Oil palm germplasm collected from Angola,Africa in 1991 were subjected to genetic variability potential studies.The collection was planted in the form of open-pollinated families as trials at the Malaysian Palm Oil Bo...Oil palm germplasm collected from Angola,Africa in 1991 were subjected to genetic variability potential studies.The collection was planted in the form of open-pollinated families as trials at the Malaysian Palm Oil Board(MPOB)Kluang Research Station,Johor,Malaysia,in 1994.Dura palms from 52 families and tenera palms from 44 families of MPOB-Angola were evaluated for their bunch yield and bunch quality components.The objectives of this study were to determine the genetic variability among the families and performance of MPOB-Angola germplasm for yield improvement.The analysis of variance(ANOVA)revealed highly significant differences between the dura and tenera families for most of the traits,suggesting the presence of high genetic variability,which is essential for breeding programmes.Among the duras,family AGO 02.02 displayed the best yield performance,with a high fresh fruit bunch,oil yield and total economic product at 240.40,29.46 and 37.93 kg palm^(-1)year^(-1),respectively.As for the teneras,family AGO 03.04 recorded the highest FFB yield and oil yield at 249.25 and 45.22 kg palm^(-1)year^(-1),respectively.Besides that,several families with big fruit sizes or producing a mean fruit weight of 14-17 g were also identified.Both dura and tenera from AGO 01.01 recorded the highest oil to bunch(O/B)of 17.76%and 28.65%,respectively.These findings will facilitate the selection of palms from the MPOB-Angola germplasm for future breeding programmes.展开更多
The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more e...The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.展开更多
Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these i...Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.展开更多
In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(...In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.展开更多
The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly re...The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.展开更多
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify sp...Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.展开更多
This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independ...This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.展开更多
Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain c...Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.展开更多
In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by re...In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.展开更多
Germplasm resources are essential for the sustainable development of biodiversity and husbandry of local chickens, as well as for the breeding and industry of superior quality chickens. Unfortunately, many local and i...Germplasm resources are essential for the sustainable development of biodiversity and husbandry of local chickens, as well as for the breeding and industry of superior quality chickens. Unfortunately, many local and indigenous chicken breeds are at risk of declining numbers, emphasizing the need to conserve breed resources for endangered chickens. Primordial germ cells(PGCs) are crucial for preserving germplasm resources by inheriting genetic information from parents to offspring and ensuring stability of genetic material between germlines. In this study,PGCs were isolated from chicken embryos' gonads and cultured in FAcs medium without feeder cells. Over a period of approximately 40 d, the cells proliferated to a number of up to 10^(6), establishing various cell lines. Particularly, 18 PGC lines were created from Rugao Yellow chicken and Shouguang chicken, with an efficiency ranging from 39.1 to 45%. Furthermore, PGCs that had been cultured for 40 passages exhibited typical PGC characteristics, suchas glycogen staining reaction, and expression of pluripotency and reproductive markers. These results confirmthat PGCs maintain stem cell properties even after long-term in vitro culture. Additionally, PGCs cryopreserved for up to 120 d remained viable, maintained typical PGC morphologies, and possessed stable cell proliferation ability. Through intravascular injection into chicken embryos, green fluorescent protein(GFP)-PGCs were found in the recipient embryos' gonads and could develop into gametes to produce offspring, indicating that even after extended culture, PGCs retain their migratory and lineage-transmitting capabilities. This research offers valuable insights into the in vitro cultivation and preservation of PGCs of Chinese indigenous chickens. The findings of this study can be applied in transgenic chicken production and the preservation of genetic resources of indigenous chicken breeds.展开更多
Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this stud...Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this study,we investigate mtDNA variants that may affect obesity risk in 2877 Han Chinese individuals from 3 independent populations.The association analysis of 16 basal mtDNA haplogroups with body mass index,waist circumference,and waist-to-hip ratio reveals that only haplogroup M7 is significantly negatively correlated with all three adiposity-related anthropometric traits in the overall cohort,verified by the analysis of a single population,i.e.,the Zhengzhou population.Furthermore,subhaplogroup analysis suggests that M7b1a1 is the most likely haplogroup associated with a decreased obesity risk,and the variation T12811C(causing Y159H in ND5)harbored in M7b1a1 may be the most likely candidate for altering the mitochondrial function.Specifically,we find that proportionally more nonsynonymous mutations accumulate in M7b1a1 carriers,indicating that M7b1a1 is either under positive selection or subject to a relaxation of selective constraints.We also find that nuclear variants,especially in DACT2 and PIEZO1,may functionally interact with M7b1a1.展开更多
