基于LSTM算法的玉米籽粒储藏温度预测

为减少储粮损失和虫霉等的发生,该文利用自制试验仓及检测系统检测储藏玉米籽粒不同位置的粮温,分析粮堆温度变化及整个粮堆热量的传递过程。试验结果表明,仓外环境温度对粮温影响较大,仓内粮食温度变化与仓外环境温度变化相比较为滞后,粮堆第一层2号位置温度在检测周期中一直处于较低状态,温度最高位置出现在第四层12号位置。基于粮堆温度变化分析,该文开展了基于长短时记忆网络(LSTM)算法的玉米籽粒储藏粮温预测研究。结果表明:(1)对比预测值与试验值可知,粮堆第一、二、三、四层测试集的粮温准确率分别为0.62、0.89、0.83、0.79;(2)位于粮堆第二层和第三层的预测结果精度较高,试验仓粮堆底层和顶层温度易受环境温度影响,粮堆热量交换速度快,温度变化迅速,导致第一层和第四层预测结果精度偏低。该研究可为粮食储藏温度预测研究提供新思路。

Estimating Monthly Surface Air Temperature Using MODIS LST Data and an Artificial Neural Network in the Loess Plateau, China

Air temperature(Ta)datasets with high spatial and temporal resolutions are needed in a wide range of applications,such as hydrology,ecology,agriculture,and climate change studies.Nonetheless,the density of weather station networks is insufficient,especially in sparsely populated regions,greatly limiting the accuracy of estimates of spatially distributed Ta.Due to their continuous spatial coverage,remotely sensed land surface temperature(LST)data provide the possibility of exploring spatial estimates of Ta.However,because of the complex interaction of land and climate,retrieval of Ta from the LST is still far from straightforward.The estimation accuracy varies greatly depending on the model,particularly for maximum Ta.This study estimated monthly average daily minimum temperature(Tmin),average daily maximum temperature(Tmax)and average daily mean temperature(Tmean)over the Loess Plateau in China based on Moderate Resolution Imaging Spectroradiometer(MODIS)LST data(MYD11A2)and some auxiliary data using an artificial neural network(ANN)model.The data from 2003 to 2010 were used to train the ANN models,while 2011 to 2012 weather station temperatures were used to test the trained model.The results showed that the nighttime LST and mean LST provide good estimates of Tmin and Tmean,with root mean square errors(RMSEs)of 1.04℃ and 1.01℃,respectively.Moreover,the best RMSE of Tmax estimation was 1.27℃.Compared with the other two published Ta gridded datasets,the produced 1 km×1 km dataset accurately captured both the temporal and spatial patterns of Ta.The RMSE of Tmin estimation was more sensitive to elevation,while that of Tmax was more sensitive to month.Except for land cover type as the input variable,which reduced the RMSE by approximately 0.01℃,the other vegetation-related variables did not improve the performance of the model.The results of this study indicated that ANN,a type of machine learning method,is effective for long-term and large-scale Ta estimation.

基于LSTM网络的海水温度剖面预报研究

基于海水温度历史观测和海洋模式数值预报数据,利用长短期记忆(LSTM)神经网络开展了海水温度剖面短时预报方法研究。以南海17°46.91′N,112°03.24′E处海水温剖面预报为研究对象,利用模式预报数据和观测数据,构建了观测、预报、观测和预报混合3个样本数据集。基于LSTM神经网络模型,建立了由编码器-解码器组成的多对多海水温度剖面序列预报模型,并开展了模型的训练和性能验证分析工作。结果表明:该模型具有较高的预报精度,对小样本问题的处理具有良好的稳定性;采用深度分层预报,可有效提高模型预报精度,改善模型泛化能力;与只使用观测数据集的预报相比,混合样本集预报误差下降明显,为基于小样本观测数据的海洋环境要素预报研究提供了一种思路。

Record-breaking High-temperature Outlook for 2023: An Assessment Based on the China Global Merged Temperature(CMST) Dataset

