The suppression of statistical fluctuations is crucial for the accurate qualitative and quantitative analysis of gamma-ray energy spectra. The nonlinear nature of these spectra in complex measurement environments presents a significant challenge to traditional smoothing methods, which are often constrained by their reliance on pre-defined models. To address this, support vector regression (SVR), an efficient supervised learning algorithm, is well-suited for managing nonlinear datasets. This paper proposes a generalized support vector regression (GSVR) model for gamma-ray spectrum smoothing, based on the principle of structural risk minimization. The performance of the proposed model was verified through a comparative study with traditional methods: multipoint moving average smoothing (MMAS), wavelet threshold denoising (WTDM), and noise-adjusted singular value decomposition (NASVD). Model performance was evaluated using a suite of metrics, including Smoothing Goodness (SG), Root Mean Square Error (RMSE), Energy Spectrum Distortion (ESD), and Signal-to-Noise Ratio (SNR). The comparison reveals that the proposed GSVR model demonstrates significant improvements. It achieves superior smoothing performance and better preservation of spectral peak shapes compared to all traditional methods evaluated. These results confirm the efficacy of the proposed model, offering an effective solution for smoothing gamma-ray energy spectra.

210Po is a natural radioactive nuclide, belonging to the uranium series decay product, with high toxicity alpha radiation characteristics, widely present in environmental media such as soil. It poses a potential internal radiation risk to human health, therefore accurate monitoring of 210Po in soil is of great significance for public health and radiation environment regulation. At present, there is a lack of standardized analysis methods for 210Po in soil media both domestically and internationally, resulting in inconsistent quality of monitoring data. Based on extensive literature research and multi institutional experimental verification, this study proposes a soil 210Po analysis method that combines wet acid digestion and spontaneous deposition followed by alpha spectroscopy. The HNO3 + HCl + HF (3:2:1) mixed acid system is used to optimize key parameters such as digestion temperature (160 °C), self deposition acidity (0.2–0.5 mol/L HCl), and ascorbic acid dosage (0.5 g). The method has been validated by 8 radiation monitoring institutions in China, showing a method detection limit (MDL) of 1.0 × 10−3 Bq/g and a limit of quantification (LOQ) of 4.0 × 10−3 Bq/g, a precision (RSD) of 2.5–14

Atmospheric pollution by potentially toxic elements (PTEs) remains a critical environmental and public health issue. In this study, total suspended particle (TSP) samples were collected at 13 monitoring sites across Chongqing from January to October 2021 and analyzed using X-ray fluorescence spectroscopy. The mean annual TSP concentration was 97.2 µg·m⁻3, with clear seasonal variability. The concentrations of PTEs followed the order Zn > Ti > Mn > Cu > Sr > Pb > Cr > Ni > As > V. Source apportionment indicated that soil and road dust, industrial emissions, coal combustion, and traffic were the dominant contributors. Ecological risk analysis highlighted As, Cu, and Pb as the main elements of concern, with the overall risk level classified as moderate. The health risk assessment indicates that inhalation-related carcinogenic and non-carcinogenic risks are generally within acceptable bounds; dermal contact presents a carcinogenic risk exceeding the commonly accepted threshold while remaining on the order of 10⁻4; and ingestion yields a non-carcinogenic hazard for children above unity, dominated by arsenic. Overall, children exhibit higher multi-pathway risks than adults, warranting targeted, pathway-specific control of As, Cr, and Ni along their dominant exposure routes.

CO2 may erode cement structure of geological disposal repository in the long-term disposal process. This study investigates the adsorption behavior and mechanism of carbonated cement for Co2+ via characterization and adsorption experiments. Characterization experiments show that the carbonation does not change the adsorption mechanism of cement for Co2+. Adsorption experiments show that carbonation reduces the adsorption capacity of cement. In general, the adsorption of Co2+ by both pristine and carbonated cement involves multiple mechanisms including ion exchange and chemical adsorption. This study provides critical data for safety assessments of the geological disposal repository.

