Tumour response following preoperative chemotherapy is affected by body mass index in patients with colorectal liver metastases

BACKGROUND Colorectal cancer is the third most prevalent malignancy globally and ranks second in cancer-related mortality,with the liver being the primary organ of metastasis.Preoperative chemotherapy is widely recommended for initially or potentially resectable colorectal liver metastases(CRLMs).Tumour pathological response serves as the most important and intuitive indicator for assessing the efficacy of chemotherapy.However,the postoperative pathological results reveal that a considerable number of patients exhibit a poor response to preoperative chemotherapy.Body mass index(BMI)is one of the factors affecting the tumori-genesis and progression of colorectal cancer as well as prognosis after various antitumour therapies.Several studies have indicated that overweight and obese patients with metastatic colorectal cancer experience worse prognoses than those with normal weight,particularly when receiving first-line chemotherapy regimens in combination with bevacizumab.AIM To explore the predictive value of BMI regarding the pathologic response following preoperative chemotherapy for CRLMs.METHODS A retrospective analysis was performed in 126 consecutive patients with CRLM who underwent hepatectomy following preoperative chemotherapy at four different hospitals from October 2019 to July 2023.Univariate and multivariate logistic regression models were applied to analyse potential predictors of tumour pathological response.The Kaplan-Meier method with log rank test was used to compare progression-free survival(PFS)between patients with high and low BMI.BMI<24.0 kg/m^(2) was defined as low BMI,and tumour regression grade 1-2 was defined as complete tumour response.RESULTS Low BMI was observed in 74(58.7%)patients and complete tumour response was found in 27(21.4%)patients.The rate of complete tumour response was significantly higher in patients with low BMI(29.7%vs 9.6%,P=0.007).Multivariate analysis revealed that low BMI[odds ratio(OR)=4.56,95%confidence interval(CI):1.42-14.63,P=0.011],targeted therapy with bevacizumab(OR=3.02,95%CI:1.10-8.33,P=0.033),preoperative carcinoembryonic antigen level<10 ng/mL(OR=3.84,95%CI:1.19-12.44,P=0.025)and severe sinusoidal dilatation(OR=0.17,95%CI:0.03-0.90,P=0.037)were independent predictive factors for complete tumour response.The low BMI group exhibited a significantly longer median PFS than the high BMI group(10.7 mo vs 4.7 mo,P=0.011).CONCLUSION In CRLM patients receiving preoperative chemotherapy,a low BMI may be associated with better tumour response and longer PFS.

An Investigation of Landslide Susceptibility Using Logistic Regression and Statistical Index Methods in Dailekh District, Nepal

Landslide distribution and susceptibility mapping are the fundamental steps for landslide-related hazard and disaster risk management activities, especially in the Himalaya region which has resulted in a great deal of death and damage to property. To better understand the landslide condition in the Nepal Himalaya, we carried out an investigation on the landslide distribution and susceptibility using the landslide inventory data and 12 different contributing factors in the Dailekh district, Western Nepal. Based on the evaluation of the frequency distribution of the landslide, the relationship between the landslide and the various contributing factors was determined.Then, the landslide susceptibility was calculated using logistic regression and statistical index methods along with different topographic(slope, aspect, relative relief, plan curvature, altitude, topographic wetness index) and non-topographic factors(distance from river, normalized difference vegetation index(NDVI), distance from road, precipitation, land use and land cover, and geology), and 470(70%) of total 658 landslides. The receiver operating characteristic(ROC) curve analysis using 198(30%) of total landslides showed that the prediction curve rates(area under the curve, AUC) values for two methods(logistic regression and statistical index) were 0.826, and 0.823with success rates of 0.793, and 0.811, respectively. The values of R-Index for the logistic regression and statistical index methods were83.66 and 88.54, respectively, consisting of high susceptible hazard classes. In general, this research concluded that the cohesive and coherent natural interplay of topographic and non-topographic factors strongly affects landslide occurrence, distribution, and susceptibility condition in the Nepal Himalaya region. Furthermore, the reliability of these two methods is verified for landslide susceptibility mapping in Nepal’s central mountain region.