According to the latest version(version 2.0) of the China global Merged Surface Temperature(CMST2.0) dataset, the global mean surface temperature(GMST) in the first half of 2023 reached its third warmest value since the period of instrumental observation began, being only slightly lower than the values recorded in 2016 and 2020, and historically record-breaking GMST emerged from May to July 2023. Further analysis also indicates that if the surface temperature in the last five months of 2023 approaches the average level of the past five years, the annual average surface temperature anomaly in 2023 of approximately 1.26°C will break the previous highest surface temperature, which was recorded in 2016of approximately 1.25°C(both values relative to the global pre-industrialization period, i.e., the average value from 1850 to1900). With El Ni?o triggering a record-breaking hottest July, record-breaking average annual temperatures will most likely become a reality in 2023.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

A bHLH transcription factor,CsSPT,regulates high-temperature resistance in cucumber

High-temperature stress threatens the growth and yield of crops. Basic helix-loop-helix(bHLH) transcription factors(TFs) have been shown to play important roles in regulating high-temperature resistance in plants. However, the bHLH TFs responsible for high-temperature tolerance in cucumbers have not been identified. We used transcriptome profiling to screen the high temperature-responsive candidate bHLH TFs in cucumber. Here, we found that the expression of 75 CsbHLH genes was altered under high-temperature stress. The expression of the CsSPT gene was induced by high temperatures in TT(Thermotolerant) cucumber plants. However, the Csspt mutant plants obtained by the CRISPR-Cas9 system showed severe thermosensitive symptoms, including wilted leaves with brown margins and reduced root density and cell activity.The Csspt mutant plants also exhibited elevated H_(2)O_(2) levels and down-regulated photosystem-related genes under normal conditions.Furthermore, there were high relative electrolytic leakage(REC), malondialdehyde(MDA), glutathione(GSH), and superoxide radical(O_(2)^(·-)) levels in the Csspt mutant plants, with decreased Proline content after the high-temperature treatment. Transcriptome analysis showed that the photosystem and chloroplast activities in Csspt mutant plants were extremely disrupted by the high-temperature stress compared with wildtype(WT) plants. Moreover, the plant hormone signal transduction, as well as MAPK and calcium signaling pathways were activated in Csspt mutant plants under high-temperature stress. The HSF and HSP family genes shared the same upregulated expression patterns in Csspt and WT plants under high-temperature conditions. However, most bHLH, NAC, and bZIP family genes were significantly down-regulated by heat in Csspt mutant plants. Thus, these results demonstrated that CsSPT regulated the high-temperature response by recruiting photosynthesis components, signaling pathway molecules, and transcription factors. Our results provide important insights into the heat response mechanism of CsSPT in cucumber and its potential as a target for breeding heat-resistant crops.

Alkyl dimethyl betaine activates the low-temperature collection capacity of sodium oleate for scheelite

The impact of alkyl dimethyl betaine (ADB) on the collection capacity of sodium oleate (NaOl) at low temperatures was evaluated using flotation tests at various scales. The low-temperature synergistic mechanism of ADB and NaOl was explored by infrared spectroscopy, X-ray photoelectron spectroscopy, surface tension measurement, foam performance test, and flotation reagent size measurement.The flotation tests revealed that the collector mixed with octadecyl dimethyl betaine (ODB) and NaOl in a mass ratio of 4:96 exhibited the highest collection capacity. The combined collector could increase the scheelite recovery by 3.48% at low temperatures of 8–12℃. This is particularly relevant in the Luanchuan area, which has the largest scheelite concentrate output in China. The results confirmed that ODB enhanced the collection capability of NaOl by improving the dispersion and foaming performance. Betaine can be introduced as an additive to NaOl to improve the recovery of scheelite at low temperatures.

Competitive oxidation behavior of Ni-based superalloy GH4738 at extreme temperature

A high thrust-to-weight ratio poses challenges to the high-temperature performance of Ni-based superalloys. The oxidation behavior of GH4738 at extreme temperatures has been investigated by isothermal and non-isothermal experiments. As a result of the competitive diffusion of alloying elements, the oxide scale included an outermost porous oxide layer (OOL), an inner relatively dense oxide layer (IOL), and an internal oxide zone (IOZ), depending on the temperature and time. A high temperature led to the formation of large voids at the IOL/IOZ interface. At 1200℃, the continuity of the Cr-rich oxide layer in the IOL was destroyed, and thus, spallation occurred. Extension of oxidation time contributed to the size of Al-rich oxide particles with the increase in the IOZ. Based on this finding,the oxidation kinetics of GH4738 was discussed, and the corresponding oxidation behavior at 900-1100℃ was predicted.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Comprehensive analyses of the proteome and ubiquitome revealed mechanism of high temperature accelerating petal abscission in tree peony