Purpose The purpose of this study is to determine the best dose processing method for deep learning-based dose prediction in brachytherapy (BT), as well as to investigate the feasibility of using the inverse dose optimization algorithm to improve treatment planning quality. Methods and materials BT data from 186 patients with cervical cancer were retrospectively collected. The data were divided into three sets: training, validation, and test, with a ratio of 150:18:18. The dose data was normalized using square-root transformation normalization, logarithmic normalization, and linear normalization. For dose distribution prediction, a 3D U-Net architecture was used. The predicted results were compared to unprocessed dose data. The four groups of dose predictions were assessed using the Dice similarity coefficient (DSC), conformity index (CI), and homogeneity index (HI). The group with the best overall performance was chosen, and the dose prediction results were fed into a gradient-based planning optimization (GBPO) algorithm for additional optimization. The target D90 % was normalized to 6 Gy. The D1cc and D2cc of the OARs were compared prior to and following optimization. Results The dose prediction method using unprocessed doses produced the best overall performance on the DSC, CI, and HI metrics. The (DSC, CI, HI) values for unprocessed dose, square-root transformation normalized, log normalized, and linear normalized were (0.94, 0.74, 0.49), (0.93, 0.72, 0.50), (0.91, 0.71, 0.45) and (0.90, 0.71, 0.47), respectively. The predicted dose results for the unprocessed dose group were further optimized by the GBPO algorithm. The outcomes demonstrated that the (D1cc, D2cc) values for the bladder, rectum, and sigmoid decreased by (2.11 %, 2.09 %), (2.62 %, 2.14 %) and (3.16 %, 2.98 %), respectively, and were statistically significant (p < 0.05). The small intestine dose increased slightly; the average increase in the D1cc and D2cc doses was 2.08 % and 1.63 %, respectively, with no statistically significant difference (P > 0.05). Conclusion When using deep learning for BT dose prediction in the 3D U-Net model with the cervical cancer BT data used in this study, dose normalization processing is not recommended The predicted dose can be further optimized using inverse dose optimization algorithms to improve the treatment plan's quality.

Naturally occurring radioactive materials (NORM) are present in waste generated during shale gas drilling activities and pose potential risks to the environment, drawing increasing public and scientific attention. In this study, soil, wastewater and effluent samples were collected across multiple operational stages of shale gas development in Southwest China. A combination of in-situ gamma absorbed dose rate in air, soil radon concentration, radionuclide activity concentrations, and conventional hazard indices was used to evaluate environmental radioactivity and potential occupational exposure. The results showed that both the gamma absorbed dose rates and soil radioactivity were comparable to those observed in other oil and gas fields, that were strongly correlated with uranium-series nuclides. A strong linear relationship between 238U and 226Ra, indicating the two radionuclides were in near radioactive equilibrium within the soil. The 210Pb/226Ra ratio was consistently greater than 1 and increased with platform operation time, suggesting an accumulation of radon progeny. Discrepancies between measured and calculated dose rates highlighted the need to prioritize direct measurements in dose assessments. Worker dose assessments revealed annual effective doses below 1 mSvy-1. Furthermore, elevated concentrations of 226Ra, 228Ra, and 40K were detected in untreated-water, which could be effectively reduced by existing treatment technologies. These findings provide a baseline for radiological risk evaluation in shale gas fields and highlight the necessity for continuous monitoring and wastewater management.

Among environment contaminants, 210Pb and 210Po have gained significant research attention due to their radioactive toxicity. Moss, with its exceptional adsorption capability for these radionuclides, serves as an indicator for environmental 210Pb and 210Po pollution. The paper reviews a total of 138 articles, summarizing the common methods and analytical results of 210Pb and 210Po research in moss. It elucidates the accumulation characteristics of 210Pb and 210Po in moss, discusses current research challenges, potential solutions, and future prospects in this field. Existing literature indicates limitations in common measurement techniques for 210Pb and 210Po in moss, characterized by high detection limits or lengthy sample processing. The concentration of 210Pb and 210Po within moss display substantial variations across different regions worldwide, ranging from <MDA to 518.75 kBq/kg and 38 Bq/kg to 170.68 kBq/kg, respectively, correlating significantly with regional pollution circumstances. Analysis of extra 210Pb (210Pb introduced by 222Rn), concentration factor and the 210Po/210Pb ratio indicates that atmospheric deposition constitutes the primary source of 210Pb and 210Po within moss, and the 210Po/210Pb ratio is influenced by the extent of local anthropogenic impact. Factors influencing their distribution include the inherent biological traits of moss, atmospheric factors, soil conditions, and human activities. Different measurement methods are recommended for different measurement requirements, and discuss the selection of the study area. Presently, research concerning 210Pb and 210Po in moss as a good bioindicator predominantly focuses on environmental monitoring within polluted regions and extends to studies encompassing heavy metal tracing and site remediation.