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

In this study, a multivariate local quadratic polynomial regression(MLQPR) method is proposed to design a model for the sludge volume index(SVI). In MLQPR, a quadratic polynomial regression function is established to describe the relationship between SVI and the relative variables, and the important terms of the quadratic polynomial regression function are determined by the significant test of the corresponding coefficients. Moreover, a local estimation method is introduced to adjust the weights of the quadratic polynomial regression function to improve the model accuracy. Finally, the proposed method is applied to predict the SVI values in a real wastewater treatment process(WWTP). The experimental results demonstrate that the proposed MLQPR method has faster testing speed and more accurate results than some existing methods.

Ki67 Proliferative Index and Peritumoral Brain Edema in Meningiomas: Do They Correlate? A Clinical Study on 56 Patients

Introduction: Meningiomas are the most common type of extra-axial neoplasm. Peritumoral brain edema (PTBE) can be seen around meningiomas while it may be absent in others. Despite that Ki67 proliferative index has been previously correlated with meningioma grades, no definite relationship has been established in relation to PTBE in meningioma patients. Objective: Correlate the peritumoral brain edema with the Ki67 proliferative index of meningiomas. Patients & Methods: Aclinical prospective study was conducted on 56 patients (47 women, 9 men;mean age 50.89 ± 12.55 years) diagnosed with meningiomas. All patients were evaluated regarding the presence of brain edema surrounding the lesion in pre-operative neuroimaging using T2W and FLAIR MR images. Immunohistochemical staining of Ki67 index (representing proliferative activity) was done. Correlation between presence of PTBE and Ki67 index values was evaluated. Results: PTBE was found in nearly half of the patients (48.2%), while the remaining (51.8%) of patients did not exhibit PTBE in their pre-operative neuroimaging. The mean value of Ki67 index in meningioma patients with PTBE was 4.83% compared to a value of 1.83% in patients without PTBE, P value = 0.014. Conclusion: High Ki67 indices are evident in meningiomas with surrounding peritumoral brain edema (PTBE).

Predicting pavement condition index based on the utilization of machine learning techniques:A case study

Pavement management systems(PMS)are used by transportation government agencies to promote sustainable development and to keep road pavement conditions above the minimum performance levels at a reasonable cost.To accomplish this objective,the pavement condition is monitored to predict deterioration and determine the need for maintenance or rehabilitation at the appropriate time.The pavement condition index(PCI)is a commonly usedmetric to evaluate the pavement's performance.This research aims to create and evaluate prediction models for PCI values using multiple linear regression(MLR),artificial neural networks(ANN),and fuzzy logic inference(FIS)models for flexible pavement sections.The authors collected field data spans for 2018 and 2021.Eight pavement distress factors were considered inputs for predicting PCI values,such as rutting,fatigue cracking,block cracking,longitudinal cracking,transverse cracking,patching,potholes,and delamination.This study evaluates the performance of the three techniques based on the coefficient of determination,root mean squared error(RMSE),and mean absolute error(MAE).The results show that the R2 values of the ANN models increased by 51.32%,2.02%,36.55%,and 3.02%compared toMLR and FIS(2018 and 2021).The error in the PCI values predicted by the ANNmodel was significantly lower than the errors in the prediction by the FIS and MLR models.

Application of Water Quality Index(WQI)and Regression Analysis of Groundwater in Budigumma Village,Anantapur District,Andhra Pradesh

