Search Results (1,554)

Thermal annealing improves the mechanical, structural, and electrical properties of polymer thin films, promoting processes like residual solvents and stress removal, as well as the crystallization and densification of the gel layer. The effects are strongly dependent on the annealing temperature, where optimal temperatures enhance film performance, while excessive thermal exposure may induce negative outcomes like amorphous structural transitions, increased roughness, and defect formation. In this work, thin films of two humidity-sensitive poly(vinyl alcohol) (PVA)-based copolymers with grafted poly(N,N-dimethylacrylamide) (PDMA) chains were investigated. The polymers differ in grafting density and chain length, enabling the assessment of macromolecular architecture’s effects. Spin-coated films with 150–200 nm thickness were annealed at three temperatures: 60 °C, 120 °C, and 180 °C. By using UV-VIS-NIR spectroscopy and the quartz crystal microbalance method, a comprehensive characterization of temperature- and humidity-induced changes in swelling, hysteresis, sensitivity, detection resolution, and water uptake is performed, elucidating the role of the macromolecular architecture on the post-deposition annealing modification of gel film properties and its humidity response. High-performance humidity sensing with a resolution of 0.8% RH is achieved through the optimization of the interplay between the macromolecular architecture and annealing temperature. In addition, the study highlights and explores the potential of these films for optical color-based moisture detection. Full article

Students and workers spend much of their day in school and office environments, where poor indoor air quality (IAQ) can negatively affect health and comfort. Indoor vegetation is increasingly proposed as a low-cost nature-based solution (NBS) to improve IAQ. This study evaluated the effects of phytoremediation on IAQ and indoor microclimate in schools across different regions and educational levels, as well as in office environments, under real-world conditions. Several C₃ plants (e.g., Chamaedorea, Schefflera, Ficus, Epipremnum, Yucca, and Spathiphyllum) were used, with crassulacean acid metabolism (CAM) plants (Sansevieria) included in selected settings. Temperature, relative humidity, CO₂, PM_2.5, and PM₁₀ were continuously monitored using intercalibrated low-cost sensors in absence and presence of vegetation. A comparable plant configuration was implemented in offices to assess its effects on volatile organic compounds (VOC). Indoor greenery reduced particulate matter, especially PM10 (18–20%), and improved microclimatic conditions by lowering air temperature (1–2 °C) and increasing relative humidity (6–15%). However, CO₂ reductions were limited and context-dependent. In the tested office environments, plant introduction was associated with reduced total VOC concentrations (25–50%). Overall, our results further support that indoor vegetation constitutes a robust, cost-effective nature-based solution (NBS) capable of complementing conventional ventilation systems in both school and office environments. Full article

The application of silk sericin as a polymeric biomaterial has recently gained interest, although its film was found to be fragile, exhibiting brittleness when subjected to relatively slight stress, and it also displayed higher water solubility. This study focused on the enhanced physico-mechanical properties of the three films obtained by the crosslinking of sericin protein from three silkworm cocoons with poly (vinyl alcohol) (PVA) to reduce phase separation and solubilization of the films by promoting miscibility between sericin and PVA. The findings demonstrated how crosslinking with glutaraldehyde enhanced thermal stability and tensile strength and controlled the solubility of the three sericin–PVA films. The sericin from G. postica, G. rufobrunnea, and Argema mimosae is composed of serine, aspartic acid, and glutamic acid, which make up 80% of the total polar amino acids. X-ray diffraction (XRD) patterns showed that sericin–PVA films have semicrystalline features, representing amorphous and crystalline regions. The XRD results also indicated that the Saturniidae sericin–PVA film (Sat-SPF), Gonometa postica sericin–PVA film (GP-SPF), and Gonometa rufobrunnea sericin–PVA film (GR-SPF) have crystallinity percentages of 66.4%, 55.9%, and 17.7%, respectively. The moisture vapor transmission rate (MVTR) values observed in this study ranged from 991.2 to 5160 g/m²/24 h, indicating that these films can effectively regulate moisture levels in wounds. The swelling capacity of the three sericin–PVA composite films depends on the crosslinking density of their structures and was also found to be sensitive to the pH of the aqueous media, demonstrating their hydrophilic nature and potential use in drug delivery systems. The water vapor permeability of sericin–PVA films increased with higher environmental relative humidity (RH) and moisture content within the films. The elongation at break for GP-SPF (107.2% ± 3.1) and Sat-SPF (73.0% ± 4.1) was significantly higher than in GR-SPF (29.3% ± 2.3). However, their tensile strength and elastic modulus were lower than those of GR-SPF. These results show that the number of polar groups (amino and hydroxyl groups) from both sericin and PVA influences all the properties of the sericin–PVA composite films. The three sericin–PVA solutions were found to have antibacterial efficacy against three Gram-positive and one Gram-negative bacteria over 24 h. Scanning electron microscopy (SEM) images revealed a rough surface with a granular network pattern, which supports the potential use of sericin–PVA films for cell adhesion and proliferation, which are essential for biomedical wound dressing applications. Full article