Brachytherapy (BT) is an effective form of cancer treatment. In recent years, significant progress has been made in applying artificial intelligence (AI) in brachytherapy, especially in the treatment of cervical cancer and prostate cancer. This paper summarizes the latest developments and applications of AI in brachytherapy, focusing on its role in improving treatment accuracy and efficacy. Integrating AI in medical image enhancement, organ segmentation, and dose calculation has significantly improved efficiency and accuracy, providing new avenues for treatment planning and quality assurance. In addition, the potential of AI in patient follow-up prediction promises better assessment of treatment outcomes and prognosis. This paper also discusses the challenges and future directions in using AI to improve the accuracy and effectiveness of brachytherapy, highlighting the significant potential of AI in making brachytherapy more accurate, adaptable, and effective.

A new method for estimating fast neutron energy, based on in situ X-ray fluorescence analysis technology, has been proposed. According to the simulation results, the fluorescence mainly originates from the interactions of intermediate particles protons, electrons, and X/gamma-rays with target atoms. The contribution of fluorescence excited by each type of particle to the overall fluorescence intensity presents distinct characteristics as neutron energy varies. Finally, by analyzing the fluorescence intensity ratios of Ag, Mo, W to Bi as examples, a power function relationship between neutron energy and the fluorescence intensity ratio was derived, preliminary demonstrating the feasibility of the method.

Background Distant metastases is the main failure mode of nasopharyngeal carcinoma. However, early prediction of distant metastases in NPC is extremely challenging. Deep learning has made great progress in recent years. Relying on the rich data features of radiomics and the advantages of deep learning in image representation and intelligent learning, this study intends to explore and construct the metachronous single-organ metastases (MSOM) based on multimodal magnetic resonance imaging. Patients and methods The magnetic resonance imaging data of 186 patients with nasopharyngeal carcinoma before treatment were collected, and the gross tumor volume (GTV) and metastatic lymph nodes (GTVln) prior to treatment were defined on T1WI, T2WI, and CE-T1WI. After image normalization, the deep learning platform Python (version 3.9.12) was used in Ubuntu 20.04.1 LTS to construct automatic tumor detection and the MSOM prediction model. Results There were 85 of 186 patients who had MSOM (including 32 liver metastases, 25 lung metastases, and 28 bone metastases). The median time to MSOM was 13 months after treatment (7–36 months). The patients were randomly assigned to the training set (N = 140) and validation set (N = 46). By comparison, we found that the overall performance of the automatic tumor detection model based on CE-T1WI was the best (6). The performance of automatic detection for primary tumor (GTV) and lymph node gross tumor volume (GTVln) based on the CE-T1WI model was better than that of models based on T1WI and T2WI (AP@0.5 is 59.6 and 55.6). The prediction model based on CE-T1WI for MSOM prediction achieved the best overall performance, and it obtained the largest AUC value (AUC = 0.733) in the validation set. The precision, recall, precision, and AUC of the prediction model based on CE-T1WI are 0.727, 0.533, 0.730, and 0.733 (95% CI 0.557–0.909), respectively. When clinical data were added to the deep learning prediction model, a better performance of the model could be obtained; the AUC of the integrated model based on T2WI, T1WI, and CE-T1WI were 0.719, 0.738, and 0.775, respectively. By comparing the 3-year survival of high-risk and low-risk patients based on the fusion model, we found that the 3-year DMFS of low and high MSOM risk patients were 95% and 11.4%, respectively (p < 0.001). Conclusion The intelligent prediction model based on magnetic resonance imaging alone or combined with clinical data achieves excellent performance in automatic tumor detection and MSOM prediction for NPC patients and is worthy of clinical application.

The variety and content of minerals in clay determine its quality and use. The x-ray fluorescence characteristics of seven elements, that is, Mg, Al, Si, K, Ca, Ti, and Fe, in clay samples were studied using the energy-dispersive x-ray fluorescence (EDXRF) method. The application of wavelet transform was used to analyze the overlapping peaks of the FeK alpha and MnK beta and the CaK alpha and KK beta spectral lines and to measure the spectral peak count rate and the content of seven elements. The weight of classified elements in clay was calculated through principal component analysis, and the clay samples from four clay-producing areas were classified using the K-means clustering method. The results showed that the accuracy of classification using the count rate of seven elements reached 97.73%. The average accuracy of the classification results of the content of seven elements reached 80.68%. A comparison of the results showed that the EDXRF spectral peak information provides a reliable scientific method for the identification and tracing of clay and its products. In addition, tracing the origin of clay and its products can also indirectly assess the radiation level and its impact on the radiation environment based on the radiation index test results.

