Genetic and Agronomic Parameter Estimates of Growth, Yield and Related Traits of Maize (Zea mays L.) under Different Rates of Nitrogen Fertilization

This study evaluated the genetic and agronomic parameter estimates of maize under different nitrogen rates. The trial was established at the Njala Agricultural Research Centre experimental site during 2021 and 2022 in a split block design with three maize varieties (IWCD2, 2009EVDT, and DMR-ESR-Yellow) and seven nitrogen (0, 30, 60, 90, 120, 150 and 180 kg∙N∙ha−1) rates. Findings showed that cob diameter and anthesis silking time (ASI) had intermediate heritability, ASI had high genetic advance, ASI and grain yield had high genotypic coefficient of variation (GCV), while traits with high phenotypic coefficient of variation (PCV) were plant height, ASI, grain yield, number of kernel per cob, number of kernel rows, ear length, and ear height. The PCV values were higher than GCV, indicating the influence of the environment in the studied traits. Nitrogen rates and variety significantly (p < 0.05) influenced grain yield production. Mean grain yields and economic parameter estimates increased with increasing nitrogen rates, with the 30 and 180 kg∙N∙ha−1 plots exhibiting the lowest and highest grain yields of 1238 kg∙ha−1 and 2098 kg∙ha−1, respectively. Variety and nitrogen effects on partial factor productivity (PFPN), agronomic efficiency (AEN), net returns (NR), value cost ratio (VCR) and marginal return (MR) indicated that these parameters were significantly affected (p < 0.05) by these factors. The highest PFPN (41.3 kg grain kg−1∙N) and AEN (29.4 kg grain kg−1∙N) were obtained in the 30 kg∙N∙ha−1 plots, while the highest VCR (2.8) and MR (SLL 1.8 SLL−1 spent on N) were obtained in the 180 kg∙N∙ha−1. The significant influence of variety and nitrogen on traits suggests that increasing yields and maximizing profits require use of appropriate nitrogen fertilization and improved farming practices that could be exploited for increased productivity of maize.

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

Method for rapid warning and activity concentration estimates in online waterγ-spectrometry systems

Onlineγ-spectrometry systems for inland waters,most of which extract samples in situ and in real time,are able to produce reliable activity concentration measurements for waterborne radionuclides only when they are distributed relatively uniformly and enter into a steady-state diffusion regime in the measurement chamber.To protect residents’health and ensure the safety of the living environment,better timeliness is required for this measurement method.To address this issue,this study established a mathematical model of the online waterγ-spectrometry system so that rapid warning and activity estimates can be obtained for water under non-steady-state(NSS)conditions.In addition,the detection efficiency of the detector for radionuclides during the NSS diffusion process was determined by applying the computational fluid dynamics technique in conjunction with Monte Carlo simulations.On this basis,a method was developed that allowed the online waterγ-spectrometry system to provide rapid warning and activity concentration estimates for radionuclides in water.Subsequent analysis of the NSS-mode measurements of^(40)K radioactive solutions with different activity concentrations determined the optimum warning threshold and measurement time for producing accurate activity concentration estimates for radionuclides.The experimental results show that the proposed NSS measurement method is able to give warning and yield accurate activity concentration estimates for radionuclides 55.42 and 69.42 min after the entry of a 10 Bq/L^(40)K radioactive solution into the measurement chamber,respectively.These times are much shorter than the 90 min required by the conventional measurement method.Furthermore,the NSS measurement method allows the measurement system to give rapid(within approximately 15 min)warning when the activity concentrations of some radionuclides reach their respective limits stipulated in the Guidelines for Drinking-water Quality of the WHO,suggesting that this method considerably enhances the warning capacity of in situ online waterγ-spectrometry systems.