We present a novel algorithm for the retrieval of non-spherical particle microphysical parameters (PMP) from 3β + 2α + 3δ optical data taken with multiwavelength lidar. The 3β + 2α + 3δ optical datasets describe particle backscatter coefficients (β) at three wavelengths, λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths, λ = 355 and 532 nm, and particle linear depolarization ratios (PLDR, δ) at three wavelengths, λ = 355, 532, and 1064 nm. The algorithm can be used for retrieving bimodal particle size distributions (PSDs). The PSDs can comprise mixtures of spheres and spheroids (SS). One or both modes can comprise spheroid-shaped particles or spherically shaped particles. The spheroids are used for approximating an arbitrary ensemble of non-spherical particles. The algorithm works on the basis of a combination of direct and analytical inversion methods. The algorithm uses the spheroid reference look-up table (RLUT) we developed and presented in part 1 of our research work. The algorithm uses constraints regarding the particle complex refractive index (CRI) and information on relative humidity (RH) in the atmosphere (in the case of aerosol lidar observation) for suppressing retrieval uncertainties. We carried out a numerical simulation study to evaluate the algorithm’s performance. In these numerical simulations, we considered perturbed synthetic 3β + 2α + 3δ optical data that mimic different organic carbon (OC)–dust (D) mixtures. Such mixtures are suitable examples for describing bimodal PSDs that consist of a fine mode of spherical particles and a coarse mode of non-spherical particles. The results of the numerical simulation show that (1) the PMPs of each mode of these particle mixtures can be found separately, (2) the mean retrieval errors of the effective radius, number, surface-area, and volume concentrations of these mixtures are 25%, 52%, 9%, and 28%, respectively, and (3) the mean retrieval error of single-scattering albedo (SSA) at 355 nm of these mixtures is as low as ±0.02. SSA retrieval accuracies at 532 and 1064 nm degrade because the complex refractive index (CRI) of OC and D particles depends on the measurement wavelength. In future studies, we will upgrade the algorithm such that it takes into account a spectrally dependent CRI. We also compare the results of our novel algorithm with our TiARA2.1 algorithm. The errors obtained from the TiARA2.1 algorithm are approximately three times larger compared to the errors we obtain with our novel ATLAS algorithm for the case of the OC-D mixtures considered in the present study. We explain the higher accuracy of the PMP retrievals by the use of three PLDRs and the extra constraints placed on CRI and RH. Full article

Buildings in hot–humid climates experience increasing thermal stress due to urban heat islands and climate change, leading to greater reliance on air conditioning. Passive cooling is therefore a crucial low-carbon strategy for maintaining thermal comfort. This paper reviews thermal comfort ranges and passive-cooling techniques across Köppen–Geiger hot–humid climate classes. A two-stage approach was adopted: thermal comfort data from 35 field studies were analyzed by climate class and ventilation mode, while more than 70 application studies were qualitatively reviewed to assess mechanisms, performance, and climate suitability. The results indicate that occupants in hot–humid areas exhibit broad thermal tolerance, particularly in naturally ventilated buildings, with neutral temperatures ranging from 19.5 °C in humid subtropical climates to 36.3 °C in tropical savanna climates. Natural ventilation is the most widely applicable passive-cooling strategy, but its effectiveness depends on integration with climate-responsive measures. Ventilation, combined with solar protection and courtyards, is most effective in Af and Am climates, whereas shading, solar chimneys, evaporative cooling, night ventilation, thermal mass, and phase-change materials provide greater benefits in Aw, Cfa, and Cwa climates. However, no single strategy is sufficient across all climates. The review provides climate-specific guidance for designing low-carbon, thermally resilient buildings in hot–humid regions. Full article