Exploring the phenotypic trait variation and diversity of kiwifruit male plant resources can support selection,breeding,and genetic improvement,ultimately enhancing agricultural production.In this study,50 kiwifruit m...Exploring the phenotypic trait variation and diversity of kiwifruit male plant resources can support selection,breeding,and genetic improvement,ultimately enhancing agricultural production.In this study,50 kiwifruit male plants were collected from the resource nursery of Sichuan Provincial Natural Resources Bureau.The phenotypic variation of the germplasm was analyzed using 16 quantitative traits.The analysis involved coefficient of variation(CV),Shannon-Wiener index(H),principal component analysis,correlation analysis,cluster analysis,and comprehensive evaluation.The results showed that the variation range of 16 phenotypic traits in kiwifruit male germplasm resources was 1.55%to 83.71%,with an average coefficient of variation of 28.62%,and an H index of 1.265 to 2.941.The average CVs of diploid,tetraploid,and hexaploid were 22.62%,18.99%and 18.18%,respectively,and the average CV of diploid was the largest.Indicated that the male germplasm resources of kiwifruit showed significant phenotypic diversity,and the diploid showed higher diversity characteristics.Principal component analysis(PCA)revealed that the cumulative variance contribution rate of the first seven principal components was 76.66%,which effectively captured the information from 21 traits.Cluster analysis divided the 50 kiwifruit male germplasm resources into 4 clusters;each cluster exhibited distinct phenotypic characteristics.The analysis also determined the trait characteristics and breeding value of each cluster.The results of this study provide valuable information for genetic improvement,protection,and evaluation of kiwifruit male germplasm resources.展开更多
Houttuynia cordata, a characteristic edible and medicinal plant in southwestern China, is prone to absorbing lead (Pb^(2+)). Excessive consumption may lead to Pb^(2+) accumulation in the human body, which has been lin...Houttuynia cordata, a characteristic edible and medicinal plant in southwestern China, is prone to absorbing lead (Pb^(2+)). Excessive consumption may lead to Pb^(2+) accumulation in the human body, which has been linked to serious health risks such as neurotoxicity, kidney damage, anemia, and developmental disorders, particularly in children. Therefore, the development of molecular markers associated with Pb^(2+) uptake and the investigation of the plant’s physiological responses to Pb^(2+) pollution are of great significance. In this study, 72 H. cordata germplasms were evaluated for Pb^(2+) accumulation after exogenous Pb^(2+) treatment. A significant variation in Pb^(2+) content was observed among the germplasms, indicating rich genetic diversity. Using RAPD markers, seven loci were identified to be significantly associated with Pb^(2+) uptake, with locus 43 (R^(2) = 6.72%) and locus 53 (R^(2) = 5.39%) showing the strongest correlations. Marker validation was performed using five low- and five high-accumulating accessions. Two representative germplasms were further subjected to 0, 500 and 1000 mg/kg Pb^(2+) treatments for 40 days. Pb^(2+) content, membrane lipid peroxidation, and redox enzyme activities (SOD, POD and CAT) were measured across different organs. Organs with greater soil contact (roots) exhibited higher Pb^(2+) accumulation and oxidative damage. POD and CAT activities were markedly induced by Pb^(2+) stress, while SOD response was limited. This study provides a theoretical foundation for breeding low Pb^(2+)-accumulating H. cordata varieties through marker-assisted selection (MAS) and supports their safe use and application in phytoremediation.展开更多
This paper investigated and analyzed the conservation and utilization of four local livestock breeds in Binzhou City:Wadi Sheep,Bohai Black Cattle,Wudi Donkey,and Lubei White Goat.Shortcomings in the protection and ut...This paper investigated and analyzed the conservation and utilization of four local livestock breeds in Binzhou City:Wadi Sheep,Bohai Black Cattle,Wudi Donkey,and Lubei White Goat.Shortcomings in the protection and utilization of local germplasm resources were pointed out,and strategies and recommendations were proposed to promote high-quality development of livestock and poultry genetic resources in Binzhou,including building a solid germplasm foundation,standardizing production,and driving innovation.This paper provides references for the conservation,development,and utilization of local genetic resources in Binzhou City.展开更多
Accurate evaluation of disease levels in wild rice germplasm and identification of disease resistance are critical for developing rice varieties resistant to blast disease.However,existing evaluation methods face limi...Accurate evaluation of disease levels in wild rice germplasm and identification of disease resistance are critical for developing rice varieties resistant to blast disease.However,existing evaluation methods face limitations that hinder progress in breeding.To address these challenges,we proposed an AI-powered method for evaluating blast disease levels and identifying resistance in wild rice.A lightweight segmentation model for diseased leaves and lesions was developed,incorporating an improved federated learning approach to enhance robustness and adaptability.Based on the segmentation results and resistance identification technical specifications,wild rice materials were evaluated into 10 disease levels(L0 to L9),further enabling disease-resistance identification through multiple replicates of the same materials.The method was successfully implemented on augmented reality glasses for real-time,first-person evaluation.Additionally,high-speed scanners and edge computing devices were integrated to enable continuous,precise,and dynamic evaluation.Experimental results demonstrate the outstanding performance of the proposed method,achieving effective segmentation of diseased leaves and lesions with only 0.22 M parameters and 5.3 G floating-point operations per second(FLOPs),with a mean average precision(mAP@0.5)of 96.3%.The accuracy of disease level evaluation and disease-resistance identification reached 99.7%,with a practical test accuracy of 99.0%,successfully identifying three highly resistant wild rice materials.This method provides strong technical support for efficiently identifying wild rice materials resistant to blast disease and advancing resistance breeding efforts.展开更多
