Utilizing third-party open source libraries is fundamental to modern software development because it can enhance productivity and software quality. However, libraries may cease maintenance and become deprecated, negatively impacting the projects that rely on them. Promptly identifying and addressing deprecated libraries can help developers mitigate potential risks within their projects. As a programming language known for its emphasis on safety, Rust’s package manager currently does not provide a direct mechanism for deprecation. Nevertheless, Rust developers can still declare deprecation using certain methods offered by GitHub and the official Rust package registry, crates.io. However, the current usage of these deprecation mechanisms in the Rust ecosystem, as well as their effectiveness, remains underexplored. This paper addresses this gap by empirically studying the prevalence of deprecation declarations in Rust libraries, the effectiveness of different ways of declarations, and the reasons for using deprecated libraries to understand how deprecation information is disseminated and perceived in the current Rust ecosystem. We found that: 1) Among the 13,289 inactive libraries in the Rust ecosystem, only 11% of them indicate their deprecated status; 2) Among the packages that released a new version after their dependent library declared deprecation, 38.9% still chose to use the deprecated library in their new releases; 3) Despite developers being able to actively or passively discover deprecated libraries within their projects through various means, unawareness of library deprecation is a significant reason for developers using deprecated libraries. Based on these findings, we discuss practice insights to help improve the deprecation mechanism and mitigate software dependency risks.

Methane is a potent greenhouse gas, and its accurate detection is critical for addressing global climate change. Although remote sensing has been a crucial technique for understanding the spatial distribution and temporal dynamics of methane emissions, it is still urgently needed that automate the identification of methane emission plume and effectively deconvolve the signal from background noise. In this study, we propose the Methane Plume Segmentation UNet (MPSUNet) to achieve precise segmentation of methane plumes from remote sensing imagery. MPSUNet incorporates the Pyramid Squeeze Attention (PSA) module to enhance feature representation and employs a joint loss function combining Dice Loss and Focal Loss to address challenges such as class imbalance and noisy data. A novel dataset, MPDataset, was constructed using EMIT methane enhancement and RGB radiance data, providing 4172 high-quality samples for model training and evaluation. Our results show that MPSUNet achieves a mean intersection over union (MIoU) of 78.20%, mean precision of 80.78%, recall of 71.11%, and mean pixel accuracy (MPA) of 85.41% on the complete four-channel MPDataset. Compared with seven classical segmentation models, the most improvents of MPSUNet in MIoU, MPrecision, Recall and MPA reach up to 5.33%, 12.28%, 18.04% and 8.94%, respectively. Notably, the integration of RGB channels enhances the segmentation of small and intricate plume structures. Cross-dataset evaluation using Sentinel-2 data further validates the model’s robustness, achieving an MIoU of 77.65% and an MPA of 83.61%. Generally, the proposed MPSUNet model marks a substantial performance in methane detection, which provides a robust technical framework for global-scale methane emission monitoring as emphasized by global climate agreements.

Nitrogen dioxide (NO2) is a critical air pollutant with significant health and environmental implications, particularly in urban areas where high levels of emissions are prevalent. Accurate nowcasting of ground-level NO2 concentrations is essential for effective air quality management and timely public health interventions. Traditional methods often struggle with balancing the spatial accuracy of ensemble learning models and the temporal forecasting strengths of time-series models like long short-term memory (LSTM) networks. In this study, we propose an improved hybrid framework, GC-LSTM, to nowcast regional ground-level NO2 concentrations on an hourly scale based on satellite-derived NO2 vertical column densities (VCDs), meteorological data, and on-site observations. GC-LSTM integrates the spatial learning capabilities of grained cascade forest (gcForest) with the temporal prediction strengths of LSTM networks, leveraging the strengths of both spatial inference and time-series prediction. This study focuses on the Beijing-Tianjin–Hebei (BTH) region, one of China’s most polluted areas, as a case study. Our results indicate that the GC-LSTM framework performs a strong correlation between predicted and observed ground-level NO2 concentrations, with an$R^{2}$of 0.746 and a mean absolute percentage error (MAPE) of 18.4% at a 1-h prediction interval. Even as the prediction intervals extended to 2 and 3 h, the GC-LSTM consistently outperforms the gcForest model across all evaluated metrics, with$R^{2}$values higher by 0.097 and 0.117, and root mean square error (RMSE) values lower by 0.666 and$1.76~\mu \text {g/m}^{3}$than those nowcasted by using the standalone gcForest model, respectively, highlighting its robustness and adaptability. Furthermore, the capacity of the GC-LSTM framework for continual learning and adaptation ensures its effectiveness in dynamic environments, making it a valuable tool for real-time air quality forecasting and environmental management.

