Variations in leaf functional traits among plant species grouped by growth and leaf types in Zhenjiang,China

Leaf functional traits are adaptations that enable plants to live under various environmental conditions. This study aims to determine the differences in leaf functional traits among plants grouped by growth habit, leaf life span,leaf lifestyle, leaf form, and origin. Specific leaf area（SLA） of perennial or evergreen species was lower than that of annual or deciduous species because longer-lived leaves of perennial or evergreen species require more investment in structural integrity and/or defense against disturbances, especially with any resource constraint. SLA of large individuals was lower than that of small individuals. The low SLA in large individuals can improve their response to changing light and water conditions because increasing plant height is advantageous for light competition, but it can also impose a cost in terms of structural support and water transport. Petioles of plants with compound leaves were significantly longer than those of simple leaves because branching is expensive in terms of gaining height. SLA of plants increased with increasing invasiveness accordingly, and SLA of invasive plants was higher than that of their native congeners because invasive plants should invest more biomass on leaf growth rather than leaf structures per unit area to achieve a higher growth rate.Overall, variation in leaf functional traits among different groups may play an adaptive role in the successful survival of plants under diverse environments because leaf functional traits can lead to pronounced effects on leaf function,especially the acquisition and use of light. Plant species with different growth and leaf traits balance resource acquisition and leaf construction to minimize trade-offs and achieve fitness advantages in their natural habitat.

Anomalies of Precursory Group and Grouped Strong Earthquakes in the Sichuan-Yunnan Region

Three methods of extracting the information of anomalies of a precursory group are put forward, i.e., the mathematical analyses of the synthetic information of earthquake precursors (S), the inhomogeneous degree of precursory groups (ID) and the values of short-term and impending anomaly in near-source area (NS). Using these methods, we calculate the observational data of deformation, underground fluid and hydrochemical constituents obtained from different seismic stations in the Sichuan-Yunnan region and conclude that the synthetic precursory anomalies of a single strong earthquake with M S6.0 differ greatly from those of the grouped strong earthquakes, for the anomalous information of precursory groups are more abundant. The three methods of extracting the synthetic precursory anomaly and the related numerical results can be applied into the practice of prediction to the grouped strong earthquakes in the Sichuan-Yunnan region. Inhomogeneous degree (ID) of synthetic precursory anomaly can be identified automatically because it takes the threshold of distributive characteristics of the anomalies of precursory group as its criterion for anomaly.

STATISTICAL INFERENCE FOR A BIVARIATE EXPONENTIAL DISTRIBUTION BASED ON GROUPED DATA

Consider the bivariate exponential distribution due to Marshall and Olkin[2], whose survival function is F(x, g) = exp[-λ1x-λ2y-λ12 max(x, y)] (x 0,y 0)with unknown Parameters λ1 > 0, λ2 > 0 and λ12 0.Based on grouped data, a newestimator for λ1, λ2 and λ12 is derived and its asymptotic properties are discussed.Besides, some test procedures of equal marginals and independence are given. Asimulation result is given, too.

On the Power Performance of Test Statistics for the Generalized Rayleigh Interval Grouped Data

In this paper, the weighted Kolmogrov-Smirnov, Cramer von-Miss and the Anderson Darling test statistics are considered as goodness of fit tests for the generalized Rayleigh interval grouped data. An extensive simulation process is conducted to evaluate their controlling of type 1 error and their power functions. Generally, the weighted Kolmogrov-Smirnov test statistics show a relatively better performance than both, the Cramer von-Miss and the Anderson Darling test statistics. For large sample values, the Anderson Darling test statistics cannot control type 1 error but for relatively small sample values it indicates a better performance than the Cramer von-Miss test statistics. Best selection of the test statistics and highlights for future studies are also explored.

Study of Paleoearthquakes by Excavation of Grouped Trenches on the Zemuhe Fault in Daqingliangzi,Xichang,Sichuan

Uncertainties are common in the dating of paleoearthquakes.To improve the credibility of the dating of paleoearthquakes,analysis was done on fault activity,sedimentary environment and seismo-geomorphology to investigate paleoearthquakes along the Zemuhe active fault zone.Grouped trenches were excavated near Daqingliangzi,which revealed three palaeoearthquake events aged 160a,3100a and 5500a～8900a,respectively,including recurrence intervals of about 3000a.Sedimentary processes related to strike-slip fault type earthquakes were discussed,and a sedimentary model was put forward for strike-slip faults at hillsides where drumlin and reverse scarp developed.

