Background Environment Research Articles

A Typical Infrared Background Radiation Prediction Model Based on RF-VMD and Optimized Hybrid Neural Network

ABSTRACT The short-term prediction and adjustment of a target’s infrared radiation hold significant value in military camouflage applications. Existing radiation prediction models generally require real-time environmental and meteorological data support, resulting in lag in active camouflage. To meet the demand for active camouflage of background infrared (IR) radiation, a short-term background IR radiation prediction method based on historical data is proposed. First, a random forest (RF) is used to filter the collected multidimensional meteorological parameters. Variational mode decomposition (VMD) is applied for time-frequency analysis on these parameters, optimizing them with Bayesian algorithms and decomposing them into multivariate intrinsic mode functions (IMFs) with similar frequencies to reduce the impact of nonlinearity in the data. Based on the superimposed IMFs as inputs, a hybrid deep neural network prediction model is established. The model optimizes the CNN-LSTM network with residual connections and introduces a multi-head self-attention mechanism to enhance spatiotemporal feature extraction of the multidimensional meteorological parameters, focusing on key temporal feature regions. According to the experimental results, the constructed model demonstrates high prediction accuracy and adaptability across different background environments, with a low parameter count and fast prediction capability, meeting the practical application needs for various complex backgrounds.

MLANet: A Robust Ship Segmentation Network Based on Multilevel Multiattention Feature Fusion for Complex Maritime Background Environments

MLANet: A Robust Ship Segmentation Network Based on Multilevel Multiattention Feature Fusion for Complex Maritime Background Environments

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions?

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

Studying single-electron traps in newly fabricated Skipper-CCDs for the Oscura experiment using the pocket-pumping technique

Understanding and characterizing very low-energy (∼eV) background sources is a must in rare-event searches. Oscura, an experiment aiming to probe electron recoils from sub-GeV dark matter using a 10 kg skipper-CCD detector, has recently fabricated its first two batches of sensors. In this work, we present the characterization of defects/contaminants identified in the buried-channel region of these newly fabricated skipper-CCDs. These defects/contaminants produce deferred charge from trap emission in the images next to particle tracks, which can be spatially resolved due to the sub-electron resolution achieved with these sensors. Using the trap-pumping technique, we measured the energy and cross section associated with these traps in three Oscura prototype sensors from different fabrication batches which underwent different gettering methods during fabrication. Results suggest that the type of defects/contaminants is more closely linked to the fabrication batch rather than to the gettering method used. The exposure-dependent single-electron rate (SER) of one of these sensors was measured ∼100 m underground, yielding (1.8±0.3)×10−3e−/pix/day at 131 K. The impact of the identified traps on the measured exposure-dependent SER is evaluated via a Monte Carlo simulation. Results suggest that the exposure-dependent SER of Oscura prototype sensors would be lower in lower background environments as expected.

Acquisition of Cognitive Properties of Animal Cultural Words Based on Learners' Understanding and Output Performance

The mastery of the cultural connotation behind the language is one of the important criteria to measure the learners' language acquisition. The Cognitive property of words involves the cultural connotation behind the language, so it is necessary to apply the Cognitive property of words to the international Chinese education. This paper takes Chinese learners in Southeast Asian countries as the research object, and explores the Cognitive Properties of words from two aspects of learners' understanding and output. With the help of the corpus and the questionnaire survey method, it was found that Chinese learners have good understanding performance but poor output performance. The reasons are analyzed from the perspectives of mother tongue background, learning process and output environment, and put forward on teaching materials, teaching content and learning effect evaluation of Chinese teaching, in order to provide new ideas for the long-term development of international Chinese education.

Development of α-ray visualization survey meter in high gamma and neutron background environment.

A survey meter was developed to reliably detect and visualize surface contamination of suits and objects by α-nuclides in high γ/n-rays background radiation environment. The survey meter features a semi-opaque ZnS:Ag scintillator mounted directly onto a multi-anode photomultiplier tube (MA-PMT) and amplification circuits, ensuring output gain equalization for all channels. α-ray events induce localized light emission in thin-film scintillators. By directly mounting the scintillator, diffusion of light before reaching the MA-PMT is suppressed, concentrating it in just a few channels, thereby facilitating discrimination from background radiation. This design also enables clear visualization of the shape of surface contamination. The prototyped survey meter is capable of responding up to 2.1×107cpm, with no γ-ray response even in high-radiation environments exceeding 1Sv/h. In actual environments with high background radiation, contamination of ~1/100th of the surface contamination density limit of 4Bq/cm2 could be reliably detected.