The research work is aimed at assessing the subsurface or groundwater suitability for human use or consumption depends upon the calculated water quality index values,correlation coefficient and regression analysis.The water quality index(WQI)is main important tool to calculate the characteristics of drinking water quality in rural,urban and industrial area.Different parameters which is measured and determination of the water quality index for selecting parameters.Further to study the correlation and regression method in this research work.Totally fifteen groundwater samples were collected from the Budigumma Village Anantapur district in the state Andhra Pradesh in India.Nine water quality parameters has been considered for the computation of water quality index such as pH,total dissolved solid(TDS),total hardness(TH),calcium(Ca),magnesium(Mg),nitrates(NO3),chlorides(Cl),sulphates(SO4),fluorides(F).The World Health Organization(WHO)has been assessed to the suitability of groundwater for drinking purposes or other uses for public and determining of WQI.This WQI index values ranged from 97.78 to 108.37.The study shows that 87%area comes under the poor category of drinking purposes and the remaining 13%comes under as good water for drinking purposes as per the WQI classification.The correlation and regression analysis gives as an outstanding device for the calculation of different parameter values within realistic degree of precision.The subsistence of strong correlation or relationship between the total hardness and magnesium is determined.The analysis of selected parameters revealed that proper treatment before use or consumption and protected from more contamination.

Assessment of functional prognosis of anterior cruciate ligament reconstruction in athletes based on a body shape index

BACKGROUND A healthy body shape is essential to maintain athletes’sports level.At present,little is known about the effect of athletes’body shape on anterior cruciate ligament reconstruction(ACLR).Moreover,the relationship between body shape and variables such as knee joint function after operation and return to the field has not been well studied.AIM To verify the relationship between a body shape index(ABSI)and the functional prognosis of the knee after ACLR in athletes with ACL injuries.METHODS We reviewed 76 athletes with unilateral ACL ruptures who underwent ACLR surgery in the First Hospital of Shanxi Medical University between 2017 and 2020,with a follow-up period of more than 24 mo.First,all populations were divided into a High-ABSI group(ABSI>0.835,n=38)and a Low-ABSI group(ABSI<0.835,n=38)based on the arithmetic median(0.835)of ABSI values.The primary exposure factor was ABSI,and the outcome indicators were knee function scores as well as postoperative complications.The correlation between ABSI and postoperative knee function scores and postoperative complications after ACLR were analyzed using multifactorial logistic regression.RESULTS The preoperative knee function scores of the two groups were similar.The surgery and postoperative rehabilitation exercises,range of motion(ROM)compliance rate,Lysholm score,and Knee Injury and Osteoarthritis Outcome Score of the two groups gradually increased,whereas the quadriceps atrophy index gradually decreased.The knee function scores were higher in the Low-ABSI group than in the High-ABSI group at the 24-mo postoperative follow-up(P<0.05).In multifactorial logistic regression,ABSI was a risk factor of low knee joint function score after surgery,specifically low ROM scores(odds ratio[OR]=1.31,95%confidence interval[CI][1.10-1.44];P<0.001),low quadriceps atrophy index(OR=1.11,95%CI[0.97-1.29];P<0.05),low Lysholm scores(OR=2.34,95%CI[1.78-2.94];P<0.001),low symptoms(OR=1.14,95%CI[1.02-1.34];P<0.05),low activity of daily living(OR=1.34,95%CI[1.18-1.65];P<0.05),low sports(OR=2.47,95%CI[1.78-2.84];P<0.001),and low quality of life(OR=3.34,95%CI[2.88-3.94];P<0.001).ABSI was also a risk factor for deep vein thrombosis of the lower limb(OR=2.14,95%CI[1.88-2.36],P<0.05]and ACL recurrent rupture(OR=1.24,95%CI[0.98-1.44],P<0.05)after ACLR.CONCLUSION ABSI is a risk factor for the poor prognosis of knee function in ACL athletes after ACLR,and the risk of poor knee function after ACLR,deep vein thrombosis of lower limb,and ACL recurrent rupture gradually increases with the rise of ABSI.

Unveiling the Predictive Capabilities of Machine Learning in Air Quality Data Analysis: A Comparative Evaluation of Different Regression Models

Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.