Using headspace solid-phase microextraction(HS-SPME)combined with gas chromatography-mass spectrometry(GC-MS),we investigated the composition of volatile compounds in 114 peach germplasms across three harvest seasons(...Using headspace solid-phase microextraction(HS-SPME)combined with gas chromatography-mass spectrometry(GC-MS),we investigated the composition of volatile compounds in 114 peach germplasms across three harvest seasons(June,July,and August),three types(round peach,nectarine,and flat peach),two flesh colors(white and yellow),three levels of total soluble solids(TSS)content(high,medium,and low),and three categories of single fruit weight(SFW)(large,medium,and small).A total of 41 volatile compounds were identified in all 114 germplasms,including nine volatile categories:aldehydes(15),esters(5),lactones(8),alcohols(3),terpenols(4),ketones(2),phenols(1),alkanes(2),and ether(1).The average contents of the nine volatile types were aldehydes>esters>lactones>alcohols>terpenols>ketones>phenols>alkanes>et hers.Most of the 24 germplasms harvested in June could be clearly distinguished from the 30 germplasms harvested in August in the principal component analysis(PCA)plots(PC1,PC2,and PC3)of the three harvest seasons,which had much better discriminability than hierarchical clustering heatmap.In the PCA plots of the three TSS classifications,considerable separations occurred between 39 high TSS germplasms and 39 low TSS ones.In the PCA plots of classifications of the three SFW,three types,and two flesh colors,germplasms of different categories highly overlapped with each other.In the PCA(PC1 and PC2)loading plots,weights for distinguishing 114 germplasms of the five classifications were all in the order of trans-2-hexenyl acetate>benzaldehyde>methyl benzoate>2-hexenal>hexanal>linalool>hexyl acetate>2-ethylhexanol>γ-decalactone>the other 32 volatile components.展开更多
Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with mult...Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with multiple-input multipleoutput(MIMO)systems.Antenna selection plays a critical role in MIMO–NOMA systems as it has the potential to significantly reduce the cost and complexity associated with radio frequency chains.This paper considers antenna selection for downlink MIMO–NOMA networks with multiple-antenna basestation(BS)and multiple-antenna user equipments(UEs).An iterative antenna selection scheme is developed for a two-user system,and to determine the initial power required for this selection scheme,a power estimation method is also proposed.The proposed algorithm is then extended to a general multiuser NOMA system.Numerical results demonstrate that the proposed antenna selection algorithm achieves near-optimal performance with much lower computational complexity in both two-user and multiuser scenarios.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure t...The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.展开更多
The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud d...The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud data centres and an important concern of research for many researchers.In this paper,we proposed a cuckoo search(CS)-based optimisation technique for the virtual machine(VM)selection and a novel placement algorithm considering the different constraints.The energy consumption model and the simulation model have been implemented for the efficient selection of VM.The proposed model CSOA-VM not only lessens the violations at the service level agreement(SLA)level but also minimises the VM migrations.The proposed model also saves energy and the performance analysis shows that energy consumption obtained is 1.35 kWh,SLA violation is 9.2 and VM migration is about 268.Thus,there is an improvement in energy consumption of about 1.8%and a 2.1%improvement(reduction)in violations of SLA in comparison to existing techniques.展开更多
文摘Oil palm germplasm collected from Angola,Africa in 1991 were subjected to genetic variability potential studies.The collection was planted in the form of open-pollinated families as trials at the Malaysian Palm Oil Board(MPOB)Kluang Research Station,Johor,Malaysia,in 1994.Dura palms from 52 families and tenera palms from 44 families of MPOB-Angola were evaluated for their bunch yield and bunch quality components.The objectives of this study were to determine the genetic variability among the families and performance of MPOB-Angola germplasm for yield improvement.The analysis of variance(ANOVA)revealed highly significant differences between the dura and tenera families for most of the traits,suggesting the presence of high genetic variability,which is essential for breeding programmes.Among the duras,family AGO 02.02 displayed the best yield performance,with a high fresh fruit bunch,oil yield and total economic product at 240.40,29.46 and 37.93 kg palm^(-1)year^(-1),respectively.As for the teneras,family AGO 03.04 recorded the highest FFB yield and oil yield at 249.25 and 45.22 kg palm^(-1)year^(-1),respectively.Besides that,several families with big fruit sizes or producing a mean fruit weight of 14-17 g were also identified.Both dura and tenera from AGO 01.01 recorded the highest oil to bunch(O/B)of 17.76%and 28.65%,respectively.These findings will facilitate the selection of palms from the MPOB-Angola germplasm for future breeding programmes.
