GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.

Long-Term Rainfall Trends in South West Asia—Saudi Arabia

In this study, rainfall data from 19 stations in Saudi Arabia (SA) for the period 1985-2019 was utilized to investigate interannual, monthly, and seasonal rainfall variations and trends. The magnitudes of these trends were characterized and tested using Mann-Kendall (MK) rank statistics at different significance levels. During this study period, the mean rainfall in SA showed a slight and significant decreasing trend by about 2 mm/35 years. Investigation of seasonal trends of rainfall revealed that Winter and Spring rainfall decreased significantly by 2.7 mm/35 years and 5.4 mm/35 years respectively. Three months showed very slight significant decreasing trends of rainfall. These were the months of February, March and April. Mann-Kendall analyses were carried out to investigate the annual trends of rainfall during three sub-periods, i.e., 1985-1996, 1997-2008, and 2009-2019. The results revealed that while rainfall increased by 5.3 mm/12 years and 7.8 mm/11 years for the first and the third periods respectively, it decreased by about 11 mm/12 years during the second period. While trends of rainfall in Saudi Arabia are affected by large scale circulations and local factors, the effect of extraterrestrial factors, such as solar activity and its consequent effects on the climate may, additionally, play a potential role in affecting the pattern of rainfall in Saudi Arabia.

Analysis of Temperature Trends and Variations in the Arabian Peninsula’s Upper Atmosphere

In this study, the trends of upper-air temperatures are analysed by utilising radiosonde observations for the barometric levels at 700, 500, 300, 200, 150, 100 and 50 hPa from five meteorological stations within the Arabian Peninsula from January 1986 to August 2015. The mean monthly variations of the temperatures at these levels are characterised and established. The magnitudes of the annual trends of the mean temperatures for each site for the selected barometric levels are studied and statistically tested using Mann-Kendall rank statistics at different significance levels. The temperature trends at different pressure levels show that the upper troposphere and lower stratosphere are warming, while the middle troposphere is cooling which is consistent with the findings of other studies. The variations in upper air temperature observed in this study can be attributed to a range of factors, including increasing greenhouse gas concentrations, changes in atmospheric circulation patterns, variations in solar activity, aerosols and volcanic eruptions, and land use and land cover change.

The long-term trend of the sea surface wind speed and the wave height (wind wave, swell, mixed wave) in global ocean during the last 44 a

Utilizing the 45 a European Centre for Medium-Range Weather Forecasts （ECMWF） reanalysis wave da- ta （ERA-40）, the long-term trend of the sea surface wind speed and （wind wave, swell, mixed wave） wave height in the global ocean at grid point 1.5°× 1.5° during the last 44 a is analyzed. It is discovered that a ma- jority of global ocean swell wave height exhibits a significant linear increasing trend （2-8 cm/decade）, the distribution of annual linear trend of the significant wave height （SWH） has good consistency with that of the swell wave height. The sea surface wind speed shows an annually linear increasing trend mainly con- centrated in the most waters of Southern Hemisphere westerlies, high latitude of the North Pacific, Indian Ocean north of 30°S, the waters near the western equatorial Pacific and low latitudes of the Atlantic waters, and the annually linear decreasing mainly in central and eastern equator of the Pacific, Juan. Fernandez Archipelago, the waters near South Georgia Island in the Atlantic waters. The linear variational distribution characteristic of the wind wave height is similar to that of the sea surface wind speed. Another find is that the swell is dominant in the mixed wave, the swell index in the central ocean is generally greater than that in the offshore, and the swell index in the eastern ocean coast is greater than that in the western ocean inshore, and in year-round hemisphere westerlies the swell index is relatively low.

Long-Term Trends in Extreme Temperatures in Hong Kong and Southern China

The observed long-term trends in extreme temperatures in Hong Kong were studied based on the meteorological data recorded at the Hong Kong Observatory Headquarters from 1885-2008. Results show that, over the past 124 years, the extreme daily minimum and maximum temperatures, as well as the length of the warm spell in Hong Kong, exhibit statistically significant long-term rising trends, while the length of the cold spell shows a statistically significant decreasing trend. The time-dependent return period analysis also indicated that the return period for daily minimum temperature at 4°C or lower lengthened considerably from 6 years in 1900 to over 150 years in 2000, while the return periods for daily maximum temperature reaching 35°C or above shortened drastically from 32 years in 1900 to 4.5 years in 2000. Past trends in extreme temperatures from selected weather stations in southern China from 1951-2004 were also assessed. Over 70% of the stations studied yielded a statistically significant rising trend in extreme daily minimum temperature, while the trend for extreme maximum temperatures was found to vary, with no significant trend established for the majority of stations.