Using Extreme Value Theory Approaches to Estimate High Quantiles for Stroke Data

This paper aims to explore the application of Extreme Value Theory (EVT) in estimating the conditional extreme quantile for time-to-event outcomes by examining the functional relationship between ambulatory blood pressure trajectories and clinical outcomes in stroke patients. The study utilizes EVT to analyze the functional connection between ambulatory blood pressure trajectories and clinical outcomes in a sample of 297 stroke patients. The 24-hour ambulatory blood pressure measurement curves for every 15 minutes are considered, acknowledging a censored rate of 40%. The findings reveal that the sample mean excess function exhibits a positive gradient above a specific threshold, confirming the heavy-tailed distribution of data in stroke patients with a positive extreme value index. Consequently, the estimated conditional extreme quantile indicates that stroke patients with higher blood pressure measurements face an elevated risk of recurrent stroke occurrence at an early stage. This research contributes to the understanding of the relationship between ambulatory blood pressure and recurrent stroke, providing valuable insights for clinical considerations and potential interventions in stroke management.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Improving the accuracy of precipitation estimates in a typical inland arid area of China using a dynamic Bayesian model averaging approach

Xinjiang Uygur Autonomous Region is a typical inland arid area in China with a sparse and uneven distribution of meteorological stations,limited access to precipitation data,and significant water scarcity.Evaluating and integrating precipitation datasets from different sources to accurately characterize precipitation patterns has become a challenge to provide more accurate and alternative precipitation information for the region,which can even improve the performance of hydrological modelling.This study evaluated the applicability of widely used five satellite-based precipitation products(Climate Hazards Group InfraRed Precipitation with Station(CHIRPS),China Meteorological Forcing Dataset(CMFD),Climate Prediction Center morphing method(CMORPH),Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record(PERSIANN-CDR),and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis(TMPA))and a reanalysis precipitation dataset(ECMWF Reanalysis v5-Land Dataset(ERA5-Land))in Xinjiang using ground-based observational precipitation data from a limited number of meteorological stations.Based on this assessment,we proposed a framework that integrated different precipitation datasets with varying spatial resolutions using a dynamic Bayesian model averaging(DBMA)approach,the expectation-maximization method,and the ordinary Kriging interpolation method.The daily precipitation data merged using the DBMA approach exhibited distinct spatiotemporal variability,with an outstanding performance,as indicated by low root mean square error(RMSE=1.40 mm/d)and high Person's correlation coefficient(CC=0.67).Compared with the traditional simple model averaging(SMA)and individual product data,although the DBMA-fused precipitation data were slightly lower than the best precipitation product(CMFD),the overall performance of DBMA was more robust.The error analysis between DBMA-fused precipitation dataset and the more advanced Integrated Multi-satellite Retrievals for Global Precipitation Measurement Final(IMERG-F)precipitation product,as well as hydrological simulations in the Ebinur Lake Basin,further demonstrated the superior performance of DBMA-fused precipitation dataset in the entire Xinjiang region.The proposed framework for solving the fusion problem of multi-source precipitation data with different spatial resolutions is feasible for application in inland arid areas,and aids in obtaining more accurate regional hydrological information and improving regional water resources management capabilities and meteorological research in these regions.

Energy-Saving Construction Technologies for Buildings

The development of the construction industry is shifting towards low-carbon construction,so it is necessary to improve and optimize related construction concepts,methods,and processes.By improving resource and energy control efficiency in building projects,minimizing construction waste,and reducing environmental impact,a foundation for the sustainable development of the industry can be established.This paper mainly analyzes the significance of low-carbon energy-saving construction technology and the control factors of construction,summarizes the status quo of the development of building energy-saving construction,and puts forward strategies for applying building energy-saving construction technology.These strategies serve to achieve low-carbon and energy-saving goals to promote the healthy development of energy-saving construction.

Application of Energy-Saving Materials in Architectural Design

The conventional process of building construction is associated with issues such as the waste of construction materials and environmental pollution.Sustainable development highlights the importance of energy conservation and eco-friendly practices.It is essential to use energy-efficient and green materials in building designs to ensure the healthy growth of construction companies.This article discusses the advantages and principles of incorporating energy-saving materials in architectural design.It examines the strategies and critical control points for using energy-saving materials in architectural design,offering guidance for the sustainable development of the construction industry.

