Search Results (146)

Road dust is an important urban reservoir of potentially toxic elements (PTEs) and a relevant source of human exposure through resuspension and inhalation, particularly in large megacities. This study provides an integrated assessment of the seasonal variability, contamination levels, source identification, particle characteristics, lung bioaccessibility, and health risk of road dust in Mexico City, one of the world’s largest urban centers. A total of 74 road dust samples were collected during the dry and wet seasons, and V, Cr, Mn, Co, Ni, Cu, As, Cd, Sb, and Pb were analyzed by ICP–MS in the <20 µm fraction. Geochemical indices, spatial analysis, Pearson correlation, principal component analysis, SEM–EDS particle characterization, in vitro lung bioaccessibility (ALF), and human health risk models were applied. Sb, Cu, and Pb were identified as the most enriched elements, exceeded local background concentrations at all sampling sites. Spatial patterns revealed recurrent hotspots in the northern, northeastern, and central sectors of the city. SEM–EDS analyses showed that most particles belonged to the 2.5–5 µm equivalent-size class and included Fe-rich spherules, Pb-rich aggregates, silicate grains, and C-rich particles. Health risk assessment indicated acceptable risks for adults, whereas children exceeded the non-carcinogenic threshold (HI = 3.85–4.60) and slightly surpassed the upper acceptable carcinogenic risk level. Lung bioaccessibility results revealed low Pb solubility but high mobility of Ni and Cu, with some samples reaching complete dissolution under ALF conditions. These findings demonstrate that traffic-derived road dust represents a persistent urban exposure pathway in Mexico City and highlight the importance of integrating total concentrations, particle characteristics, and bioaccessibility data to improve environmental and health-risk assessments in urban environments. Full article

The outcomes of a standard geochemical, geophysical and petrographical approach to exploration at Lead Trial, a small prospect in central Scotland, have been compared to the interpretation of a parallel gold compositional study describing 703 gold particles from local in situ and alluvial occurrences. Standard exploration approaches identified a 4.5 km² zone hosting an array of numerous auriferous (to 17 g/t Au), vuggy, brecciated quartz-galena ± sphalerite veins culminating in the identification of a drill target. The gold study identified three gold compositional types: two 23–32 wt.% Ag alloys with a Zn-Pb-Cu mineral inclusion assemblage differentiated by sphalerite abundance, and a 5–16 wt.% Ag alloy with a Mo-Bi-Pb-Cu-Fe inclusion signature, yet to be correlated with either float or outcrop. Spatial distribution of the gold types indicates lateral variation and probably vertical variation within a single magmatic hydrothermal system. Integration of gold particle studies with early stages of exploration offers rapid insights into the nature and distribution of mineralization when very limited information is available and is mutually supportive of standard exploration approaches. Full article

Microplastics (MPs; <5 mm) represent a growing environmental concern that increasingly challenges environmental monitoring, governance, and evidence-based decision-making. This review critically examines how current scientific understanding of microplastic sources, classification, occurrence, and environmental behavior can support environmental governance. MPs are classified as primary and secondary particles; however, persistent inconsistencies in size definitions, shape descriptors, and polymer identification limit the comparability of monitoring data and constrain the development of coherent regulatory frameworks. Evidence on the occurrence of MPs in surface waters and sediments highlights widespread contamination and pronounced spatial variability, raising challenges for risk assessment and policy harmonization across regions. Key transport pathways, including atmospheric deposition, terrestrial runoff, and riverine fluxes, are analyzed to illustrate how local emissions translate into large-scale environmental impacts. Rivers emerge as key components linking sources to receptors, offering relevant points for policy intervention and management measures. The review evaluates current policy responses to microplastic pollution, identifying significant gaps in standardized monitoring, data integration, and risk assessment approaches. It emphasizes the need for stronger alignment between scientific outputs and policy requirements, including the co-production of knowledge involving scientists, regulators, and stakeholders. By outlining pathways through which scientific evidence can inform regulatory design and environmental management, this study provides actionable insights for improving policy effectiveness. Advancing harmonized methodologies and integrating science into decision-making processes are essential steps toward mitigating microplastic pollution and supporting sustainable environmental governance. Full article

