Scale Features Research Articles

Enhancing work commitment through workplace design: development and prioritization of evaluation criteria in South Korea

ABSTRACT Workplace design elements influence work commitment; however, previous studies broadly categorized these elements, lacking specific practical design information. This study aims to classify workplace design elements that influence work commitment into structural and decorative elements, then further categorize them into top-level factors and sub-items. Using two surveys with employees, this study derived evaluation criteria, resulting in 6 factors and 19 sub-items for structural elements, and 6 factors and 27 sub-items for decorative elements. Subsequently, an additional survey was conducted with employees to prioritize the derived workplace design elements for work commitment. The results indicated that workplaces designed for work commitment should be as follows: first, structural elements should be prioritized over decorative elements. Second, structural elements should be considered in the order of outdoor space perception, scale, functionality, circulation impact, visual impact and aesthetic characteristics. Third, decorative elements should be considered in the order of furniture, colour, material, interior landscaping, personal lighting devices and graphics. This study establishes criteria for workplace design to enhance work commitment and provides valuable insights for improving work commitment through design.

A multi-scale attributes fusion model for travel mode identification using GPS trajectories

ABSTRACT Travel mode recognition is a key issue in urban planning and transportation research. While traditional travel surveys use manual data collection and have limited coverage, poor timeliness, and insufficient sample capacity, recent advancements in Global Positioning System (GPS) technology allow large-scale data collection and offer novel opportunities to enhance travel mode recognition. However, existing studies often neglect regular differences and changes in motion states across different travel modes and fail to fully integrate multi-scale spatio-temporal features, which limits the accurate classification of travel modes. To fill this gap, this study proposes a multi-scale spatio-temporal attribute fusion (MSAF) model for precise travel mode identification using solely GPS trajectories without altering their sampling rate. The MSAF model segments GPS trajectories into various temporal and spatial scales, extracting local motion states and spatial features at multiple scales. The spatio-temporal feature extraction module is constructed to extract local motion states and capture spatio-temporal dependencies. Additionally, the model incorporates a multi-scale feature fusion module, which effectively combines features of various scales through a series of fusion techniques to obtain a comprehensive representation, enabling automatic and accurate travel mode identification. Experiments on real-world datasets, including the GeoLife Trajectories dataset and the Sussex-Huawei Locomotion-Transportation (SHL) dataset, demonstrate the effectiveness of the MSAF model, achieving a competitive accuracy of 95.16% and 91.70%. This represents an improvement of 2.50% to 7.95% and 0.8% to 6.62% over several state-of-the-art baselines, effectively addressing sample imbalance challenges. Moreover, the experiments demonstrate the significant role of multiscale feature fusion in improving model performance.

Modeling and Simulation of UAV Task Assignment and Execution in Designated Area Based on Order Strike Theory

Abstract Since 2023, local conflicts have intensified and local wars have occurred frequently. The term “order strike” has appeared on the battlefield many times and is one of the hot spots in the study of modern warfare. Order strike has the characteristics of small scale, fast response, low cost, etc. It is a distributed combat mode proposed by the US military on the basis of theories of “Kill Chain” and “Mosaic Warfare”, etc. This paper takes the UAV penetration and strike against large targets as research case, and builds a new type of UAV penetration and strike combat platform based on the theory of “order strike”, combined with the technologies of “ StarLink “ satellite network reconnaissance and intelligent UAV control. This combat platform can continuously monitor the designated area and effectively strike the hostile targets invading the area. Finally, we conducted a preliminary simulation and initial effectiveness evaluation of the method proposed in this paper through multi-agent simulation.

Advancing Stigma Measurement: Comprehensive Validation of the Workplace Stigma Characteristics Scale

Advancing Stigma Measurement: Comprehensive Validation of the Workplace Stigma Characteristics Scale

Bridging length scales in hard materials with ultra-small angle X-ray scattering - a critical review.

