Robust adaptive radar beamforming based on iterative training sample selection using kurtosis of generalized inner product statistics

In engineering application,there is only one adaptive weights estimated by most of traditional early warning radars for adaptive interference suppression in a pulse reputation interval(PRI).Therefore,if the training samples used to calculate the weight vector does not contain the jamming,then the jamming cannot be removed by adaptive spatial filtering.If the weight vector is constantly updated in the range dimension,the training data may contain target echo signals,resulting in signal cancellation effect.To cope with the situation that the training samples are contaminated by target signal,an iterative training sample selection method based on non-homogeneous detector(NHD)is proposed in this paper for updating the weight vector in entire range dimension.The principle is presented,and the validity is proven by simulation results.

Analysis of environmental selection pressure of superoxide dismutase in deep-sea sea cucumber

Manganese superoxide dismutase(MnSOD)is an antioxidant that exists in mitochondria and can effectively remove superoxide anions in mitochondria.In a dark,high-pressure,and low-temperature deep-sea environment,MnSOD is essential for the survival of sea cucumbers.Six MnSODs were identified from the transcriptomes of deep and shallow-sea sea cucumbers.To explore their environmental adaptation mechanism,we conducted environmental selection pressure analysis through the branching site model of PAML software.We obtained night positive selection sites,and two of them were significant(97F→H,134K→V):97F→H located in a highly conservative characteristic sequence,and its polarity c hange might have a great impact on the function of MnSOD;134K→V had a change in piezophilic a bility,which might help MnSOD adapt to the environment of high hydrostatic pressure in the deepsea.To further study the effect of these two positive selection sites on MnSOD,we predicted the point mutations of F97H and K134V on shallow-sea sea cucumber by using MAESTROweb and PyMOL.Results show that 97F→H,134K→V might improve MnSOD’s efficiency of scavenging superoxide a nion and its ability to resist high hydrostatic pressure by moderately reducing its stability.The above results indicated that MnSODs of deep-sea sea cucumber adapted to deep-sea environments through their amino acid changes in polarity,piezophilic behavior,and local stability.This study revealed the correlation between MnSOD and extreme environment,and will help improve our understanding of the organism’s adaptation mechanisms in deep sea.

Flexible nest site selection of the endangered Oriental Storks(Ciconia boyciana):Trade-off from adaptive strategies

Nest site selection is a vital component of bird reproduction success,and an adaptive behavior conducted to decrease nest predation risk with avoiding external disturbances.Understanding patterns of nest site selection can provide insights into how species adapt to changes in their habitat and has important conservation implications.In this study,we used microhabitat variables and multi-scale data with a field survey of nest occurrence to determine nest site selection patterns and adaptive strategies of the breeding Oriental Storks(Ciconia boyciana)in different nest areas.Results demonstrate that the nest site microhabitat characteristics of the breeding Oriental Storks significantly differed among the three nesting areas,and nest height was higher in the middle and lower Yangtze River floodplain than in the Northeast China and Bohai Bay nest areas.The food resources and intensity of human disturbance had the greatest effects on the nest site selection of the breeding Oriental Storks.The intensity of human disturbance was positively correlated with the nest height of the breeding Oriental Storks in Bohai Bay and the middle and lower Yangtze River floodplain;however,nest height decreased with the abundance of food resources in the Northeast China nest area.Our findings indicate that the nest site selection patterns of Oriental Storks showed flexible adaptive strategies.In safer environments,nests were lower and closer to food resources,which allows parent storks to invest more in the nestlings.However,in areas where human activity was intense,nests were higher to ensure the safety of their offspring.Some measures that could be taken to improve the breeding habitat of Oriental Storks include increasing the percentage of wetland areas in nesting areas to enhance food resources availability and setting artificial nests at suitable heights in potential nesting grounds to encourage nesting.Finally,the establishment of soft barriers around the nesting areas could increase the safety of nests.

Accelerated Particle Swarm Optimization Algorithm for Efficient Cluster Head Selection in WSN

Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.