Tree peony(Paeonia suffruticosa Andrews)is a well-known ornamental plant with high economic value,but the short fluorescence is a key obstacle to its ornamental value and industry development.High temperature accelerates flower senescence and abscission,but the associated mechanisms are poorly understood.In this study,the tandem mass tag(TMT)proteome and label-free quantitative ubiquitome from tree peony cut flowers treated with 20℃for 0 h(RT0),20℃or 28℃for 60 h(RT60 or HT60)were examined based on morphological observation,respectively.Totally,6970 proteins and 1545 lysine ubiquitinated(Kub)sites in 844 proteins were identified.Hydrophilic residues(such as glutamate and aspartate)neighboring the Kub sites were in preference,and 36.01%of the Kub sites were located on the protein surface.The differentially expressed proteins(DEPs)and Kub-DEPs in HT60 vs RT60 were mainly enriched in ribosomal protein,protein biosynthesis,secondary metabolites biosynthesis,flavonoid metabolism,carbohydrate catabolism,and auxin biosynthesis and signaling revealed by GO and KEGG analysis,accompanying the increase of endogenous abscisic acid(ABA)accumulation and decrease of endogenous indoleacetic acid(IAA)level.Additionally,the expression patterns of six enzymes(SAMS,ACO,YUC,CHS,ANS and PFK)putatively with Kub modifications were analyzed by proteome and real-time quantitative RT-PCR.The cell-free degradation assays showed PsSAMS and PsACO proteins could be degraded via the 26 S proteasome system in tree peony flowers.Finally,a working model was proposed for the acceleration of flower senescence and abscission by high temperature.In summary,all results contributed to understanding the mechanism of flower senescence induced by high temperature and prolonging fluorescence in tree peony.

Revealing the key role of non-solvating diluents for fast-charging and low temperature Li-ion batteries

Fast-charging and low temperature operation are of vital importance for the further development of lithium-ion batteries(LIBs),which is hindered by the utilization of conventional carbonate-based electrolytes due to their slow kinetics,narrow operating temperature and voltage range.Herein,an acetonitrile(AN)-based localized high-concentration electrolyte(LHCE)is proposed to retain liquid state and high ionic conductivity at ultra-low temperatures while possessing high oxidation stability.We originally reveal the excellent thermal shielding effect of non-solvating diluent to prevent the aggregation of Li^(+) solvates as temperature drops,maintaining the merits of fast Li transport and facile desolvation as at room temperature,which bestows the graphite electrode with remarkable low temperature performance(264 mA h g^(-1) at-20 C).Remarkably,an extremely high capacity retention of 97%is achieved for high-voltage high-energy graphite||NCM batteries after 250 cycles at-20 C,and a high capacity of 110 mA h g^(-1)(71%of its room-temperature capacity)is retained at-30°C.The study unveils the key role of the non-solvating diluents and provides instructive guidance in designing electrolytes towards fast-charging and low temperature LIBs.

Low-energy-consumption temperature swing system for CO_(2) capture by combining passive radiative cooling and solar heating

Temperature-swing adsorption(TSA)is an effective technique for CO_(2) capture,but the temperature swing procedure is energy-intensive.Herein,we report a low-energy-consumption system by combining passive radiative cooling and solar heating for the uptake of CO_(2) on commercial activated carbons(CACs).During adsorption,the adsorbents are coated with a layer of hierarchically porous poly(vinylidene fluoride-co-hexafluoropropene)[P(VdF-HFP)HP],which cools the adsorbents to a low temperature under sunlight through radiative cooling.For desorption,CACs with broad absorption of the solar spectrum are exposed to light irradiation for heating.The heating and cooling processes are completely driven by solar energy.Adsorption tests under mimicked sunlight using the CACs show that the performance of this system is comparable to that of the traditional ones.Furthermore,under real sunlight irradiation,the adsorption capacity of the CACs can be well maintained after multiple cycles.The present work may inspire the development of new temperature swing procedures with little energy consumption.