文摘The rapid rise of cyberattacks and the gradual failure of traditional defense systems and approaches led to using artificial intelligence(AI)techniques(such as machine learning(ML)and deep learning(DL))to build more efficient and reliable intrusion detection systems(IDSs).However,the advent of larger IDS datasets has negatively impacted the performance and computational complexity of AI-based IDSs.Many researchers used data preprocessing techniques such as feature selection and normalization to overcome such issues.While most of these researchers reported the success of these preprocessing techniques on a shallow level,very few studies have been performed on their effects on a wider scale.Furthermore,the performance of an IDS model is subject to not only the utilized preprocessing techniques but also the dataset and the ML/DL algorithm used,which most of the existing studies give little emphasis on.Thus,this study provides an in-depth analysis of feature selection and normalization effects on IDS models built using three IDS datasets:NSL-KDD,UNSW-NB15,and CSE–CIC–IDS2018,and various AI algorithms.A wrapper-based approach,which tends to give superior performance,and min-max normalization methods were used for feature selection and normalization,respectively.Numerous IDS models were implemented using the full and feature-selected copies of the datasets with and without normalization.The models were evaluated using popular evaluation metrics in IDS modeling,intra-and inter-model comparisons were performed between models and with state-of-the-art works.Random forest(RF)models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86%and 96.01%,respectively,whereas artificial neural network(ANN)achieved the best accuracy of 95.43%on the CSE–CIC–IDS2018 dataset.The RF models also achieved an excellent performance compared to recent works.The results show that normalization and feature selection positively affect IDS modeling.Furthermore,while feature selection benefits simpler algorithms(such as RF),normalization is more useful for complex algorithms like ANNs and deep neural networks(DNNs),and algorithms such as Naive Bayes are unsuitable for IDS modeling.The study also found that the UNSW-NB15 and CSE–CIC–IDS2018 datasets are more complex and more suitable for building and evaluating modern-day IDS than the NSL-KDD dataset.Our findings suggest that prioritizing robust algorithms like RF,alongside complex models such as ANN and DNN,can significantly enhance IDS performance.These insights provide valuable guidance for managers to develop more effective security measures by focusing on high detection rates and low false alert rates.
基金supported by the National Natural Science Foundation of China(42250101)the Macao Foundation。
文摘Earth’s internal core and crustal magnetic fields,as measured by geomagnetic satellites like MSS-1(Macao Science Satellite-1)and Swarm,are vital for understanding core dynamics and tectonic evolution.To model these internal magnetic fields accurately,data selection based on specific criteria is often employed to minimize the influence of rapidly changing current systems in the ionosphere and magnetosphere.However,the quantitative impact of various data selection criteria on internal geomagnetic field modeling is not well understood.This study aims to address this issue and provide a reference for constructing and applying geomagnetic field models.First,we collect the latest MSS-1 and Swarm satellite magnetic data and summarize widely used data selection criteria in geomagnetic field modeling.Second,we briefly describe the method to co-estimate the core,crustal,and large-scale magnetospheric fields using satellite magnetic data.Finally,we conduct a series of field modeling experiments with different data selection criteria to quantitatively estimate their influence.Our numerical experiments confirm that without selecting data from dark regions and geomagnetically quiet times,the resulting internal field differences at the Earth’s surface can range from tens to hundreds of nanotesla(nT).Additionally,we find that the uncertainties introduced into field models by different data selection criteria are significantly larger than the measurement accuracy of modern geomagnetic satellites.These uncertainties should be considered when utilizing constructed magnetic field models for scientific research and applications.
基金supported by the CAS Project for Young Scientists in Basic Research under Grant YSBR-035Jiangsu Provincial Key Research and Development Program under Grant BE2021013-2.
文摘In covert communications,joint jammer selection and power optimization are important to improve performance.However,existing schemes usually assume a warden with a known location and perfect Channel State Information(CSI),which is difficult to achieve in practice.To be more practical,it is important to investigate covert communications against a warden with uncertain locations and imperfect CSI,which makes it difficult for legitimate transceivers to estimate the detection probability of the warden.First,the uncertainty caused by the unknown warden location must be removed,and the Optimal Detection Position(OPTDP)of the warden is derived which can provide the best detection performance(i.e.,the worst case for a covert communication).Then,to further avoid the impractical assumption of perfect CSI,the covert throughput is maximized using only the channel distribution information.Given this OPTDP based worst case for covert communications,the jammer selection,the jamming power,the transmission power,and the transmission rate are jointly optimized to maximize the covert throughput(OPTDP-JP).To solve this coupling problem,a Heuristic algorithm based on Maximum Distance Ratio(H-MAXDR)is proposed to provide a sub-optimal solution.First,according to the analysis of the covert throughput,the node with the maximum distance ratio(i.e.,the ratio of the distances from the jammer to the receiver and that to the warden)is selected as the friendly jammer(MAXDR).Then,the optimal transmission and jamming power can be derived,followed by the optimal transmission rate obtained via the bisection method.In numerical and simulation results,it is shown that although the location of the warden is unknown,by assuming the OPTDP of the warden,the proposed OPTDP-JP can always satisfy the covertness constraint.In addition,with an uncertain warden and imperfect CSI,the covert throughput provided by OPTDP-JP is 80%higher than the existing schemes when the covertness constraint is 0.9,showing the effectiveness of OPTDP-JP.