Selective footprints and genes relevant to cold adaptation and other phenotypic traits are unscrambled in the genomes of divergently selected chicken breeds

Background The genomes of worldwide poultry breeds divergently selected for performance and other phenotypic traits may also be affected by,and formed due to,past and current admixture events.Adaptation to diverse environments,including acclimation to harsh climatic conditions,has also left selection footprints in breed genomes.Results Using the Chicken 50K_CobbCons SNP chip,we genotyped four divergently selected breeds:two aboriginal,cold tolerant Ushanka and Orloff Mille Fleur,one egg-type Russian White subjected to artificial selection for cold tolerance,and one meat-type White Cornish.Signals of selective sweeps were determined in the studied breeds using three methods:(1)assessment of runs of homozygosity islands,(2)F_(ST) based population differential analysis,and(3)haplotype differentiation analysis.Genomic regions of true selection signatures were identified by two or more methods or in two or more breeds.In these regions,we detected 540 prioritized candidate genes supplemented them with those that occurred in one breed using one statistic and were suggested in other studies.Amongst them,SOX5,ME3,ZNF536,WWP1,RIPK2,OSGIN2,DECR1,TPO,PPARGC1A,BDNF,MSTN,and beta-keratin genes can be especially mentioned as candidates for cold adaptation.Epigenetic factors may be involved in regulating some of these important genes(e.g.,TPO and BDNF).Conclusion Based on a genome-wide scan,our findings can help dissect the genetic architecture underlying various phenotypic traits in chicken breeds.These include genes representing the sine qua non for adaptation to harsh environments.Cold tolerance in acclimated chicken breeds may be developed following one of few specific gene expression mechanisms or more than one overlapping response known in cold-exposed individuals,and this warrants further investigation.

Genomic Mutations Within the Host Microbiome: Adaptive Evolution or Purifying Selection

Next-generation sequencing technology has transformed our ability to assess the taxonomic composition functions of host-associated microbiota and microbiomes. More human microbiome research projects—particularly those that explore genomic mutations within the microbiome—will be launched in the next decade. This review focuses on the coevolution of microbes within a microbiome, which shapes strain-level diversity both within and between host species. We also explore the correlation between microbial genomic mutations and common metabolic diseases, and the adaptive evolution of pathogens and probiotics during invasion and colonization. Finally, we discuss advances in methods and algorithms for annotating and analyzing microbial genomic mutations.

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

A novel adaptive harmonic balance method with an asymptotic harmonic selection

The harmonic balance method(HBM)is one of the most widely used methods in solving nonlinear vibration problems,and its accuracy and computational efficiency largely depend on the number of the harmonics selected.The adaptive harmonic balance(AHB)method is an improved HBM method.This paper presents a modified AHB method with the asymptotic harmonic selection(AHS)procedure.This new harmonic selection procedure selects harmonics from the frequency spectra of nonlinear terms instead of estimating the contribution of each harmonic to the whole nonlinear response,by which the additional calculation is avoided.A modified continuation method is proposed to deal with the variable size of nonlinear algebraic equations at different values of path parameters,and then all solution branches of the amplitude-frequency response are obtained.Numerical experiments are carried out to verify the performance of the AHB-AHS method.Five typical nonlinear dynamic equations with different types of nonlinearities and excitations are chosen as the illustrative examples.Compared with the classical HBM and Runge-Kutta methods,the proposed AHB-AHS method is of higher accuracy and better convergence.The AHB-AHS method proposed in this paper has the potential to investigate the nonlinear vibrations of complex high-dimensional nonlinear systems.

An Adaptive Sequential Replacement Method for Variable Selection in Linear Regression Analysis

With the rapid development of DNA technologies, high throughput genomic data have become a powerful leverage to locate desirable genetic loci associated with traits of importance in various crop species. However, current genetic association mapping analyses are focused on identifying individual QTLs. This study aimed to identify a set of QTLs or genetic markers, which can capture genetic variability for marker-assisted selection. Selecting a set with k loci that can maximize genetic variation out of high throughput genomic data is a challenging issue. In this study, we proposed an adaptive sequential replacement (ASR) method, which is considered a variant of the sequential replacement (SR) method. Through Monte Carlo simulation and comparing with four other selection methods: exhaustive, SR method, forward, and backward methods we found that the ASR method sustains consistent and repeatable results comparable to the exhaustive method with much reduced computational intensity.

