Optical redox imaging of ANT1-de-cient muscles

Adenine nucleotide translocator(ANT)is a mitochondrial protein involved in the exchange of ADP and ATP across the mitochondrial inner membrane.It plays a crucial role in cellular energy metabolism by facilitating the transport of ATP synthesized within the mitochondria to the cytoplasm.The isoform ANT1 predominately expresses in cardiac and skeletal muscles.Mutations or dysregulation in ANT1 have been implicated in various mitochondrial disorders and neuromuscular diseases.We aimed to examine whether ANT1 deletion may affect mitochondrial redox state in our established ANT1-de-cient mice.Hearts and quadriceps resected from age-matched wild type(WT)and ANT1-de-cient mice were snap-frozen in liquid nitrogen.The Chance redox scanner was utilized to perform 3D optical redox imaging.Each sample underwent scanning across 3–5 sections.Global averaging analysis showed no signi-cant differences in the redox indices(NADH,flavin adenine dinucleotide containing-flavoproteins Fp,and the redox ratio Fp/(NADH+Fp)between WT and ANT1-de-cient groups.However,quadriceps had higher Fp than hearts in both groups(p¼0:0004 and 0.01,respectively).Furthermore,the quadriceps were also more oxidized(a higher redox ratio)than hearts in WT group(p¼0:004).NADH levels were similar in all cases.Our data suggest that under non-stressful physical condition,the ANT1-de-cient muscle cells were in the same mitochondrial state as WT ones and that the signi-cant difference in the mitochondrial redox state between quadriceps and hearts found in WT might be diminished in ANT1-de-cient ones.Redox imaging of muscles under physical stress can be conducted in future.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Micro-Arthropods in the Surroundings of Nests of Messor arenarius Ants in the Negev Desert in Israel

The present research investigated a segment of the micro-arthropod populations residing within nests of Messor arenarius ants in the Negev Desert of Israel. The total frequencies of micro-arthropods in the chaff of those ants’ nests were found to be higher than in the surrounding soil of the same nests. Acari (mites) were observed to be more abundant during the spring season, whereas their presence decreased during the summer months. Springtails (Collembola) were found to follow the Acari pattern, commonly found within the nests of those ants during spring but were absent during summer. Psocoptera order inhabiting soil habitats were infrequently encountered during spring, but their prevalence increased significantly during summer, particularly within the chaff of the ants’ nests, suggesting that chaff is their primary food source in the Negev Desert. Our research suggests that shifts in seasonality have important consequences on the distribution of soil invertebrate communities with implications on nutrient cycling.

基于Ant Design Pro的物流系统前端开发与用户体验优化研究

本文探讨了Ant Design Pro在物流系统开发中的应用,特别是其对用户体验和数据可视化的影响。通过采用案例研究方法,分析Ant Design Pro在构建企业级物流系统中的应用。研究发现,Ant Design Pro的一致性设计和丰富的组件库显著提升了开发效率和用户交互体验。此外,其数据可视化组件有效支持了复杂物流数据的呈现和分析。然而,其在性能优化和定制化需求方面面临着挑战。最终,本文提出了一系列基于Ant Design Pro的物流系统开发最佳实践,并对其潜在的改进方向进行了讨论。研究结果为物流行业的信息系统开发提供了实用的指导,并为Ant Design Pro社区贡献了宝贵的实践经验。

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.

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).

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 Effective Optimization Algorithm for Ant Colony Vehicular Congestion Management

Adaptability and dynamicity are special properties of social insects derived from the decentralized behavior of the insects. Authors have come up with designs for software solution that can regulate traffic congestion in a network transportation environment. The effectiveness of various researches on traffic management has been verified through appropriate metrics. Most of the traffic management systems are centered on using sensors, visual monitoring and neural networks to check for available parking space with the aim of informing drivers beforehand to prevent traffic congestion. There has been limited research on solving ongoing traffic congestion in congestion prone areas like car park with any of the common methods mentioned. This study focus however is on a motor park, as a highly congested area when it comes to traffic. The car park has two entrance gate and three exit gates which is divided into three Isle of parking lot where cars can park. An ant colony optimization algorithm (ACO) was developed as an effective management system for controlling navigation and vehicular traffic congestion problems when cars exit a motor park. The ACO based on the nature and movement of the natural ants, simulates the movement of cars out of the car park through their nearest choice exit. A car park simulation was also used for the mathematical computation of the pheromone. The system was implemented using SIMD because of its dual parallelization ability. The result showed about 95% increase on the number of vehicles that left the motor park in one second. A clear indication that pheromones are large determinants of the shortest route to take as cars followed the closest exit to them. Future researchers may consider monitoring a centralized tally system for cars coming into the park through a censored gate being.