基金supported by the National Natural Science Foundation of China(32160782 and 32060737).
文摘The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.
基金the Deanship of Scientifc Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/421/45supported via funding from Prince Sattam bin Abdulaziz University project number(PSAU/2024/R/1446)+1 种基金supported by theResearchers Supporting Project Number(UM-DSR-IG-2023-07)Almaarefa University,Riyadh,Saudi Arabia.supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1F1A1055408).
文摘Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
文摘This article constructs statistical selection procedures for exponential populations that may differ in only the threshold parameters. The scale parameters of the populations are assumed common and known. The independent samples drawn from the populations are taken to be of the same size. The best population is defined as the one associated with the largest threshold parameter. In case more than one population share the largest threshold, one of these is tagged at random and denoted the best. Two procedures are developed for choosing a subset of the populations having the property that the chosen subset contains the best population with a prescribed probability. One procedure is based on the sample minimum values drawn from the populations, and another is based on the sample means from the populations. An “Indifference Zone” (IZ) selection procedure is also developed based on the sample minimum values. The IZ procedure asserts that the population with the largest test statistic (e.g., the sample minimum) is the best population. With this approach, the sample size is chosen so as to guarantee that the probability of a correct selection is no less than a prescribed probability in the parameter region where the largest threshold is at least a prescribed amount larger than the remaining thresholds. Numerical examples are given, and the computer R-codes for all calculations are given in the Appendices.
基金funded by the Natural Science Foundation of China(Grant Nos.42377164 and 41972280)the Badong National Observation and Research Station of Geohazards(Grant No.BNORSG-202305).
文摘Landslide susceptibility prediction(LSP)is significantly affected by the uncertainty issue of landslide related conditioning factor selection.However,most of literature only performs comparative studies on a certain conditioning factor selection method rather than systematically study this uncertainty issue.Targeted,this study aims to systematically explore the influence rules of various commonly used conditioning factor selection methods on LSP,and on this basis to innovatively propose a principle with universal application for optimal selection of conditioning factors.An'yuan County in southern China is taken as example considering 431 landslides and 29 types of conditioning factors.Five commonly used factor selection methods,namely,the correlation analysis(CA),linear regression(LR),principal component analysis(PCA),rough set(RS)and artificial neural network(ANN),are applied to select the optimal factor combinations from the original 29 conditioning factors.The factor selection results are then used as inputs of four types of common machine learning models to construct 20 types of combined models,such as CA-multilayer perceptron,CA-random forest.Additionally,multifactor-based multilayer perceptron random forest models that selecting conditioning factors based on the proposed principle of“accurate data,rich types,clear significance,feasible operation and avoiding duplication”are constructed for comparisons.Finally,the LSP uncertainties are evaluated by the accuracy,susceptibility index distribution,etc.Results show that:(1)multifactor-based models have generally higher LSP performance and lower uncertainties than those of factors selection-based models;(2)Influence degree of different machine learning on LSP accuracy is greater than that of different factor selection methods.Conclusively,the above commonly used conditioning factor selection methods are not ideal for improving LSP performance and may complicate the LSP processes.In contrast,a satisfied combination of conditioning factors can be constructed according to the proposed principle.
文摘In this study,we examine the problem of sliced inverse regression(SIR),a widely used method for sufficient dimension reduction(SDR).It was designed to find reduced-dimensional versions of multivariate predictors by replacing them with a minimally adequate collection of their linear combinations without loss of information.Recently,regularization methods have been proposed in SIR to incorporate a sparse structure of predictors for better interpretability.However,existing methods consider convex relaxation to bypass the sparsity constraint,which may not lead to the best subset,and particularly tends to include irrelevant variables when predictors are correlated.In this study,we approach sparse SIR as a nonconvex optimization problem and directly tackle the sparsity constraint by establishing the optimal conditions and iteratively solving them by means of the splicing technique.Without employing convex relaxation on the sparsity constraint and the orthogonal constraint,our algorithm exhibits superior empirical merits,as evidenced by extensive numerical studies.Computationally,our algorithm is much faster than the relaxed approach for the natural sparse SIR estimator.Statistically,our algorithm surpasses existing methods in terms of accuracy for central subspace estimation and best subset selection and sustains high performance even with correlated predictors.