Mechanisms of species divergence through visual adaptation and sexual selection:Perspectives from a cichlid model system

The theory of ecological speciation suggests that assortative mating evolves most easily when mating preferences aredirectly linked to ecological traits that are subject to divergent selection. Sensory adaptation can play a major role in this process,because selective mating is often mediated by sexual signals: bright colours, complex song, pheromone blends and so on. Whendivergent sensory adaptation affects the perception of such signals, mating patterns may change as an immediate consequence.Alternatively, mating preferences can diverge as a result of indirect effects: assortative mating may be promoted by selectionagainst intermediate phenotypes that are maladapted to their (sensory) environment. For Lake Victoria cichlids, the visual environmentconstitutes an important selective force that is heterogeneous across geographical and water depth gradients. We investigatethe direct and indirect effects of this heterogeneity on the evolution of female preferences for alternative male nuptial colours(red and blue) in the genus Pundamilia. Here, we review the current evidence for divergent sensory drive in this system, extractgeneral principles, and discuss future perspectives [Current Zoology 56 (3): 285-299, 2010].

Identification of genes under positive selection reveals evolutionary adaptation of Ulva mutabilis

Ulvophytes are attractive model systems for understanding the evolution of growth,development,and environmental stress responses.They are untapped resources for food,fuel,and high-value compounds.The rapid and abundant growth of Ulva species makes them key contributors to coastal biogeochemical cycles,which can cause significant environmental problems in the form of green tides and biofouling.Until now,the Ulva mutabilis genome is the only Ulva genome to have been sequenced.To obtain further insights into the evolutionary forces driving divergence in Ulva species,we analyzed 3905 single copy ortholog family from U.mutabilis,Chlamydomonas reinhardtii and Volvox carteri to identify genes under positive selection(GUPS)in U.mutabilis.We detected 63 orthologs in U.mutabilis that were considered to be under positive selection.Functional analyses revealed that several adaptive modifications in photosynthesis,amino acid and protein synthesis,signal transduction and stress-related processes might explain why this alga has evolved the ability to grow very rapidly and cope with the variable coastal ecosystem environments.

Positive selection analysis reveals the deep-sea adaptation of a hadal sea cucumber ( Paelopatides sp.) to the Mariana Trench

The Mariana Trench,the deepest trench on the earth,is ideal for deep-sea adaptation research due to its unique characters,such as the highest hydrostatic pressure on the Earth,constant ice-cold temperature,and eternal darkness.In this study,tissues of a the hadal holothurian(Paelopatides sp.)were fi xed with RNA later in situ at~6501-m depth in the Mariana Trench,which,to our knowledge,is the deepest in-situ fi xed animal sample.A high-quality transcript was obtained by de-novo transcriptome assembly.A maximum likelihood tree was constructed based on the single copy orthologs across nine species with their available omics data.To investigate deep-sea adaptation,113 positively selected genes(PSGs)were identifi ed in Paelopatides sp.Some PSGs such as microphthalmia-associated transcription factor(MITF)may contribute to the distinct phenotype of Paelopatides sp.,including its translucent white body and degenerated ossicles.At least eight PSGs(transcription factor 7-like 2[TCF7L2],ETS-related transcription factor Elf-2-like[ELF2],PERQ amino acid-rich with GYF domain-containing protein[GIGYF],cytochrome c oxidase subunit 7a,[COX7A],type I thyroxine 5′-deiodinase[DIO1],translation factor GUF1[GUF1],SWI/SNF related-matrix-associated actin-dependent regulator of chromatin subfamily C and subfamily E,member 1[SMARCC]and[SMARCE1])might be related to cold adaptation.In addition,at least nine PSGs(cell cycle checkpoint control protein[RAD9A],replication factor A3[RPA3],DNA-directed RNA polymerases I/II/III subunit RPABC1[POLR2E],putative TAR DNA-binding protein 43 isoform X2[TARDBP],ribonucleoside-diphosphate reductase subunit M1[RRM1],putative serine/threonine-protein kinase[SMG1],transcriptional regulator[ATRX],alkylated DNA repair protein alkB homolog 6[ALKBH6],and PLAC8 motif-containing protein[PLAC8])may facilitate the repair of DNA damage induced by the high hydrostatic pressure,coldness,and high concentration of cadmium in the upper Mariana Trench.

