我国事故指标与经济社会发展指标多元回归分析

以安全生产相对指标工矿商贸十万就业人员生产安全事故死亡率为研究对象,通过文献调研分析,最终从经济发展、经济发展结构、经济活动水平和社会活动水平角度筛选出14个指标为观察对象,采用逐步多元回归分析法筛选出影响我国安全生产的主要经济社会发展指标为：第三产业增加值产值占GDP比重、第三产业就业人数占总就业人数的比重和固定资产投资增长速度,并建立了回归模型。应用弹性系数法对主要影响因素进行重要性排序,并推测工矿商贸就业人员十万人事故死亡率将保持持续下降的趋势。     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.     

基于Logistic回归模型的大兴安岭地区林火发生概率预测研究*

为实现对大兴安岭地区林火发生概率的基本预测,根据该地区历史火灾资料、地形因子、气象因子和人为因子,基于Logistic回归模型建立该地区林火发生概率预测模型。研究结果表明:坡度、海拔、平均气压、平均气温、平均相对湿度、最小相对湿度是该地区林火发生的主要驱动因子;坡度、海拔、平均气压、平均气温与林火发生概率呈正相关,平均相对湿度和最小相对湿度与林火发生概率呈负相关;ROC曲线下面积值（AUC）为0.91,最佳临界值为0.425;建立的大兴安岭地区林火发生概率预测模型,建模样本总体准确率为82.4%,验证样本总体准确率为80.5%;该地区夏季林火发生概率明显高于春、秋季;林火发生概率较高的Ⅳ级和Ⅴ级火险区主要集中在夏季该地区东南部、西部以及春、秋季该地区东南部。研究结果可为该地区林火预测提供1种参考方法。     

Differences in the quality of steamed hull-less barley and wheat cakes北大核心CSCD

In this study, three different hull-less barley varieties were used to prepare steamed cakes, and their nutritional value, sensory qualities, textural properties, and in vitro starch hydrolysis were evaluated. The results showed that the contents of total dietary fiber (4.50%-5.12%), β-glucan (2.96%-3.96%), total flavonoids (12.56-38.73 mg/100 g), and γ-aminobutyric acid (5.08-9.53 mg/100 g) in the steamed hull-less barley cakes were significantly higher than that in the steamed wheat cake, which were 0.65%, 0.23%, 3.93 mg/(100 g), and 2.63 mg/(100 g), respectively. The sensory properties of steamed ordinary hull-less barley and wheat cakes were not significantly different, but the steamed 08-1127 (waxy hull-less barley) cake was softer and out of shape. The springiness, resilience, cohesiveness, gumminess, and adhesiveness of steamed ordinary hull-less barley cakes were similar to those of steamed wheat cake, while those of steamed 08-1127 cakes were significantly lower than those of steamed ordinary hull-less barley cakes. Steamed hull-less barley cake also showed a maximum starch hydrolysis rate (38.76%-42.74%) that was lower than that of steamed wheat cake (49.92%), and the contents of rapidly (11.58%-13.16%) and slowly digested starch (5.34%-7.56%) were lower than that of steamed wheat cake (17.21% and 15.97%, respectively). In addition, the glycemic (59.37-61.67) and hydrolysis indexes (35.82-40.00) were lower than those of steamed wheat cake (76.66 and 67.30, respectively), and the contents of resistant starch (2.74%-3.55%) were higher than those of wheat steamed cake (1.68%). Therefore, the steamed cakes of ordinary hull-less barley had a higher content of nutritional components than the steamed cake of wheat, and the in vitro starch hydrolysis parameters were better than those of steamed wheat cake. When it is necessary to consider both nutritional and sensory qualities, ordinary hull-less barley can be selected as the raw material for steamed cakes, but waxy hull-less barley is not suitable for making steamed cakes. © 2022 Science Press. All rights reserved.     

重点生态功能区生态资产保护成效及驱动力研究

重点生态功能区是保障国家生态安全、扩大绿色生态空间的重要区域,在实现全国生态资产保值增值方面发挥着重要作用。然而,现阶段围绕重点生态功能区生态资产保护成效及驱动力开展的研究较少。为明确重点生态功能区生态资产保护的驱动力以及存在的问题,本文利用遥感影像并结合图像解译、GIS空间分析和数理统计等方法对重点生态功能区内生态资产展开分析,并将其与非重点生态功能区进行比较。研究结果显示:重点生态功能区内森林、灌丛、草地和湿地生态资产总面积为349.30万km~2,优级和良级生态资产占生态资产总面积的42.42%,生态资产综合指数为21.19。15年间,重点功能区内生态资产总面积增加0.05%,优级和良级生态资产面积占比分别增加0.59%和0.85%,生态资产综合指数增加2.94。重点生态功能区生态资产面积和质量增幅均小于非重点生态功能区,然而重点生态功能区生态资产指数增量高于非重点生态功能区。生态恢复是驱动重点生态功能区生态资产面积变化的主因,对原有生态资产的保护是促进生态资产指数增加的最主要驱动力(贡献率达81%),城镇化和农业开发对生态资产的不利影响不容忽视(贡献率达-6.81%)。研究表明,对重点生态功能区生态资产保护成效显著,同时还有较大提升空间。本研究也为优化重点生态功能区的城镇化和农业开发的管理提供了证据和参考。     