Accurate satellite-derived aerosol optical depth (AOD) with high temporal resolution is crucial for monitoring diurnal aerosol variations and understanding their impacts on atmospheric processes and air quality. The Advanced Geostationary Radiation Imager (AGRI) aboard the Fengyun 4A (FY-4A) satellite offers high spatiotemporal resolution, making it suitable for continuous atmospheric aerosol monitoring. In this study, an improved AOD retrieval algorithm is proposed for FY-4A/AGRI based on the generalized retrieval of atmosphere and surface properties (GRASP) framework. The algorithm incorporates multitemporal and multispectral FY-4A/AGRI observations within a 30-min window to enhance observational constraints for AOD retrieval. Reasonable prior information from Moderate Resolution Imaging Spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF) products and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol components is introduced, enabling hourly AOD retrievals with high accuracy and robustness. Compared with AOD derived from the single-temporal strategy with fixed BRDF and aerosol models, results of validation against aerosol robotic network (AERONET) AOD measurements over Beijing-Tianjin–Hebei (BTH) region indicate that our improved FY-4A/AGRI AOD retrievals increase the R from 0.543 to 0.864, and reduce root-mean-square error (RMSE) from 0.149 to 0.09, with the percentage of data falling within the expected error (EE) range rising from 46.1% to 69.9%. In Asia, such advancements led to significant improvements in AOD retrieval performance in 2021, with validation results demonstrating a strong correlation ($R =0.826$for hourly retrievals and$R =0.891$for daily means) and high accuracy (RMSE =0.118 for hourly retrievals and RMSE =0.09 for daily means) against ground-based AOD measurements from 32 AERONET sites. Comparative analyses reveal that FY-4A/AGRI AOD retrievals outperform Himawari-8/AHI products and are comparable to MODIS multiangle implementation of atmospheric correction (MAIAC) data, particularly in capturing diurnal variations and spatial distributions of aerosols. The algorithm also demonstrates robustness across diverse land cover types and vegetation densities. Our AOD retrieval strategy provides a scalable approach for geostationary satellite aerosol retrieval, with implications for regional air quality monitoring and climate studies.

Precisely measuring the concentration profile of ozone (O3) in the troposphere is a necessary prerequisite for studying its climatic and environmental effects and for effectively preventing photochemical pollution. Currently, thermal infrared satellite sensors mainly utilize the absorption characteristics of ozone near 9.6 μm to retrieve the O3profile. However, the retrieval of the O3profile is an ill-posed problem, and it is necessary to introduce a priori ozone information as a constraint to expand the solvable domain of the underdetermined problem. Therefore, the a priori ozone profile is crucial for the retrieval accuracy. Moreover, because tropospheric O₃ accounts for only about 10% of the total atmospheric ozone, the detectable signals and information content from thermal infrared observations are extremely limited, making it necessary to enhance the degrees of freedom and accuracy of tropospheric ozone retrievals. In this study, tropospheric ozone profiles are retrieved using the Cross-track Infrared Sounder (CrIS) thermal infrared hyperspectral imager aboard the Suomi-NPP satellite. By comparing ozone profiles obtained from a Long Short-Term Memory (LSTM) model, the Empirical Orthogonal Function (EOF) method, the ERA5 ozone reanalysis data, and the radiosonde data, the results show that the ozone profiles obtained from the model established by the LSTM-based a priori are closer to the true vertical distribution of tropospheric ozone. To further improve retrieval accuracy, the total ozone column amount from the Ozone Mapping and Profiler Suite (OMPS) ultraviolet payload on the same satellite platform is incorporated to redefine the cost function within the optimal estimation framework. The retrieval model is verified using the ozone radiosonde data from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC), and results are compared with retrievals that exclude the OMPS constraint. The results indicate that the retrieval results with the constraint of the total ozone column amount have a smaller relative error compared to the retrieval results without the constraint.

The Multi-angle Imaging SpectroRadiometer (MISR) measurement with a large range of scattering angles provides valuable information about aerosol microphysical properties. The current MISR algorithm utilizes pre-defined aerosol mixtures in lookup tables (LUT) to infer aerosol types and microphysical parameters, which performs well globally but remains subject to considerable uncertainties in regional scales. To make efficient use of MISR measurement, we developed a physics-informed Deep Learning (PDL) method to retrieve aerosol optical/microphysical parameters over land in eastern China. By combining the physical constraint of radiative transfer simulation and modeling ability of DL methods, each aerosol parameter can be modeled with the whole used MISR measurements separately with high computational efficiency. PDL Aerosol Optical Depth (AOD) and fine AOD(FAOD) have high correlation coefficients (R>0.95) with Aerosol Robotic Network (AERONET) observations, with 89% and 81% values falling into expected error (EE) envelope of ± (0.05+20%AODAERONET) respectively. Despite only a slightly higher accuracy than recent MISR Version 23 products, PDL retrievals have solved the underestimation problem of AOD and FAOD at moderate-high values (>0.4). Besides better constraint of abnormal values in coarse AOD(CAOD), PDL algorithm significantly improves retrieval accuracy of MISR Single Scattering Albedo (SSA). With reliable and robust performance, PDL algorithm provides a flexible and efficient aerosol retrieval framework for emerging multi-angle polarimetric measurements.

Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.

The multi-angle polarimetric (MAP) instruments have been a focus of recent satellite missions dedicated to enhanced detection of global aerosol microphysical properties. Considering that satellite observations can hardly infer all the unknowns of atmosphere and surface, it’s crucial to know how many and which aerosol parameters can be accurately retrieved from these different MAP measurements as well as their uncertainties. In this study, we present a comprehensive insight into the information content of POLDER-3 and 3MI observations for aerosol retrievals and estimate posterior errors of corresponding parameters based on Bayesian theory. The total degree of freedom for signal (DFS) of aerosol retrievals is around 6-8 from POLDER-3, and is raised by ~1.8-3.5 with 3MI. The retrieval accuracy of volume concentration and effective radius are high (<4%) in the fine-dominant case for both POLDER-3 and 3MI, but get much lower (~8% and ~15%) in coarse-dominant conditions. Furthermore, the advanced 3MI measurements can upgrade the retrieval uncertainties of POLDER-3 by ~50%. Though additional shortwave infrared bands of 3MI provide more information regarding coarse particles, the influence of aerosols on surface BRDF leads to a decrease of the total DFS. With a prior assumption that variations of refractive index depending on wavelength, satellite retrieval accuracy of the real (<0.03) and imaginary part (<0.003) reaches close levels with that of ground-based Sun photometers. Our results can provide a fundamental reference for MAP satellite retrieval of aerosol microphysical properties.

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ($\text{R}^{2}>$0.8 in polluted areas and uncertainty$\ll 5~\mu \text{g}/\text{m}^{3}$for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

Heap-based memory corruption could cause serious hazards such as system crash, denial of service, arbitrary code execution and data leakage. In most cases, these wrong and dangerous behaviors do not immediately lead program to crash. So finding such vulnerabilities in applications is critical for security. However, some existing dynamic analysis tools tend to be specialized for specific classes of heap-based memory vulnerability rather than comprehensive detection of heap-based memory corruption. Some tools do not actively traverses different execation paths and automatically generate test inputs. In this paper, we propose a new method called concolic testing for heapbased memory corruption (CTHM) to discover comprehensive vulnerabilities. We present different heuristics to select initial inputs based on types and numbers of input paramter, which effectively increase the coverage and find the targets to be analyzed as soon as possible. We propose a custom memory model of dynamic symbolic execution, which minimizes the system performance overhead and is strong consistency with the real program running environment. We provide a comprehensive analysis engine, which could detect different types of heap-based memory vulnerabilities and correctly locate their locations. We have implemented a prototype system of CTHM. Through the analysis and comparison of its experimental data, the result shows that CTHM can find nearly 70% more bugs than S2E with only increasing the overhead by 10%.

Software updates that bring new features to the users or that fix old errors can easily introduce new errors, which makes it necessary for users to repeatedly consider whether to update the software to the latest version.Therefore, the security testing for updated software is indispensable before its releasing.State-of-the-art increasing number of researchers have been devoting to develop new techniques that can automatically generate high-coverage test suites and detect software errors introduced by patches.In this paper, we proposed a technique based on concolic execution to ensure the correctness and reliability of a patch.Our method generates test inputs to cover the changed lines of the patch and the relevant function by using a target-based search strategy which combines the selector based on the mapped address and the selector based on the priority.A prototype system called FCEP was implemented and evaluated with 5 C-programs.The experimental results demonstrated that our method reaches the new code introduced by patches quickly and achieves a high coverage.

BACKGROUND: Janus-activated kinase-1 (JAK1) plays a crucial role in many aspects of cell proliferation, differentiation, apoptosis and immune regulation. However, correlations of JAK1 with prognosis and immune infiltration in NSCLC have not been documented. METHODS: We analyzed the relationship between JAK1 expression and NSCLC prognosis and immune infiltration using multiple public databases. RESULTS: JAK1 expression was significantly decreased in NSCLC compared with that in paired normal tissues. JAK1 overexpression indicated a favourable prognosis in NSCLC. In subgroup analysis, high JAK1 expression was associated with a preferable prognosis in lung adenocarcinoma (OS: HR, 0.74, 95% CI from 0.58 to 0.95, log-rank P = 0.017), not squamous cell carcinoma. In addition, data from Kaplan-Meier plotter revealed that JAK1 overexpression was associated with a preferable prognosis in male and stage N2 patients and patients without distant metastasis. Notably, increased levels of JAK1 expression were associated with an undesirable prognosis in patients with stage 1 (OS: HR, 1.46, 95% CI from 1.06 to 2.00, P = 0.02) and without lymph node metastasis (PFS: HR, 2.18, 95% CI from 1.06 to 4.46, P = 0.029), which suggests that early-stage NSCLC patients with JAK1 overexpression may have a bleak prognosis. Moreover, multiple immune infiltration cells, including NK cells, CD8 + T and CD4 + T cells, B cells, macrophages, neutrophils, and dendritic cells (DCs), in NSCLC were positively correlated with JAK1 expression. Furthermore, diverse immune markers are associated with JAK1 expression. CONCLUSIONS: JAK1 overexpression exhibited superior prognosis and immune infiltration in NSCLC.