Metallographic microstructure analysis is essential for understanding the evolution of steel microstructures during heat treatment and mechanical processing. However, accurate analysis of optical micrographs remains difficult because of blurred grain boundaries, grayscale inhomogeneity within grains, and irregular grain morphologies. To address these issues, this work proposes an automated metallographic image-processing method based on superpixels, DPSS (dual-phase steel segmentation), with the main contribution focused on microstructure segmentation. First, image contrast and boundary visibility are enhanced by edge detection and sharpening. Then, superpixel segmentation is combined with extracted edge information to improve boundary localization and preserve irregular grain morphology, enabling more complete extraction of grain or particle regions from optical images. The proposed method is validated on optical micrographs of Mn-Si low-alloy steel, and the results show that it provides more accurate and complete segmentation than conventional ImageJ (Version: 1.54f)-based processing. Based on the segmented regions, a lightweight neural network is further used for phase identification. The final classification recognition accuracy can reach 99.91%. This classification result serves to demonstrate that the improved segmentation results can provide more reliable inputs for subsequent microstructure recognition. Overall, the proposed method offers an effective and automated solution for metallographic image segmentation and supports more accurate downstream phase analysis. Full article

Three-phase induction motors account for nearly two-thirds of industrial electricity consumption, making accurate parameter identification essential for efficiency optimization, predictive maintenance, and digital twin calibration. This paper introduces the stochastic spheric navigator algorithm (SSNA) for estimating the equivalent circuit parameters (stator and rotor resistances, leakage reactances, and magnetizing reactance) of induction motors by minimizing the normalized squared error between manufacturer-provided torque characteristics (starting, peak, and full-load) and their analytical counterparts derived from the steady-state Thévenin model. The SSNA employs an adaptive spherical search mechanism with a decaying radius schedule that progressively narrows the exploration neighborhood, enabling a balanced transition from global exploration to local refinement. Validated on 5 hp and 25 hp motors against the genetic algorithm (GA), particle swarm optimizer (PSO), hybrid GA-PSO, and sine–cosine algorithm (SCA), the SSNA demonstrates distinct advantages. For the 5 hp motor, it achieves the lowest errors in maximum torque ($1.34\times {10}^{-4}\%$) and full-load torque ($5.08\times {10}^{-4}\%$). For the previously unreported 25 hp motor, the SSNA yields an objective function value of $4.68\times {10}^{-12}$—six orders of magnitude lower than the SCA—and reduces magnetizing reactance estimation error from 46.55% (SCA) to 16.18%. Statistical analysis over 100 independent runs reveals that the SSNA uniquely combines the lowest minimum (best) value, the lowest maximum (worst) value, and the lowest standard deviation, demonstrating superior accuracy, reliability, and consistency. These results position the SSNA as a highly competitive optimization framework for induction motor parameter identification, with particular suitability for applications demanding high precision and robust performance. Full article

The reconstruction of acoustic (AC) logging curves is of great significance for reservoir evaluation, lithology identification, and velocity modeling, particularly in the presence of missing or degraded logging data. However, conventional reconstruction methods and existing deep learning models often suffer from limited feature representation capability and rely heavily on manual hyperparameter tuning, leading to suboptimal performance. To address these challenges, this study proposes a reinforcement learning-based optimization framework for AC logging curve reconstruction. Specifically, a hybrid deep learning architecture integrating convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM), and an attention mechanism is developed to effectively capture local spatial features, long-range temporal dependencies, and key feature contributions from multi-logging data. Furthermore, a Q-learning-based optimization strategy is introduced to adaptively tune model hyperparameters by formulating the optimization process as a Markov Decision Process (MDP), enabling dynamic and data-driven parameter adjustment. To validate the effectiveness of the proposed method, comparative experiments are conducted using several baseline and optimized models, including CNN–BiLSTM, CNN–BiLSTM–Attention, particle swarm optimization (PSO)-optimized CNN–BiLSTM–Attention, and genetic algorithm (GA)-optimized CNN–BiLSTM–Attention. The results demonstrate that the proposed approach achieves superior reconstruction accuracy for AC curves, with improved convergence efficiency and model stability. In addition, it exhibits stronger robustness and generalization capability under limited data conditions, effectively mitigating the risk of overfitting and local optima. This study provides a novel reinforcement learning-driven solution for AC logging curve reconstruction and offers practical value for intelligent reservoir characterization in complex geological environments. Full article