单应估计是许多计算机视觉任务中一个基础且重要的步骤.传统单应估计方法基于特征点匹配,难以在弱纹理图像中工作.深度学习已经应用于单应估计以提高其鲁棒性,但现有方法均未考虑到由于物体尺度差异导致的多尺度问题,所以精度受限.针对上述问题,提出了一种用于单应估计的多尺度残差网络.该网络能够提取图像的多尺度特征信息,并使用多尺度特征融合模块对特征进行有效融合,此外还通过估计四角点归一化偏移进一步降低了网络优化难度.实验表明,在MS-COCO数据集上,该方法平均角点误差仅为0.788个像素,达到了亚像素级的精度,并且在99％情况下能够保持较高的精度.由于综合利用了多尺度特征信息且更容易优化,该方法精度显著提高,并具有更强的鲁棒性.

加强党对高校的领导,加强和改进高校党的建设,是办好中国特色社会主义大学的根本保证.教育部于2018年发布了《中共教育部党组关于高校党组织"对标争先"建设计划的实施意见》,提出开展新时代高校党建示范创建和质量创优工作.全国各地高校基层党组织陆续掀起对标争先建设热潮.该文通过分析当前部分高校基层党支部建设所面临的问题,结合成都理工大学核技术教工党支部建设的情况,开展全国样板党支部建设路径的探索实践,总结出支部活动标准化,党建与业务融合发展以及提高工作执行力三点建设经验.

Compared with traditional manual inspection, inspection robots can not only meet the all-weather, real-time, and accurate inspection needs of substation inspection, they also reduce the work intensity of operation and maintenance personnel and decrease the probability of safety accidents. For the urgent demand of substation inspection robot intelligence enhancement, an environment understanding algorithm is proposed in this paper, which is an improved DeepLab V3+ neural network. The improved neural network replaces the original dilate rate combination in the ASPP (atrous spatial pyramid pooling) module with a new dilate rate combination with better segmentation accuracy of object edges and adds a CBAM (convolutional block attention module) in the two up-samplings, respectively. In order to be transplanted to the embedded platform with limited computing resources, the improved neural network is compressed. Multiple sets of comparative experiments on the standard dataset PASCAL VOC 2012 and the substation dataset have been made. Experimental results show that, compared with the DeepLab V3+, the improved DeepLab V3+ has a mean intersection-over-union (mIoU) of eight categories of 57.65% on the substation dataset, with an improvement of 6.39%, and the model size of 13.9 M, with a decrease of 147.1 M.

The major rivers in a region are usually vital sources of drinking water for local populations, and the concentration of radionuclides in the water is intimately tied to people's health. The varying concentration limits set by the World Health Organization are appropriate as screening values for determining the pollution of water sources, but their capacities as regulatory or early warning limits are restricted. In daily management, the regulatory authority needs to manage water bodies by level based on the concentration of radionuclide to indicate the potential pollution risks. From 2017 to 2019, a statistical analysis and dosage evaluation were conducted on the water radioactivity level in the Chongqing section of the Yangtze River in this study. The Modified Nemerow Index method based on the dose conversion coefficients was applied for the grading evaluation of the water radioactivity level, allowing the grading effect discussed. The results showed that the concentration of radionuclides in the Chongqing section of the Yangtze River and its contribution to the annual effective dose of the human body were lower than the limits stated in the Guidelines for Drinking Water Quality (Fourth Edition). And the samples in the section were 52.94% in Grade Ⅰand 47.06% in Grade Ⅱ, meaning few potential radioactive pollution risks exist there. Compared with other methods. The Modified Nemerow Index method combines the Traditional Nemerow Index method with the dose conversion coefficient of nuclides making it more realistic for the early warning and control of radioactive pollution in water bodies, which is worth popularizing and implementing.