基金supported by the National Key Research and Development Program of China (2021YFD1200301 and 2021YFD1200302)the Natural Science Foundation of Jiangsu Province, China (BK20210813)+1 种基金the National Natural Science Foundation of China (32102534)the Yangzhou International Science and Technology Cooperation Projects, China (YZ2021175)。
文摘Germplasm resources are essential for the sustainable development of biodiversity and husbandry of local chickens, as well as for the breeding and industry of superior quality chickens. Unfortunately, many local and indigenous chicken breeds are at risk of declining numbers, emphasizing the need to conserve breed resources for endangered chickens. Primordial germ cells(PGCs) are crucial for preserving germplasm resources by inheriting genetic information from parents to offspring and ensuring stability of genetic material between germlines. In this study,PGCs were isolated from chicken embryos' gonads and cultured in FAcs medium without feeder cells. Over a period of approximately 40 d, the cells proliferated to a number of up to 10^(6), establishing various cell lines. Particularly, 18 PGC lines were created from Rugao Yellow chicken and Shouguang chicken, with an efficiency ranging from 39.1 to 45%. Furthermore, PGCs that had been cultured for 40 passages exhibited typical PGC characteristics, suchas glycogen staining reaction, and expression of pluripotency and reproductive markers. These results confirmthat PGCs maintain stem cell properties even after long-term in vitro culture. Additionally, PGCs cryopreserved for up to 120 d remained viable, maintained typical PGC morphologies, and possessed stable cell proliferation ability. Through intravascular injection into chicken embryos, green fluorescent protein(GFP)-PGCs were found in the recipient embryos' gonads and could develop into gametes to produce offspring, indicating that even after extended culture, PGCs retain their migratory and lineage-transmitting capabilities. This research offers valuable insights into the in vitro cultivation and preservation of PGCs of Chinese indigenous chickens. The findings of this study can be applied in transgenic chicken production and the preservation of genetic resources of indigenous chicken breeds.
基金supported by the National Natural Science Foundation of China(32270670,32288101,32271186,and 32200482)the National Basic Research Program of China(2015FY111700)the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-066).
文摘Mitochondria play a key role in lipid metabolism,and mitochondrial DNA(mtDNA)mutations are thus considered to affect obesity susceptibility by altering oxidative phosphorylation and mitochondrial function.In this study,we investigate mtDNA variants that may affect obesity risk in 2877 Han Chinese individuals from 3 independent populations.The association analysis of 16 basal mtDNA haplogroups with body mass index,waist circumference,and waist-to-hip ratio reveals that only haplogroup M7 is significantly negatively correlated with all three adiposity-related anthropometric traits in the overall cohort,verified by the analysis of a single population,i.e.,the Zhengzhou population.Furthermore,subhaplogroup analysis suggests that M7b1a1 is the most likely haplogroup associated with a decreased obesity risk,and the variation T12811C(causing Y159H in ND5)harbored in M7b1a1 may be the most likely candidate for altering the mitochondrial function.Specifically,we find that proportionally more nonsynonymous mutations accumulate in M7b1a1 carriers,indicating that M7b1a1 is either under positive selection or subject to a relaxation of selective constraints.We also find that nuclear variants,especially in DACT2 and PIEZO1,may functionally interact with M7b1a1.
基金funded by Sichuan Science and Technology Program(2023YFH0006).
文摘Exploring the phenotypic trait variation and diversity of kiwifruit male plant resources can support selection,breeding,and genetic improvement,ultimately enhancing agricultural production.In this study,50 kiwifruit male plants were collected from the resource nursery of Sichuan Provincial Natural Resources Bureau.The phenotypic variation of the germplasm was analyzed using 16 quantitative traits.The analysis involved coefficient of variation(CV),Shannon-Wiener index(H),principal component analysis,correlation analysis,cluster analysis,and comprehensive evaluation.The results showed that the variation range of 16 phenotypic traits in kiwifruit male germplasm resources was 1.55%to 83.71%,with an average coefficient of variation of 28.62%,and an H index of 1.265 to 2.941.The average CVs of diploid,tetraploid,and hexaploid were 22.62%,18.99%and 18.18%,respectively,and the average CV of diploid was the largest.Indicated that the male germplasm resources of kiwifruit showed significant phenotypic diversity,and the diploid showed higher diversity characteristics.Principal component analysis(PCA)revealed that the cumulative variance contribution rate of the first seven principal components was 76.66%,which effectively captured the information from 21 traits.Cluster analysis divided the 50 kiwifruit male germplasm resources into 4 clusters;each cluster exhibited distinct phenotypic characteristics.The analysis also determined the trait characteristics and breeding value of each cluster.The results of this study provide valuable information for genetic improvement,protection,and evaluation of kiwifruit male germplasm resources.
