Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning

Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

Simplified quantitative analysis method and its application in the insitu synthesized copper-based azide chips

Copper-based azide(Cu(N_(3))2 or CuN_(3),CA)chips synthesized by in-situ azide reaction and utilized in miniaturized explosive systems has become a hot research topic in recent years.However,the advantages of in-situ synthesis method,including small size and low dosage,bring about difficulties in quantitative analysis and differences in ignition capabilities of CA chips.The aim of present work is to develop a simplified quantitative analysis method for accurate and safe analysis of components in CA chips to evaluate and investigate the corresponding ignition ability.In this work,Cu(N_(3))2 and CuN_(3)components in CA chips were separated through dissolution and distillation by utilizing the difference in solubility and corresponding content was obtained by measuring N_(3)-concentration through spectrophotometry.The spectrophotometry method was optimized by studying influencing factors and the recovery rate of different separation methods was studied,ensuring the accuracy and reproducibility of test results.The optimized method is linear in range from 1.0-25.0 mg/L,with a correlation coefficient R^(2)=0.9998,which meets the requirements of CA chips with a milligram-level content test.Compared with the existing ICP method,component analysis results of CA chips obtained by spectrophotometry are closer to real component content in samples and have satisfactory accuracy.Moreover,as its application in miniaturized explosive systems,the ignition ability of CA chips with different component contents for direct ink writing CL-20 and the corresponding mechanism was studied.This study provided a basis and idea for the design and performance evaluation of CA chips in miniaturized explosive systems.

Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection methods.This paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching method.The proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune elements.Then,to improve the accuracy of similarity calculation,a quantitative matching method is proposed.The model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown intrusions.The proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional methods.The experiment results show that the proposed model can detect intrusions effectively.It has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

Quantitative hepatitis B core antibody and quantitative hepatitis B surface antigen:Novel viral biomarkers for chronic hepatitis B management

The management of hepatitis B virus(HBV)infection now involves regular and appropriate monitoring of viral activity,disease progression,and treatment response.Traditional HBV infection biomarkers are limited in their ability to predict clinical outcomes or therapeutic effectiveness.Quantitation of HBV core antibodies(qAnti-HBc)is a novel non-invasive biomarker that may help with a variety of diagnostic issues.It was shown to correlate strongly with infection stages,hepatic inflammation and fibrosis,chronic infection exacerbations,and the presence of occult infection.Furthermore,qAnti-HBc levels were shown to be predictive of spontaneous or treatment-induced HBeAg and HBsAg seroclearance,relapse after medication termination,re-infection following liver transplantation,and viral reactivation in the presence of immunosuppression.qAnti-HBc,on the other hand,cannot be relied on as a single diagnostic test to address all problems,and its diagnostic and prognostic potential may be greatly increased when paired with qHBsAg.Commercial qAnti-HBc diagnostic kits are currently not widely available.Because many methodologies are only semi-quantitative,comparing data from various studies and defining universal cut-off values remains difficult.This review focuses on the clinical utility of qAnti-HBc and qHBsAg in chronic hepatitis B management.

Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data

The Gated Recurrent Unit(GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity(Z), radar echo-top height(ET) is also a good indicator of rainfall rate(R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-R relationship(Z=300R1.4), the optimal Z-R relationship(Z=79R1.68) and the GRU neural network with only Z as the independent input variable(GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-R relationship performs the worst. The performances of the rest two methods are similar.To further evaluate the performance of the GRU_Z-ET, 200 rainfall events with 21882 total samples during May–July of 2018 are used for statistical analysis. Results demonstrate that the spatial correlation coefficients, threat scores and probability of detection between the observed and estimated precipitation are the largest for the GRU_Z-ET and the smallest for the traditional Z-R relationship, and the root mean square error is just the opposite. In addition, these statistics of GRU_Z are similar to those of optimal Z-R relationship. Thus, it can be concluded that the performance of the GRU_ZET is the best in the four methods for the quantitative precipitation estimation.