中国碳排放影响因素分解及峰值预测研究

随着我国对2030年前达到碳排放峰值意愿的逐渐增强,首先,利用对数平均迪氏分解(Logarithmic Mean Divisia Index, LMDI)模型对我国2011—2019年人均碳排放量变化的影响因素进行分解,以明确各影响因素的贡献量、贡献率,并得出2011—2019年我国人均碳排放累积增长约为1.09 t。其中,经济发展起主要促进作用,累积贡献值约为5.61 t;能源结构优化和能源强度降低起抑制作用,累积贡献值分别约为-0.66 t和-3.86 t。其次,依据相关政策公布的经济社会发展预定目标,设定3种模拟情景下的指标变动量,并预测2022—2030年的CO_(2)排放量。结果显示:基准情景下CO_(2)排放量在2027年达到峰值,约为110.87亿t,人均CO_(2)排放量为7.69 t;低减排情景下CO_(2)排放量在2029年达到峰值,约为112.04亿t,人均CO_(2)排放量为7.75 t;高减排情景下CO_(2)排放量峰值出现在2023年,约为110.00亿t,人均CO_(2)排放量为7.74 t。

芙蓉李果实成熟期间的综合品质评价指标筛选与表观预测模型构建

目的科学评价芙蓉李果实成熟期间的营养品质,建立色度值表观特征与营养品质的关系。方法以福建省主栽品种芙蓉李为研究对象,对其成熟期间果糖、葡萄糖、蔗糖、苹果酸、奎尼酸、琥珀酸、柠檬酸、富马酸、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷、多酚、黄酮、类胡萝卜素等13个品质指标进行分析和综合评价。结果芙蓉李成熟期间,各品质指标的含量变化存在显著差异(P<0.05),综合运用相关分析、因子分析、绝对因子分析-多元线性回归(absolute principal component scores-multiple linear regression,APCS-MLR)分析筛选可反映芙蓉李综合品质的主要指标。因子分析提取出3个主因子,贡献率分别为52.677%、23.468%、11.649%,累计贡献率为87.794%。综合APCS-MLR等数理统计分析,主因子1主要对果糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷贡献较大,贡献率分别为53.00%、73.85%、55.54%;主因子2主要对蔗糖、富马酸、果糖、柠檬酸的贡献率较大,分别为28.26%、18.70%、16.14%、15.59%;主因子3主要对多酚(29.13%)和黄酮(28.28%)有较大贡献率;选取3个主因子总贡献率高于60%的果糖、葡萄糖、矢车菊素-3-芸香糖苷、矢车菊素-3-葡萄糖苷作为综合品质评价的主要指标。分别对已筛选出的4个主要评价指标与色度值进行多元线性逐步回归分析,建立4个主要指标与色度值的表观预测模型,各模型均具有较好的拟合度,预测值与实测值的均方根误差较小;进一步验证结果表明,通过色度值对4个指标的预测具有较高的可靠性和准确性。结论本研究筛选出的主要指标及预测模型可更加简单、便捷地评价芙蓉李果实成熟期间的综合品质。

快速城镇化地区生态环境质量时空格局演变及对土地利用变化响应——以廊坊市北三县为例

对生态环境质量时空格局及其对土地利用变化的响应进行研究可为区域制定生态保护修复政策提供参考。通过构建遥感生态指数,对廊坊市北三县2000-2020年生态环境质量分布格局分析评价,利用地理加权回归模型探索生态环境质量变化对土地利用变化的响应。结果表明,北三县2000-2020年生态环境质量等级以“良”和“一般”为主,整体呈下降趋势,等级“较差”面积呈快速增长趋势。建设用地变化、土地利用强度变化和生态环境质量变化呈负相关关系,回归系数在空间上呈现较大的异质性;生态用地变化与生态环境质量变化呈正相关关系,回归系数在空间上异质性不强。城镇快速扩张的区域生态环境质量变化对建设用地变化和土地利用强度变化响应最显著。大量耕地向建设用地转变是区域生态环境变化的最主要影响因素,在国土空间规划中应划定城市边界,严格控制建设用地规模,提高集约节约利用水平,确保区域可持续发展。

