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.

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.

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.

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).

Hybrid Hierarchical Particle Swarm Optimization with Evolutionary Artificial Bee Colony Algorithm for Task Scheduling in Cloud Computing

Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment.Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios.In this work,the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm(HPSO-EABC)has been proposed,which hybrids our presented Evolutionary Artificial Bee Colony(EABC),and Hierarchical Particle Swarm Optimization(HPSO)algorithm.The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm.Comprehensive testing including evaluations of algorithm convergence speed,resource execution time,load balancing,and operational costs has been done.The results indicate that the EABC algorithm exhibits greater parallelism compared to the Artificial Bee Colony algorithm.Compared with the Particle Swarm Optimization algorithm,the HPSO algorithmnot only improves the global search capability but also effectively mitigates getting stuck in local optima.As a result,the hybrid HPSO-EABC algorithm demonstrates significant improvements in terms of stability and convergence speed.Moreover,it exhibits enhanced resource scheduling performance in both homogeneous and heterogeneous environments,effectively reducing execution time and cost,which also is verified by the ablation experimental.

Intermittent fasting boosts antitumor immunity by restricting CD11b^(+)Ly6C^(low)Ly6G^(low) cell viability through glucose metabolism in murine breast tumor model

Intermittent fasting can benefit breast cancer patients undergoing chemotherapy or immunotherapy.However,it is still uncertain how to select immunotherapy drugs to combine with intermittent fasting.Herein we observed that two cycles of fasting treatment significantly inhibited breast tumor growth and lung tissue metastasis,as well as prolonged overall survival in mice bearing 4T1 and 4T07 breast cancer.During this process,both the immunosuppressive monocytic-(M-)and granulocytic-(G-)myeloid-derived suppressor cell(MDSC)decreased,accompanied by an increase in interleukin(IL)7R^(+)and granzyme B^(+)T cells in the tumor microenvironment.Interestingly,we observed that Ly6G^(low)G-MDSC sharply decreased after fasting treatment,and the cell surface markers and protein mass spectrometry data showed potential therapeutic targets.Mechanistic investigation revealed that glucose metabolism restriction suppressed the splenic granulocytemonocyte progenitor and the generation of colony-stimulating factors and IL-6,which both contributed to the accumulation of G-MDSC.On the other hand,glucose metabolism restriction can directly induce the apoptosis of Ly6G^(low)G-MDSC,but not Ly6G^(high)subsets.In summary,these results suggest that glucose metabolism restriction induced by fasting treatment attenuates the immune-suppressive milieu and enhances the activation of CD3^(+)T cells,providing potential solutions for enhancing immune-based cancer interventions.

A Study on Outlier Detection and Feature Engineering Strategies in Machine Learning for Heart Disease Prediction

This paper investigates the application ofmachine learning to develop a response model to cardiovascular problems and the use of AdaBoost which incorporates an application of Outlier Detection methodologies namely;Z-Score incorporated with GreyWolf Optimization(GWO)as well as Interquartile Range(IQR)coupled with Ant Colony Optimization(ACO).Using a performance index,it is shown that when compared with the Z-Score and GWO with AdaBoost,the IQR and ACO,with AdaBoost are not very accurate(89.0%vs.86.0%)and less discriminative(Area Under the Curve(AUC)score of 93.0%vs.91.0%).The Z-Score and GWO methods also outperformed the others in terms of precision,scoring 89.0%;and the recall was also found to be satisfactory,scoring 90.0%.Thus,the paper helps to reveal various specific benefits and drawbacks associated with different outlier detection and feature selection techniques,which can be important to consider in further improving various aspects of diagnostics in cardiovascular health.Collectively,these findings can enhance the knowledge of heart disease prediction and patient treatment using enhanced and innovativemachine learning(ML)techniques.These findings when combined improve patient therapy knowledge and cardiac disease prediction through the use of cutting-edge and improved machine learning approaches.This work lays the groundwork for more precise diagnosis models by highlighting the benefits of combining multiple optimization methodologies.Future studies should focus on maximizing patient outcomes and model efficacy through research on these combinations.