A Distributed Ant Colony Optimization Applied in Edge Detection

With the rise of image data and increased complexity of tasks in edge detection, conventional artificial intelligence techniques have been severely impacted. To be able to solve even greater problems of the future, learning algorithms must maintain high speed and accuracy through economical means. Traditional edge detection approaches cannot detect edges in images in a timely manner due to memory and computational time constraints. In this work, a novel parallelized ant colony optimization technique in a distributed framework provided by the Hadoop/Map-Reduce infrastructure is proposed to improve the edge detection capabilities. Moreover, a filtering technique is applied to reduce the noisy background of images to achieve significant improvement in the accuracy of edge detection. Close examinations of the implementation of the proposed algorithm are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

Toxicity and horizontal transfer of bifenthrin and dimefluthrin against the red imported fire ant, Solenopsis invicta Buren(Hymenoptera: Formicidae), and the efficacy of their dust applications in the field

The red imported fire ant,Solenopsis invicta Buren,poses a significant threat to biodiversity,agriculture,and public health in its introduced ranges.While chemicals such as toxic baits and dust are the main methods for S.invicta control,toxic baits are slow,requiring approximately one or two weeks,but dust can eliminate the colony of fire ants rapidly in just three to five days.To explore more active ingredients for fire ant control using dusts,the toxicity of bifenthrin and dimefluthrin,the horizontal transfer of bifenthrin and dimefluthrin dust and their efficacy in the field were tested.The results showed that the LD50(lethal dose) values of bifenthrin and dimefluthrin were 3.40 and 1.57 ng/ant,respectively.The KT50(median knockdown time) and KT95(95%knockdown time) values of a 20μg mL^(–1)bifenthrin dose were 7.179and 16.611 min,respectively.The KT50and KT95of a 5μg mL^(–1)dimefluthrin dose were 1.538 and 2.825 min,respectively.The horizontal transfers of bifenthrin and dimefluthrin among workers were effective.The mortality of recipients (secondary mortality) and secondary recipients (tertiary mortality) were both over 80%at 48 h after 0.25,0.50 and 1.00%bifenthrin dust treatments.The secondary mortality of recipients was over 99%at 48 h after 0.25,0.50 and 1.00% dimefluthrin dust treatments,but the tertiary mortality was below 20%.The field trial results showed that both bifenthrin and dimefluthrin exhibited excellent fire ant control effects,and the comprehensive control effects of 1.00%bifenthrin and dimefluthrin dusts at 14 d post-treatment were 95.87 and 85.70%,respectively.

Improved Ant Colony Algorithm for Vehicle Scheduling Problem in Airport Ground Service Support

Support vehicles are part of the main body of airport ground operations,and their scheduling efficiency directly impacts flight delays.A mathematical model is constructed and the responsiveness of support vehicles for current operational demands is proposed to study optimization algorithms for vehicle scheduling.The model is based on the constraint relationship of the initial operation time,time window,and gate position distribution,which gives an improvement to the ant colony algorithm(ACO).The impacts of the improved ACO as used for support vehicle optimization are compared and analyzed.The results show that the scheduling scheme of refueling trucks based on the improved ACO can reduce flight delays caused by refueling operations by 56.87%,indicating the improved ACO can improve support vehicle scheduling.Besides,the improved ACO can jump out of local optima,which can balance the working time of refueling trucks.This research optimizes the scheduling scheme of support vehicles under the existing conditions of airports,which has practical significance to fully utilize ground service resources,improve the efficiency of airport ground operations,and effectively reduce flight delays caused by ground service support.

Monitoring the little fire ant,Wasmannia auropunctata(Roger 1863),in the early stage of its invasion in China:Predicting its geographical distribution pattern under climate change

Invasive alien ants(IAAs)are among the most aggressive,competitive,and widespread invasive alien species(IAS)worldwide.Wasmannia auropunctata,the greatest IAAs threat in the Pacific region and listed in“100 of the world’s worst IAS”,has established itself in many countries and on islands worldwide.Wild populations of W.auropunctata were recently reported in southeastern China,representing a tremendous potential threat to China’s agricultural,economic,environmental,public health,and social well-being.Estimating the potential geographical distribution(PGD)of W.auropunctata in China can illustrate areas that may potentially face invasion risk.Therefore,based on the global distribution records of W.auropunctata and bioclimatic variables,we predicted the geographical distribution pattern of W.auropunctata in China under the effects of climate change using an ensemble model(EM).Our findings showed that artificial neural network(ANN),flexible discriminant analysis(FDA),gradient boosting model(GBM),Random Forest(RF)were more accurate than categorical regression tree analysis(CTA),generalized linear model(GLM),maximum entropy model(MaxEnt)and surface distance envelope(SRE).The mean TSS values of ANN,FDA,GBM,and RF were 0.820,0.810,0.843,and 0.857,respectively,and the mean AUC values were 0.946,0.954,0.968,and 0.979,respectively.The mean TSS and AUC values of EM were 0.882 and 0.972,respectively,indicating that the prediction results with EM were more reliable than those with the single model.The PGD of W.auropunctata in China is mainly located in southern China under current and future climate change.Under climate change,the PGD of W.auropunctata in China will expand to higher-latitude areas.The annual temperature range(bio7)and mean temperature of the warmest quarter(bio10)were the most significant variables affecting the PGD of W.auropunctata in China.The PGD of W.auropunctata in China was mainly attributed to temperature variables,such as the annual temperature range(bio7)and the mean temperature of the warmest quarter(bio10).The populations of W.auropunctata in southern China have broad potential invasion areas.Developing strategies for the early warning,monitoring,prevention,and control of W.auropunctata in southern China requires more attention.