Deep learning assisted variational Hilbert quantitative phase imaging

We propose a high-accuracy artifacts-free single-frame digital holographic phase demodulation scheme for relatively lowcarrier frequency holograms-deep learning assisted variational Hilbert quantitative phase imaging(DL-VHQPI).The method,incorporating a conventional deep neural network into a complete physical model utilizing the idea of residual compensation,reliably and robustly recovers the quantitative phase information of the test objects.It can significantly alleviate spectrum-overlapping-caused phase artifacts under the slightly off-axis digital holographic system.Compared to the conventional end-to-end networks(without a physical model),the proposed method can reduce the dataset size dramatically while maintaining the imaging quality and model generalization.The DL-VHQPI is quantitatively studied by numerical simulation.The live-cell experiment is designed to demonstrate the method's practicality in biological research.The proposed idea of the deep learning-assisted physical model might be extended to diverse computational imaging techniques.

Application of X-band Polarimetric Phased-array Radars in Quantitative Precipitation Estimation

The performance of different quantitative precipitation estimation(QPE) relationships is examined using the polarimetric variables from the X-band polarimetric phased-array radars in Guangzhou,China.Three QPE approaches,namely,R(ZH),R(ZH,ZDR) and R(KDP),are developed for horizontal reflectivity,differential reflectivity and specific phase shift rate,respectively.The estimation parameters are determined by fitting the relationships to the observed radar variables using the T-matrix method.The QPE relationships were examined using the data of four heavy precipitation events in southern China.The examination shows that the R(ZH) approach performs better for the precipitation rate less than 5 mm h-1, and R(KDP) is better for the rate higher than 5 mm h-1, while R(ZH,ZDR) has the worst performance.An adaptive approach is developed by taking the advantages of both R(ZH) and R(KDP) approaches to improve the QPE accuracy.

Quantitative characterization of deep fault-karst carbonate reservoirs: A case study of the Yuejin block in the Tahe oilfield

The Ordovician reservoirs in the Tahe oilfield are dominated by fractured-vuggy carbonate reservoirs, of which fault-karst reservoirs are a hot topic in recent years. Fault-karst reservoirs feature high production, large burial depth, and strong heterogeneity under the control of faulting and karstification. Based on geological, logging, and seismic data, this study classified the Ordovician fault-karst reservoirs in the Yuejin block of the Tahe oilfield into three types, namely karst-cave, dissolved-vug, and fractured types, and established the integrated identification criteria of the three types of reservoirs. This study characterized karst caves, dissolved vugs, and multi-scale faults through seismic wave impedance inversion and frequency-domain detection of multi-scale faults. 3D geological models of different types of reservoirs were built using the combined deterministic and stochastic methods and characterized the spatial distribution of multi-scale faults, karst caves, dissolved vugs, and physical property parameters of reservoir. This study established the method for the geological modeling of fault-karst reservoirs, achieved the quantitative characterization and revealed the heterogeneity of fault-karst reservoirs. The karst-cave and dissolved-vug types are high in porosity and act as reservoirs, while the fractured type is high in permeability and act as flow pathway. This study lays the foundation for the development index prediction, well emplacement, and efficient development of the fault-karst carbonate reservoirs.

Evaluation of Reference Genes for Quantitative Real-Time PCR Analysis in Manila Clam Ruditapes philippinarum Under Hypoxic Stress