基于PCA-LM的空战目标威胁评估

空战过程中态势瞬息万变,获取敌目标的威胁是我方取得攻击占位优势和采取战术规避的前提条件。提出主成分分析法和阻尼最小二乘法相结合的回归模型对目标的威胁进行评估。利用主成分分析法,分析指标之间的相关性,转化成相互独立的分量,确定主成分分量,重构目标威胁评估体系;对目标威胁与主成分分量进行回归分析,利用阻尼最小二乘法对回归模型参数进行估计,得到主成分分量与目标威胁之间的统计关系;利用目标威胁估计值与实际值之间的误差大小,验证了回归模型的有效性。消除了指标之间的相关性对评估结果的影响,提高了评估结果的客观性,解决了传统评估方法忽略指标之间耦合性的问题。

基于可持续发展视角我国稀土资源贸易安全评价研究

稀土是支撑高端技术创新和新兴产业发展的关键矿产资源,已经成为大国资源竞争和国际地缘经济政治博弈的重要对象。全球稀土供应链重构进程加剧,如何评估我国稀土资源贸易安全,破解稀土贸易贫困化增长困境,促进稀土产业可持续发展,是当前亟须关注和解决的重要课题。本研究基于“压力—状态—响应”模型(Pressure-State-Response Model)构建稀土贸易安全评价指标体系,测算不同时期我国稀土资源的压力指数、状态指数、响应指数,揭示我国稀土贸易安全指数演变特征,进一步建立多元线性回归模型,识别我国稀土贸易安全的影响因素。研究发现:我国稀土资源贸易安全度在持续下降,亟需优化贸易结构,降低供应链风险;我国稀土资源贸易的驱动因素主要来源于市场需求牵引、资源禀赋优势以及产业有序供给;全球稀土产业分工格局、资源价值核算方法缺失,限制了我国稀土资源贸易可持续发展。在此基础上,提出通过健全稀土资源收储体系、建设再生稀土研发和回收网络等方式,保障战略性稀土资源供给安全;同时,整合稀土行业优势资源与优化稀土产业链及供应链,深度参与全球稀土供应链治理,为我国稀土资源开发利用与可持续发展提供决策依据。

乡村振兴背景下兵团农产品加工业转型升级效率测度及影响因素研究

该文基于2006—2021年兵团农产品加工业面板数据,构建DEA-Malmquist指数模型和Tobit回归模型,对兵团农产品加工业转型升级的效率以及影响因素进行测评。结果表明:兵团农产品加工业转型升级的效率(ML指数)由2006年的0.968上涨至2021年的1.230,总体呈现出上升趋势;兵团农产品加工业转型升级的技术进步变化指数(TC)均值为1.267大于综合技术效率变化指数(EC)的均值1.130,表明兵团农产品加工业转型升级的ML指数增长主要归结于TC指数的增长;科研投入对兵团农产品加工业转型升级效率产生显著的正向影响,产业集聚程度对效率的正向影响作用并不显著,外商直接投资、金融支持力度和政府支持力度均对效率产生显著的负向影响。基于此,该文对促进兵团农产品加工业转型升级提出相应建议。

黄河流域生态环境质量时空演变及驱动力分析

开展黄河流域生态环境质量时空格局演变及驱动因素研究,为黄河流域生态文明建设和人地和谐发展提供科学决策支持。基于2000-2022年的MODIS数据,利用遥感生态指数(RSEI)评估黄河流域生态环境质量,采用Sen趋势分析法和MK检验全面刻画RSEI的空间演变特征。结合分位数回归模型和残差分析探究自然环境和人类活动对不同分位数RSEI变化的影响机制及相对贡献率。结果表明:2000-2022年,黄河流域RSEI年均值为0.465,年均增速为0.55%。黄河流域中游RSEI上升最为显著。流域RSEI改善的区域面积占比为74.94%,显著改善的区域为内蒙古中部及晋陕两省北部地区。同一因素对不同分位数RSEI的影响效应存在显著分异。随着RSEI分位点的提高,温度、降水及高程等3个变量影响程度均呈下降趋势。自然环境和人类活动共同作用导致黄河流域RSEI变化面积占比为64.00%,由人类活动导致RSEI变化的区域面积占比为20.62%,由自然环境导致RSEI变化的区域面积占比为15.38%。