基金supported by the Guizhou Provincial Department of Agriculture and Rural Afairs,the Guizhou Provincial Department of Science and Technology,and the Guizhou Provincial Department of Education.Funding Project are Guizhou Highland Specialty Vegetable Green Production Science,Technology Innovation Talent Team(Qiankehe Platform Talent-CXTD[2022]003)Guizhou Mountain Agriculture Key Core Technology Research Project(GZNYGJHX 2023013)Platform construction project of Engineering Research Center for Protected Vegetable Crops in Higher Learning Institutions of Guizhou Province(Qian Jiao Ji[2022]No.040).
文摘Houttuynia cordata, a characteristic edible and medicinal plant in southwestern China, is prone to absorbing lead (Pb^(2+)). Excessive consumption may lead to Pb^(2+) accumulation in the human body, which has been linked to serious health risks such as neurotoxicity, kidney damage, anemia, and developmental disorders, particularly in children. Therefore, the development of molecular markers associated with Pb^(2+) uptake and the investigation of the plant’s physiological responses to Pb^(2+) pollution are of great significance. In this study, 72 H. cordata germplasms were evaluated for Pb^(2+) accumulation after exogenous Pb^(2+) treatment. A significant variation in Pb^(2+) content was observed among the germplasms, indicating rich genetic diversity. Using RAPD markers, seven loci were identified to be significantly associated with Pb^(2+) uptake, with locus 43 (R^(2) = 6.72%) and locus 53 (R^(2) = 5.39%) showing the strongest correlations. Marker validation was performed using five low- and five high-accumulating accessions. Two representative germplasms were further subjected to 0, 500 and 1000 mg/kg Pb^(2+) treatments for 40 days. Pb^(2+) content, membrane lipid peroxidation, and redox enzyme activities (SOD, POD and CAT) were measured across different organs. Organs with greater soil contact (roots) exhibited higher Pb^(2+) accumulation and oxidative damage. POD and CAT activities were markedly induced by Pb^(2+) stress, while SOD response was limited. This study provides a theoretical foundation for breeding low Pb^(2+)-accumulating H. cordata varieties through marker-assisted selection (MAS) and supports their safe use and application in phytoremediation.
基金Supported by Binzhou Social Sciences Planning Project in 2024(24-SKGH-051)Binzhou Comprehensive Experimental Station Project of Shandong Provincial Forage Industry Technology System(SDAIT-23-10).
文摘This paper investigated and analyzed the conservation and utilization of four local livestock breeds in Binzhou City:Wadi Sheep,Bohai Black Cattle,Wudi Donkey,and Lubei White Goat.Shortcomings in the protection and utilization of local germplasm resources were pointed out,and strategies and recommendations were proposed to promote high-quality development of livestock and poultry genetic resources in Binzhou,including building a solid germplasm foundation,standardizing production,and driving innovation.This paper provides references for the conservation,development,and utilization of local genetic resources in Binzhou City.
基金supported by the National Key Research and Development Program of China(Grant Nos.2022YFF0711805,2022YFF0711801,and 2021YFF0704204)the Project of Sanya Yazhou Bay Science and Technology City,China(Grant No.SCKJ-JYRC-2023-45)+3 种基金the National Natural Science Foundation of China(Grant Nos.31971792 and 32160421)the Innovation Project of the Chinese Academy of Agricultural Sciences(CAAS)(Grant Nos.CAAS-ASTIP-2024-AII and ZDXM23011)the Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonweal Research Institutes(Grant No.JBYW-AII-2024-05)the Nanfan Special Project,CAAS,China(Grant No.YBXM2312).
文摘Accurate evaluation of disease levels in wild rice germplasm and identification of disease resistance are critical for developing rice varieties resistant to blast disease.However,existing evaluation methods face limitations that hinder progress in breeding.To address these challenges,we proposed an AI-powered method for evaluating blast disease levels and identifying resistance in wild rice.A lightweight segmentation model for diseased leaves and lesions was developed,incorporating an improved federated learning approach to enhance robustness and adaptability.Based on the segmentation results and resistance identification technical specifications,wild rice materials were evaluated into 10 disease levels(L0 to L9),further enabling disease-resistance identification through multiple replicates of the same materials.The method was successfully implemented on augmented reality glasses for real-time,first-person evaluation.Additionally,high-speed scanners and edge computing devices were integrated to enable continuous,precise,and dynamic evaluation.Experimental results demonstrate the outstanding performance of the proposed method,achieving effective segmentation of diseased leaves and lesions with only 0.22 M parameters and 5.3 G floating-point operations per second(FLOPs),with a mean average precision(mAP@0.5)of 96.3%.The accuracy of disease level evaluation and disease-resistance identification reached 99.7%,with a practical test accuracy of 99.0%,successfully identifying three highly resistant wild rice materials.This method provides strong technical support for efficiently identifying wild rice materials resistant to blast disease and advancing resistance breeding efforts.
