Red Study Greenway Route Selection at County Scale: A Case Study of Ji’an County, Jiangxi Province, China

Using multi-source data like remote sensing images,resource point coordinates,road networks and land type,a suitability assessment system for red study greenway route selection is constructed with red study resource layer,traffic condition layer,ecological condition layer,and service radius layer as the selection elements.Analytic Hierarchy Process(AHP)and Delphi method aree used to determine the selection factors and weight allocation of each element,and the single factor evaluation and multi factor overlaying analysis are used to accurately identify suitable selection corridors.The potential position is determined based on the lowest cost path.Finally,the final red study greenway is obtained through manual optimization based on the current situation of the road network.The analysis results show that the areas with the highest suitability were those with the richest distribution of first,second,and third level red study resources.An effective connection of red resource points and various elements helps to enhance the competitiveness of red study tours in Ji’an County,providing a realistic path for selecting study greenways for cities rich in red resources.

A Throughput-Aware Joint Vehicle Route and Access Network Selection Approach Based on SMDP

In intelligent transportation system(ITS), the interworking of vehicular networks(VN) and cellular networks(CN) is proposed to provide high-data-rate services to vehicles. As the network access quality for CN and VN is location related, mobile data offloading(MDO), which dynamically selects access networks for vehicles, should be considered with vehicle route planning to further improve the wireless data throughput of individual vehicles and to enhance the performance of the entire ITS. In this paper, we investigate joint MDO and route selection for an individual vehicle in a metropolitan scenario. We aim to improve the throughput of the target vehicle while guaranteeing its transportation efficiency requirements in terms of traveling time and distance. To achieve this objective, we first formulate the joint route and access network selection problem as a semi-Markov decision process(SMDP). Then we propose an optimal algorithm to calculate its optimal policy. To further reduce the computation complexity, we derive a suboptimal algorithm which reduces the action space. Simulation results demonstrate that the proposed optimal algorithm significantly outperforms the existing work in total throughput and the late arrival ratio.Moreover, the heuristic algorithm is able to substantially reduce the computation time with only slight performance degradation.

Chemical Reaction Route Selection Based on Green Chemical Engineering

In the preliminary stage of chemical process design, the choice of chemical reaction route is the key design decision, and the concepts of atom utilization and environmental quotient have become extremely useful tools. However, the waste quality such as chemical toxicity and other engineering factors have not been taken into account. Therefore, a synthetic route selection index, Iroute, is proposed to determine the suitability of a chemical route in this paper. Iroute considers the effects of 'extended atom economy', material renewability, chemical characteristics and some engineering factors. The extended atom economy concept regards not only the value of the desired product but also the value of byproducts. The methodology by using Iroute to compare different routes is illustrated in case study of cyclohexanone oxime and acrylonitrile manufacture.

Genetic algorithm for pareto optimum-based route selection

A quality of service （QoS） or constraint-based routing selection needs to find a path subject to multiple constraints through a network. The problem of finding such a path is known as the multi-constrained path （MCP） problem, and has been proven to be NP-complete that cannot be exactly solved in a polynomial time. The NPC problem is converted into a multiobjective optimization problem with constraints to be solved with a genetic algorithm. Based on the Pareto optimum, a constrained routing computation method is proposed to generate a set of nondominated optimal routes with the genetic algorithm mechanism. The convergence and time complexity of the novel algorithm is analyzed. Experimental results show that multiobjective evolution is highly responsive and competent for the Pareto optimum-based route selection. When this method is applied to a MPLS and metropolitan-area network, it will be capable of optimizing the transmission performance.

Engineering Geological Assessment for Route Selection of Railway Line in Geologically Active Area:A Case Study in China

The Cheng-Lan railway links Chengdu, a central city in Southwestern China, and Lanzhou, a central city in Northwestern China. The railway passes through the Longmenshan fault zone (Wenchuan earthquake happened there on May 12, 2008), Minjiang fault zone, and Dongkunlun fault zone, which are all active. It runs over the Yangtze River and the Yellow River, and crosses high mountains and deep valleys. There exists, along the railway's alignment, different kinds of strata of hard granite and soft, weak metamorphic rocks such as carbonaceous slate, schist, and phyllite. It is, therefore, a key issue for such an infrastructure construction to assess the engineering geological conditions and risks, so as to mitigate or avoid possible georisks and to offer optional designs. Geological survey and georisk assessment along the railway corridor are carried out. Special attention is given to active faults, earthquakes and seismic zones. Based on these, discussions about geological aspects for route selection of the railway are conducted and countermeasures for georisk control are proposed accordingly. Main conclusions are achieved as follows: (1) Geohazards such as landslides, rockfalls and debries flows dominate both the route selection of the railway and the engineering structures (e.g., tunnels or bridges) adopted; (2) Tunnel has been proved to be an excellent structure for linear engineering in geologically active area; and (3) In the case where avoiding is impractical, necessary protection measures should be taken to engineering slopes in high earthquake intensity areas, especially the area with earthquake of Ms. 8 or greater.

