Phylogenetic Relationships and Status Quo of Colonies for Gayal Based on Analysis of Cytochrome b Gene Partial Sequences

Thirty-three mutations and four different haplotypes were found when cytochrome b（Cytb） gene partial sequences of 12 gayals were analyzed. Together with sequences of Bos indicus, Bos taurus, Bos grunniens, and Bos gaurus with Bubalus bubalis as the out group, the partial sequences of Cytb gene of gayals were aligned and base composition and nucleotide variation of Cytb gene were analyzed. The phylogenetic trees were constructed by the NJ method and the MP method respectively, both supporting almost the same topology. Gayal is an independent species of Bos from Bos indicus, Bos taurus, and Bos gaurus. The results also indicate that a great proportion of gayal bloodline was invaded by other species, and the protection of gayal is facing a formidable situation.

Deadly oasis:Recurrent annihilation of Cretaceous desert bryophyte colonies;the role of solar,climate and lithospheric forcing

Many oases(wet interdunes)are sedimentary systems characterized by high-frequency water-level oscillations,marked changes in salinity and intense biological activity at their margins.They are considered to be one of the most challenging environments on Earth for ecosystem development.These dynamic,depositional settings are usually unfavourable for fossilization and subsequent preservation of vegetal remains.This paper describes bryophyte(liverwort)assemblages occurring in three successive horizons interpreted to represent(i)recurrent early successional phases of biological soil crust colonization of wet interdune margins or(ii)exceptional preservation of floating or riparian liverworts in oasis pond waters associated with a progressive fall of the interdune water level.The record of in situ colonization surfaces characterized by delicate(e.g.lignin-free)three-dimensional structures represents an exceptional type of preservation herein associated with a rapid variation in phreatic interdune water level and the subsequent establishment of anoxic and reducing conditions.The occurrence of exceptionally preserved liverwort colonies coincides with the sedimentary record of,at least,three seismite levels in the oasis.Data gathered from the site suggests that the water table of the oasis was controlled by a combination of(i)a positive creation of accommodation space due to subsidence associated with movement on syn-sedimentary extensional faults,and(ii)the rise and fall of the oasis water table controlled by the oscillations of the groundwater system due to orbital changes which appear to drive the variability of the climate system.Rising groundwater levels flooded the oasis soil crusts and lead to the exceptional recurrent preservation of liverwort colonies at the oasis margins.Alternatively,considering the hypothesis of floating or riparian liverworts in the oasis pond waters,the fall in the level of the oasis water table placed the floating liverworts in contact with the oasis bottom sediments.This fall in the level of the oasis water table could indicate a cessation of accommodation space by syn-sedimentary extensional faults and/or a regional lowering of the groundwater system level associated with drought periods.Preliminary results indicate that oasis lamination between liverwort colonies records decadal and sub-decadal cyclicity,related with 11-year Schwabe sunspot and sub-decadal NAO cyclicities,conferring for every sedimentary cycle between liverwort colonies a duration of approximately 200 years,that otherwise matches the expected recurrence period for the De Vries cycle of solar activity.

Growth of Circulating Tumor Cell-Derived Colonies from Peripheral Blood of Melanoma Patients: Preliminary Characterization of Colony Composition

Circulating tumor cells (CTC) are rarely detected in the blood of cancer patients, even though they are a direct harbinger of eventual patient demise. We developed an innovative CTC culture technology to allow more sensitive isolation, expansion, and characterization of viable colonies from patient blood. In this assay, the entire leukocyte fraction from 10 ml of anticoagulated patient blood is placed into culture medium without any pre-selection. After 16 days in culture, CTC derived colonies are counted. As a proof-of-principle, blood samples from 58 Stage IIa-IV melanoma patients were tested. Ninety percent of these samples grew colonies. The colony numbers ranged from 0 - 308 (mean 63 ± 9.5 SEM). Ten normal volunteers had virtually no growth (mean 0.5 ± 1.4 colonies). Colonies were harvested using a micropipette for characterization. Tumor-cell containing spheroids were embedded in paraffin, sectioned, and stained with melanoma-specific mAb for histologic characterization. MITF proved to be the most consistent immunostain that identified melanoma cells in these colonies. A host-cell component in colonies was also identified using CD68 and CD43 mAb staining. Following enzymatic dissociation of colonies, a variety of immunostains were tested. Papanicolau staining proved most useful for identifying the abnormal nuclei of tumor cells. Flow cytometry could readily distinguish host and tumor cell populations based on DNA content and forward/side scatter in dissociated colonies. The stem cell marker ALDH1A1 associated with the aneuploid population, but CD45 was expressed on both diploid and aneuploid cells. The ability to repeatedly isolate CTC derived colonies from cancer patient blood samples opens the door to a novel type of long-term clinical monitoring. This novel CTC culture technology may prove useful to perform molecular characterization, assessment of treatment response, and testing of drug sensitivity and resistance in patients during treatment.