Quantitative real-time PCR(qRT-PCR)has been widely used for gene expression analysis,and selection of reference genes is a key point to obtain accurate results.To find out optimal reference genes for qRT-PCR in Manila clam Ruditapes philippinarum in response to hypoxia,different tissues were used and compared to evaluate the stability of candidate reference genes under low oxygen stress(DO 0.5mgL^(−1) and DO 2.0mgL^(−1))and normal condition(DO 7.5mgL^(−1)).Seven candidate reference genes were selected to evaluate the stability of their expression levels.The reference genes were evaluated by Delta Ct,BestKeeper,NormFinder and geNorm,and then screened by RefFinder calculation.Under hypoxic stress of 0.5mgL^(−1),the most suitable reference gene for gill and hepatopancreas was RPL31,and the optimal reference genes for axe foot and adductor muscle were TUB and HIS,respectively.For hypoxic stress of 2.0mgL^(−1),the most stable reference gene for gill and hepatopancreas was RPL31,and the optimal reference genes for axe foot and adductor muscle were RPS23 and EF1A,respectively.At the normal condition,HIS and EF1A were identified as the optimal internal reference genes in gill and hepatopancreas respectively,and GFRP2 was the best internal reference gene for axe foot and adductor muscle.The present findings will provide important basis for the selection of reference genes for qRT-PCR analysis of gene expression level in bivalves under hypoxic stress,which might be helpful for the analysis of other molluscs too.

Quantitative Parameters Analysis for Prenatally Echocardiographic Diagnosis of Atrioventricular Septal Defects

Background:Atrioventricular septal defects(AVSDs)are screened and diagnosed usually rely on the imaging characteristics of fetal echocardiography(FE).However,diagnosis on images is heavily depended on sonographers’experience and the quantitative data are rarely studied.Objective:This study aimed to realize the prenatal diagnosis of AVSDs by analyzing the quantitative data on FE.Methods:One hundred and thirteen cardiac quantitative data was analyzed in 370 normal and 49 AVSDs fetuses retrospectively.The top six with the highest diagnostic accuracy rate were acquired according to the area under the curve(AUC),and the diagnostic value of six variables was analyzed.Results:Six parameters obtained on the four-chamber view(4CHV),including the atrial to ventricular length ratio in end-diastole(AVLR-ED),AVLR-ED combined with the atrial to ventricular length ratio in end-systole(AVLR-ES),quantile score(Q score)of AVLR-ED,Q score of AVLR-ES,Q score of ventricle length in end-diastole(VL-ED),and AVLR-ES,were the top six with the highest diagnostic value,and the AUC was 0.99(95%CI 0.99–1.00),0.99(95%CI 0.99–1.00),0.99(95%CI 0.98–1.00),0.95(95%CI 0.91–0.99),0.93(95%CI 0.87–0.99),and 0.91(95%CI 0.83–1.00),respectively.And within the 20%false positive rate,the diagnostic sensitivity was greater than 100%,100%,100%,90%,90%,and 88%,respectively.Conclusions:Six variables could be used for prenatal diagnosis of AVSDs.Among them,AVLR-ED and Q score of AVLR-ED,obtained on the 4CHV,were more convenient to acquire and had higher diagnostic accuracy.

Spectra-assisted laser focusing in quantitative analysis of laser-induced breakdown spectroscopy for copper alloys

Laser-induced breakdown spectroscopy(LIBS)is a capable technique for elementary analysis,while LIBS quantitation is still under development.In quantitation,precise laser focusing plays an important role because it ensures the distance between the laser and samples.In the present work,we employed spectral intensity as a direct way to assist laser focusing in LIBS quantitation for copper alloys.It is found that both the air emission and the copper line could be used to determine the position of the sample surface by referencing the intensity maximum.Nevertheless,the fine quantitation was only realized at the position where the air emission(e.g.O(I)777.4 nm)reached intensity maximum,and also in this way,a repeatable quantitation was successfully achieved even after 120 days.The results suggested that the LIBS quantitation was highly dependent on the focusing position of the laser,and spectra-assisted focusing could be a simple way to find the identical condition for different samples’detection.In the future,this method might be applicable in field measurements for LIBS analysis of solids.