Exploration of Energy-Saving Technologies in Building Electrical System Design

Green energy conservation is the mainstream trend in the current development of the construction industry.The application of energy-saving technology in building electrical system design can effectively reduce energy consumption,avoid unnecessary energy consumption,and truly achieve energy conservation and environmental protection.Based on this,the article elaborates on the principles of energy-saving design in building electrical systems,and actively explores the application of energy-saving technologies from different perspectives such as optimizing power supply and distribution system design,adopting high-efficiency energy-saving lighting equipment,applying renewable energy,promoting smart home technology,and improving the efficiency of building electrical equipment.

Green,Sustainable Architectural Bamboo with High Light Transmission and Excellent Electromagnetic Shielding as a Candidate for Energy-Saving Buildings

Currently,light-transmitting,energy-saving,and electromagnetic shielding materials are essential for reducing indoor energy consumption and improving the electromagnetic environment.Here,we developed a cellulose composite with excellent optical transmittance that retained the natural shape and fiber structure of bamboo.The modified whole bamboo possessed an impressive optical transmittance of approximately 60%at 6.23 mm,illuminance of 1000 luminance(lux),water absorption stability(mass change rate less than 4%),longitudinal tensile strength(46.40 MPa),and surface properties(80.2 HD).These were attributed to not only the retention of the natural circular hollow structure of the bamboo rod on the macro,but also the complete bamboo fiber skeleton template impregnated with UV resin on the micro.Moreover,a multilayered device consisting of translucent whole bamboo,transparent bamboo sheets,and electromagnetic shielding film exhibited remarkable heat insulation and heat preservation performance as well as an electromagnetic shielding performance of 46.3 dB.The impressive optical transmittance,mechanical properties,thermal performance,and electromagnetic shielding abilities combined with the renewable and sustainable nature,as well as the fast and efficient manufacturing process,make this bamboo composite material suitable for effective application in transparent,energy-saving,and electromagnetic shielding buildings.

Low-content Pt-triggered the optimized d-band center of Rh metallene for energy-saving hydrogen production coupled with hydrazine degradation

Utilizing the hydrazine-assisted water electrolysis for energy-efficient hydrogen production shows a promising application, which relies on the development and design of efficient bifunctional electrocatalysts. Herein, we reported a low-content Pt-doped Rh metallene(Pt-Rhene) for hydrazine-assisted water electrolysis towards energy-saving hydrogen(H_(2)) production, where the ultrathin metallene is constructed to provide enough favorable active sites for catalysis and improve atom utilization.Additionally, the synergistic effect between Rh and Pt can optimize the electronic structure of Rh for improving the intrinsic activity. Therefore, the required overpotential of Pt-Rhene is only 37 mV to reach a current density of-10 mA cm^(-2) in the hydrogen evolution reaction(HER), and the Pt-Rhene exhibits a required overpotential of only 11 mV to reach a current density of 10 mA cm^(-2) in the hydrazine oxidation reaction(HzOR). With the constructed HER-HzOR two-electrode system, the Pt-Rhene electrodes exhibit an extremely low voltage(0.06/0.19/0.28 V) to achieve current densities of 10/50/100 mA cm^(-2) for energy-saving H_(2) production, which greatly reduces the electrolysis energy consumption. Moreover,DFT calculations further demonstrate that the introduction of Pt modulates the electronic structure of Rh and optimizes the d-band center, thus enhancing the adsorption and desorption of reactant/intermediates in the electrocatalytic reaction.