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

Improvement of YOLOv5s-Ghost model

Abstract Based on the YOLOv5s-Ghost model, the changes in the number of channels and feature scales of the YOLOv5s-Ghost model, as well as the structure and function of GhostConv convolution, were studied. It was found that the YOLOv5s-Ghost model has insufficient information extraction and a YOLOv5s-EGhost model with fewer model parameters and higher detection accuracy was designed. GhostConv convolution has the functions of compressing channels and downsampling. The YOLOv5s-Ghost model has a lower number of channels at low channels, and each channel doubling of the model is accompanied by a downsampling operation. Applying GhostConv convolution to the low channel of the YOLOv5s-Ghost model can cause insufficient information extraction. Therefore, ECRS and EC3Ghost2 modules were designed to be placed in the low channel of the model, and a regular 3×3 convolution was used to replace GhostConv convolution at the third downsampling point, improving the situation of insufficient information extraction in the low channel of the YOLOv5s-Ghost model. Gc3-Ghost and GSPPF modules were designed in the high channels of the model, which can reduce the number of model parameters while maintaining model accuracy. On the self-made defect dataset, the improved YOLOv5s-EGhost model reduces the number of parameters by 58.6% and 21.1% respectively compared to YOLOv5s and YOLOv5s Ghost, improves the R index by 2.0% and 1.8 percentage points respectively, and improves Map_0.5 by 0.7 percentage points and 1.8 percentage points respectively, which can meet the requirements of real-time detection.

Side-channel attacks based on attention mechanism and multi-scale convolutional neural network

One widely utilized attack technique for side-channel attacks is deep learning. However, the efficiency of deep learning attacks can be impacted by the differences in data collected from various devices or environmental conditions. These differences arise from factors such as hardware discrepancies, environmental noise, and countermeasures. Such differences can disrupt the correlation between the leaked information from the target device during operations and sensitive data, leading to failures in the attacks. In order to better extract the feature information and temporal information of traces in this challenge, we propose an Attention Mechanism and Multi-Scale Convolutional Neural Network (AMCNNet). The critical components of the AMCNNet are the multi-scale convolution module and the feature extraction module. The multi-scale convolution module extracts feature of different scales from traces, improving the network's ability to capture high-level meanings and details. Then, the feature extraction module dynamically weights and rescales the features to better captures the long-term dependencies in the traces.. Through experiments conducted on publicly available datasets such as DPA Contest v4, ASCAD, and AES_HD, as well as analyzing the guessing entropy, the experimental results demonstrate that the network exhibits strong generalization capability and robustness across datasets with diverse features.

Foreign Object Detection Network for Transmission Lines from Unmanned Aerial Vehicle Images

Foreign objects such as balloons and nests often lead to widespread power outages by coming into contact with transmission lines. The manual detection of these is labor-intensive work. Automatic foreign object detection on transmission lines is a crucial task for power safety and is becoming the mainstream method, but the lack of datasets is a restriction. In this paper, we propose an advanced model termed YOLOv8 Network with Bidirectional Feature Pyramid Network (YOLOv8_BiFPN) to detect foreign objects on power transmission lines. Firstly, we add a weighted cross-scale connection structure to the detection head of the YOLOv8 network. The structure is bidirectional. It provides interaction between low-level and high-level features, and allows information to spread across feature maps of different scales. Secondly, in comparison to the traditional concatenation and shortcut operations, our method integrates information between different scale features through weighted settings. Moreover, we created a dataset of Foreign Object detection on Transmission Lines from a Drone-view (FOTL_Drone). It consists of 1495 annotated images with six types of foreign object. To our knowledge, FOTL_Drone stands out as the most comprehensive dataset in the field of foreign object detection on transmission lines, which encompasses a wide array of geographic features and diverse types of foreign object. Experimental results showcase that YOLOv8_BiFPN achieves an average precision of 90.2% and an mAP@.50 of 0.896 across various categories of foreign objects, surpassing other models.

ComBatLS: A location- and scale-preserving method for multi-site image harmonization.

Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals' morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features' variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features' locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals' normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic "sites". Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.