Cucumber Leaf Segmentation Based on Bilayer Convolutional Network

When monitoring crop growth using top-down images of the plant canopies, leaves in agricultural fields appear very dense and significantly overlap each other. Moreover, the image can be affected by external conditions such as background environment and light intensity, impacting the effectiveness of image segmentation. To address the challenge of segmenting dense and overlapping plant leaves under natural lighting conditions, this study employed a Bilayer Convolutional Network (BCNet) method for accurate leaf segmentation across various lighting environments. The major contributions of this study are as follows: (1) Utilized Fully Convolutional Object Detection (FCOS) for plant leaf detection, incorporating ResNet-50 with the Convolutional Block Attention Module (CBAM) and Feature Pyramid Network (FPN) to enhance Region of Interest (RoI) feature extraction from canopy top-view images. (2) Extracted the sub-region of the RoI based on the position of the detection box, using this region as input for the BCNet, ensuring precise segmentation. (3) Utilized instance segmentation of canopy top-view images using BCNet, improving segmentation accuracy. (4) Applied the Varifocal Loss Function to improve the classification loss function in FCOS, leading to better performance metrics. The experimental results on cucumber canopy top-view images captured in glass greenhouse and plastic greenhouse environments show that our method is highly effective. For cucumber leaves at different growth stages and under various lighting conditions, the Precision, Recall and Average Precision (AP) metrics for object recognition are 97%, 94% and 96.57%, respectively. For instance segmentation, the Precision, Recall and Average Precision (AP) metrics are 87%, 83% and 84.71%, respectively. Our algorithm outperforms commonly used deep learning algorithms such as Faster R-CNN, Mask R-CNN, YOLOv4 and PANet, showcasing its superior capability in complex agricultural settings. The results of this study demonstrate the potential of our method for accurate recognition and segmentation of highly overlapping leaves in diverse agricultural environments, significantly contributing to the application of deep learning algorithms in smart agriculture.

Take good care of your fish: fish re-identification with synchronized multi-view camera system

IntroductionFish re-identification (re-ID) is of great significance for fish monitoring and can contribute to aquaculture and fish breeding. Synchronizing information from different cameras is beneficial for optimizing re-ID performance.MethodsWe constructed the first underwater fish re-identification benchmark dataset (FS48) under three camera conditions. FS48 encompasses 48 different fish identities, 10,300 frames, and 39,088 bounding boxes, covering various lighting conditions and background environments. Additionally, we developed the first robust and accurate fish re-identification baseline, FSNet, which fuses information from three camera positions by extracting features from synchronized video frames of each position and combining the synchronized information.ResultsThe experimental results show that FS48 is universal and of high quality. FSNet has an effective network design and demonstrates good performance, achieving better re-identification performance by combining information from three positions, helping improve overall re-test accuracy, and evaluating the effectiveness of re-identification among detectors.DiscussionOur dataset will be released upon acceptance of this paper, which is expected to further promote the development of underwater fish re-identification.

The Law of Data-driven on Employee Growth in Enterprise Human Resource Management in the Era of Digital Transformation

In the era of digital transformation, the popularity of artificial intelligence technology has driven the development of the field of Human Resource Management (HRM) in enterprises, and data-driven HRM has become the key to the development and success of enterprises. Due to the late start, data-driven HRM is still in the development stage in China. To address the problem that traditional HRM lacks accuracy and real-time, this study proposes a Growth Potential Prediction (GPP) model based on the combination of all class features and a Random Forest (RF) classifier. The results of the study showed that the prediction accuracy of interpersonal background, work ability, educational status and organisational environment characteristics under the screening index based on [Formula: see text]1 value were 85.01%, 66.34%, 66.41%, 44.19% and 64.27%, respectively. The accuracy of ROS under the RF classifier and the macro [Formula: see text]1 value were 87.31% and 87.24%. After adding the network features, the accuracy and macro [Formula: see text]1 values of the feature ensemble could reach 90%, and the accuracy and macro [Formula: see text]1 values of all class feature combinations were 89.01% and 89.10%, respectively. In summary, the GPP model based on the combination of all class features and the RF classifier proposed in the study can accurately predict the growth potential of employees and provide some guarantee for mining the growth pattern of employees.