Quantitative Easing: Money Supply and Commodity Prices of Oil, Gold, and Cocoa in Ghana

This study investigates the dynamic relationships between the money supply (M2) and key commodity prices (Cocoa, Gold, and Crude) in the context of Ghana. Utilizing Vector Error Correction Model (VECM) analysis, we analyze the short-term and long-term Granger causality relationships among these variables, aiming to shed light on the potential linkages between monetary policy and commodity markets. The analysis covers the period from December 1999 to April 2023, using lag structures of 1 and 8 to capture both short-term and more enduring effects. Our findings reveal significant Granger causality relationships between the money supply and various commodities, with nuanced patterns emerging across different lags. In the short-run, our results suggest bidirectional causal relationships between COCOA and M2, CRUDE and M2, and GOLD and M2. Additionally, M2 Granger causes changes in COCOA, CRUDE, and GOLD. However, the causal relationship between COCOA and GOLD appears to be unidirectional, with COCOA not significantly Granger causing changes in GOLD. The short-term findings highlight the intricate interplay between monetary policy and commodity markets. In the long-run (lag 8), our analysis unveils robust Granger causality relationships between the variables. Past values of COCOA, CRUDE, and GOLD Granger cause changes in M2, indicating a notable influence of commodity markets on the money supply. Similarly, M2 Granger causes changes in CRUDE and GOLD. Notably, the findings underscore a more comprehensive and intertwined relationship between monetary policy and commodity prices in the long-run. Based on these results, we derive several policy implications. Policymakers should carefully consider the potential impact of monetary policy decisions, such as quantitative easing, on commodity markets and price dynamics. Measures to stabilize commodity prices, promote export diversification, manage inflation expectations, and enhance economic resilience are recommended. Additionally, effective data monitoring, international collaboration, and proactive risk management strategies are essential components for navigating the complex interactions between monetary policy and commodity markets. This study contributes to a deeper understanding of the intricate connections between monetary policy and commodity prices in Ghana, offering insights for policymakers, researchers, and stakeholders seeking to promote sustainable economic growth and stability. Further research can delve into the mechanisms underlying these relationships and explore their broader implications for trade balances, economic performance, and policy formulation.

A methodological framework of landslide quantitative risk assessment in areas with incomplete historical landslide information

Landslide risk assessment(LRA)is of great significance to hazard prevention and mitigation.However,the historical landslide information is incomplete in most areas,which makes the landslide quantitative risk assessment(LQRA)extremely difficult.This research proposed a set of frameworks for LQRA,so as to achieve LQRA in areas with incomplete historical landslide information.Firstly,we constructed the convolutional neural network(CNN)model suitable for landslide susceptibility assessment(LSA)by studying the structure and hyperparameters optimization of CNN.Secondly,we proposed a method to calculate the temporal probability by using the Poisson model based on the time range of historical landslides occurrence,and then conducted landslide hazard assessment(LHA).Then,we established a mathematical model for landslide intensity of shallow landslide based on landslide area and slope,aiming at solving the problem that it is difficult to calculate landslide intensity due to the lack of landslide volume and velocity.Based on the landslide intensity and the hazard-resistant capacity of the element at risk,we assessed the landslide vulnerability.Finally,population risk map and economic risk map are obtained based on the landslide hazard,vulnerability,and estimated value of the elements at risk.The proposed LQRA framework was applied to Tumen City,China for testing and field validation.From the results,the CNN model built can help improve the accuracy of LSA.The proposed temporal probability calculation method is conducive to the completion of LHA in areas with incomplete historical landslide information.The established landslide intensity mathematical model has certain credibility.Since the landslide risk map is obtained through appropriate simplification and substitution estimation,its final value cannot be used as an accurate prediction of future losses,but it can be used as a reference for the extent of potential losses,so as to determine the areas where hazard prevention and mitigation measures need to be taken.