Electrolyte Design for Low‑Temperature Li‑Metal Batteries:Challenges and Prospects

Electrolyte design holds the greatest opportunity for the development of batteries that are capable of sub-zero temperature operation.To get the most energy storage out of the battery at low temperatures,improvements in electrolyte chemistry need to be coupled with optimized electrode materials and tailored electrolyte/electrode interphases.Herein,this review critically outlines electrolytes’limiting factors,including reduced ionic conductivity,large de-solvation energy,sluggish charge transfer,and slow Li-ion transportation across the electrolyte/electrode interphases,which affect the low-temperature performance of Li-metal batteries.Detailed theoretical derivations that explain the explicit influence of temperature on battery performance are presented to deepen understanding.Emerging improvement strategies from the aspects of electrolyte design and electrolyte/electrode interphase engineering are summarized and rigorously compared.Perspectives on future research are proposed to guide the ongoing exploration for better low-temperature Li-metal batteries.

Attribution of Biases of Interhemispheric Temperature Contrast in CMIP6 Models

One of the basic characteristics of Earth's modern climate is that the Northern Hemisphere(NH) is climatologically warmer than the Southern Hemisphere(SH). Here, model performances of this basic state are examined using simulation results from 26 CMIP6 models. Results show that the CMIP6 models underestimate the contrast in interhemispheric surface temperatures on average(0.8 K for CMIP6 mean versus 1.4 K for reanalysis data mean), and that there is a large intermodel spread, ranging from -0.7 K to 2.3 K. A box model energy budget analysis shows that the contrast in interhemispheric shortwave absorption at the top of the atmosphere, the contrast in interhemispheric greenhouse trapping, and the crossequatorial northward ocean heat transport, are all underestimated in the multimodel mean. By examining the intermodel spread, we find intermodel biases can be tracked back to biases in midlatitude shortwave cloud forcing in AGCMs. Models with a weaker interhemispheric temperature contrast underestimate the shortwave cloud reflection in the SH but overestimate the shortwave cloud reflection in the NH, which are respectively due to underestimation of the cloud fraction over the SH extratropical ocean and overestimation of the cloud liquid water content over the NH extratropical continents.Models that underestimate the interhemispheric temperature contrast exhibit larger double ITCZ biases, characterized by excessive precipitation in the SH tropics. Although this intermodel spread does not account for the multimodel ensemble mean biases, it highlights that improving cloud simulation in AGCMs is essential for simulating the climate realistically in coupled models.

基于LSTM神经网络的烟丝水分恒定控制系统设计

在烟丝加工过程中,水分分布受到温度、湿度多个因素的影响,控制系统无法准确反映整体水分情况;为全面提高加工型香烟的质量水平,设计基于LSTM神经网络的烟丝水分恒定控制系统;部署Profibus控制总线,并在线路体系中连接水分检测仪与水分恒定器,完成烟丝水分恒定控制系统的硬件设计;在系统软件设计方面,构建LSTM神经网络单元,根据烟叶吸湿能力分析条件,求解具体的水分分布模型,实现基于LSTM神经网络的烟丝水分模型建模;分别计算烟叶出口湿度与出口温度,并联合传递函数逼近参量与恒定时滞参数,完成对控制参数的整定处理,再联合相关应用部件,实现基于LSTM神经网络的烟丝水分恒定控制系统设计;实验结果表明,LSTM神经网络模型作用下,生丝含水量被稳定控制在13%~18%数值之间,不会因水分过量问题而导致香烟质量水平无法达到实际加工标准。

基于LSTM的多因素石灰窑煅烧带温度预测研究

针对石灰窑煅烧过程易出现燃烧不平衡的问题以及石灰窑煅烧系统的滞后性,提出了大数据分析+神经网络的解决方案。利用大数据分析对石灰窑多源历史数据进行数据融合插补,采用多元线性回归方程分析空间因素对温度的影响,通过时间滑窗提取特征,在此基础上利用长短期记忆神经网络(LSTM)算法构建多因素模型,并采用自适应运动估计算法进行优化。实验结果表明:较单因素LSTM模型,多因素LSTM模型有效提高了石灰窑温度预测精度,现场可根据预测值提前调整工艺参数,实现了石灰窑局部温度预测。