Ardeid nesting colonies in central Guizhou,southwestern China

A survey of Ardeid nesting colonies in central Guizhou,southwestern China in May 2006,which covered about one-third of the area of this province,recorded a total of 1587 nests of five species,i.e.,Little Egret (Egretta garzetta),Cattle Egret (Bubulcus ibis),Chinese Pond Heron (Ardeola bacchus),Black-crowned Night Heron (Nycticorax nycticorax) and Grey Heron (Ardea cinerea) at 11 nesting colonies.The largest colonies were recorded at Fengyunyan and Yanfu resorts,the smallest one at Luobolin.The Little Egret was the dominant nesting species (49.7% of the total number of nests) and the Grey Heron was the least abundant (0.1%).All nesting colonies were located in the northern and central parts of Guizhou;no colonies were found in the Leigongshan area,the southern part of this province.The distribution of nesting colonies in central Guizhou appears to be affected by the relative composition of winter (wheat and vegetable) and summer (rice) crops during the breeding season (between late March and June).No colonies were found around farmlands with winter crops and most colonies were situated around rice paddies.The farmland use pattern is affected by rainfall.The summer crop is delayed if the rainy season starts late and the rainfall is light.Priority for conservation of the Ardeids in central Guizhou should be given to the larger colonies i.e.,Yanfu and Fengyunyan resorts.

Inhibitory effect of parvovirus H-1 on the formation of colonies of human hepatoma cell line in vitro and its tumors in nude mice

The inhibitory effect of parvovirus H-1 on the colonyforming ability in vitro of QGY-7703, a cultured human hepatoma cell line, and on the formation and growth of its tumors in nude mice was studied. With higher multiplicity of infection (MOI) of H-1 given, survival of the QGY-7703 cells was found to be decreased. H-1 DNA amplification level at 30 h postinfection(p.i.) was detected to be 7.4 times higher than that at 2 h by dispersed cells assay, while the cells were delayed to enter into S phase.Plaques were formed in the indicator cells (new-born human kidney cell line, NBK) by progeny H-1 virus particles released from the infected QGY-7703 cells by infectious cell center assay. The formation of tumors in nude mice by QGY-7703 cells which were injected s c at 2 h postinfection was observed to be prevented in 2 groups with given MOI 25 and 50. The tumor growth of MOI 10 group occurred at a lower exponential rate than that of control,after a 20 d latent period. It was evident that parvovirus H-1 exhibited a direct inhibitory effect on the formation and growth of human hepatoma cells in vivo as well as in vitro.

Optimal Placement and Sizing of Distributed Energy Generation in an Electrical Network Using the Hybrid Algorithm of Bee Colonies and Newton Raphson

Distributed generation (DG) is gaining in importance due to the growing demand for electrical energy and the key role it plays in reducing actual energy losses, lowering operating costs and improving voltage stability. In this paper, we propose to inject distributed power generation into a distribution system while minimizing active energy losses. This injection should be done at a grid node (which is a point where energy can be injected into or recovered from the grid) that will be considered the optimal node when total active losses in the radial distribution system are minimal. The focus is on meeting energy demand using renewable energy sources. The main criterion is the minimization of active energy losses during injection. The method used is the algorithm of bee colony (ABC) associated with Newtonian energy flow transfer equations. The method has been implemented in MATLAB for optimal node search in IEEE 14, 33 and 57 nodes networks. The active energy loss results of this hybrid algorithm were compared with the results of previous searches. This comparison shows that the proposed algorithm allows to have reduced losses with the power injected that we have found.