Urban air pollution exhibits pronounced spatial heterogeneity, yet conventional fixed-site monitoring often cannot resolve fine-scale hotspot patterns. To address this issue, this study conducted a winter intensive observation campaign combining mobile measurements and synchronous fixed-site observations in Chengdu. The mobile observation was used to characterize the spatial distribution of particulate pollution, while fixed-site pollutant and meteorological data were used to provide temporal and background context. Three mobile observation sessions were performed each day at fixed local times (09:00–11:00, 14:00–16:00, and 19:00–21:00). Based on the PM_2.5 concentration, the observation period was categorized into two episodes: polluted episodes (PM_2.5 > 75 μg m⁻³) and clean episodes (<75 μg m⁻³). Polluted episodes were characterized by substantially elevated PM_2.5, PM₁₀, NOx, CO, and particle number concentrations, together with relatively weak wind speed, indicating enhanced accumulation under stagnant conditions. In contrast, clean episodes generally occurred under stronger ventilation and lower pollutant levels. The results revealed marked small-scale spatial variability and distinct temporal changes in particulate pollution. PCA was suitable for the dataset (Kaiser–Meyer–Olkin = 0.788; Bartlett’s test, p < 0.001), and the first three principal components explained 82.7% of the total variance. Cluster analysis further identified three pollution regimes among 224 samples: clean/ventilated (34.4%), intermediate accumulation (39.7%), and heavy accumulation (25.9%). These findings demonstrate that short-term intensive mobile monitoring can serve as a cost-effective supplement to conventional monitoring for hotspot identification and targeted urban air-pollution management. Full article

This study focuses on modelling the spatial dependence of airborne particle contamination using Response Surface Methodology (RSM), with consideration of its implications for technical cleanliness and employee health. The analysis is based on two measurement campaigns conducted in an industrial production hall, where particle concentrations were recorded across multiple size fractions using a TROTEC PC220 device. The results demonstrate that RSM effectively captures nonlinear relationships and spatial gradients, enabling the identification of local extrema and contamination hotspots. Statistical analysis confirmed a significant influence of spatial coordinates on particle concentration across all fractions, with finer particles exhibiting stronger spatial dependence, consistent with aerosol behaviour in indoor environments. Quadratic model terms revealed stable hotspot regions persisting even after corrective measures, indicating persistent contamination sources or structural factors. Residual analysis suggested additional unmodeled local sources or transport mechanisms. Based on the integration of RSM and multi-fraction analysis, a mechanistic contamination model (source–transport–receptor framework with deposition processes) is proposed, linking particle behaviour with surface contamination and potential human exposure. The approach enables data-driven, localised contamination control and supports optimisation of technical cleanliness and occupational health conditions. Full article

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

Soybean lodging severely impairs yield and quality, and its precise grading is a key prerequisite for intelligent agricultural management and loss assessment in agricultural insurance. Most existing studies have focused primarily on soybean lodging identification and lodging resistance evaluation, whereas methods for the precise differentiation of lodging grades remain to be refined. This study presents an improved AlexNet model integrated with a Local Feature Aggregation (LFA) attention mechanism and a dynamic optimization strategy for the accurate grading of soybean lodging. RGB imagery of soybean canopies during the grain-filling to early maturity stages was acquired via a multispectral unmanned aerial vehicle (UAV). A dynamic Dropout strategy was adopted to enhance model stability and mitigate overfitting, and the Particle Swarm Optimization (PSO) algorithm was employed to intelligently optimize key hyperparameters of the model. The results demonstrate that the optimized model achieved an overall accuracy of 94.23% on the test set, with an average loss of 0.0682 and an inference speed of 0.422 s/step. In independent field validation, the grading accuracies for the five lodging grades were 90.12%, 86.35%, 89.47%, 88.93%, and 92.76%, respectively, with a mean accuracy of 89.53%. The proposed model enables the rapid and precise grading of soybean lodging under field conditions, thereby providing effective technical support for intelligent field management and disaster loss assessment in soybean production. Full article