In systems biology, filtering the discriminative features from complex high-dimensional data is a crucial issue. This paper proposes a feature selection algorithm based on feature overlapping and group overlapping (FS-FOGO) to calculate the feature importance. FS-FOGO weighs feature from two aspects: overlapping degree based on the ratio of overlapping area on the effective range of each class and the overlapping degree based on the proportion of heterogeneous samples in every sample's nearest neighbors. To show the validation of FS-FOGO, it is compared with effective range based gene selection (ERGS), which calculates the feature weights based on overlapping area of the effective range, on six public biological data sets and one serum metabolomics data set about liver disease. Naive Bayes and Support Vector Machine are used as classifiers, respectively. The experiment results show that the top ranked features by FS-FOGO are more discriminative and get higher classification accuracy rates than those by ERGS in most cases. And in the metabolomics data, the top ranked metabolites by FS-FOGO could separate different liver diseases well.

Global climate change is one of the most challenging issues facing the world today. Atmospheric aerosols and carbon dioxide (CO2), as two key factors driving the global climate change, have earned enormous attention from scientist around the world [1]. One challenge for the satellite measurements of CO2using this SWIR wavelength range (∼1.6µm) is the impact of multiple scattering by aerosols and cirrus [2]. Since the rapid economic growth and associated increase in fossil fuel consumption have caused serious particulate pollution in many regions of China [3], remote sensing of CO2using SWIR band in China needs to pay more attention to the scattering properties of aerosol particles and the multiple scattering. Considering the complexity of morphological and chemical properties, aerosol particles are grouped based on a large number of TEM/SEM images, and then their scattering properties at 1.6µm band are calculated by the T-matrix method [4] and GMM method [5]. In this study, the Monte Carlo method is used to solve the multiple scattering problem by simulating photons transport in the scattering media. We combined this multiple scattering model with the LBLRTM [6] as a forward radiative transfer model for studying the impact of aerosol scattering on the satellite observations of CO2using SWIR band. Finally, based on the GOCART aerosol component products, AERONET aerosol size distribution products, CALIPSO aerosol profile products, and MODIS aerosol optical depth and surface albedo products, the monthly variability of errors in CO2concentrations over China were calculated and analyzed. The results indicate that AOD and surface albedo are two of most important factors for the satellite observations of CO2. For low surface albedo, the retrieved CO2columns are undervalued when aerosol scattering is neglected. While for moderate and high surface albedos, the retrieved CO2columns are overvalued. As shown in Fighre 1, CO2concentrations are overestimated in western regions of China, especially in desert areas (a maximum of ∼7.08% in September), and those are underestimated in eastern regions (a minimum of ∼−6.9% in June).

An improved Optimal Estimation (OE) method is presented for methane (CH4) column density retrieval from satellite observations in short-wave infrared band (SWIR), to avoid non-convergence of iteration process for CH4retrieval caused by the singularity or non-positivity of the Hessian matrix. We add a constraining factor γ and a step factor α to the OE iteration algorithm. Then, total column averaged CH4dry air mole fraction, XCH4is retrieved using GOSAT Level 1b data. Retrievals are validated by comparisons with ground-based FTIR measurements from TCCON stations. Comparison shows good agreement and the correlation coefficient is more than 0.55. Preliminary validations approve the utility of proposed retrieval algorithm.

In this paper, we explored the changes of air quality over Jing-Jin-Tang (Beijing-Tianjin-Tangshan) district during the period from 2002 to 2009. Based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, Dense Dark Vegetation (DDV) algorithm is employed to retrieve the aerosol optical thickness (AOT) with 1-km resolution. Comparison of the satellite inferred AOT and the values from ground-based Aerosol Robotic Network (AERONET) sun/sky radiometer measurements indicates a good agreement (R2=0.786) in Beijing site. We compared the spatial, monthly and annual variation over Jing-Jin-Tang district and analyzed the main factors of these changes. Our study indicates that there is a decreasing trend in the annual variation of AOT since 2004. The averages of AOT were commonly higher in spring and summer than those in autumn and winter, and the retrieved AOT over cities and southern areas is obviously larger than that over rural and northern areas respectively.