The value of spatial experience and group size for ant colonies in direct competition

Animals often search for food more efficiently with experience.However,the contribution of experience toforaging success under direct competition has rarelybeen examined.Here we used colonies of an individually foraging desert ant to investigate the value of spatial experience.First,we trained worker groups of equal numbers to solve either a complex or a simple maze.We then tested pairs of both groups against one another in reaching a food reward.This task required solving the same complex maze that one of the groups had been trained in,to determine which group would exploit better the food reward.The worker groups previously trained in the complex mazes reached the food reward faster and more of these workers fed on the food than those trained in simple mazes,but only in the intermediate size group.To determine the relative importance of group size versus spatial experience in exploiting food patches,we then tested smaller trained worker groups against larger untrained ones.The larger groups outcompeted the smaller ones,despite the latter's advantage of spatial experience.The contribution of spatial experience,as found here,appears to be small,and depends on group size:an advantage of a few workers of the untrained group over the trained group negates its benefits.

Prediction model of surrounding rock deformation in doublecontinuous-arch tunnel based on the ABC-WNN

Purpose–The wavelet neural network(WNN)has the drawbacks of slow convergence speed and easy falling into local optima in data prediction.Although the artificial bee colony(ABC)algorithm has strong global optimization ability and fast convergence speed,it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.Design/methodology/approach–This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model.Based on the example of the Jinan Yuhan underground tunnel project,the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed,and the analysis results are compared with the actual detection amount.Findings–The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data,with a maximum relative error of only 4.73%.On this basis,the results show that the statistical features of ABC-WNN are the lowest,with the errors at 0.566 and 0.573,compared with the single back propagation(BP)neural network model and WNN model.Therefore,it can be derived that the ABC-WNN model has higher prediction accuracy,better computational stability and faster convergence speed for deformation.Originality/value–This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels.This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multiarch tunnels and small clearance tunnels.It can provide a new and effective way for deformation prediction in similar projects.

Effects of aeration induced turbulence on colonial morphology and microcystin release of the bloom-forming cyanoabcterium Microcystis

Aeration induced turbulence was considered as an important measure to control the occurrence of cyanobacterial blooms in many lakes.Different aeration intensities were set for the culture of Microcystis aeruginosa based on the formation of Microcystis colony at high iron concentrations.The turbulent dissipation rate was calculated using a computational fluid dynamics(CFD)model to evaluate the intensity of turbulence.The effects of turbulence on the formation of Microcystis colony and the release of microcystin were analyzed.Results show that turbulence produced by aeration promoted the growth of Microcystis compared to that in stagnant water.Low intensity turbulence(4×10^(-8)-1×10^(-7) m2/s3)promoted the formation of Microcystis colonies,but high intensity turbulence(1.28×10^(-6)-1.8×10^(-5) m^(2)/s^(3))did not.The increase in the number of cells per colony was slower than that in total biomass,indicating that the low intensity turbulence induced colony formation via cell division,while the high level turbulence disaggregated colonies formed by both cell division and cell adhesion.Low aeration intensity induced more production of reactive oxygen species(ROS)and malondialdehyde(MDA)in the cells of Microcystis than those in high aeration intensity.In addition,the content of microcystin(MC)-LR in the cells was positively correlated with turbulence intensity,showing that turbulence affected not only the growth and aggregation of Microcystis colonies but also their toxin production.These findings provide a better understanding of the cyanobacterial bloom formation mechanisms and help to propose feasible methods to prevent the formation of Microcystis colonies in a natural environment.