In recent years, due to the fluidity and thermal conductivity of the liquid metal, the use of liquid metal as an anode target is one of the ways to improve the brightness of X-ray tubes. In this paper, the optimal thickness and conversion efficiency of three kinds of target materials under different energies were discussed by the Monte Carlo method. The optimal thickness of the gallium target is 6.2 mu m when the incident electron beam energy is 50 keV and the optimal thickness of the indium target and the tin target was 4 mu m. The X-ray conversion efficiency of the gallium target was 44.28% higher than that of the silver target as well as the ratio of X-ray count with the K lines to total count was 53.38%. The gallium target is suitable to be the material of a liquid metal anode target of transmission X-ray tube within the concerned energy range.

本研究基于我国生态环境部空气质量自动监测站逐时监测数据分析了淮安市洪泽区细颗粒物(PM2.5)及臭氧(O3)时空变化特征及其来源特征.结果表明,该地区PM2.5浓度冬季高,夏季低;O3浓度春秋季高,冬季低;PM2.5和O3均呈现显著的日变化特征,PM2.5呈U型分布,15时浓度最低,O3呈单峰分布,15时达峰值,谷值出现在08时.与江苏省及淮安市区域平均值相比,洪泽区PM2.5浓度较低而O3浓度则较高.对比洪泽区内两测站PM2.5及O3表明,湖畔PM2.5浓度较低而O3浓度较高,两站差异在冬季,特别是2-3月差异最大,这很可能与工业园区一次排放、氮氧化物(NOx)的滴定作用以及湖陆风效应等有关.后向轨迹聚类和PSCF方法对洪泽区PM2.5潜在源的分析结果表明,污染物浓度在冬季受区域传输的影响较大,安徽北部、东北部为洪泽区最主要的潜在贡献源区.洪泽区内冬季细颗粒物在线源解析表明,监测期间PM2.5的上升主要受二次无机源、机动车尾气源、工业工艺源、燃煤源增多的影响;作为O3重要前体物的可挥发性有机物(VOCs),其监测结果表明,洪泽工业园区VOCs主要以芳香烃占比最高,为58％;新华书店站VOCs也主要为芳香烃,占比39％,其次为烷烃、卤代烃及含氧含氮烃,分别占比20％、17％及16％.

单能X射线光源是由X射线光机、双晶单色器、标准探测器以及准直系统组成.X射线光机产生的连续X射线,通过与双晶单色器发生布拉格衍射完成单色化,调节不同的特定布拉格角度得到能量范围30～160 keV的单能X射线.为了研究标定装置的能量展宽,需要对该装置产生的单能X射线的能量分辨率进行研究.结果 表明Si(220)晶体产生的单能X射线的能量分辨率为0.91％@30 keV和2.3％@70.6 keV,Si(551)晶体为1.97％@80.1 keV和3.45％@142.6 keV.使用这套装置对溴化镧晶体探测器的能量响应进行校准验证,实验发现该装置的能量分辨率良好,可以应用在多种类型探测器的标定实验、X射线质量衰减系数测量以及多层膜反射率测量等领域.

Purpose: Motivated by recent advances in deep learning, the purpose of this study was to investigate a deep learning method in automatic segment and reconstruct applicators in computed tomography (CT) images for cervix brachytherapy treatment planning. Material and methods: U-Net model was developed for applicator segmentation in CT images. Sixty cervical cancer patients with Fletcher applicator were divided into training data and validation data according to ratio of 50 : 10, and another 10 patients with Fletcher applicator were employed to test the model. Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) were used to evaluate the model. Segmented applicator coordinates were calculated and applied into RT structure file. Tip error and shaft error of applicators were evaluated. Dosimetric differences between manual reconstruction and deep learning-based reconstruction were compared. Results: The averaged overall 10 test patients' DSC, HD95, and reconstruction time were 0.89, 1.66 mm, and 17.12 s, respectively. The average tip error was 0.80 mm, and the average shaft error was less than 0.50 mm. The dosimetric differences between manual reconstruction and automatic reconstruction were 0.29% for high-risk clinical target volume (HR-CTV) D90%, and less than 2.64% for organs at risk D2cc at a scenario of doubled maximum shaft error. Conclusions: We proposed a deep learning-based reconstruction method to localize Fletcher applicator in three-dimensional CT images. The achieved accuracy and efficiency confirmed our method as clinically attractive. It paves the way for the automation of brachytherapy treatment planning.