DecodeSTORM:A user-friendly ImageJ plug-in for quantitative data analysis in single-molecule localization microscopy

Quantitative data analysis in single-molecule localization microscopy(SMLM)is crucial for studying cellular functions at the biomolecular level.In the past decade,several quantitative methods were developed for analyzing SMLM data;however,imaging artifacts in SMLM experiments reduce the accuracy of these methods,and these methods were seldom designed as user-friendly tools.Researchers are now trying to overcome these di±culties by developing easyto-use SMLM data analysis software for certain image analysis tasks.But,this kind of software did not pay su±cient attention to the impact of imaging artifacts on the analysis accuracy,and usually contained only one type of analysis task.Therefore,users are still facing di±culties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs.In this paper,we report an ImageJ plug-in called DecodeSTORM,which not only has a simple GUI for human–computer interaction,but also combines artifact correction with several quantitative analysis methods.DecodeSTORM includes format conversion,channel registration,artifact correction(drift correction and localization¯ltering),quantitative analysis(segmentation and clustering,spatial distribution statistics and colocalization)and visualization.Importantly,these data analysis methods can be combined freely,thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods.We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in,which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

Establishment and Modification of Ninety-seven Pneumococcal Serotyping Assays Based on Quantitative Real-time Polymerase Chain Reaction

Objective To establish and modify quantitative real-time polymerase chain reaction(qPCR)-based serotyping assays to distinguish 97 pneumococcal serotypes.Methods A database of capsular polysaccharide(cps)loci sequences was generated,covering 97 pneumococcal serotypes.Bioinformatics analyses were performed to identify the cps loci structure and target genes related to different pneumococcal serotypes with specific SNPs.A total of 27 novel qPCR serotyping assay primers and probes were established based on qPCR,while 27 recombinant plasmids containing serotype-specific DNA sequence fragments were constructed as reference target sequences to examine the specificity and sensitivity of the qPCR assay.A panel of pneumococcal reference strains was employed to evaluate the capability of pneumococcal serotyping.Results A total of 97 pneumococcal serotyping assays based on qPCR were established and modified,which included 64 serotypes previously reported as well as an additional 33 serotypes.Twenty-seven novel qPCR serotyping target sequences were implemented in the pneumococcal qPCR serotyping system.A total of 97 pneumococcal serotypes,which included 52 individual serotypes and 45 serotypes belonging to 20 serogroups,could not be identified as individual serotypes.The sensitivity of qPCR assays based on 27 target sequences was 1–100 copies/μL.The specificity of the qPCR assays was 100%,which were tested by a panel of 90 serotypes of the pneumococcal reference strains.Conclusion A total of 27 novel qPCR assays were established and modified to analyze 97pneumococcal serotypes.

Quantitative morphometric analysis of a deep-water channel in the Taranaki Basin, New Zealand

The morphological changes of deep-water channels have an important influence on the distributions of channel sand reservoirs,so it is important to explore the morphological change process of deep-water channel for the exploration and development of deep-water oil and gas.Based on a typical sinuous Quaternary channel(Channel I)in the Taranaki Basin,New Zealand,a variety of seismic interpretation techniques were applied to quantitatively characterize the morphological characteristics of the Channel I,and the relationships between the quantitative parameters and the morphological changes of the Channel I,as well as the controlling factors affecting those morphological changes,were discussed.The results are as follows:(1)in the quantitative analysis,six parameters were selected:the channel depth,width,sinuosity,and aspect ratio(width/depth),the channel swing amplitude(λ)and the channel bend frequency(ω);(2)according to the quantitative morphological parameters of the channel(mainly including three parameters such as channel sinuosity,ωandλ),the Channel I was divided into three types:the low-sinuous channel(LSC),the high-sinuous channel(HSC),the moderate-sinuous channel(MSC).U-shaped channel cross-sections developed in the LSC,V-shaped channel cross-sections developed in the HSC,including inclined-V and symmetric-V cross-sections,and dish-shaped channel cross-sections developed in the MSC;(3)the morphological characteristics of the LSC and MSC were related to their widths and depths,while the morphology of the HSC was greatly affected by the channel width,a change in depth did not affect the HSC morphology;(4)the morphological changes of the Channel I were controlled mainly by the slope gradient,the restricted capacity of the channel and the differential in fluid properties.