基于ESTIMATE算法探究TCGA数据库免疫相关基因在乳腺癌中的预后价值

目的根据ESTIMATE算法探究TCGA数据库中免疫相关基因在乳腺癌中的预后价值。方法从TCGA数据库获取606例乳腺癌患者的临床信息和肿瘤样本的转录组数据,采用edgeR方法对转录组数据进行差异表达分析,通过ESTIMATE算法筛选出基于免疫分数或间质分数高低分组的差异表达基因;使用R软件绘制差异表达基因的聚类分析热图;使用韦恩图对基于免疫分数和间质分数得到的差异表达基因进行交集分析;运用STRING数据库,搜索并预测表达显著差异基因编码蛋白质之间的相互作用;通过单因素Cox回归分析评估差异表达基因的预后作用;运用DAVID数据库对差异表达基因进行GO富集分析以及KEGG通路富集分析。结果生存分析结果显示,高免疫分数组患者的总生存期中位值943天,而低分组患者总生存期中位值860天,显著高分组患者总生存期显著高于低分组患者(P<0.05);而间质分数与乳腺癌患者总生存期之间无差异(P>0.05)。进一步对免疫分数高分组和低分组患者表达上调的951个差异表达基因进行生存分析,发现160个基因与乳腺癌患者的总生存期显著相关;对上述160个基因进行蛋白质互作分析,富集出与乳腺癌患者总生存率呈正相关的CTLA4、CD8A、CD19、CD27、CD2、IL2、GZMB、IL2RB、CD3E、CD40LG等基因。对上述160个基因进行GO富集和KEGG通路分析,结果显示免疫功能相关信号通路T细胞受体信号通路、造血细胞系、T细胞活化等通路显著富集。结论本研究发现160个具有预后价值的免疫分数相关基因,免疫分数与乳腺癌患者的总生存期、预后改善相关,将为乳腺癌预后判断和靶向治疗提供新的潜在靶点。

基于ESTIMATE评分挖掘肝癌微环境相关的预后基因

目的基于ESTIMATE评分筛选与肝癌肿瘤微环境预后相关的基因。方法从TCGA数据库中下载肝癌转录组数据,使用ESTIMATE法计算肝癌组织的免疫和基质评分。分析肝癌基质、免疫评分与肿瘤分级、分期、生存时间的相关关系。Limma包分析差异表达基因,进行基因功能富集分析和通路富集分析。采用Cytoscape中Mcode模块筛选核心差异表达基因,并分析核心基因与肝癌预后、诊断和肿瘤免疫浸润细胞的相关关系。结果筛选出肝癌微环境相关差异表达基因329个。基因功能富集分析显示其主要参与免疫激活和调节等过程。Cytoscape筛选PPI网络核心基因CCR5、SLAMF1、TRAF3IP3,在肝癌中高表达,且基因表达量高的患者的总生存期较低表达患者长,与肿瘤免疫浸润细胞比例呈正相关关系。结论CCR5、SLAMF1、TRAF3IP3基因表达与肝癌的诊断、预后显著相关。

Design of Diversified Intelligent Control System for Energy-saving Optimization of Solar Greenhouse in North China

Intelligent greenhouse can promote the development of modern agriculture, realize the high quality and high yield of crops, and also bring greater economic benefits. In accordance with the climate conditions in northwest China, a set of intelligent control system for diversified environment of solar greenhouse was designed. The system divides the annual greenhouse control into six stages according to the optimal energy saving. It uses modern detection technology to collect the greenhouse environmental temperature, environmental humidity, soil humidity, CO_(2) concentration and illumination parameters under different working modes. It uses programmable logic control technology to realize the data processing of various parameters and the action control of rolling film, wet curtain fan and other actuators. It uses KingView monitoring software to realize the monitoring and manual control of greenhouse environment parameters. The operation results indicate that the control system runs stably and basically meets the control requirements.

Behavior characteristics and market operation problems of existing building energy-saving renovation market

The long-term and effective implementation of the existing building energy efficiency renovation depends on the development of the existing building energy efficiency renovation market.The key to the development of the existing building energy efficiency renovation market is the joint role of the market players.Starting with the analysis of the externalities and information asymmetry of the existing building energy efficiency renovation market,this paper analyzes the behavioral characteristics and influencing factors of the existing building energy efficiency renovation market entities(central government,local government,owners,energy conservation service enterprises,third-party evaluation institutions,and other market entities),and reveals the problems of the existing building energy efficiency renovation market,such as the absence of government,the lack of main power,and the lack of financing channels,Thus,it lays a platform foundation for the research on the behavior strategy and security system of the existing building energy-s aving renovation market.