基金supported by the Key Project Fund of the Shanghai Municipal Committee of Agriculture,China[Hu Nong Ke Chuang Zi(2021)NO.1-1]the Science and Technology Commission of Shanghai Municipality,China(Nos.22015810400 and 23N11900400)+3 种基金the National Natural Science Foundation of China(No.32302522)the National Peach Production System of Agriculture Ministry of China(No.CARS-30)the Project of Shanghai Academy of Agricultural Sciences,China(Germplasm Preservation and Identificationthe Outstanding Team Program)(No.2022-004).
文摘Using headspace solid-phase microextraction(HS-SPME)combined with gas chromatography-mass spectrometry(GC-MS),we investigated the composition of volatile compounds in 114 peach germplasms across three harvest seasons(June,July,and August),three types(round peach,nectarine,and flat peach),two flesh colors(white and yellow),three levels of total soluble solids(TSS)content(high,medium,and low),and three categories of single fruit weight(SFW)(large,medium,and small).A total of 41 volatile compounds were identified in all 114 germplasms,including nine volatile categories:aldehydes(15),esters(5),lactones(8),alcohols(3),terpenols(4),ketones(2),phenols(1),alkanes(2),and ether(1).The average contents of the nine volatile types were aldehydes>esters>lactones>alcohols>terpenols>ketones>phenols>alkanes>et hers.Most of the 24 germplasms harvested in June could be clearly distinguished from the 30 germplasms harvested in August in the principal component analysis(PCA)plots(PC1,PC2,and PC3)of the three harvest seasons,which had much better discriminability than hierarchical clustering heatmap.In the PCA plots of the three TSS classifications,considerable separations occurred between 39 high TSS germplasms and 39 low TSS ones.In the PCA plots of classifications of the three SFW,three types,and two flesh colors,germplasms of different categories highly overlapped with each other.In the PCA(PC1 and PC2)loading plots,weights for distinguishing 114 germplasms of the five classifications were all in the order of trans-2-hexenyl acetate>benzaldehyde>methyl benzoate>2-hexenal>hexanal>linalool>hexyl acetate>2-ethylhexanol>γ-decalactone>the other 32 volatile components.
文摘Non-orthogonal multiple access(NOMA)is a promising technology for the next generation wireless communication networks.The benefits of this technology can be further enhanced through deployment in conjunction with multiple-input multipleoutput(MIMO)systems.Antenna selection plays a critical role in MIMO–NOMA systems as it has the potential to significantly reduce the cost and complexity associated with radio frequency chains.This paper considers antenna selection for downlink MIMO–NOMA networks with multiple-antenna basestation(BS)and multiple-antenna user equipments(UEs).An iterative antenna selection scheme is developed for a two-user system,and to determine the initial power required for this selection scheme,a power estimation method is also proposed.The proposed algorithm is then extended to a general multiuser NOMA system.Numerical results demonstrate that the proposed antenna selection algorithm achieves near-optimal performance with much lower computational complexity in both two-user and multiuser scenarios.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘The rapid evolution of smart cities through IoT,cloud computing,and connected infrastructures has significantly enhanced sectors such as transportation,healthcare,energy,and public safety,but also increased exposure to sophisticated cyber threats.The diversity of devices,high data volumes,and real-time operational demands complicate security,requiring not just robust intrusion detection but also effective feature selection for relevance and scalability.Traditional Machine Learning(ML)based Intrusion Detection System(IDS)improves detection but often lacks interpretability,limiting stakeholder trust and timely responses.Moreover,centralized feature selection in conventional IDS compromises data privacy and fails to accommodate the decentralized nature of smart city infrastructures.To address these limitations,this research introduces an Interpretable Federated Learning(FL)based Cyber Intrusion Detection model tailored for smart city applications.The proposed system leverages privacy-preserving feature selection,where each client node independently identifies top-ranked features using ML models integrated with SHAP-based explainability.These local feature subsets are then aggregated at a central server to construct a global model without compromising sensitive data.Furthermore,the global model is enhanced with Explainable AI(XAI)techniques such as SHAP and LIME,offering both global interpretability and instance-level transparency for cyber threat decisions.Experimental results demonstrate that the proposed global model achieves a high detection accuracy of 98.51%,with a significantly low miss rate of 1.49%,outperforming existing models while ensuring explainability,privacy,and scalability across smart city infrastructures.
文摘The cloud data centres evolved with an issue of energy management due to the constant increase in size,complexity and enormous consumption of energy.Energy management is a challenging issue that is critical in cloud data centres and an important concern of research for many researchers.In this paper,we proposed a cuckoo search(CS)-based optimisation technique for the virtual machine(VM)selection and a novel placement algorithm considering the different constraints.The energy consumption model and the simulation model have been implemented for the efficient selection of VM.The proposed model CSOA-VM not only lessens the violations at the service level agreement(SLA)level but also minimises the VM migrations.The proposed model also saves energy and the performance analysis shows that energy consumption obtained is 1.35 kWh,SLA violation is 9.2 and VM migration is about 268.Thus,there is an improvement in energy consumption of about 1.8%and a 2.1%improvement(reduction)in violations of SLA in comparison to existing techniques.