Long-Term Trends in Photosynthetically Active Radiation in Beijing

A long-term dataset of photosynthetically active radiation （Qp） is reconstructed from a broadband global solar radiation （Rs） dataset through an all-weather reconstruction model. This method is based on four years＇ worth of data collected in Beijing. Observation data of Rs and Qp from 2005-2008 are used to investigate the temporal variability of Qp and its dependence on the clearness index and solar zenith angle. A simple and effcient all-weather empirically derived reconstruction model is proposed to reconstruct Qp from Rs. This reconstruction method is found to estimate instantaneous Qp with high accuracy. The annual mean of the daily values of Qp during the period 1958-2005 period is 25.06 mol m-2 d-1. The magnitude of the long-term trend for the annual averaged Qp is presented （-0.19 mol m-2 yr-1 from 1958-1997 and -0.12 mol m-2 yr-1 from 1958-2005）. The trend in Qp exhibits sharp decreases in the spring and summer and more gentle decreases in the autumn and winter.

foF2 Long-Term Trend at a Station Located near the Crest of the Equatorial Ionization Anomaly

Critical frequency foF2 long-term trends at Dakar station (14.4°N, 342.74°E) located near the crest of the equatorial ionization anomaly EIA, are analysed taking into account geomagnetic activity, increasing greenhouse gases concentration and Earth’s magnetic field secular variation. After filtering solar activity effect using F10.7 as a solar activity proxy, we determined the relative residual trends slopes α values for three different levels of geomagnetic activity. For example, at 1200 LT, the value of α goes from -0.27%/year for very magnetically quiet days to -0.19%/year for magnetically quiet days and to -0.13%/year for all days. It appears from the slopes α obtained, that they increase with the level of geomagnetic activity and their negative values are qualitatively consistent with the expected decreasing trend due to the increase in greenhouse gases concentration but are greater than 0.003%/year which would result from a 20% increase in CO2 emissions which actually took place during the analysis period. Regarding Earth’s magnetic field magnitude, B secular variation and the dip equator secular movement, Dakar station is located near the crest of the equatorial ionization anomaly, Earth’s magnetic field magnitude, B decreases there and the trough approaches the position of Dakar during the period of analysis. These two phenomena induce a decrease in foF2 which is in agreement with the decreasing trend observed at this station.

Long-Term Visibility Trends in the Riyadh Megacity, Central Arabian Peninsula and Their Possible Link to Solar Activity

In this study, atmospheric visibility (AV) data from Riyadh, Saudi Arabia (24.91˚N, 46.41˚E, 760 m), for the period 1976-2011 were utilized to investigate the interannual, monthly, and seasonal AV variations and trends. The magnitudes of these trends were characterized and tested using mann-kendall (MK) rank statistics at different significance levels. No significant trend in AV was observed during the 36-year period. However, a significant increase in the annual mean AV by 0.24 km per year for the period between 1976 and 1999 was found. For the period 1999-2011, AV decreased significantly by 0.16 km per year. The potential effects of air temperature and relative humidity on AV were investigated. While these two variables could explain the observed trend of AV over some periods, they failed to do so for the whole study period. To search for extraterrestrial causes for long-term AV variations, correlation analyses between the time series of cosmic ray (CR) data (measured by NM and muon detector) and solar activity (represented by sunspot number) and AV were conducted and showed that these two variables are able to explain the AV variations for the whole study period. Additionally, power spectra analyses were conducted to investigate periodicities in the AV time series. Several significant periodicities, such as 9.8, 5.2, 2.2, 1.7, and 1.3 years were recognized. The obtained periodicities were similar to those reported by several investigators and found in solar, interplanetary, and CR parameters. The spectral and correlation results suggested that, with the expected effects of terrestrial and meteorological conditions on AV, long-term AV variations can also be related to the solar activity and associated CR modulations.