Co/Fe layered double hydroxides (LDHs) are among the most promising materials for advanced industrial and energy applications. Controlling the synthesis conditions of LDH materials is thus crucial to precisely tailoring cation composition and distribution, thereby regulating surface charge, ion sorption, and electron transfer required for optimal chemical and electrochemical performance. Therefore, characterizing Co/Fe precipitates (chemical composition, purity, morphology, and crystallinity) is also required to further exploit their controlled properties. Thus, solids with Co/Fe cation ratios between 2/2 and 10/2 were synthesized under an air atmosphere, at pH 8 or 11.5. For the first time, multiscale physicochemical techniques (FTIR, TEM-EELS, SEM, AAS, TGA, CHN elemental analysis, and XRD complemented by Rietveld refinement) were used to provide a fully documented characterization of the structure, texture, purity, chemical composition, and thermal properties of Co/Fe LDH-based materials. The combined interpretation of data from these complementary techniques enabled the precise identification and chemical characterization of the mineralogical phases formed. Both acid–base and redox reactions govern the overall Co^II/Fe^III LDH formation process. Well-crystallized LDHs were synthesized, except for the 2/2 ratio at pH 11.5, which led to the formation of α-Co(OH)₂, γ-Fe₂O₃, and Co₃O₄ byproducts. A pH value of 8.0 provides valuable LDH materials made of quasi-hexagonal particles with diagonal lengths between 200 and 500 nm. Rietveld refining showed the presence of LDH phases in the range of 95–98%. Multiple local chemical analyses using EDX on chosen particles demonstrated pure 4/2 and 6/2 LDHs. For the 2/2 ratio, the cumulative mass fraction of two LDH-type products consistently reached 97%, distributed between Co/Fe 1.5/2 (71%) and Co/Fe 4/2 (29%). For the 10/2 ratio, only partial Co precipitation was observed, forming 95% Co/Fe LDH phases distributed between Co/Fe 10/2 (72%) and 7/2 (28%). Full article

Fast and reliable fault detection is critical in photovoltaic (PV) systems to improve reliability and energy yield and reduce maintenance costs, ensuring safe and efficient operation under varying operating conditions. Although recent data-driven PV fault detection techniques (FDTs) in literature have demonstrated high diagnostic accuracies, they often suffer from practical limitations, offline operation, lack of fault localization and/or inability to concurrently identify faults. To address these challenges, a unified framework is proposed that simultaneously integrates real-time operation, fault classification and localization, and concurrent-fault identification in a single compact diagnostic approach. This is realized by developing a parallel feedforward neural network (FFNN) architecture whose performance is enhanced by a residual model-based structure, resulting in a more interpretable, scalable, reliable and accurate scheme. In addition, Grey Wolf Optimizer–Support Vector Machine (GWO–SVM) feature selection is incorporated to select the most influential diagnostic features, thus reducing data redundancy, enhancing diagnostic accuracy, and shortening training time. The proposed approach was tested to diagnose five types of PV faults (open circuit, short circuit, partial shading, degradation, and simultaneous faults), as well as classify their intensity and location. Simulation results show that the proposed FFNNs consistently outperform conventional Support Vector Machines (SVMs) in classification accuracy, with accuracies exceeding 98% and 99% for fault classification and localization, respectively, and above 95% for noisy data. Moreover, GWO-SVM proved to offer more stable feature subsets compared to Particle Swarm Optimization–SVM (PSO–SVM) in the considered feature selection structure. Simulation results validated the effectiveness of the proposed unified multiclassification fault diagnosis approach, even under system uncertainties, making it suited for real-world PV systems. Full article