基于地下结构整体损伤表征的复合地震动参数构造及其性能验证

在地下结构抗震设计中,不同的输入地震动引起的地下结构响应有显著差异,因此,合理通过地震动参数选择输入地震动是正确开展地下结构抗震设计的重要前提。针对单一地震动参数难以表征地下结构地震动潜在破坏势问题,文章构造了能更好表征地下结构损伤破坏的复合地震动参数。具体开展了以下工作:提出基于变形与滞回耗能的地下结构整体损伤指标作为结构需求参数,以定量化评估地下结构的整体破坏状态。选取64条真实地震动记录作为输入地震动,开展四层三跨地铁车站地震弹塑性动力时程分析。基于分析结果提供的数据样本,采用偏最小二乘法从统计角度构造复合地震动参数。最后,选用100条真实地震动记录开展两层三跨地铁车站弹塑性动力时程分析,对文章所构造的复合地震动参数进行验证。对比分析复合地震动参数、12个常用地震动参数与地下结构整体损伤指数的回归统计特征。结果表明:复合地震动参数与结构需求数之间具有更好拟合优度值,其Pearson相关性、有效性也优于单一地震动参数。

基于小波分解和ARIMA-GARCH-GRU组合模型的制造业PMI预测

制造业采购经理人指数(PMI)是反映国家经济运行情况的重要指标,而传统预测模型对该类时序数据预测精度不高。针对制造业PMI指数的非线性、波动性和数据量少的特点,提出一种基于一维离散小波变换进行数据预处理的组合模型。时序数据经过小波变换,由整合移动平均自回归–广义自回归条件异方差模型(ARIMA-GARCH)处理稳态低频数据,门控循环单元(GRU)处理波动性强的高频数据,将各频段预测结果进行融合得到最终预测结果。为验证模型有效性,选取一定数据量的PMI指数进行实验。结果表明,与其他常见模型对比,本文构建的组合模型具有较好的预测精度与性能,平均绝对误差(MAE)、均方根误差(RMSE)、平均绝对百分比误差(MAPE)分别达到0.00329、0.004162、0.65%。

中国海洋渔业碳排放效率区域差异与时空分异

海洋渔业碳排放效率大小反映了海洋渔业经济与生态是否实现协调发展。基于我国9个沿海地区的面板数据,利用非期望产出超效率SBM模型,测算出沿海地区海洋渔业碳排放效率值,进一步引入泰尔指数和莫兰指数分析沿海地区海洋渔业碳排放效率区域差异和空间分布特征,并用Tobit回归探究其影响因素。结果表明:(1)我国沿海地区海洋渔业碳排放效率排序为:东海地区>南海地区>黄渤海地区。(2)在考察期内海洋渔业碳排放效率的总体差异主要来源于区域内差异,但到2018年区域间差异贡献率与区域内贡献率几乎相等。(3)我国沿海地区海洋渔业碳排放效率的空间格局由随机相异态势逐步转向为相似集聚,且形成较为明显的H-H型和L-L型空间集聚。(4)海洋渔业发展水平、科技创新和生态保护对海洋渔业碳排放效率具有显著的正向影响,产业结构对其有负向作用。