Long-Term Trends and Its Best Functional Form Estimation of Yearly Maximum and Minimum Temperatures at Cotonou City by Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Method

The understanding of the long-term trend in climatic variables is necessary for the climate change impacts studies and for modeling several processes in environmental engineering. However, for climatic variables, long-term trend is usually unknown whether there is a trend component and, if so, the functional form of this trend is also unknown. In this context, a conventional strategy consists to assume randomly the shape of the local trends in the time series. For example, the polynomial forms with random order are arbitrarily chosen as the shape of the trend without any previous justification. This study aims to 1) estimate the real long-term nonlinear trend and the changing rate of the yearly high temperature among the daily minimum (YHTaDMinT) and maximum temperatures (YHTaDMaxT) observed at Cotonou city, 2) find out for these real trend and trend increment, the best polynomial trend model among four trend models (linear, quadratic, third-order and fourth-order polynomial function). For both time series, the results show that YHTaDMinT and YHTaDMaxT time series are characterized by nonlinear and monotonically increasing trend. The trend increments present different phases in their nonmonotone variations. Among the four trend estimations models, the trend obtained by third-order and fourth-order polynomial functions exhibits a close pattern with the real long-term nonlinear trend given by the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). But, the fourth-order polynomial function is optimal, therefore, it can be used as the functional form of trend. In the trend increment case, for the YHTaDMaxT time series, the fourth-order fit is systematically the best among the four proposed trend models. Whereas for the YHTaDMinT time series, the third-order and fourth-order polynomial functions present the same performance. They can both be used as the functional form of trend increments. Overall, the fourth-order polynomial function presents a good performance in terms of trend and trend increments estimation.

海洋悬浮物浓度的长期演变特征及机制

受气候变化和人类活动的影响,21世纪以来全球海洋的悬浮物浓度发生了显著变化。悬浮物浓度的剧烈变化不可避免地对海洋生态环境、地貌演化和生物地球化学循环产生重大影响。随着气候变化对人类活动的环境影响研究的深入,以及卫星遥感数据的不断积累,悬浮物浓度的长期变化趋势及其机制已成为地球科学研究的热点。本文概括性地总结了海洋悬浮物浓度长期变化的国内外研究进展,指出了中国近海相关研究的不足之处,并对未来发展方向作出了展望。

单位根KPSS检验趋势项检验量的改进

为完善KPSS检验流程,借鉴单位根右侧检验理论,将递归估计方法引入KPSS趋势项检验中,构造两种递归估计上确界趋势项检验量和一种非递归估计趋势项检验量。理论研究表明,所有趋势项检验量在大样本下都收敛于维纳过程的泛函,但与已有检验量的分布不同,需要重新模拟以获取临界值。蒙特卡洛模拟显示,和非递归估计趋势项检验量相比,递归估计上确界趋势项检验量具有满意的检验水平,但临界值更大。在三种突变结构模型中,所有检验量在全局结构突变模型中检验功效最高,且符合理论研究结果。在三种检验量中,狭义递归估计趋势项检验量检验功效总体占优,说明递归估计上确界趋势项检验量具有优越性。

面向动态交通分配的交通需求深度学习预测方法

为满足动态交通分配对高精度、高时效性交通需求的要求,本文建立了一种交通需求深度学习预测方法。根据动态交通分配要求确定交通需求数据的时间间隔,构建对复杂交通需求预测性能较优的长短期记忆神经网络预测方法;针对动态交通分配中交通需求的周期性、随机性和非线性等特征,为减少数据噪声的干扰,引入局部加权回归周期趋势分解方法将交通需求数据分解,将其中的趋势分量和余项分量作为深度学习预测方法的输入量,周期分量采用周期估计进行预测;选用具有随机寻优能力强、寻优效率高等特点的布谷鸟寻优算法优化预测方法的隐藏层单元数量、学习速率和训练迭代次数等核心参数。应用西安市长安区的卡口车牌数据验证该方法。结果表明:本文模型的预测结果在高峰及平峰各连续4个时段内相比于自回归滑动平均模型、长短期记忆神经网络模型、支持向量回归模型,平均绝对误差降低了10.55%~19.80%,均方根误差降低了11.20%~17.99%,决定系数提升了8.62%~12.48%;相比遗传算法、粒子群算法优化的模型,平均绝对误差降低了7.36%~13.81%,均方根误差降低了4.23%~10.67%,决定系数提升了3.50%~7.01%,且本文模型运行时间最短。说明与对比模型相比,本文所建立的预测方法在面向动态交通分配的交通需求预测中具有更高的预测精度。

基于周期特征提取的DLnet预测模型研究

现有的预测方法很少独立分析能源消耗的周期性特征。本文提出了一个短期办公建筑能耗预测模型(DLnet),以解决周期性能耗数据利用效率低下的问题。首先,利用STL对能耗数据的周期成分进行分解,通过网格搜索算法寻找能耗数据的最优周期;然后,根据最优周期构建周期块;再根据周期块的数据形状构建时间序列块数据;之后,利用长短期记忆(LSTM)对时间序列块数据和周期块数据进行训练和学习;最后,通过线性回归将时间序列块数据和周期块数据的预测结果进行融合。事实证明,所提出的模型的4个预测精度指标分别比LSTM模型高7%,21%,25%和26%。