Network autoregression model with grouped factor structures

Network autoregression and factor model are effective methods for modeling network time series data.In this study,we propose a network autoregression model with a factor structure that incorporates a latent group structure to address nodal heterogeneity within the network.An iterative algorithm is employed to minimize a least-squares objective function,allowing for simultaneous estimation of both the parameters and the group structure.To determine the unknown number of groups and factors,a PIC criterion is introduced.Additionally,statistical inference of the estimated parameters is presented.To assess the validity of the proposed estimation and inference procedures,we conduct extensive numerical studies.We also demonstrate the utility of our model using a stock dataset obtained from the Chinese A-Share stock market.

Novel Grouped Probability Data Association Algorithm for MIMO Detection

To bridge the performance gap between original probability data association （PDA） algorithm and the optimum maximum a posterior （MAP） algorithm for multi-input multi-output （MIMO） detection, a grouped PDA （GP-PDA） detection algorithm is proposed. The proposed GP-PDA method divides all the transmit antennas into groups, and then updates the symbol probabilities group by group using PDA computations. In each group, joint a posterior probability （APP） is computed to obtain the APP of a single symbol in this group, like the MAP algorithm. Such new algorithm combines the characters of MAP and PDA. MAP and original PDA algorithm can be regarded as a special case of the proposed GP-PDA. Simulations show that the proposed GP-PDA provides a performance and complexity trade, off between original PDA and MAP algorithm.

High mobility group box 1 in the central nervous system:regeneration hidden beneath inflammation

High-mobility group box 1 was first discovered in the calf thymus as a DNA-binding nuclear protein and has been widely studied in diverse fields,including neurology and neuroscience.High-mobility group box 1 in the extracellular space functions as a pro-inflammatory damage-associated molecular pattern,which has been proven to play an important role in a wide variety of central nervous system disorders such as ischemic stroke,Alzheimer’s disease,frontotemporal dementia,Parkinson’s disease,multiple sclerosis,epilepsy,and traumatic brain injury.Several drugs that inhibit high-mobility group box 1 as a damage-associated molecular pattern,such as glycyrrhizin,ethyl pyruvate,and neutralizing anti-high-mobility group box 1 antibodies,are commonly used to target high-mobility group box 1 activity in central nervous system disorders.Although it is commonly known for its detrimental inflammatory effect,high-mobility group box 1 has also been shown to have beneficial pro-regenerative roles in central nervous system disorders.In this narrative review,we provide a brief summary of the history of high-mobility group box 1 research and its characterization as a damage-associated molecular pattern,its downstream receptors,and intracellular signaling pathways,how high-mobility group box 1 exerts the repair-favoring roles in general and in the central nervous system,and clues on how to differentiate the pro-regenerative from the pro-inflammatory role.Research targeting high-mobility group box 1 in the central nervous system may benefit from differentiating between the two functions rather than overall suppression of high-mobility group box 1.

AN APPROXIMATE ALGORITHM OF OPTIMAL DIGIT-GROUPED PARTITION OF SEQUENCES

In this note we give a rather good approximate algorithm of optimal digit-grouped partition of sequences, showing that the optimal complete digit-grouped partition number of a dumbbell type sequence S is equal to its optimal digit-grouped partition number N(S), hence an estimation of N(S) is obtained. The thickness of a sequence introduced in this note is a key.

ASYMPTOTIC PROPERTIES OF MLE FOR WEIBULL DISTRIBUTION WITH GROUPED DATA

Abstract. A grouped data model for Weibull distribution is considered. Under mild con-ditions, the maximum likelihood estimators(MLE) are shown to be identifiable, stronglyconsistent, asymptotically normal, and satisfy the law of iterated logarithm. Newton iter-ation algorithm is also considered, which converges to the unique solution of the likelihoodequation. Moreover, we extend these results to a random case.

Parameters estimation and application of generalized exponential distribution under grouped and right-censored data

Generalized exponential distribution is a class of important distribution in lifedata analysis,especially in some skewed lifedata.The Parameter estimation problem for generalized exponential distribution model with grouped and right-censored data is considered.The maximum likelihood estimators are obtained using the EM algorithm.Some simulations are carried out to illustrate that the proposed algorithm is effective for the model.Finally,a set of medicine data is analyzed by generalized exponential distribution.

Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models

Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.