Intersection-union dual-stream cross-attention Lova-SwinUnet for skin cancer hair segmentation and image repair

Skin cancer images have hair occlusion problems, which greatly affects the accuracy of diagnosis and classification. Current dermoscopic hair removal methods use segmentation networks to locate hairs, and then uses repair networks to perform image repair. However, it is difficult to segment hair and capture the overall structure between hairs because of the hair being thin, unclear, and similar in color to the entire image. When conducting image restoration tasks, the only available images are those obstructed by hair, and there is no corresponding ground truth (supervised data) of the same scene without hair obstruction. In addition, the texture information and structural information used in existing repair methods are often insufficient, which leads to poor results in skin cancer image repair. To address these challenges, we propose the intersection-union dual-stream cross-attention Lova-SwinUnet (IUDC-LS). Firstly, we propose the Lova-SwinUnet module, which embeds Lovasz loss function into Swin-Unet, enabling the network to better capture features of various scales, thus obtaining better hair mask segmentation results. Secondly, we design the intersection-union (IU) module, which takes the mask results obtained in the previous step for pairwise intersection or union, and then overlays the results on the skin cancer image without hair to generate the labeled training data. This turns the unsupervised image repair task into the supervised one. Finally, we propose the dual-stream cross-attention (DC) module, which makes texture information and structure information interact with each other, and then uses cross-attention to make the network pay attention to the more important texture information and structure information in the fusion process of texture information and structure information, so as to improve the effect of image repair. The experimental results show that the PSNR index and SSIM index of the proposed method are increased by 5.4875 and 0.0401 compared with the other common methods. Experimental results unequivocally demonstrate the effectiveness of our approach, which serves as a potent tool for skin cancer detection, significantly surpassing the performance of comparable methods.

YOLO-FMDI: A Lightweight YOLOv8 Focusing on a Multi-Scale Feature Diffusion Interaction Neck for Tomato Pest and Disease Detection

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications.

HP-YOLOv8: High-Precision Small Object Detection Algorithm for Remote Sensing Images.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small objects in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-Level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small objects. Finally, we propose the Shape Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes, and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conducted comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the mAP@0.5 metric, and 72.03%, 65.37%, and 38.91% in the more stringent mAP@0.5:0.95 metric, respectively.

Road extraction from remote sensing images based on a multi-scale asymmetric dual attention mechanism

ABSTRACT Aiming at the problems of road fracture and detail loss caused by not considering the geometric features in the road extraction method. We proposed an encoder-decoder architecture based on a multi-scale asymmetric dual attention mechanism. Firstly, A multi-scale convolution block in the shape of ‘Union Jack’ is designed. It includes symmetric convolution and asymmetric convolution along horizontal, vertical, left diagonal, and right diagonal spatial directions, and a multi-scale dilated convolution for extracting features of different scales. Remote dependence relationships are highly converged by using it, and road fracture problems caused by occlusion can be solved effectively. Secondly, a directional dual attention mechanism is proposed, which consists of directional channel attention using strip pooling and a directional spatial attention mechanism using asymmetric convolution along left diagonal and right diagonal spatial directions. It can use the directivity of asymmetric convolution to allocate attention mechanism adaptively in attention mechanism, and effectively avoid the road detail loss problem. Finally, we conducted corresponding experiments on the DeepGlobe and Ottawa road datasets, and the experimental results are superior to the current state-of-the-art methods.

Sexual Developmental Characteristics of Intellectually Disabled Adolescents in Türkiye: A Cross-Sectional Analysis

Purpose: This study aimed to examine the sexual development characteristics of adolescents with intellectual disability in terms of several variables. The data were collected with the Personal Information Form and the Sexual Development Characteristics Scale of Children with Adolescent Mental Disabilities. Method: Independent samples tests, one-way ANOVA tests, and Bonferroni post hoc analyses were conducted to compare the descriptive characteristics of the adolescents and parents with their scale scores. Multiple linear regression analysis was employed to examine the relationships between the dependent and independent variables. Significant differences were identified between certain subdimensions of the scale and the variables. Findings: The sexual arousal subdimension of the scale accounts for 12% of the variance in the dependent variable, the knowledge about physical development subdimension accounts for 10% of the variance in the dependent variable, the sexual satisfaction subdimension accounts for 6% of the variance in the dependent variable, and the sexual self-care subdimension accounts for 11% of the variance in the dependent variable. This difference was explained by the independent variables (R2 adjusted= .12, .10, .06, .11, respectively). Conclusion: In this study, it was found that the sexual development characteristics of adolescents with intellectual disability were affected by the educational status of the mother, the place of residence of the family, the help of the spouse in childcare, the gender of the child, the verbal communication status of the child, the degree of inadequacy of the child, and the ability to meet the personal needs of the child.