1990-2019年中国归因于高体质指数的2型糖尿病疾病负担分析与预测研究

背景中国的糖尿病患者数位居全球首位。近年来糖尿病患病率和死亡率不断上升,威胁人们健康水平,给我国人民群众带来沉重负担。随着肥胖患病率不断上升,预计糖尿病疾病负担将持续上升,糖尿病已成为我国不容忽视的公共卫生问题。目的描述和分析1990—2019年中国归因于高BMI的2型糖尿病疾病负担状况及其变化趋势,预测2020—2024年中国归因于高BMI的2型糖尿病疾病负担状况,旨在为中国2型糖尿病科学防控提供依据。方法于2023年5月,从2019年全球疾病负担(GBD 2019)中提取1990—2019年中国2型糖尿病伤残调整寿命年(DALYs)、DALYs率、标化DALYs率、死亡人数、死亡率及标化死亡率等疾病负担指标的数据,采用联结点回归模型通过年度变化百分比(APC)和平均年度变化百分比(AAPC)分析其变化趋势。基于1990—2016年数据(训练集),构建归因于高BMI的2型糖尿病DALYs率和死亡率的自回归移动平均(ARIMA)模型,利用2017—2019年数据(测试集)进行模型评价。用预测值与实际值得到的相对误差、模型的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)、均方误差(MASE)及均方根误差(RMSE)判断模型预测效果,选择最佳模型预测2020—2024年中国归因于高BMI的2型糖尿病疾病负担。结果1990—2019年:疾病负担整体呈上升趋势(标化DALYs率AAPC=2.85%,标化死亡率AAPC=2.32%,均P<0.05),标化DALYs率从80.21/10万增至181.54/10万,标化死亡率从1.25/10万增至2.39/10万;男性和女性的标化DALYs率和标化死亡率均呈快速上升趋势,与1990年相比,2019年男性标化DALYs率增长了173%,女性增长了89%,男性标化死亡率增长了146%,女性增长了58%;DALYs率和死亡率随年龄增加明显增加,DALYs率在30岁后迅速增加,高峰基本维持在65~69岁(1990年337.47/10万,2019年711.09/10万)和70~74岁年龄组(1990年323.64/10万,2019年730.47/10万),人群死亡率在45岁后迅速增加,高峰维持在95岁以上(1990年12.78/10万,2019年33.29/10万);与全球相比,我国归因于高BMI的2型糖尿病的DALYs率和死亡率整体增速均较高。在1990—2019年中均有4个时间拐点,标化DALYs率和标化死亡率分别在2000—2004年和1996—2004年增速最快。经ARIMA模型预测得到2020—2024年中国归因于高BMI的2型糖尿病标化DALYs率和标化死亡率均呈持续上升趋势,到2024年分别达到205.142/10万(95%CI=189.775/10万~220.508/10万)和2.621/10万(95%CI=2.343/10万~2.900/10万)。结论我国归因于高BMI的2型糖尿病的疾病负担总体呈上升趋势,表现为由伤残导致的疾病负担与死亡人数升高,且增速高于全球。我国男性归因于高BMI的2型糖尿病的疾病负担逐渐高于女性,归因于高BMI的2型糖尿病的DALYs率和死亡率有年轻化趋势,ARIMA模型显示归因于高BMI的2型糖尿病的疾病负担预计将持续上升。为减轻2型糖尿病疾病负担,应该对重点人群(男性、中老年人群)加强健康教育,以提高对糖尿病防治的知晓度,可以通过提倡健康饮食和生活习惯加强体质量管理。

基于无人机多光谱NDVI值估测玉米产量

【目的】研究基于UAS-8无人机采集数据,运用归一化植被指数(Normalized Difference Vegetation Index)模型估测玉米产量,为大田无人机多光谱预测玉米产量提供理论依据。【方法】以新疆18份春播玉米为研究对象,获取开花期多光谱图像,经过辐射校正、大气校正、建立掩膜、提取NDVI图,计算植被覆盖率,得到区光谱反射率和归一化植被指数实际数值,将NDVI值与田间实测产量值进行模型拟合。【结果】幂函数Y=23411.46-10997.99/X(R^(2)=0.4886),二次函数为Y=39003.00-117963.03X+103130.25X 2(R^(2)=0.562),正反比函数(Inverse Proportional Function)为Y 2=2840.5 X/(1-X)(R^(2)=0.495),利用偏最小二乘回归(Partial Least Squares Regression),其线性函数Y=24458.22X-9620.55(R^(2)=0.521)。【结论】在数值0.5~0.8区间,NDVI与玉米产量具有较高的相关性,线性函数方程NDVI值可预测玉米的产量。