青藏高原1981-2015年暖季降水变化趋势:受控于大尺度环流型变化

利用最新的高时空分辨率融合降水数据集,研究青藏高原的长期降水变化趋势。结果表明,在1981—2015年的暖季(5—9月),青藏高原降水量显著增加(格点平均趋势为0.9 mm/a),其东北部、中部和西部最为显著。采用T模态倾斜旋转主成分分析法(PCT),将青藏高原500 hPa位势高度场分为9个典型环流型,发现第2和第4环流型(T2和T4)是暖季降水量增加的主导环流型(DT)。DT位势高度场为西低东高的“槽脊对峙”分布,青藏高原暖季降水量增加体现在DT主导降水日数和日均降水量的共同增加。除DT环流型数量的增加导致青藏高原中西部降水增加外,降水增加机制还包括降水条件的优化,即T2以“槽脊对峙”加强为驱动的动力条件优化为主导,T4以“水汽滞增”加强为驱动的热力条件优化为主导。

1961一2020年中国复合湿热的变化特征

随着全球变暖加剧,复合湿热天气在世界各地呈现显著加剧趋势,中国东部也是极端湿热事件的高发区。为更好了解中国复合湿热事件的变化特征,基于1961—2020年中国日最高湿球温度观测数据,利用趋势分析、小波功率谱分析和广义极值分布分析等方法,对中国日最高湿球温度的时空变化特征进行了深入分析。结果表明:1)1961—2020年中国日最高湿球温度平均值和最大值主要呈“南高北低”的分布特点,最大值高值区集中在中国南部和四川盆地。全国日最高湿球温度平均值呈增强趋势,最大值无明显的变化趋势。全国平均值有2~6 a尺度的周期震荡,全国最大值在多个时间段和时间尺度有短周期。2)全国极端湿热阈值分布与日最高湿球温度最大值比较类似,极端湿热强度呈现增强趋势,全国极端湿热频次也以0.098 d/a的速率增多。西北东部地区极端湿热强度增强幅度最大,但南方地区呈减弱趋势;西北东部、南方和东北地区极端湿热频次持续增多。3)多年一遇事件的阈值分布同样与最大值分布类似,多年一遇事件频次呈现显著的区域特征,多年一遇事件主要发生在四川盆地,其中西北东部地区显著增多,南方地区有减少趋势。

中国海风能和波浪能的长期趋势与演变规律分析

海上风能和波浪能是极具吸引力的可再生能源。在气候变化的背景下,基于ERA5再分析数据产品研究并对比了1979~2020年的中国海风能和波浪能的长期历史趋势和多尺度演化规律;采用Mann-Kendall检验和Theil-Sen方法分别量化了趋势显著性、趋势幅度和突变点。结果表明,海上风能和波浪能在气候特征和长期趋势上存在显著差异。风能的增长趋势在统计上不显著,平均增长率为每10年0.55%;而波浪能呈现强劲的增长趋势,以每10年4.8%的正向速率上升。海浪特性普遍向大波高、长周期和高能量的海况转移。不同季节和地区的长期趋势区别明显,其中冬季是风与波资源储量和趋势增长的优势季节、东海等开阔海域显示了风能和波浪能持续开发的潜力。基于这些分析结果,进一步探究了风浪能和涌浪能的时空多样性,揭示了海浪场的强化以及风波系统在能量场中的差异主要由涌浪的资源量所贡献,并受涌浪变化的调制。这些结果为海上资源评估、风和海浪气候预测以及海洋工程等众多领域提供了科学参考。