Plant allelochemicals inhibit the growth and colony formation of Microcystis

Genus Microcystis is the most common dominant species of bloom-forming cyanobacteria.Growth and colony formation are critical processes for the occurrence of Microcystis blooms.To identify allelochemicals that can inhibit the growth and colony formation of Microcystis,Microcystis aeruginosa was cultivated and treated with various concentrations of gramine,catechol,berberine,and sanguinarine in laboratory.Results indicate that the growth,colony formation,and extracellular polysaccharides(EPS)content of Microcystis were significantly inhibited when treated with gramine,catechol,and berberine in appropriate concentrations at high iron level(6.8 mg/L).Microcystis growth was inhibited and colony formation and EPS content were promoted when treated with sanguinarine at high iron level.These findings suggest that cyanobacterial blooms can be controlled by regulating the input of gramine,catechol,and berberine.Furthermore,this study revealed that plant allelochemicals could affect the growth and morphology of algae in aquatic ecological restoration,which may be potentially important for improving water quality.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Planning of a Single Flow Channel in Valve Blocks Based on Additive Manufacturing and the Ant Colony Algorithm

As electro-hydrostatic actuator(EHA)technology advances towards lightweight and integration,the demand for enhanced internal flow pathways in hydraulic valve blocks intensifies.However,owing to the constraints imposed by traditional manufacturing processes,conventional hydraulic integrated valve blocks fail to satisfy the demands of a more compact channel layout and lower energy dissipation.Notably,the subjectivity in the arrangement of internal passages results in a time-consuming and labor-intensive process.This study employed additive manufacturing technology and the ant colony algorithm and B-spline curves for the meticulous design of internal passages within an aviation EHA valve block.The layout environment for the valve block passages was established,and path optimization was achieved using the ant colony algorithm,complemented by smoothing using B-spline curves.Three-dimensional modeling was performed using SolidWorks software,revealing a 10.03%reduction in volume for the optimized passages compared with the original passages.Computational fluid dynamics(CFD)simulations were performed using Fluent software,demonstrating that the algorithmically optimized passages effectively prevented the occurrence of vortices at right-angled locations,exhibited superior flow characteristics,and concurrently reduced pressure losses by 34.09%-36.36%.The small discrepancy between the experimental and simulation results validated the efficacy of the ant colony algorithm and B-spline curves in optimizing the passage design,offering a viable solution for channel design in additive manufacturing.

Improved artificial bee colony algorithm for pressure source parameter inversion of Sakurajima volcano from InSAR data

A novel artificial bee colony algorithm was introduced for the eruption event of the Sakurajima volcano on August 9,2020,to invert the magma source characteristics below the volcano based on the point source Mogi model.Considering that the Sakurajima volcano is surrounded by sea,all the deformation data are used to obtain the location and magma eruption volume of the volcano.In response to the weak local search capability of the artificial swarm algorithm,the difference between the global optimal individual and the un-roulette screened individual is introduced as the variance component in the onlooker stage.Detailed simulation experiments verify the improvement of the algorithm in terms of convergence speed.In real experiments,the Sakurajima volcano inversion shows closer fitting results and smaller residuals compared to the existing literature.Meanwhile,the convergence speed of the algorithm echoes with the simulation experiments.

An Improved Image Steganography Security and Capacity Using Ant Colony Algorithm Optimization

This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)algorithm.Image steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least suspect.The contemporary methods of steganography are at best a compromise between these two.In this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic embedding.The approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret data.This approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale images.The ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual quality.Pheromone evaporation is introduced through iterations to avoid stagnation in solution refinement.The levels of pheromone are modified to reinforce successful pixel choices.Experimental results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of 0.98.The approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.

A Prognostic Model Based on Colony Stimulating Factors-related Genes in Triple-negative Breast Cancer