Double adaptive selection strategy for MOEA/D

Since most parameter control methods are based on prior knowledge, it is difficult for them to solve various problems.In this paper, an adaptive selection method used for operators and parameters is proposed and named double adaptive selection(DAS) strategy. Firstly, some experiments about the operator search ability are given and the performance of operators with different donate vectors is analyzed. Then, DAS is presented by inducing the upper confidence bound strategy, which chooses suitable combination of operators and donates sets to optimize solutions without prior knowledge. Finally, the DAS is used under the framework of the multi-objective evolutionary algorithm based on decomposition, and the multi-objective evolutionary algorithm based on DAS(MOEA/D-DAS) is compared to state-of-the-art MOEAs. Simulation results validate that the MOEA/D-DAS could select the suitable combination of operators and donate sets to optimize problems and the proposed algorithm has better convergence and distribution.

Target Vehicle Selection Algorithm for Adaptive Cruise Control Based on Lane-changing Intention of Preceding Vehicle

To improve the ride comfort and safety of a traditional adaptive cruise control(ACC)system when the preceding vehicle changes lanes,it proposes a target vehicle selection algorithm based on the prediction of the lane-changing intention for the preceding vehicle.First,the Next Generation Simulation dataset is used to train a lane-changing intention prediction algorithm based on a sliding window support vector machine,and the lane-changing intention of the preceding vehicle in the current lane is identified by lateral position offset.Second,according to the lane-changing intention and collision threat of the preceding vehicle,the target vehicle selection algorithm is studied under three different conditions:safe lane-changing,dangerous lane-changing,and lane-changing cancellation.Finally,the effectiveness of the proposed algorithm is verified in a co-simulation platform.The simulation results show that the target vehicle selection algorithm can ensure the smooth transfer of the target vehicle and effectively reduce the longitudinal acceleration fluctuation of the subject vehicle when the preceding vehicle changes lanes safely or cancels their lane change maneuver.In the case of a dangerous lane change,the target vehicle selection algorithm proposed in this paper can respond more rapidly to a dangerous lane change than the target vehicle selection method of the traditional ACC system;thus,it can effectively avoid collisions and improve the safety of the subject vehicle.

Estimating the State of Health for Lithium-ion Batteries:A Particle Swarm Optimization-Assisted Deep Domain Adaptation Approach

The state of health(SOH)is a critical factor in evaluating the performance of the lithium-ion batteries(LIBs).Due to various end-user behaviors,the LIBs exhibit different degradation modes,which makes it challenging to estimate the SOHs in a personalized way.In this article,we present a novel particle swarm optimization-assisted deep domain adaptation(PSO-DDA)method to estimate the SOH of LIBs in a personalized manner,where a new domain adaptation strategy is put forward to reduce cross-domain distribution discrepancy.The standard PSO algorithm is exploited to automatically adjust the chosen hyperparameters of developed DDA-based method.The proposed PSODDA method is validated by extensive experiments on two LIB datasets with different battery chemistry materials,ambient temperatures and charge-discharge configurations.Experimental results indicate that the proposed PSO-DDA method surpasses the convolutional neural network-based method and the standard DDA-based method.The Py Torch implementation of the proposed PSO-DDA method is available at https://github.com/mxt0607/PSO-DDA.

Multi-Source Adaptive Selection and Fusion for Pedestrian Dead Reckoning

Accurate multi-source fusion is based on the reliability, quantity, and fusion mode of the sources. The problem of selecting the optimal set for participating in the fusion process is nondeterministic-polynomial-time-hard and is neither sub-modular nor super-modular. Furthermore, in the case of the Kalman filter(KF) fusion algorithm, accurate statistical characteristics of noise are difficult to obtain, and this leads to an unsatisfactory fusion result. To settle the referred cases, a distributed and adaptive weighted fusion algorithm based on KF has been proposed in this paper. In this method, on the basis of the pseudo prior probability of the estimated state of each source, the reliability of the sources is evaluated and the optimal set is selected on a certain threshold. Experiments were performed on multi-source pedestrian dead reckoning for verifying the proposed algorithm. The results obtained from these experiments indicate that the optimal set can be selected accurately with minimal computation, and the fusion error is reduced by 16.6% as compared to the corresponding value resulting from the algorithm without improvements.The proposed adaptive source reliability and fusion weight evaluation is effective against the varied-noise multi-source fusion system, and the fusion error caused by inaccurate statistical characteristics of the noise is reduced by the adaptive weight evaluation.The proposed algorithm exhibits good robustness, adaptability,and value on applications.

