaners and to open windows for ventilation. After controlling for age and education, women were significantly more likely than men to use traditional chemical-based cleaners (β = 0.69; p < 0.001) and to open windows for ventilation (β = 0.78; p < 0.001), while men more frequently relied on mechanical methods such as air filters and ceiling fans. Women were more likely to express interest in learning about IAP (adjusted OR = 2.55; 95% CI: 1.71–3.79) and to prefer community-based, informal channels such as awareness campaigns and social media. These gender differences persisted after adjustment for age and education, suggesting that indoor air quality behaviours and communication preferences may vary systematically by gender in this sample. Among this English-speaking convenience sample, gender differences were observed in household practices and preferred communication channels. These findings provide preliminary evidence that may help inform future public health communication strategies in Dubai but should not be interpreted as representative of the wider Dubai population. Full article

Barbecue (Moo Kratha) restaurants are prevalent in Chiang Mai City, Thailand. However, their PM_2.5-bound polycyclic aromatic hydrocarbon (PAH) emissions, compounded by the region’s severe seasonal smoke haze, remain poorly characterized. This study investigated the chemical composition and carcinogenic risk of PM_2.5-bound PAHs in this present environment. Ten PM_2.5 samples were collected from ten charcoal Moo Kratha restaurants during peak evening hours in February 2026 (early dry season) using a portable air sampler operated as a fixed-location (area) sampler positioned at the customer dining table (2 L min⁻¹, 180 min) on quartz fiber filters, and the 16 US EPA priority PAHs were analyzed by GC-MS. Carcinogenic risk was assessed using benzo[a]pyrene toxicity equivalent (TEQ_BaP) concentrations and incremental lifetime cancer risk (ILCR) for adult and child customers (diners) via the inhalation pathway. The mean concentrations of PM_2.5 and total PAHs were 87.64 µg m⁻³ and 132.74 ng m⁻³, respectively, with carcinogenic PAHs contributing 52.3%; the mean TEQBaP of 15.34 ng m⁻³ provides an internal index of the carcinogenic potency of the PAH mixture. The mean inhalation ILCR for a frequent diner was 2.82 × 10⁻⁵ for adults and 2.12 × 10⁻⁵ for children, rising to 6.16 × 10⁻⁵ and 4.62 × 10⁻⁵, respectively, at the most contaminated site; even an occasional (monthly) diner exceeded the 10⁻⁶ negligible threshold. These findings indicate that charcoal Moo Kratha restaurants are distinct, persistent sources of carcinogenic PAHs that place diners within the upper part of the tolerable cancer-risk range; worker exposure is expected to be higher but was not quantified here, underlining the need for ventilation and occupational-health strategies. Full article

Cyanobacterial and harmful algal blooms are common components of the waters in and around Southwest Florida and are sufficiently frequent to be of concern with respect to adverse effects on short- and long-term human and animal health. Currently, the contribution of airborne exposure to cyanobacterial toxins is not as advanced as for the other known human exposure routes such as consumption of contaminated water or fish. An airborne monitoring device named Airborne Detection for Algae Monitoring (ADAM) was developed to collect air samples in the proximity of algal and/or cyanobacterial blooms to examine the relationship between naturally occurring toxins in the air and those in proximal Floridian waters. Twenty-one air and water samplings were performed between July and December 2021 using a filter and impinger system for airborne components, along with a water sample from the same location. Samples were assessed for microcystins, anatoxin-a, cylindrospermopsin, saxitoxin and BMAA and isomers. Furthermore, as Karenia brevis is common in this area, brevetoxins were also assessed in air samples. Although very few large-scale cyanobacterial bloom events were observed at sampling locations, cyanobacterial and algal toxins were present in the majority of samples at low concentration. Data obtained from ADAM indicate that people may be chronically exposed to low concentrations of cyanobacterial toxins and that further assessment is required to help protect human health. Full article