1985—2019年中国18个少数民族17岁学生身高长期趋势分析

目的:分析1985—2019年我国18个少数民族17岁学生身高生长长期变化趋势。方法:提取7次中国学生体质与健康调研报告(除1991年)中蒙古族、回族、维吾尔族、壮族、朝鲜族、藏族、苗族、布依族、侗族、白族、土家族、哈尼族、傣族、傈僳族、佤族、纳西族、土族、羌族等18个少数民族学生身高均值进行研究。结果:34年间各少数民族男生17岁身高均值显著增长(P<0.05)。2019年18个少数民族17岁男学生中,回族男生身高均值最高(173.09 cm),苗族男生身高均值最低(162.66 cm),每10年身高增长>1 cm的民族有侗族(1.96 cm/10年)、白族(1.96 cm/10年)、土家族(1.95 cm/10年)、羌族(1.80 cm/10年)、回族(1.80 cm/10年)、蒙古族(1.79 cm/10年)、土族(1.72 cm/10年)、维吾尔族(1.63 cm/10年)、藏族(1.61 cm/10年)、布依族(1.55 cm/10年)、纳西族(1.52 cm/10年)、傣族(1.35 cm/10年)、傈僳族(1.29 cm/10年)、哈尼族(1.25 cm/10年)、壮族(1.16 cm/10年)、佤族(1.06 cm/10年);每10年身高增长<1 cm的民族是苗族(0.95 cm/10年)。2019年,18个少数民族17岁女学生中,回族女生身高均值最高(161.71 cm),布依族女生身高均值最低(151.63 cm),每10年身高增长>1 cm的民族分别是土家族(1.80 cm/10年)、回族(1.52 cm/10年)、土族(1.25 cm/10年)、白族(1.23 cm/10年)、侗族(1.18 cm/10年)、朝鲜族(1.12 cm/10年)、羌族(1.04 cm/10年);34年间除维吾尔族、苗族女生外,其余少数民族女生身高增长显著(P<0.05)。结论:我国少数民族男生均出现了较为明显的生长长期趋势,大部分少数民族女生出现了较为明显的生长长期趋势,各民族男女生身高增长不平衡,证明我国不同地区、不同少数民族学生生长潜力得以发挥,对于身高增长较慢的民族需要给予更高的关注,而对于那些身高增长较快的民族也要预防其带来的负面效应。

多源数据预测重庆市肝炎发病趋势的时滞输入神经网络研究

目的利用多源互联网数据构建时序分析融合工具,继而精准预测重庆市肝炎的发病趋势。方法利用卫生疾控中心数据库获取肝炎发病率数据,大气污染物数据来源于中国环境监测总站官网,气候数据来源于国家气象星系中心,网络指数数据来源于百度搜索引擎,时间范围均为2013年11月至2023年5月。基于现有的时序分析方法,利用多源数据对分解模型的残差部分进行校正。基于非自回归(non-autoregressive,NAR)和长短期记忆递归神经网络(long short term memory,LSTM)的各自优势,构建了时滞输入神经网络(delayed input neural network,DINN)。之后,还在其基础上加入了星雀优化算法(nutcracker optimizer algorithm,NOA)和联合四分位-Huber损失函数(joint quantile Huber loss,JQHL)等优化模块,继而构建了DINN+。结果相较于常见的单输入模型及同步多输入模型,DINN可取得最为优异的预测效果。在加入超参数和损失函数优化后,DINN+的预测性能进一步提升,其测试集MSE为0.1709、MAE为0.4612、RMSE为0.5821、MAPE为0.0626、R 2为0.8840。结论基于多样方法和多元数据融合的思想,在既往的时序分析方法基础上,本文提出了一个准确性和泛化能力良好的DINN+优化模型。该模型丰富和补充了利用多源数据校准传染病时序预测分析的方法学研究内容,可作为未来传染病公共卫生层面影响因素分析及趋势预测的全新基准。

高速电梯振动测试与故障诊断研究

针对高速电梯激励频率高、零部件负荷大导致的共振现象与安全隐患,为高效且精细化地进行振动测试,以某型号高速电梯为例,将实际原始信号的测量均值作为真实信号的修正值,消除低频信号趋势项带来的误差,并结合对瞬时响应捕捉能力更强的离散小波变换算法,更加精准地实现高速电梯导轨缺陷与导靴故障诊断。同时,基于高速电梯离散位移-时间变化率提出的状态分割算法,可快捷实现高速电梯的5个运行状态信号自动提取,为高速电梯振动特性分析与故障诊断提供了便利。实验结果表明,所提方法可以快速高效地实现不同工况下高速电梯振动信号的特征提取和电梯运行故障诊断。

我国长期护理保险研究热点及趋势分析

目的:分析我国长期护理保险的研究现状和发展趋势,以期为我国长期护理保险领域的研究与实践提供参考依据。方法:通过中国知网数据库、万方数据库和维普数据库,获取2010—2022年国内长期护理保险研究文献相关信息,运用Cite Space软件绘制科学知识图谱,对该领域发文作者、研究机构、研究热点和演变趋势进行可视化分析。结果:学者间联系不够紧密、研究团队较少,且未形成紧密的研究网络。研究热点包括实施现状、老龄化背景、服务供给等。研究趋势包括制度探索、运行模式、护理服务等。结论:我国长期护理保险领域的研究需要进一步加强合作与交流,加强政策制度研究,并完善护理服务体系,从而推进长期护理保险制度发展。