A Pre-Selection-Based Ant Colony System for Integrated Resources Scheduling Problem at Marine Container Terminal

Marine container terminal(MCT)plays a key role in the marine intelligent transportation system and international logistics system.However,the efficiency of resource scheduling significantly influences the operation performance of MCT.To solve the practical resource scheduling problem(RSP)in MCT efficiently,this paper has contributions to both the problem model and the algorithm design.Firstly,in the problem model,different from most of the existing studies that only consider scheduling part of the resources in MCT,we propose a unified mathematical model for formulating an integrated RSP.The new integrated RSP model allocates and schedules multiple MCT resources simultaneously by taking the total cost minimization as the objective.Secondly,in the algorithm design,a pre-selection-based ant colony system(PACS)approach is proposed based on graphic structure solution representation and a pre-selection strategy.On the one hand,as the RSP can be formulated as the shortest path problem on the directed complete graph,the graphic structure is proposed to represent the solution encoding to consider multiple constraints and multiple factors of the RSP,which effectively avoids the generation of infeasible solutions.On the other hand,the pre-selection strategy aims to reduce the computational burden of PACS and to fast obtain a higher-quality solution.To evaluate the performance of the proposed novel PACS in solving the new integrated RSP model,a set of test cases with different sizes is conducted.Experimental results and comparisons show the effectiveness and efficiency of the PACS algorithm,which can significantly outperform other state-of-the-art algorithms.

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

Optimal Location and Sizing ofMulti-Resource Distributed Generator Based onMulti-Objective Artificial Bee Colony Algorithm

Distribution generation(DG)technology based on a variety of renewable energy technologies has developed rapidly.A large number of multi-type DG are connected to the distribution network(DN),resulting in a decline in the stability of DN operation.It is urgent to find a method that can effectively connect multi-energy DG to DN.photovoltaic(PV),wind power generation(WPG),fuel cell(FC),and micro gas turbine(MGT)are considered in this paper.A multi-objective optimization model was established based on the life cycle cost(LCC)of DG,voltage quality,voltage fluctuation,system network loss,power deviation of the tie-line,DG pollution emission index,and meteorological index weight of DN.Multi-objective artificial bee colony algorithm(MOABC)was used to determine the optimal location and capacity of the four kinds of DG access DN,and compared with the other three heuristic algorithms.Simulation tests based on IEEE 33 test node and IEEE 69 test node show that in IEEE 33 test node,the total voltage deviation,voltage fluctuation,and system network loss of DN decreased by 49.67%,7.47%and 48.12%,respectively,compared with that without DG configuration.In the IEEE 69 test node,the total voltage deviation,voltage fluctuation and system network loss of DN in the MOABC configuration scheme decreased by 54.98%,35.93%and 75.17%,respectively,compared with that without DG configuration,indicating that MOABC can reasonably plan the capacity and location of DG.Achieve the maximum trade-off between DG economy and DN operation stability.

Furtherance and Shaping:On the Media-Making of Hankow in the“Foreign Newspaper Era”(1866-1900)

“The Era of Foreign Newspapers”refers to the period from the emergence of the first modern newspaper in Hankow in 1866 to 1900 when Wuhan’s newspaper industry was dominated by foreign newspapers.The well-known foreign newspapers in Wuhan during this period mainly included Hankow Times,The New Edition of Tan Dao,and Han Bao.The subjective purpose of foreigners’early endeavors of running newspapers in Wuhan was mainly to use newspapers to convey business information,spread religion,or influence public opinion in order to safeguard their own interests in China.However,foreign newspapers in this period played a constructive role in the development of Wuhan’s local society:It gave birth to the emergence and development of the first private and official newspapers in Wuhan and shaped the local social,cultural,and political changes in Wuhan in the late Qing Dynasty.Sorting out and explaining the constructive influence of Hankow’s foreign newspaper in this period has certain significance for restoring the social and political landscape of Wuhan at that time and better understanding the context of historical development.