17O Destruction Rate in Stars

In recent years, several laboratory studies of CNO cycle-related nuclear reactions have been carried out. Nevertheless, extant models of stellar nucleosynthesis still adopt CNO reaction rates reported in old compilations, such as NACRE or CF88. In order to update these rates, we performed new calculations based on a Monte Carlo R-Matrix analysis. In more detail, a method was developed that is based on the collection of all the available data, including recent low-energy measurements obtained by the LUNA collaboration in the reduced background environment of the INFN-LNGS underground laboratory, on R-Matrix cross-section calculations with the AZURE2 code and on uncertainty evaluations with a Monte Carlo analysis. As a first scientific benchmark case, the reactions 17O(p,γ)18F and 17O(p,α)14N were investigated. Among the different stellar scenarios they can influence, the 16O/17O abundance ratio in RGB and AGB stars is the one that can be directly confirmed from spectroscopic measurements. The aim is to reduce the nuclear physics uncertainties, thus providing a useful tool to constrain deep mixing processes eventually taking place in these stars. In this work, we present the procedure we followed to calculate the 17O(p,γ)18F and the 17O(p,α)14N reaction stellar rates and preliminary comparisons with similar rates reported in widely used nuclear physics libraries are discussed.

Visible color camouflage and infrared, laser band stealth, compatible with dual-band radiative heat dissipation

Infrared stealth technology garners significant attention due to its value in military applications. With the rise of multi-band detection technologies, the infrared stealth target is easily detected. Therefore, it is urgent to study multi-band camouflage devices. Here, this paper proposes a multi-band camouflage device composed of ZnS, Ge, TiO2, crystalline Ge2Sb2Te5 (cGST) and Ag multilayer films, which can realize multi-band camouflage across visible, mid-wave infrared (MWIR, ε3∼5 μm = 0.22), long-wave infrared (LWIR, ε8∼14 μm = 0.16), and laser (R1.06 μm = 0.01) spectra. In addition, effective radiative heat dissipation was realized in two non-atmospheric transparent windows (ε2.5–3 μm = 0.51 and ε5∼8 μm = 0.65). Among them, visible camouflage in different background environments can be realized independently by adjusting the thickness of the ZnS film to produce different structural colors. Notably, this emissivity spectrum remains robust even as the incident angle increases to 60°. These results can guarantee the realization of multi-band infrared camouflage, laser camouflage and radiation heat dissipation. This study not only addresses high-efficiency infrared camouflage and thermal management but also achieves outstanding visible and laser camouflage performance, offering a comprehensive guidance for advancing multi-band camouflage technology.

Vehicle Localization Method in Complex SAR Images Based on Feature Reconstruction and Aggregation.

Due to the small size of vehicle targets, complex background environments, and the discrete scattering characteristics of high-resolution synthetic aperture radar (SAR) images, existing deep learning networks face challenges in extracting high-quality vehicle features from SAR images, which impacts vehicle localization accuracy. To address this issue, this paper proposes a vehicle localization method for SAR images based on feature reconstruction and aggregation with rotating boxes. Specifically, our method first employs a backbone network that integrates the space-channel reconfiguration module (SCRM), which contains spatial and channel attention mechanisms specifically designed for SAR images to extract features. The network then connects a progressive cross-fusion mechanism (PCFM) that effectively combines multi-view features from different feature layers, enhancing the information content of feature maps and improving feature representation quality. Finally, these features containing a large receptive field region and enhanced rich contextual information are input into a rotating box vehicle detection head, which effectively reduces false alarms and missed detections. Experiments on a complex scene SAR image vehicle dataset demonstrate that the proposed method significantly improves vehicle localization accuracy. Our method achieves state-of-the-art performance, which demonstrates the superiority and effectiveness of the proposed method.