Global population estimate and conservation gap analysis for the Nordmann's Greenshank(Tringa guttifer)

Nordmann's Greenshank(Tringa guttifer)is a globally endangered species that has received little research attention.It is threatened by rapid habitat loss,an incomplete network of protected sites,and lack of long-term data on population dynamics.Citizen science data can be combined with survey data to support population estimation and conservation gap analysis.From 2020 to 2021,Nordmann's Greenshank was surveyed in Tiaozini,Xiaoyangkou,and Dongling on the southern coast of Jiangsu Province,China,and the global population of the species was re-evaluated using the data obtained.We integrated citizen science data from eBird and the China Bird Report from 2000 to 2020 with the survey results to identify important habitats harboring over 1%of its total population,and compared this data with existing protected areas to identify gaps in its global conservation.Our survey found that Tiaozini supported at least 1194 individuals.Consequently,its global population was reestimated to be 1500-2000.Moreover,45 important habitats were identified based on citizen data and survey results.Although 44.4%and 50.0%of the priority sites in the world and China,respectively,are located outside protected areas,the Conservation Effectiveness Index(C)is 68.4%and 71.1%,respectively,showing that the current coverage of protected areas for this part of its range is reasonable.This study presents the most complete and recent population data to date.Tiaozini is the most important migration stopover site for Nordmann's Greenshanks.The species is under threat in terms of breeding,wintering,and stopover sites.Therefore,we suggest improving monitoring,establishing new protected sites to complete the habitat protection network,and improving the effectiveness of existing habitat protection strategies,including further developing high tide roosting sites.

Cascaded ELM-Based Joint Frame Synchronization and Channel Estimation over Rician Fading Channel with Hardware Imperfections

Due to the interdependency of frame synchronization(FS)and channel estimation(CE),joint FS and CE(JFSCE)schemes are proposed to enhance their functionalities and therefore boost the overall performance of wireless communication systems.Although traditional JFSCE schemes alleviate the influence between FS and CE,they show deficiencies in dealing with hardware imperfection(HI)and deterministic line-of-sight(LOS)path.To tackle this challenge,we proposed a cascaded ELM-based JFSCE to alleviate the influence of HI in the scenario of the Rician fading channel.Specifically,the conventional JFSCE method is first employed to extract the initial features,and thus forms the non-Neural Network(NN)solutions for FS and CE,respectively.Then,the ELMbased networks,named FS-NET and CE-NET,are cascaded to capture the NN solutions of FS and CE.Simulation and analysis results show that,compared with the conventional JFSCE methods,the proposed cascaded ELM-based JFSCE significantly reduces the error probability of FS and the normalized mean square error(NMSE)of CE,even against the impacts of parameter variations.

Multi-subject and multi-objective integrated optimization system and implementation strategy for energy-saving renovation of the existing residential buildings

The core of the healthy and orderly operation of the existing residential building energy-saving renovation market lies in the exploration of the implementation mechanism of multi-subject and multi-objective integrated optimization.The multi-agent and multi-objective integrated optimization system framework is a powerful tool to guide the scientific decision-making of the market core structural entities in the future market practice. This paper analyzes the practical dilemma of energy-saving renovation of theexisting residential buildings in China, summarizes the practical experience of multi-subject and multi-objective integrated optimization of energy-saving renovation of the existing residential buildings in foreign countries, and puts forward beneficial practical enlightenment on the basis of comparison at home and abroad;The design principles of the target integrated optimization system have established a multi-subject and multi-objective integrated optimization system framework for the energy-saving renovation of the existing residential buildings, from six aspects: government guidance, trust consensus, value co-creation, risk sharing, revenue sharing, and social responsibility sharing. This paper proposes a multi-subject and multi-objective integrated practice strategy, in order to promote the efficient and orderly development of China's existing residential building energy-saving renovation market.

A Posteriori Error Estimate of Two Grid Mixed Finite Element Methods for Semilinear Elliptic Equations

In this paper, we present the a posteriori error estimate of two-grid mixed finite element methods by averaging techniques for semilinear elliptic equations. We first propose the two-grid algorithms to linearize the mixed method equations. Then, the averaging technique is used to construct the a posteriori error estimates of the two-grid mixed finite element method and theoretical analysis are given for the error estimators. Finally, we give some numerical examples to verify the reliability and efficiency of the a posteriori error estimator.