Prediction of the concentration of chlorophyll-a for Liuhai urban lakes in Beijing City

The weekly water quality monitor data of Liuhai lakes between April 2003 and November 2004 in Beijing City were used as an example to build an artificial neural networks （ANN） model and a multi-varieties regression model respectively for predicting the fresh water algae bloom. The different predicted abilities of the two methods in Liuhai lakes were compared. A principle analysis method was first used to select the input variables of the models to avoid the phenomenon of collinearity in the data. The results showed that the input variables for the artificial neural networks were T, TP, transparency（SD）, DO, chlorophyll-a （Chl-a）,pH and the output variable was Chl-a. A three layer Levenberg-Marguardt feed forward leaming algorithm in ANN was used to model the eutrophication process of Liuhai lakes. 20 nodes in hidden layer and 1 node of output for the ANN model had been optimized by trial and error method. A sensitivity analysis of the input variables was performed to evaluate their relative significance in determining the predicted values. The correlation coefficient between predicted value and observed value in all data and in test data were 0.717 and 0.816 respectively in the artificial neural networks. The stepwise regression method was used to simulate the linear relation between Chl-a and temperature, of which the correlation coefficient was 0.213. By comparing the results of the two models, it was found that neural network models were able to simulate non-linear behavior in the water eutrophication process of Liuhai lakes reasonably and could successfully estimate some extreme values from calibration and test data sets.     

Biomineralogy of human urinary calculi (kidney stones) from some geographic regions of Sri Lanka

Kidney stones (urinary calculi) have become a global scourge since it has been recognized as one of the most painful medical problems. Primary causative factors for the formation of these stones are not clearly understood, though they are suspected to have a direct relationship to the composition of urine, which is mainly governed by diet and drinking water. Sixty nine urinary calculi samples which were collected from stone removal surgeries were analyzed chemically for their Na, K, Ca, Mg, Cu, Zn, Pb, Fe and phosphate contents. Structural and mineralogical properties of stones were studied by XRD and FT-IR methods. The mean contents of trace elements were 1348 mg kg⁻¹ (Na); 294 mg kg⁻¹ (K); 32% (Ca); 1426 mg kg⁻¹ (Mg); 8.39 mg kg⁻¹ (Mn); 258 mg kg⁻¹ (Fe); 67 mg kg⁻¹ (Cu); 675 mg kg⁻¹ (Zn); 69 mg kg⁻¹ (Pb); and 1.93% (PO₄³⁻). The major crystalline constituent in the calculi of Sri Lanka is calcium oxalate monohydrate. Principal component analysis was used to identify the multi element relationships in kidney stones. Three components were extracted and the first component represents positively correlated Na-K-Mg-PO₄³⁻ whereas the␣second components represent the larger positively weighted Fe–Cu–Pb. Ca–Zn correlated positively in the third component in which Mn–Cu correlated negatively. This study indicates that during the crystallization of human urinary stones, Ca shows more affinity towards oxalates whereas other alkali and alkaline earths precipitate with phosphates.Contribution from the Environmental Geology Research Group (EGRG), Department of Geology, University of Peradeniya, Sri Lanka.     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

活性污泥数学模型(ASM1)入流组分测定及应用

阐述了活性污泥 1号模型 (ASM1)入流中 7个含碳有机物、4个含氮物质、碱度等四大类 13种组分的分析方法 ,并用这些方法测定了西安市北石桥、电子村两个排放口的城市污水水质。实验结果表明快速可生物降解有机物 (SS)采用间歇实验法、慢速可生物降解有机物 (XS)采用测定BOD5间接计算法既操作简单又准确可靠 ,能为利用活性污泥 1号模型进行污水处理厂的设计、模拟及管理提供入流组分分析依据     

Degradation–environment–society spiral: A spatial auto‐logistic model in Thailand

In this contemporary interpretation of the widespread land degradation problem in Southeast Asia, it is hypothesized that spatial interplay of environmental and socioeconomic predictors determines the occurrence of land degradation. Village surveys, remote sensing and spatial auto‐logistic modelling of the relationship between degradation and land use dynamics in Lam Phra Phloeng watershed of Thailand enabled 80.2% of land to be classified correctly in terms of the presence or absence of erosion and explained 53.2% of the total variation. Cultivation and dependence on agriculture for livelihood positively and significantly affect degradation. Lack of access to institutional credit and land titles significantly increased the probability of occurrence of degradation. On the other hand, education and social cohesion are negatively associated with the occurrence of degradation. The Relative Operating Characteristic (ROC) curve was used to measures the performance of the model. The calculated area under the curve (0.879) suggests that significant predictor variables in the model can be confidently used to forecast the likelihood of occurrence of degradation and thus to identify priority areas for intervention. Policies to reduce land degradation should include measures to reduce pressure on the land, including alternative income sources. Policies could mobilize capital to invest in encouraging nature‐based tourism and other off‐farm income options.