The relationship between the home literacy environment and development of orthographic skills in Chinese beginning readers

Within the framework of the home literacy model, this study investigated the relationship between home formal and informal literacy experiences and the development of orthographic skills among Chinese beginning readers. A total of 143 children and their parents participated in the study, with parents completing questionnaires on family background and home literacy environment, and children completing orthography identification and selection tasks. The results indicated that children who had just entered primary school could differentiate character-like stimuli from non-character-like stimuli and exhibited some knowledge of radicals and awareness of radical positions. Both home formal and informal literacy experiences were significantly correlated with children’s performance in various orthographic skills. However, after controlling for factors such as children’s age, gender, parents’ education, and family income, and simultaneously considering both formal and informal literacy experiences, only the informal literacy environment significantly predicted children’s performance on various orthographic skills. These findings suggest that home literacy environments, particularly informal home literacy experiences, play a crucial role in supporting the development of children’s orthographic skills.

Characterizing Wet Season Precipitation in the Central Amazon Using a Mesoscale Convective System Tracking Algorithm

AbstractTo comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features.

Variations of black carbon, particulate matter and nitrogen oxides mass concentrations in urban environment with respect to winter heating period and meteorological conditions

The atmospheric concentrations of particulate pollution are of great scientific concern due to their impact on both human health and environment. This study aimed to investigate the concentration of black carbon (BC), particulate matter with an aerodynamic diameter of less than 10 micrometres (PM10) and nitrogen oxides (NOx) at an urban background environment throughout the year, and understand the impact of winter heating and meteorology on its concentration level. The campaign covered heating and non-heating periods, from 1 June 2021 to 31 May 2022. During the heating period, the mass concentrations of BC, PM10 and NOx were 1.17, 24.9 and 19.4 µg m–3, respectively. The analysis revealed that the mass concentrations of BC and NOx were 1.9 and 1.4 times greater during the heating period, respectively, compared to the non-heating period. In contrast, PM10 remained almost constant during the heating (19.4 µg m–3) and non-heating periods (20.0 µg m–3). Throughout the year, the BC mass concentration was dominated by BCFF (71.2%) originating from fossil fuel combustion with a maximum (8.43 µg m–3) during the heating period. Moreover, wind speed presented a weak negative correlation with BC (r = –0.40), PM10 (r = –0.19) and NOx (r = –0.40) during the heating period.

Virtual Reality in Waste Management: Evaluating Its Impact on Community Classification Behavior

The purpose of this study is to explore the impact of virtual reality (VR) technology on improving community garbage sorting behavior through a questionnaire survey. Targeting residents of varying ages, educational backgrounds, and lengths of residence in the community, the study utilized VR technology for garbage classification education and behavior guidance. This comprehensive method evaluated improvements in individuals' willingness, cognition, and practices regarding garbage classification. We designed a multi-dimensional questionnaire and employed a random sampling method to select residents who met specific classification criteria as survey subjects. The research involved analyzing the influence of VR technology on enhancing garbage classification cognition across different demographics, exploring how VR can shift attitudes and willingness towards garbage classification, and investigating its role in promoting actual sorting behavior and frequency. Methodologically, we implemented an immersive VR experience that simulated various types of garbage and classification scenarios, followed by preand post-intervention surveys to measure changes in cognition and behavior. Additionally, we examined the interrelationship between VR technology and other influencing factors, such as policy advocacy, educational background, and community environment. The findings indicate that VR technology significantly enhances residents' understanding of garbage classification standards and improves classification accuracy. Furthermore, the visual representation of the negative environmental impacts of garbage, depicted through VR, profoundly heightened residents' environmental awareness and willingness to engage in garbage classification. This study confirms the practical application value and significant impact of VR technology in improving community garbage sorting behavior, providing a scientific basis for its further promotion and application in community waste management practices.

Cell Mergers, Boundary Interactions, and Convective Systems in Cases of Significant Tornadoes and Hail