Nutritional Composition and Anti-Nutrient Levels in Raw and Processed Varieties of Finger Millet Promoted for Nutritional Security

Finger millet (FM) is rich in nutrients such as minerals, vitamins, and amino acids. However, the levels of nutrients and their bioaccessibility depend on the variety, the levels of ant nutrients, the chemical form of nutrients, and the type of processing methods used. The study determined the levels of selected nutrients, anti-nutrients, and bioaccessibility in raw and processed varieties of finger millet being developed by the Kenya Agricultural and Livestock Research Organization (KALRO) in Kenya. Raw finger millet seeds from KALRO Centers in Kenya were processed by malting for 60 hours and roasting at 110°C for 5 minutes as the optimal conditions. Levels of minerals were determined by AAS and AES, anti-nutrients by UV-visible spectrophotometer, proteins by the Pierce kit method, and vitamins by HPLC. The IE4115 and IE3779 showed the highest levels of nutrients and lowest levels of antinutrients hence preferred for processing and bioaccessibility studies. The level (mg/100 g) of selected minerals;K, Cr3+, Mg, Ca, P, Fe, and Zn were found to be highest in the following varieties of the FM;IE3779 (688.519 ± 1.57), IE 4115 (1.29 ± 0.07), IE4115 (294.38 ± 1.93), IE3779 (466.67 ± 4.17), IE4115 (250.92 ± 0.33), KERICHO P (16.98 ± 0.05) and IE4115 (64.10 ± 2.35) respectively. For β-carotene, vitamin B, B2, B3, B6 and B9 the levels were highest in the following varieties of FM;KAKW3 (0.023 ± 0.02), IE4115 (14.85 ± 0.16), IE4115 (12.998 ± 0.04), IE4115 (5.843 ± 0.07), IE3779 (0.06 ± 0.04) and KAKW4 (9.832 ± 0.08). Phytates, tannins, phenols, and oxalates were found to be lowest in the following varieties: IE3779 (14.20 ± 2.90, IE4115 (27.83 ± 0.73), NKFM1 (9.69 ± 0.07) and IE4115 (0.25 ± 0.01). The highest bioaccessibility values reported for K, Mg, Ca, P, Cr3+, Fe, and Zn were 89.53% (malting, IE3779), 49.28% (malting, IE4115), 60.41% (Malting, IE4115), 69.40% (malting, IE4115), 12.9% (malting, IE4115), 59.84% (malting, KAKW3) and 66.89% (roasting, IE3779) respectively (Table 8). For beta carotene, vitamin B1, B2, B3, B6 and B9 the values were 73.33% (malting, p224), 78.84% (malting, IE4115), 78.34 (malting, IE3779), 97.63% (malting, IE4115), 91.64% (malting, IE4115), and 77.52% (roasting, IE4115) (table The result on levels and bioaccessibility showed that IE4115 and IE3779 varieties were more nutritious and therefore should be promoted for nutritional security.

Security Test Case Prioritization through Ant Colony Optimization Algorithm

Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems.One of the challenges in software security testing is test case prioritization,which aims to reduce redundancy in fault occurrences when executing test suites.By effectively applying test case prioritization,both the time and cost required for developing secure software can be reduced.This paper proposes a test case prioritization technique based on the Ant Colony Optimization(ACO)algorithm,a metaheuristic approach.The performance of the ACO-based technique is evaluated using the Average Percentage of Fault Detection(APFD)metric,comparing it with traditional techniques.It has been applied to a Mobile Payment Wallet application to validate the proposed approach.The results demonstrate that the proposed technique outperforms the traditional techniques in terms of the APFD metric.The ACO-based technique achieves an APFD of approximately 76%,two percent higher than the second-best optimal ordering technique.These findings suggest that metaheuristic-based prioritization techniques can effectively identify the best test cases,saving time and improving software security overall.