Objective Triple-negative breast cancer(TNBC)is the breast cancer subtype with the worst prognosis,and lacks effective therapeutic targets.Colony stimulating factors(CSFs)are cytokines that can regulate the production of blood cells and stimulate the growth and development of immune cells,playing an important role in the malignant progression of TNBC.This article aims to construct a novel prognostic model based on the expression of colony stimulating factors-related genes(CRGs),and analyze the sensitivity of TNBC patients to immunotherapy and drug therapy.Methods We downloaded CRGs from public databases and screened for differentially expressed CRGs between normal and TNBC tissues in the TCGA-BRCA database.Through LASSO Cox regression analysis,we constructed a prognostic model and stratified TNBC patients into high-risk and low-risk groups based on the colony stimulating factors-related genes risk score(CRRS).We further analyzed the correlation between CRRS and patient prognosis,clinical features,tumor microenvironment(TME)in both high-risk and low-risk groups,and evaluated the relationship between CRRS and sensitivity to immunotherapy and drug therapy.Results We identified 842 differentially expressed CRGs in breast cancer tissues of TNBC patients and selected 13 CRGs for constructing the prognostic model.Kaplan-Meier survival curves,time-dependent receiver operating characteristic curves,and other analyses confirmed that TNBC patients with high CRRS had shorter overall survival,and the predictive ability of CRRS prognostic model was further validated using the GEO dataset.Nomogram combining clinical features confirmed that CRRS was an independent factor for the prognosis of TNBC patients.Moreover,patients in the high-risk group had lower levels of immune infiltration in the TME and were sensitive to chemotherapeutic drugs such as 5-fluorouracil,ipatasertib,and paclitaxel.Conclusion We have developed a CRRS-based prognostic model composed of 13 differentially expressed CRGs,which may serve as a useful tool for predicting the prognosis of TNBC patients and guiding clinical treatment.Moreover,the key genes within this model may represent potential molecular targets for future therapies of TNBC.

Dynamical Artificial Bee Colony for Energy-Efficient Unrelated Parallel Machine Scheduling with Additional Resources and Maintenance

Unrelated parallel machine scheduling problem(UPMSP)is a typical scheduling one and UPMSP with various reallife constraints such as additional resources has been widely studied;however,UPMSP with additional resources,maintenance,and energy-related objectives is seldom investigated.The Artificial Bee Colony(ABC)algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources,among other factors.In this study,an energy-efficient UPMSP with additional resources and maintenance is considered.A dynamical artificial bee colony(DABC)algorithm is presented to minimize makespan and total energy consumption simultaneously.Three heuristics are applied to produce the initial population.Employed bee swarm and onlooker bee swarm are constructed.Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met.Dynamical employed bee phase is implemented by computing resource shifting and solution migration.Computing resource shifting and feedback are used to construct dynamical onlooker bee phase.Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given.The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP.

Multi-Label Feature Selection Based on Improved Ant Colony Optimization Algorithm with Dynamic Redundancy and Label Dependence

The world produces vast quantities of high-dimensional multi-semantic data.However,extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging.Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features.The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection,because of its simplicity,efficiency,and similarity to reinforcement learning.Nevertheless,existing methods do not consider crucial correlation information,such as dynamic redundancy and label correlation.To tackle these concerns,the paper proposes a multi-label feature selection technique based on ant colony optimization algorithm(MFACO),focusing on dynamic redundancy and label correlation.Initially,the dynamic redundancy is assessed between the selected feature subset and potential features.Meanwhile,the ant colony optimization algorithm extracts label correlation from the label set,which is then combined into the heuristic factor as label weights.Experimental results demonstrate that our proposed strategies can effectively enhance the optimal search ability of ant colony,outperforming the other algorithms involved in the paper.

Computation Offloading in Edge Computing for Internet of Vehicles via Game Theory

With the rapid advancement of Internet of Vehicles(IoV)technology,the demands for real-time navigation,advanced driver-assistance systems(ADAS),vehicle-to-vehicle(V2V)and vehicle-to-infrastructure(V2I)communications,and multimedia entertainment systems have made in-vehicle applications increasingly computingintensive and delay-sensitive.These applications require significant computing resources,which can overwhelm the limited computing capabilities of vehicle terminals despite advancements in computing hardware due to the complexity of tasks,energy consumption,and cost constraints.To address this issue in IoV-based edge computing,particularly in scenarios where available computing resources in vehicles are scarce,a multi-master and multi-slave double-layer game model is proposed,which is based on task offloading and pricing strategies.The establishment of Nash equilibrium of the game is proven,and a distributed artificial bee colonies algorithm is employed to achieve game equilibrium.Our proposed solution addresses these bottlenecks by leveraging a game-theoretic approach for task offloading and resource allocation in mobile edge computing(MEC)-enabled IoV environments.Simulation results demonstrate that the proposed scheme outperforms existing solutions in terms of convergence speed and system utility.Specifically,the total revenue achieved by our scheme surpasses other algorithms by at least 8.98%.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).