Contextual Enhancement–Interaction and Multi-Scale Weighted Fusion Network for Aerial Tracking

Siamese-based trackers have been widely utilized in UAV visual tracking due to their outstanding performance. However, UAV visual tracking encounters numerous challenges, such as similar targets, scale variations, and background clutter. Existing Siamese trackers face two significant issues: firstly, they rely on single-branch features, limiting their ability to achieve long-term and accurate aerial tracking. Secondly, current tracking algorithms treat multi-level similarity responses equally, making it difficult to ensure tracking accuracy in complex airborne environments. To tackle these challenges, we propose a novel UAV tracking Siamese network named the contextual enhancement–interaction and multi-scale weighted fusion network, which is designed to improve aerial tracking performance. Firstly, we designed a contextual enhancement–interaction module to improve feature representation. This module effectively facilitates the interaction between the template and search branches and strengthens the features of each branch in parallel. Specifically, a cross-attention mechanism within the module integrates the branch information effectively. The parallel Transformer-based enhancement structure improves the feature saliency significantly. Additionally, we designed an efficient multi-scale weighted fusion module that adaptively weights the correlation response maps across different feature scales. This module fully utilizes the global similarity response between the template and the search area, enhancing feature distinctiveness and improving tracking results. We conducted experiments using several state-of-the-art trackers on aerial tracking benchmarks, including DTB70, UAV123, UAV20L, and UAV123@10fps, to validate the efficacy of the proposed network. The experimental results demonstrate that our tracker performs effectively in complex aerial tracking scenarios and competes well with state-of-the-art trackers.

Structural associations between self-perception of support and knowledge of disability characteristics of autism spectrum disorder among staff in facilities providing after-school day services.

We constructed a hypothetical model of the knowledge of autism spectrum disorder (ASD) and self-perception of support of staff working in after-school day services to clarify structural relationships. A questionnaire survey was conducted at 194 facilities providing after-school day services in Fukushima Prefecture (October 2020), including a basic attributes questionnaire, the Literacy Scale of Characteristics of Autistic Spectrum Disorder (LS-ASD), and a staff questionnaire. We developed a hypothetical model of the relationship between self-perception and LS-ASD total scores of after-school service staff. To obtain latent variables for structural equation modeling (SEM) to confirm factor extraction and the interrelationships among variables, exploratory factor analysis was performed. SEM was used to examine the fit of the hypothetical model to the data and the relationships among variables. The study included 302 staff members from 58 of 194 facilities. Four factors (Factor 1, motivation; 2, self-perception of knowledge; 3, information sharing; 4, self-confidence) were extracted. The final model showed that Factor 2 had a positive direct effect (path coefficient = 0.64) and Factor 4 had a negative direct effect (path coefficient = -0.22) on LS-ASD scores. The model goodness of fit was acceptable (Goodness-of-Fit Index = 0.890; Comparative Fit Index = 0.912; Root Mean Square Error of Approximation = 0.086; Akaike's Information Criterion = 392.7). Self-perception of knowledge contributes greatly to knowledge acquisition, while excessive confidence may hinder knowledge retention.

Prediction of hypertension risk based on multiple feature fusion

ObjectiveIn the application of machine learning to the prediction of hypertension, many factors have seriously affected the classification accuracy and generalization performance. We propose a pulse wave classification model based on multi-feature fusion for accuracy prediction of hypertension. Methods and MaterialsWe propose an ensemble under-sampling model with dynamic weights to decrease the influence of class imbalance on classification, further to automatically classify of hypertension on inquiry diagnosis. We also build a deep learning model based on hybrid attention mechanism, which transforms pulse waves to feature maps for extraction of in-depth features, so as to automatically classify hypertension on pulse diagnosis. We build the multi-feature fusion model based on dynamic Dempster/Shafer (DS) theory combining inquiry diagnosis and pulse diagnosis to enhance fault tolerance of prediction for multiple classifiers. In addition, this study calculates feature importance ranking of scale features on inquiry diagnosis and temporal and frequency-domain features on pulse diagnosis. ResultsThe accuracy, sensitivity, specificity, F1-score and G-mean after 5-fold cross-validation were 94.08%, 93.43%, 96.86%, 93.45% and 95.12%, respectively, based on the hypertensive samples of 409 cases from Longhua Hospital affiliated to Shanghai University of Traditional Chinese Medicine and Hospital of Integrated Traditional Chinese and Western Medicine. We find the key factors influencing hypertensive classification accuracy, so as to assist in the prevention and clinical diagnosis of hypertension. ConclusionCompared with the state-of-the-art models, the multi-feature fusion model effectively utilizes the patients’ correlated multimodal features, and has higher classification accuracy and generalization performance.