Effect of grouped ST36 or RN12 on gastric motility and sensation

Objective: To investigate the effects of paired ST36 and RN12 acupuncture stimulation on gastric motility and the firing rate of responsible neurons in anesthetized rats.Methods: Using electrophysiological methods, we measured the effects of acupuncture at RN12, ST36, or RN12+ST36 on gastric motility as well as nerve discharges from the gastric sympathetic, vagal nerve, and wide dynamic range(WDR) neurons in the spinal dorsal horn T7-9 in anesthetized rats.Results: 1) Acupuncture at RN12 inhibited gastric motility(83.84±4.49)% and vagal nerve discharge(65.64±5.67)%, but promoted sympathetic nerve activity(243.70±40.67)%; acupuncture at ST36 induced opposing effects on gastric motility(111.31±2.01)% and vagal nerve discharge(166.98±15.92)%. Interestingly, acupuncture at RN12+ST36 together yielded effects similar to that of acupuncture at RN12 alone(93.59±2.05)%,(54.52±7.12)%. 2) WDR neuron discharges induced by gastric distension were inhibited by acupuncture at RN12(194.56 ±18.79)%. However, acupuncture at ST36 induced a more significant inhibitory effect.Conclusion: Acupuncture at RN12 inhibits gastric motility by activation of the sympathetic reflex at the spinal level; acupuncture at ST36 promotes gastric motility by activation of the parasympathetic reflex at the supraspinal level, and acupuncture at RN12+ST36 produces a similar decrease in gastric motility as RN12 stimulation alone. Both acupoints decrease the effect of noxious gastric distention on WDR neuron activity, but paired RN12+ST36 stimulation does not significantly affect WDR neuron discharge.

FLIGHT:Federated Learning with IRS for Grouped Heterogeneous Training

In recent years,federated learning(FL)has played an important role in private data-sensitive scenarios to perform learning tasks collectively without data exchange.However,due to the centralized model aggregation for heterogeneous devices in FL,the last updated model after local training delays the convergence,which increases the economic cost and dampens clients’motivations for participating in FL.In addition,with the rapid development and application of intelligent reflecting surface(IRS)in the next-generation wireless communication,IRS has proven to be one effective way to enhance the communication quality.In this paper,we propose a framework of federated learning with IRS for grouped heterogeneous training(FLIGHT)to reduce the latency caused by the heterogeneous communication and computation of the clients.Specifically,we formulate a cost function and a greedy-based grouping strategy,which divides the clients into several groups to accelerate the convergence of the FL model.The simulation results verify the effectiveness of FLIGHT for accelerating the convergence of FL with heterogeneous clients.Besides the exemplified linear regression(LR)model and convolutional neural network(CNN),FLIGHT is also applicable to other learning models.

金融机构ESG投资的“漂绿”与“反漂绿”——基于DWSGroup的案例分析

深化绿色金融发展、助力经济绿色发展,是实现“双碳”目标的重要途径,特别是在ESG理念盛行的今天,金融机构的ESG投资行为因其深远影响而颇受投资者青睐,但层出不穷的“漂绿”现象也牢牢制约着绿色金融的发展,制约着ESG投资的发展。西方国家作为绿色金融发展先驱,从信息披露、制度监管、实践探索等方面对“漂绿”现象进行了研究和治理。本文在学习其先进经验的基础上,基于舞弊三角理论对DWSGroup案例进行分析,从压力、机会和自我合理化因素角度对金融机构ESG投资“漂绿”行为的动因进行分析,并据此提出我国“反漂绿”体系框架,以期为绿色金融的平稳发展和“双碳”目标的实现作出贡献。

SARS-CoV-2 Pooled Testing Methodology for PCR Testing Applied in Private Laboratory in Armenia

Since the beginning of COVID-19 pandemics many countries were facing challenges with testing capacity recourse limitations. Throughout the waves of the pandemic countries were trying to address the existing constrains exploring solutions to increase the testing capacity with more cost-effective approaches. Pooled methodology was one of the methods which many have validated and used. It is evident that in case of pooled sample testing the sensitivity becomes lower, however the variation highly depends on the pool size as well as the incidence rate at the certain point. Armenia as well as many other countries has adopted regulations for mandatory COVID-19 PCR testing for all the travelers. Current study aimed to explore the efficiency of COVID-19 pooled PCR testing for nasopharyngeal swabs of individuals with no symptoms in a time period with good epidemiological state of the infection. Nasopharingeal swab samples from individuals were collected. The manual extraction of RNAs of samples was performed after pooling up to 5 samples. The pools with Cycle Threshold (CT) of < 37 were considered positive and were retested individually. In total 28,015 samples were grouped in 667 pools of which 57 were positive. The total number of positive samples was 65. The median difference (CT-pool–CT samples) was 2.4 (ranging from–3.0 to 8.9). The correlation of CT of pools and positive samples was positive. The correlation coefficient r = 0.84, P < 0.000, 95% CI range 0.7423 to 0.9243). The total economic saving when using pools compared to the individual testing was 72%. The minor difference between CT values of pools and samples can be explained by the dilution effect in the pool. However, the positive correlation between the values as well as the amount of cost saving demonstrate that pooling on nasopharyngeal samples for COVID-19 PCR testing can be a good method for efficient screening with significant resource saving. One of the most important advantages of the proposed method is the fact that samples are pooled prior extraction, which avoids the possibilities with misinterpretation of IC due to low yield of RNA in the extraction process.