Diamondback moth (DBM, Plutella xylostella) outbreaks are shaped by delayed meteorological conditions, yet most forecasting models compress weather into a few monthly summaries and provide limited ecological interpretation. We propose MeteoSCOPE, an ontology-aware sparse Perceiver framework for interpretable, multi-horizon retrospective forecasting of DBM abundance from historical pest records and rich meteorological descriptors. Each feature-lag value is encoded as a token carrying feature identity, ecological group, descriptor type, lag position, and seasonal information; in the rich setting, 138 descriptors across 12 months yield 1656 tokens per sample. Sparse cross-attention compresses these tokens into a compact latent representation, while horizon-specific queries produce one- to four-month-ahead forecasts. Attention tensors and a common-plus-residual branch are aggregated into feature-, group-, descriptor-, lag-, horizon-, and residual-level explanations. Using DBM records from Huiyang and Shantou, Guangdong, MeteoSCOPE achieved the strongest overall retrospective performance, with robust gains at Shantou and metric-dependent gains at Huiyang. The explanations identified pest history as the leading attended group at both sites and surfaced site-specific secondary attributions for soil moisture, weather state, wind, soil temperature, and humidity, treated as model evidence rather than causal ecological effects and corroborated by independent occlusion and KernelSHAP analyses. Strict zero-shot cross-site transfer degrades substantially, so prospective field validation and broader multi-site testing remain required before operational deployment. MeteoSCOPE thus provides a transferable methodological framework (not a deployable forecaster) for interpretable analysis of high-dimensional agricultural time series. Full article

Phytolacca americana L. is an invasive perennial plant that has become increasingly widespread in China, but its current climatic suitability and future redistribution under climate change remain insufficiently quantified. This study aimed to identify the major environmental drivers of P. americana distribution and to project its potential habitat suitability under future climate scenarios. We compiled a national occurrence dataset and retained 683 quality-controlled presence records after taxonomic verification, coordinate checking, and 5 km spatial thinning. A BIOMOD2 ensemble modelling framework was used to integrate nine algorithms, and future projections were generated using CMIP6 climate data under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 across four time periods from 2021 to 2100. The ensemble model showed strong predictive performance, with TSS = 0.804 and ROC = 0.967. May shortwave radiation, January mean temperature, and annual temperature range were identified as the dominant predictors of habitat suitability. Under current climate conditions, highly suitable habitats were mainly concentrated in warm and humid regions of eastern and southern China. Future projections indicated that suitable habitats may expand toward northern, northwestern, and higher-elevation regions, whereas highly suitable habitats may become redistributed or fragmented under stronger climate forcing. Centroid analyses further suggested non-linear, scenario-dependent shifts rather than a simple poleward expansion. These findings provide a spatial basis for early warning, targeted monitoring, and pathway-focused management of P. americana in China. Full article

The Yarlung Zangbo River Basin (YZRB) stores abundant solid water resources. These components are highly sensitive to climate warming and play a critical role in regulating downstream water availability. However, the spatiotemporal responses of the thermal state to ongoing climate change and its potential atmospheric forcing remain poorly understood. Here, we use satellite-based land surface temperature (LST) to characterize the thermal dynamics of the YZRB during 2000–2024. Further, a machine learning model combined with Shapley Additive Explanations (SHAP) is applied to quantify the pixel-level statistical contributions of meteorological variables to LST trends. The climatological LST exhibits pronounced spatial and seasonal heterogeneity, with lower temperatures in the northwestern and northeastern regions and higher temperatures in the central and southeastern regions. The intra-annual cycle follows a unimodal pattern, peaking in early summer, while downstream sub-basins show a delay in peaking times. Mean LST increases at a rate of 0.18 °C decade⁻¹, while maximum LST warms at nearly twice this rate (0.40 °C decade⁻¹) with widespread warming across the basin. However, minimum LST shows no significant long-term trend, mainly due to the polarization trend within the year. The warming signal shows strong season dependence, with the largest monthly warming trend exceeding 0.80 °C decade⁻¹ for all three LST metrics. Attribution analysis identifies precipitation as the primary meteorological factor statistically associated with basin-scale LST trends. Wind speed may largely represent a response to increasing LST rather than a direct driving factor. Downward shortwave radiation, air temperature and specific humidity exhibit stronger influences in specific regions rather than at the basin scale. The dominant control of precipitation reflects strong monsoon influence on LST dynamics along the southern margin of the Tibetan Plateau. Full article