In view of the highly coupled, time-varying, and susceptible to differences in aircraft configuration of the Boeing 787 Electric Environmental Control System (E-ECS), a simplified mechanism model based on effectiveness-number of transfer units is proposed. Firstly, considering the influence of differences in aircraft configuration, part number, and optional components, a heat conduction correction coefficient is introduced to adjust the calculation process of heat exchange efficiency. Secondly, the steady-state characteristic equation of the electric compressor/turbine is established by utilizing the principle of isentropic work. Then, the outlet temperature value of the water removal component is calculated by using secondary heat recovery technology. Finally, to solve the problem of easily getting stuck in local optima during high-dimensional parameter identification, an adaptive hybrid optimization algorithm combining Dung Beetle Optimization (DBO) with mutation operator and Particle Swarm Optimization (PSO) is proposed. The experimental results show that the proposed mechanism model can achieve dynamic representation of the outlet temperature of each component of E-ECS under different aircraft stages. The DBO-PSO algorithm has a fast convergence speed and a low probability of falling into local optima. The temperature values calculated by the model have high computational accuracy, which can provide reliable data support for component level E-ECS health monitoring and early fault warning. Full article

Following the discovery of therapeutic molecules and the identification of specific biological targets, preparation of regulatory dossiers entails extensive product development and characterization to support their safety, efficacy, and stability. We have examined the drug development and relevant regulatory considerations related to inhaled biological proteins in the accompanying article. This review focuses on the characterization of locally acting inhaled biological proteins. Drug product characterization is a regulatory requirement, and it ensures drug product safety, efficacy, stability, and usability by the target populations. Together, these two articles provide a comprehensive discussion based on our review and analysis of the available open literature. We have attempted to fill gaps and simulate discussion of challenges following sound scientific pathways. This approach has the prospect of addressing regulatory expectations leading to rapid solutions to unmet medical needs. The robustness of characterization strategies and the development of analytical methods used in the in vitro testing for the evaluation of drug product attributes is assured through application of the Design-of-Experiment (DOE) and Quality-by-Design (QBD) approaches. Drug product characterization entails a variety of in vitro studies evaluating drug products for purity and contamination, and determination of drug delivery by the intended route of administration. Measurement of the proportion of the labeled amount per dose and the form suitable for delivery to the intended target sites is central to this assessment. For respiratory Drug–Device combination products, the testing may vary with the product designs. However, determination of the single-dose content, delivered-dose uniformity, aerodynamic particle size distribution, and device robustness when used by the target populations is common to all combination products. Characterization of aerosol plumes is limited to inhalation aerosols that produce specific aerosol clouds upon actuation. The flow rate dependency of devices is also examined. Product characterization also includes safety-related product attributes such as degradation products and leachables. For inhaled biological proteins, safety-related in vitro testing includes additional testing to assure maintenance of the three-dimensional structural integrity and the sustained biological activity of the drug substance in the formulation, during aerosolization and upon deposition. This article discusses various tests employed for regulatory-compliant product characterization. In addition, the stability testing and handling of possible changes during product development and post-approval are discussed. Full article

Airborne microplastics (AMPs) undergo ultraviolet (UV)-driven physicochemical aging during atmospheric transport, influencing cloud processes, greenhouse-gas release, and potential respiratory health impacts. Quantifying this transformation is particularly challenging for particles smaller than 10 µm and for polymers such as polyethylene terephthalate (PET), whose intrinsic ester carbonyl band obscures newly formed acid carbonyls in conventional infrared analyses. Here, we develop a µFTIR attenuated total reflection (µFTIR-ATR) imaging method combined with a fourth-derivative oxidation index (carbonyl ratio at 1701/1716 cm⁻¹) that resolves these overlapping bands and enables sensitive, quantitative evaluation of PET surface oxidation. The approach automates detection, identification, and oxidation analysis of particles down to ~2 µm. Laboratory UV irradiation experiments show a systematic increase in this derivative-based oxidation index with exposure dose. Application to ambient PET collected from Mt. Fuji, Tokyo, Osaka (Japan), and Siem Reap (Cambodia) reveals clear regional differences corresponding to local UV-A environments: PET from Siem Reap exhibited the highest oxidation, whereas particles from the Japanese sites showed moderate but variable aging. These results demonstrate that derivative-based µFTIR-ATR imaging provides a practical and highly sensitive tool for quantifying photo-oxidative degradation in fine AMPs and highlight the value of chemical-aging metrics for interpreting atmospheric processing and transport pathways. Full article