Scale Characteristics of Six Fish Species of the Genus Cyprinion (Teleostei: Cypriniformes): A Microscopic Analysis.

This study investigated the morphological characteristics of scales in six Cyprinion species, using light and scanning electron microscopy focusing on key features such as scale type, key scales, lateral line scales, radius/radii, rostral margin, focus, circuli, lepidonts, tubercles, and scale indices. The research analyzed the scales using ultramicroscopy and light microscopy imaging, categorizing them based on size classes and body regions. The morphological variations in scale characteristics were examined across different species, regions, and size classes. Notable findings included the tetra-sectioned form of scales, representing a unique characteristic of the Cyprinion genus. Morphological changes in scale features were observed with fish growth, particularly in the overall shape, focus shape, and size. Quantitative analysis revealed variations in average relative scale length and width among different species, regions, and size classes. The study utilized canonical discriminant analysis for multivariate assessment, classifying the species into distinct groups based on morphometric indices. The findings contribute to the understanding of scale morphology in Cyprinion species and exploring morphological variation between the examined species.

Fabric defect detection based on feature enhancement and complementary neighboring information

Abstract Fabric defect detection is a crucial aspect of quality control in the textile industry. Given the complexities of fabric backgrounds, the high similarity between patterned backgrounds and defects, and the variety of defect scales, we propose a fabric defect detection method based on feature enhancement and complementary neighboring information . The core of this method lies in two main components: the feature enhancement module and the neighboring information complementation strategy. The feature enhancement module includes two sub-modules: similarity feature enhancement(SFE) and edge detail feature enhancement(EDFE). The SFE aims to capture the similarities between features to strengthen the distinction between defects and complex backgrounds, thereby highlighting the correlations among defects and the differences between defects and the background. The EDFE focuses on improving the network's ability to capture the edge details of fabrics, preventing edge information from becoming blurred or lost due to deeper network layers. The neighboring information complementation strategy consists of shallow-level information complementation(SLIC) and top-down information fusion complementation(TDIFC). The SLIC integrates newly introduced shallow features with neighboring features that have a smaller semantic gap, injecting richer detail information into the network. The TDIFC adaptively guides the interaction of information between adjacent feature maps, effectively aggregating multi-scale features to ensure information complementarity between features of different scales. Additionally, to further optimize model performance, we introduced partial convolution(PConv) in the backbone of the feature extraction network. PConv reduces redundant computations and decreases the model's parameter count. Experimental results show that our proposed method achieved an mAP@50 of 82.4%, which is a 6.6% improvement over the baseline model YOLOv8s. The average inference frame rate reached 61.8 FPS, meeting the real-time detection requirements for fabric defects. Moreover, the model demonstrated good generalization capabilities, effectively adapting to detecting defects in different types and colors of fabrics.

Effect of NO2 on SO2–SO42- conversion in atmosphere: Evidence from long-term precipitation

Sulfate aerosol (SO42−) is an important factor leading to acid rain and aggravating atmospheric particulate pollution. SO42− is mainly converted from gaseous SO2, in which the oxidant gas NO2 may play a key role. In real atmosphere with complexity and nonlinearity, the impact of NO2 on SO2–SO42- conversion exhibits scale dependence due to the combined effect of meteorology, atmospheric chemistry, and pollution emission. Here, based on the long-term atmospheric precipitation data from 2014 to 2021 of Nanchong City in the Sichuan Basin, the Extended Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) methods have been employed to analyze the nonlinear correlations between gaseous SO2 and SO42− in rainwater at different time scales. A multifractal parameter Δh has been described as the scaling features of SO2–SO42- conversion efficiency in the real atmosphere. Then, the impact of NO2 on SO2–SO42- conversion in cold and warm seasons has been quantified, using sliding window analysis and Pearson correlation analysis. The results show that NO2 significantly promoted SO2–SO42- conversion in the cold season, but had little effect on it in the warm season. As direct evidence from atmospheric precipitation analysis, this finding has provided a new theoretical basis for evaluating the impact of NO2 on secondary aerosol generation.