Inaccurate prediction of shrinkage in self-compacting concrete (SCC) may result in underestimated cracking risk, increased permeability, serviceability deterioration, and reduced long-term durability of concrete structures. Although conventional empirical shrinkage models are widely used in engineering practice, their accuracy is often limited when applied to SCC mixtures with high paste volume, mineral admixtures, manufactured sand, and high-range water-reducing admixtures. Recent machine-learning-based models provide an alternative approach, but single learning algorithms may show limited robustness for small and heterogeneous datasets. In addition, random sample-level data splitting may introduce information leakage when shrinkage measurements obtained at different curing ages from the same mixture are assigned to both training and testing sets. To address these issues, this study develops a stacking-based ensemble learning framework for SCC shrinkage prediction using mixture proportions and curing age as input variables. A multi-source database containing 61 mixture designs and 448 data samples was established from published experimental studies. To obtain a more realistic assessment of model generalization, a mixture-level validation strategy was adopted, in which all age-dependent samples from the same mixture were assigned exclusively to either the training set or the testing set. Under this strategy, 358 data samples were used for model training and 90 data samples were used for independent testing. Four base learners, including multilayer perceptron (MLP), support vector regression (SVR), decision tree (DT), and gradient boosting decision tree (GBDT), were constructed and integrated through different ensemble configurations. The Stacking-SVR model achieved the best overall performance on the independent testing set, with a mean absolute error (MAE) of 13.6 με and a mean absolute percentage error (MAPE) of 7.5%. Compared with GBDT, Stacking-GBDT, and DT models, the proposed Stacking-SVR model reduced the MAPE by approximately 10.7%, 11.8%, and 35.3%, respectively. Stability and applicability analyses further indicate that the proposed framework can provide reliable shrinkage predictions within the investigated mixture and curing-age ranges. However, because the model was developed from a compiled database and does not explicitly include environmental variables such as relative humidity and temperature, its use should be limited to parameter ranges represented in the database. Overall, the results demonstrate that stacking ensemble learning combined with mixture-level validation offers a leakage-controlled and engineering-oriented approach for SCC shrinkage prediction. Full article

This study examines the meteorological and gaseous pollutant drivers of PM_2.5 under mild, moderate, and severe pollution conditions in the Beijing–Tianjin–Hebei region, with the aim of supporting sustainable air quality management. Daily observations from approximately 65 monitoring stations from 1 November 2021 to 31 October 2024 were used, including PM_2.5, four gaseous pollutants (SO₂, NO₂, CO, and O₃), and five meteorological variables: temperature, pressure, relative humidity, precipitation, and wind speed. A CatBoost–SHAP framework was adopted, with CatBoost used for station-level spatial prediction of PM_2.5 and SHAP applied to interpret variable contributions. Based on predefined PM_2.5 thresholds, 425 pollution days were classified into those three pollution-level scenarios. These pollution days occurred mainly in winter and spring, with higher frequencies in Handan, Baoding, and Shijiazhuang, followed by Tianjin and Beijing. The model performed well across the three pollution-level scenarios. The severe-pollution scenario achieved the highest R², indicating a clearer spatial structure under high-PM_2.5 conditions. Although absolute RMSE and MAE increased with pollution severity, their normalized values changed little, suggesting that larger errors mainly reflected stronger spatial heterogeneity at higher PM_2.5 concentrations. SHAP results showed that CO, precipitation, wind speed, and temperature dominated the prediction structure. CO was the most stable and influential predictor, but its importance should be interpreted as an indicator of combustion-related pollution accumulation rather than direct causality. Precipitation represented event-dependent wet scavenging, wind speed reflected dispersion conditions, and temperature captured seasonal and thermal background effects. SHAP dependence analysis further indicated that CO had the clearest direct dependence, whereas wind speed and temperature were more background-dependent, and precipitation acted as an episodic nonlinear regulator. Full article

Climate change presents significant challenges to agriculture worldwide, leading to food insecurity and impacting rural livelihoods. Maize farming is especially vulnerable to extreme weather, such as heavy rainfall, high temperatures, soil acidity, humidity, and poor irrigation, which reduce crop yields and raise concerns about food security. The study aimed to develop a reliable and accurate machine learning method to predict maize crop yields using historical climate data to facilitate decision-making. This allows farmers and agronomists to forecast maize production based on past data for adaptation. A dataset from Meteo Rwanda and maize yield data from the Kayonza district, Rwanda, were used for training and testing. The weather data included annual mean temperature, maximum temperature, minimum temperature, rainfall, and soil temperature over the past thirteen years. The data were analyzed using machine learning techniques such as Random Forest regressor, Extreme Boost regressor, Gradient, Support Vector Machine, and LASSO (Least Absolute Shrinkage and Selection Operator). The results show that developing a high-yield crop depends on predicting and integrating climate variables, especially temperature and rainfall. Overall, Random Forest, Support Vector Machine, and Extreme Boost outperformed LASSO, with R² values of 0.957, 0.955, and 0.953, compared to 0.256 for LASSO. Full article