A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.

A developed ant colony algorithm for cancer molecular subtype classification to reveal the predictive biomarker in the renal cell carcinoma

Background:Recently,researchers have been attracted in identifying the crucial genes related to cancer,which plays important role in cancer diagnosis and treatment.However,in performing the cancer molecular subtype classification task from cancer gene expression data,it is challenging to obtain those significant genes due to the high dimensionality and high noise of data.Moreover,the existing methods always suffer from some issues such as premature convergence.Methods:To address those problems,we propose a new ant colony optimization(ACO)algorithm called DACO to classify the cancer gene expression datasets,identifying the essential genes of different diseases.In DACO,first,we propose the initial pheromone concentration based on the weight ranking vector to accelerate the convergence speed;then,a dynamic pheromone volatility factor is designed to prevent the algorithm from getting stuck in the local optimal solution;finally,the pheromone update rule in the Ant Colony System is employed to update the pheromone globally and locally.To demonstrate the performance of the proposed algorithm in classification,different existing approaches are compared with the proposed algorithm on eight high-dimensional cancer gene expression datasets.Results:The experiment results show that the proposed algorithm performs better than other effective methods in terms of classification accuracy and the number of feature sets.It can be used to address the classification problem effectively.Moreover,a renal cell carcinoma dataset is employed to reveal the biological significance of the proposed algorithm from a number of biological analyses.Conclusion:The results demonstrate that CAPS may play a crucial role in the occurrence and development of renal clear cell carcinoma.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Path Planning of UAV by Combing Improved Ant Colony System and Dynamic Window Algorithm

A fusion algorithm is proposed to enhance the search speed of an ant colony system(ACS)for the global path planning and overcome the challenges of the local path planning in an unmanned aerial vehicle(UAV).The ACS search efficiency is enhanced by adopting a 16-direction 24-neighborhood search way,a safety grid search way,and an elite hybrid strategy to accelerate global convergence.Quadratic planning is performed using the moving average(MA)method.The fusion algorithm incorporates a dynamic window approach(DWA)to deal with the local path planning,sets a retracement mechanism,and adjusts the evaluation function accordingly.Experimental results in two environments demonstrate that the improved ant colony system(IACS)achieves superior planning efficiency.Additionally,the optimized dynamic window approach(ODWA)demonstrates its ability to handle multiple dynamic situations.Overall,the fusion optimization algorithm can accomplish the mixed path planning effectively.

An Efficient Allocation for Lung Transplantation Using Ant Colony Optimization

A relationship between lung transplant success and many features of recipients’/donors has long been studied.However,modeling a robust model of a potential impact on organ transplant success has proved challenging.In this study,a hybrid feature selection model was developed based on ant colony opti-mization(ACO)and k-nearest neighbor(kNN)classifier to investigate the rela-tionship between the most defining features of recipients/donors and lung transplant success using data from the United Network of Organ Sharing(UNOS).The proposed ACO-kNN approach explores the features space to identify the representative attributes and classify patients’functional status(i.e.,quality of life)after lung transplantation.The efficacy of the proposed model was verified using 3,684 records and 118 input features from the UNOS.The developed approach examined the reliability and validity of the lung allocation process.The results are promising regarding accuracy prediction to be 91.3%and low computational time,along with better decision capabilities,emphasizing the potential for automatic classification of the lung and other organs allocation pro-cesses.In addition,the proposed model recommends a new perspective on how medical experts and clinicians respond to uncertain and challenging lung alloca-tion strategies.Having such ACO-kNN model,a medical professional can sum-marize information through the proposed method and make decisions for the upcoming transplants to allocate the donor organ.

Route Search Method for Railway Replacement Buses Adopting Ant Colony Optimization

In recent years, Japan, and especially rural areas have faced the growing problems of debt-ridden local railway lines along with the population decline and aging population. Therefore, it is best to consider the discontinuation of local railway lines and introduce replacement buses to secure the transportation methods of the local people especially in rural areas. Based on the above background, targeting local railway lines that may be discontinued in the near future, appropriate bus stops when provided with potential bus stops were selected, the present study proposed a method that introduces routes for railway replacement buses adopting ant colony optimization (ACO). The improved ACO was designed and developed based on the requirements set concerning the route length, number of turns, road width, accessibility of railway lines and zones without bus stops as well as the constraint conditions concerning the route length, number of turns and zones without bus stops. Original road network data were generated and processed adopting a geographic information systems (GIS), and these are used to search for the optimal route for railway replacement buses adopting the improved ACO concerning the 8 zones on the target railway line (JR Kakogawa line). By comparing the improved ACO with Dijkstra’s algorithm, its relevance was verified and areas needing further improvements were revealed.