Quantitative measurement of the charge carrier concentration using dielectric force microscopy

The charge carrier concentration profile is a critical factor that determines semiconducting material properties and device performance.Dielectric force microscopy(DFM)has been previously developed to map charge carrier concentrations with nanometer-scale spatial resolution.However,it is challenging to quantitatively obtain the charge carrier concentration,since the dielectric force is also affected by the mobility.Here,we quantitative measured the charge carrier concentration at the saturation mobility regime via the rectification effect-dependent gating ratio of DFM.By measuring a series of n-type GaAs and GaN thin films with mobility in the saturation regime,we confirmed the decreased DFM-measured gating ratio with increasing electron concentration.Combined with numerical simulation to calibrate the tip–sample geometry-induced systematic error,the quantitative correlation between the DFM-measured gating ratio and the electron concentration has been established,where the extracted electron concentration presents high accuracy in the range of 4×10^(16)–1×10^(18)cm^(-3).We expect the quantitative DFM to find broad applications in characterizing the charge carrier transport properties of various semiconducting materials and devices.

Analyses and identifications of quantitative trait loci and candidate genes controlling mesocotyl elongation in rice

Rice direct seeding has the significant potential to save labor and water,conserve environmental resources,and reduce greenhouse gas emissions tremendously.Therefore,rice direct seeding is becoming the major cultivation technology applied to rice production in many countries.Identifying and utilizing genes controlling mesocotyl elongation is an effective approach to accelerate breeding procedures and meet the requirements for direct-seeded rice(DSR) production.This study used a permanent mapping population with 144 recombinant inbred lines(RILs) and 2 828 bin-markers to detect quantitative trait loci(QTLs) associated with mesocotyl length in 2019 and 2020.The mesocotyl lengths of the rice RILs and their parents,Lijiangxintuanheigu(LTH) and Shennong 265(SN265),were measured in a growth chamber at 30°C in a dark environment.A total of 16 QTLs for mesocotyl length were identified on chromosomes 1(2),2(4),3(2),4,5,6,7,9,11(2),and 12.Seven of these QTLs,including qML1a,qML1b,qML2d,qML3a,qML3b,qML5,and qML11b,were reproducibly detected in both years via the interval mapping method.The major QTL,qML3a,was reidentified in two years via the composite interval mapping method.A total of 10 to 413 annotated genes for each QTL were identified in their smallest genetic intervals of 37.69 kb to 2.78 Mb,respectively.Thirteen predicted genes within a relatively small genetic interval(88.18 kb) of the major mesocotyl elongation QTL,qML3a,were more thoroughly analyzed.Finally,the coding DNA sequence variations among SN265,LTH,and Nipponbare indicated that the LOC_Os03g50550 gene was the strongest candidate gene for the qML3a QTL controlling the mesocotyl elongation.This LOC_Os03g50550 gene encodes a mitogen-activated protein kinase.Relative gene expression analysis using qRT-RCR further revealed that the expression levels of the LOC_Os03g50550 gene in the mesocotyl of LTH were significantly lower than in the mesocotyl of SN265.In conclusion,these results further strengthen our knowledge about rice’s genetic mechanisms of mesocotyl elongation.This investigation’s discoveries will help to accelerate breeding programs for new DSR variety development.

Impact of minimalism on consumers’ low-carbon innovation behavior: Interactive role of quantitative behavior

Minimalism has become a new consumer trend that can guide low-carbon innovation behaviors.This study uses two experiments and one survey to explore the mechanisms underlying the relationships among minimalism,quantitative behavior,and low-carbon innovation behavior.The results show that(1)minimalism can positively influence consumers’low-carbon innovation behavior;(2)the interactive effect of minimalist and quantitative behavior on low-carbon innovation behavior is significant;and(3)meaning in life can play a significant mediating role in the relationship between minimalism and low-carbon innovation behavior.Finally,corresponding strategies for theory and management practices are proposed.

Solubility study of hydrogen in direct coal liquefaction solvent based on quantitative structure–property relationships model

Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.