Abstract Though discrete supercells are usually emphasized in severe weather forecasting, hazard production is often preceded by their interaction with external features. Past studies have examined the impacts of cell mergers, boundaries, other supercells, convective systems, etc. but usually in isolation. Here, we investigate 230 significant tornadoes, 246 significant hail events, and 191 null cases across the United States using WSR-88D data. We find that in over 90% of cases, supercells that produced significant hazards were accompanied by external features. These features varied between hazards; for example, hailstorms were more frequently near boundaries than tornadic storms. That said, the positions of these features with respect to the storm (and storm-relative inflow) distinguished between hazard potential and type. For example, tornadic storms were predominantly on the more unstable side of a boundary, while nontornadic storms and hailstorms were on the less unstable side. Similarly, tornadic storms had more cells in their rear flanks than forward flanks, while hailstorms had more cells in their forward flanks than rear flanks. Although these conditions were observed regardless of the background environment, they were affected by certain variables in the vertical profile, especially in tornadic cases. Namely, when storm-relative inflow was stronger and lifting condensation level (LCL) was lower, tornadic storms were accompanied by more rear-flank cells that were closer to the storm, more directly opposite the storm-relative inflow, for a longer period of time. We propose that these interactions likely modulate hazard potential, in ways that are not accounted for in traditional environmental parameter-based forecasting.

Phase stability of convergence zone propagation at mid-frequency.

Mid-frequency transmissions (1.5 to 7.5 kHz) from a towed source to a drifting array one convergence zone away demonstrate smoothly varying phase. Calculations of the strength and diffraction parameters Φ and Λ for a representative background environment predict partially saturated propagation. For a single convergence zone path, the evolving phase rate due to the changing internal wave field corresponds to a narrowband coherent integration time, defined by the length of a discrete Fourier transform that likely captures the signal, from ∼370 s at 10 kHz to ∼1370 s at 1 kHz in the absence of source motion. Simulated propagation with a split-step parabolic equation solver through a sound speed field with thermocline fine structure and a Garrett-Munk internal wave displacement field provides a full wave picture that is consistent with the statistical predictions. Experimental data from the Philippine Sea demonstrates signal phase that is highly correlated with source tow body vertical excursions, measured by a pressure sensor, that couple into horizontal motion. The received signal can be integrated for up to 128 s at 5.5 kHz, with the upper limit thought to be due to non-uniform source motion rather than ocean dynamics.

Multi-band compatible camouflage enabled by phase transition modulation of flexible GST films

With the rapid development of detection technology, there is an urgent need for multi-band compatible camouflage technology, particularly in the visible-infrared band. However, the conflicting requirements between camouflage across different bands and surface thermal management pose significant challenges in achieving synchronized control for multiple bands in diverse application environments and targets. Herein, we propose a spectrally selective emitter leveraging phase-change modulation of Ge2Sb2Te5, capable of achieving effective camouflage in both the visible band and two atmospheric windows over a wide temperature range. The designed emitters exhibit colorful appearances, compatible with various background environments, thereby ensuring the visible camouflage. Meanwhile, the emitters demonstrate low emissivity values of 0.1 in the MWIR band and 0.08 in the LWIR band, effectively reducing the infrared emission through atmospheric windows. The radiative temperatures of the optimized emitters in the MWIR and LWIR bands are reduced by 105°C and 140°C relative to the target surface (200°C), respectively, providing excellent radiative suppression compared to similar studies. Remarkably, the designed emitter still demonstrates robust flexibility, making it suitable for a variety of targets with complex curved surfaces. This work paves the way for designing the flexible visible-infrared compatible camouflage coatings with integrated thermal management functionality.

Édouard Louis´s novel The End of Eddy: A representation of hegemonic masculinity?

Background In this paper we analyse the novel The End of Eddy by Édouard Louis. The motivation for this paper is Bourdeau’s (2020) observation that Louis’s book explores working class politics, sexuality, and masculinity. Methods We analysed the amendment through narrative content analysis, the application of which allows us to answer the following question: Édouard Louis’s novel The End of Eddy: A representation of hegemonic masculinity? Results We conclude that this narrative is built on contradictions that can be summarized as a conflict between a socio-cultural norm anchored in a French village and a person who does not fulfil this concept, who is outside of it. We believe that hegemonic masculinity, that is, one part of the cultural norm of a given village, causes Eddy’s inclination or consciousness of homonationalism. Thus, on the one hand, hegemonic masculinity is undoubtedly present in this novel; on the other hand, it forms a kind of background or socio-cultural environment which, although it defines itself against the given, unconsciously causes the “birth of the conscious homosexual”. Conclusions Thus, we dare to claim that the narrative under analysis is not only a representation of hegemonic masculinity, but also an accentuation of its external and internal influence on one’s own perception of (sexual) difference.