Structural properties of residual carbon in coal gasification fine slag and their influence on flotation separation and resource utilization:A review

Coal gasification fine slag(FS)is a typical solid waste generated in coal gasification.Its current disposal methods of stockpil-ing and landfilling have caused serious soil and ecological hazards.Separation recovery and the high-value utilization of residual carbon(RC)in FS are the keys to realizing the win-win situation of the coal chemical industry in terms of economic and environmental benefits.The structural properties,such as pore,surface functional group,and microcrystalline structures,of RC in FS(FS-RC)not only affect the flotation recovery efficiency of FS-RC but also form the basis for the high-value utilization of FS-RC.In this paper,the characteristics of FS-RC in terms of pore structure,surface functional groups,and microcrystalline structure are sorted out in accordance with gasification type and FS particle size.The reasons for the formation of the special structural properties of FS-RC are analyzed,and their influence on the flotation separation and high-value utilization of FS-RC is summarized.Separation methods based on the pore structural characterist-ics of FS-RC,such as ultrasonic pretreatment-pore-blocking flotation and pore breaking-flocculation flotation,are proposed to be the key development technologies for improving FS-RC recovery in the future.The design of low-cost,low-dose collectors containing polar bonds based on the surface and microcrystalline structures of FS-RC is proposed to be an important breakthrough point for strengthening the flotation efficiency of FS-RC in the future.The high-value utilization of FS should be based on the physicochemical structural proper-ties of FS-RC and should focus on the environmental impact of hazardous elements and the recyclability of chemical waste liquid to es-tablish an environmentally friendly utilization method.This review is of great theoretical importance for the comprehensive understand-ing of the unique structural properties of FS-RC,the breakthrough of the technological bottleneck in the efficient flotation separation of FS,and the expansion of the field of the high value-added utilization of FS-RC.

Combined Promoting Effects of Specific Organic Functional Groups and Alumina Surface Characteristics for the Design of a Highly Efficient NiMo/Al_(2)O_(3) Hydrodesulfurization Catalyst

To prepare a highly efficient NiMo/Al_(2)O_(3) hydrodesulfurization catalyst,the combined effects of specific organic functional groups and alumina surface characteristics were investigated.First,the correlation between the surface characteristics of four different alumina and the existing Mo species states was established.It was found that the Mo equilibrium adsorption capacity can be used as a specific descriptor to quantitatively evaluate the changes in surface characteristics of different alumina.A lower Mo equilibrium adsorption capacity for alumina means weaker metal-support interaction and the loaded Mo species are easier to transform into MoS2.However,the Mo-O-Al bonds still exist at the metal-support interface.The introduction of cationic surfactant hecadecyl trimethyl ammonium bromide(CTAB)can further improve Mo species dispersion through electrostatic attraction with Mo anions and interaction of its alkyl chain with the alumina surface;meanwhile,the introduction of ethylenediamine tetraacetic acid(EDTA)can complex with Ni ions to enhance the Ni-promoting effect on Mo.Therefore,the NiMo catalyst designed using alumina with lower Mo equilibrium adsorption capacity and the simultaneous addition of EDTA and CTAB exhibits the highest hydrodesulfurization activity for 4,6-dimethyl dibenzothiophene because of its proper metal-support interaction and more well-dispersed Ni-Mo-S active phases.

Tailoring MXene Thickness and Functionalization for Enhanced Room‑Temperature Trace NO_(2) Sensing

In this study,precise control over the thickness and termination of Ti3C2TX MXene flakes is achieved to enhance their electrical properties,environmental stability,and gas-sensing performance.Utilizing a hybrid method involving high-pressure processing,stirring,and immiscible solutions,sub-100 nm MXene flake thickness is achieved within the MXene film on the Si-wafer.Functionalization control is achieved by defunctionalizing MXene at 650℃ under vacuum and H2 gas in a CVD furnace,followed by refunctionalization with iodine and bromine vaporization from a bubbler attached to the CVD.Notably,the introduction of iodine,which has a larger atomic size,lower electronegativity,reduce shielding effect,and lower hydrophilicity(contact angle:99°),profoundly affecting MXene.It improves the surface area(36.2 cm^(2) g^(-1)),oxidation stability in aqueous/ambient environments(21 days/80 days),and film conductivity(749 S m^(-1)).Additionally,it significantly enhances the gas-sensing performance,including the sensitivity(0.1119Ωppm^(-1)),response(0.2% and 23%to 50 ppb and 200 ppm NO_(2)),and response/recovery times(90/100 s).The reduced shielding effect of the–I-terminals and the metallic characteristics of MXene enhance the selectivity of I-MXene toward NO2.This approach paves the way for the development of stable and high-performance gas-sensing two-dimensional materials with promising prospects for future studies.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.