Feasibility Study of Parameter Identification Method Based on Symbolic Time Series Analysis and Adaptive Immune Clonal Selection Algorithm

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

Improving Video Watermarking through Galois Field GF(2^(4)) Multiplication Tables with Diverse Irreducible Polynomials and Adaptive Techniques

Video watermarking plays a crucial role in protecting intellectual property rights and ensuring content authenticity.This study delves into the integration of Galois Field(GF)multiplication tables,especially GF(2^(4)),and their interaction with distinct irreducible polynomials.The primary aim is to enhance watermarking techniques for achieving imperceptibility,robustness,and efficient execution time.The research employs scene selection and adaptive thresholding techniques to streamline the watermarking process.Scene selection is used strategically to embed watermarks in the most vital frames of the video,while adaptive thresholding methods ensure that the watermarking process adheres to imperceptibility criteria,maintaining the video's visual quality.Concurrently,careful consideration is given to execution time,crucial in real-world scenarios,to balance efficiency and efficacy.The Peak Signal-to-Noise Ratio(PSNR)serves as a pivotal metric to gauge the watermark's imperceptibility and video quality.The study explores various irreducible polynomials,navigating the trade-offs between computational efficiency and watermark imperceptibility.In parallel,the study pays careful attention to the execution time,a paramount consideration in real-world scenarios,to strike a balance between efficiency and efficacy.This comprehensive analysis provides valuable insights into the interplay of GF multiplication tables,diverse irreducible polynomials,scene selection,adaptive thresholding,imperceptibility,and execution time.The evaluation of the proposed algorithm's robustness was conducted using PSNR and NC metrics,and it was subjected to assessment under the impact of five distinct attack scenarios.These findings contribute to the development of watermarking strategies that balance imperceptibility,robustness,and processing efficiency,enhancing the field's practicality and effectiveness.

Genome-Wide Scan for Positively Selected Genes in Sessile Molluscs Highlights the Genetic Basis for Their Adaptation to Attached Lifestyle

Molluscs are one of the most diverse groups of animals and exhibit a rich and diverse variety of form and lifestyle. Most molluscs live with a free-moving lifestyle, while some molluscs are sessile. The adaptation to the two distinct lifestyles required complex changes, from molecules to organs, and physiology to morphology. In this study, we conducted a genome-wide scan for positive selection by comparing the available genomes of two sessile molluscs with four free-moving molluscs. A total of 40 genes were identified undergoing positive selection in the sessile molluscs by the branch-site model. Functional characterization showed that they were mainly enriched in two pathways, Oxidative phosphorylation(OXPHOS) and TGF-beta signaling pathway. The unexpected positive selection on OXPHOS genes in sessile molluscs suggests that the adaptation of OXPHOS involves many factors beyond enhancing ATP production. A modified OXPHOS regulatory system may allow sessile molluscs to better cope with biotic and abiotic stresses. Moreover, positively selected genes in TGF-beta signaling pathway probably have played a key role in the patterning of body plans and growth in metazoans. We speculate that these genes are associated with the body structure and organic adaptation to a sedentary lifestyle in sessile bivalve molluscs.

Adaptive Robust Waveform Selection for Unknown Target Detection in Clutter

A basic assumption of most recently proposed waveform design algorithms is that the target impulse response is a known deterministic function or a stochastic process with a known power spectral density （PSD）. However, it is well-known that a target impulse response is neither easily nor accurately obtained; besides it changes sharply with attitude angles. Both of the aforementioned cases complicate the waveform design process. In this paper, an adaptive robust waveform selection method for unknown target detection in clutter is proposed. The target impulse response is considered to be unknown but belongs to a known uncertainty set. An adaptive waveform library is devised by using a signal-to-clutter-plus-noise ratio （SCNR）- based optimal waveform design method. By applying the minimax robust waveform selection method, the optimal robust waveform is selected to ensure the lowest performance bound of the unknown target detection in clutter. Results show that the adaptive waveform library outperforms the predefined linear frequency modulation （LFM） waveform library on the SCNR bound.