利用Sentinel-3 SLSTR气候参数对江西省干旱事件的分析

利用Sentinel-3 SLSTR数据研究地表温度(land surface temperature,LST)、植被覆盖度(fractional vegetation cover,FVC)、总柱水汽含量(total column water vapor content,TCWV)等气象参数及其与DEM的相互关系,分析了2018—2022年江西短期干旱等气象事件。分析结果表明:全省气候事件中FVC之间呈现显著正相关,相关系数在0.86~0.93且FVC逐年在增加;LST之间存在显著正相关;干旱事件中TCWV之间存在显著正相关,洪涝和干旱事件间TCWV之间存在中度负相关。同时,LST和FVC呈负相关,TCWV和FVC呈正相关,且FVC之间和TCWV之间的变化趋势相似。另外,DEM与FVC存在中度正相关,DEM与LST存在负相关,DEM与TCWV存在双重相关。因此,Sentinel-3 SLSTR数据可作为江西省气象异常事件监测的有效数据源,TCWV和FVC数据的利用对异常数据监测具有重要意义。

Room Temperature Synthesis of Vertically Aligned Amorphous Ultrathin NiCo-LDH Nanosheets Bifunctional Flexible Supercapacitor Electrodes

Developing a simple scalable method to fabricate electrodes with high capacity and wide voltage range is desired for the real use of electrochemical supercapacitors.Herein,we synthesized amorphous NiCo-LDH nanosheets vertically aligned on activated carbon cloth substrate,which was in situ transformed from Co-metal-organic framework materials nano-columns by a simple ion exchange process at room temperature.Due to the amorphous and vertically aligned ultrathin structure of NiCo-LDH,the NiCo-LDH/activated carbon cloth composites present high areal capacities of 3770 and 1480 mF cm^(-2)as cathode and anode at 2 mA cm^(-2),and 79.5%and 80%capacity have been preserved at 50 mA cm^(-2).In the meantime,they all showed excellent cycling performance with negligible change after>10000 cycles.By fabricating them into an asymmetric supercapacitor,the device achieves high energy densities(5.61 mWh cm^(-2)and 0.352 mW cm^(-3)).This work provides an innovative strategy for simplifying the design of supercapacitors as well as providing a new understanding of improving the rate capabilities/cycling stability of NiCo-LDH materials.

Accelerated Sequential Deposition Reaction via Crystal Orientation Engineering for Low-Temperature,High-Efficiency Carbon-Electrode CsPbBr_(3) Solar Cells

Low-temperature,ambient processing of high-quality CsPbBr_(3)films is demanded for scalable production of efficient,low-cost carbon-electrode perovskite solar cells(PSCs).Herein,we demonstrate a crystal orientation engineering strategy of PbBr_(2)precursor film to accelerate its reaction with CsBr precursor during two-step sequential deposition of CsPbBr_(3)films.Such a novel strategy is proceeded by adding CsBr species into PbBr_(2)precursor,which can tailor the preferred crystal orientation of PbBr_(2)film from[020]into[031],with CsBr additive staying in the film as CsPb_(2)Br_(5)phase.Theoretical calculations show that the reaction energy barrier of(031)planes of PbBr_(2)with CsBr is lower about 2.28 eV than that of(O2O)planes.Therefore,CsPbBr_(3)films with full coverage,high purity,high crystallinity,micro-sized grains can be obtained at a low temperature of 150℃.Carbon-electrode PSCs with these desired CsPbBr_(3)films yield the record-high efficiency of 10.27%coupled with excellent operation stability.Meanwhile,the 1 cm^(2)area one with the superior efficiency of 8.00%as well as the flexible one with the champion efficiency of 8.27%and excellent mechanical bending characteristics are also achieved.

Analysis of piezoelectric semiconductor fibers under gradient temperature changes

Piezoelectric semiconductors(PSs)possess both semiconducting properties and piezoelectric coupling effects,making them optimal building blocks for semiconductor devices.PS fiber-like structures have wide applications in multi-functional semiconductor devices.In this paper,a one-dimensional(1D)theoretical model is established to describe the piezotronic responses of a PS fiber under gradient temperature changes.The theoretical model aims to explain the mechanism behind the resistance change caused by such gradient temperature changes.Numerical results demonstrate that a gradient temperature change significantly affects the physical fields within the PS fiber,and can induce changes in its surface resistance.It provides important theoretical guidance on the development of piezotronic devices that are sensitive to temperature effects.