Crimped electrical connections must maintain electrical continuity and mechanical load transfer capability under combined environmental and operational stressors throughout their service life. Although the environmental durability of electrical connectors has been extensively studied, previous studies have mainly focused on material, environmental, or electrical effects in isolation, whereas the coupled influence of crimp geometry on electrical–mechanical degradation and contact evolution remains insufficiently understood. In this study, crimp geometry was isolated as the primary independent variable to investigate geometry-driven degradation behavior in crimped connections. Three crimp configurations (Type A, Type B, and Type C) were subjected to temperature cycling (−55 °C to +70 °C), high humidity (90–95% RH), and combined chemical–electrical loading conditions involving representative fluids and short-circuit current. Electrical and mechanical responses were evaluated using relative resistance variation ΔR (%) and tensile strength change ΔT (%), while factorial ANOVA quantified parameter contributions. The results indicate that crimp geometry dominates the response under thermal–humidity exposure, whereas the chemical exposure type becomes the governing factor for electrical degradation under coupled chemical–electrical conditions. SEM analysis reveals that geometry-dependent plastic deformation governs contact continuity and void formation, leading to a transition from continuous conductive networks to fragmented contact structures. These findings are further supported by FEM analyses, which provide qualitative insight into the deformation response as a function of the geometric parameters. This work presents a geometry-based experimental framework for understanding the degradation behavior of crimped bonding structures under dual-exposure test conditions. Full article

We develop a leading-order continuum framework for thin-film hydrodynamics on inclined solid substrates, integrating capillarity, intermolecular forces, gravitational symmetry breaking, confined transport, and stochastic wetting into a single formulation. Starting from lubrication theory with capillary curvature and disjoining-pressure interactions, we obtain a lubrication-scale thin-film equation that incorporates inclination-driven advection, nanoscale stabilization, and humidity-controlled source–sink fluxes. A dimensionless analysis shows that, within the long-wave lubrication approximation, inclination induces a coordinated leading-order coupling among the Bond (Bo), Péclet (Pe), and Damköhler (Da) numbers. This coupling defines a characteristic inclination-angle-dependent scaling trajectory Γ(θ) in the (Bo, Pe, Da) space: material parameters set the system’s position along this curve, while the geometric constraint organizes the ordering of hydrodynamic, transport, and confinement regimes. We further derive leading-order crossover criteria associated with transport transitions (Pe ≃ 1) and reactive-confinement loss (Da ≃ 1), providing explicit regime boundaries that can be evaluated for representative parameter ranges. A representative parameterization of an ultrathin atmospheric electrolyte film is then used to make these crossovers explicit, yielding illustrative inclination thresholds that depend on the chosen parameter set. Coupling the deterministic structure to a minimal stochastic closure captures intermittent wet–dry dynamics under environmental forcing. In this closure, inclination selectively accelerates the drying pathway through the drainage time (and thus drying rate λ_dry), while rewetting remains primarily humidity-controlled, to leading order, providing a scaling-based description of wet-state persistence and time-of-wetness versus θ. The resulting framework provides a continuum-scale physical description of confined films under geometric asymmetry, relevant to wetting, interfacial drainage, confined transport, and thin-film systems in which symmetry breaking and coupled interfacial–transport processes coexist. Full article

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM_2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min. Full article

Oil palm (Elaeis guineensis Jacq.) production in Côte d’Ivoire depends on insect-mediated pollination by Elaeidobius weevils. From October 2011 to November 2013, we monitored 432 female inflorescences in three major production zones (La Mé, Grand Béréby, and Iboké). Each month, visits by E. kamerunicus, E. plagiatus, E. singularis, and E. subvittatus were recorded on the day of full anthesis during three discrete time periods (09:00–10:00, 11:00–12:00, and 16:00–17:00). Temperature and relative humidity were measured concurrently. Visitation peaked at 11:00 across sites, with abundance ~4-fold higher than at 09:00 and ~20-fold higher than at 16:00; mixed-model results indicated that this temporal pattern was consistent among sites (non-significant Site × Time interaction). E. subvittatus dominated visits to female inflorescences (71–74% of individuals), whereas E. kamerunicus showed lower visitation rates. Pollinator abundance was positively correlated with temperature (ρ = 0.18) and negatively correlated with relative humidity (ρ = −0.13), although these relationships were weak. These results identify late morning as a key activity period within the observation windows and suggest that adjusting field practices (e.g., insecticide timing) may reduce non-target impacts on pollinators. Full article