Cooperative Channel and Optimized Route Selection in Adhoc Network

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is enormous.Its uses have expanded in lockstep with its growth.Due to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is essential.In this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the confidence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization technique.At that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated forwarder.The simulation work is validated in this section using the MATLAB program.Additionally,steps show how the node acts as a confident forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between them.We cal-culate the confidence of the node at the start of the network by combining the reliability report for thefirst hop and the reliability report for the secondary hop.We then refer to the same node as the confident node in order to operate as a forwarder.As a result,we witness an increase in the leftover energy in the output.The percentage of data packets delivered has also increased.

Criterions and Measures of Route Selection of Shallowly Embedded Long-Distance Oil and Gas Pipeline in Mountain Areas

According to the engineering investigation of long-distance oil and gas pipelines, the criterions and measures of route selection are drawn as follows： the flat landform is the first choice in route alignment. The foot of mountain is the first choice when the route passes by the valley. The route should pass by but the shady and deposited slope and not in sunny and erosive slope as possible as it can. The pipeline should be vertical to contour climbing and descending the mountain except steep slope. Tunnel can be used in crossing foothill. Perpendicularly traversing the river is better than beveling; the worst choice is to put the pipeline along the river. Bypass is the best choice in karsts area. The order of route selection should be pre-choosing, investigation, optimization and adjustment.

Energy Efficient Constrained Shortest Path First-Based Joint Resource Allocation and Route Selection for Multi-Hop CRNs

Cognitive radio networks(CRNs) are expected to improve spectrum utilization efficiently by allowing secondary users(SUs) to opportunistically access the licensed spectrum of primary users(PUs).In CRNs,source and destination SUs may achieve information interaction in an ad hoc manner.In the case that no direct transmission link between the SU transmission pairs is available,multi-hop relay SUs can be applied to forward information for the source and destination SUs,resulting in multi-hop CRNs.In this paper,we consider a multi-hop CRN consisting of multiple PUs,SU transmission pairs and relay SUs.Stressing the importance of transmission hops and the tradeoff between data rate and power consumption,we propose an energy efficient constrained shortest path first(CSPF)-based joint resource allocation and route selection algorithm,which consists of two sub-algorithms,i.e.,CSPF-based route selection sub-algorithm and energy efficient resource allocation sub-algorithm.More specifically,we first apply CSPF-based route selection sub-algorithm to obtain the shortest candidate routes(SCRs) between the SU pair under the transmission constraints.Then,an energy efficient resource allocation problem of the SCRs is formulated and solved by applying iterative algorithm and Lagrange dual method.Simu-lation results demonstrate the effectiveness of the proposed algorithm.

Energy-Aware Service Selection Method Based on Sharing Routes in Wireless Sensor Networks

This paper presents a novel energy-aware algorithm for service composition based on sharing routes in Wireless Sensor Networks (WSNs). The method integrates the resource of the overlapping WSNs to a virtual resource pool in the execution cycles of the workflow. This approach chooses the suitable service instances according to the current execution environment and user requirements with minimum energy consumption. Finally, the performance of sharing routes service composition selection in WSNs has been evaluated.

Optimal Route Selection Method Based on Vague Sets

Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.

Route selection of sporting goods industry in China based on global value chain

Based on the global value chain, the paper analyzes the condition of Chinese sporting goods industry in the global value chain, and studies the problems existing in Chinese sporting goods industry. And then, the paper explores the upgrade and route selection of Chinese sporting goods industry in the global value chain.

ROUTE SELECTION IN VIRTUAL CIRCUIT DATA NETWORKS BASED ON GENETIC ALGORITHMS

This paper addresses the problem of selecting a route for every pair of communicating nodes in a virtual circuit data network in order to minimize the average delay encountered by messages. The problem was previously modeled as a network of M/M/1 queues. Agenetic algorithm to solve this problem is presented. Extensive computational results across a variety of networks are reported. These results indicate that the presented solution procedure outperforms the other methods in the literature and is effective for a wide range of traffic loads.

Research on Greenway Route Selection of Traditional Villages Based on GIS: A Case Study of Xingtai County, China

Greenway is a green linear corridor connecting scenic spots, residential areas, nature reserves, road traffic, etc. Rational greenway route selection can not only attract tourists to increase the income of local villages, but also arouse people’s awareness of the protection of traditional villages. This paper took Xingtai County, Hebei Province, China as an example to practice greenway route selection in the county territory. Eight factors were selected for greenway route planning, namely elevation, water body, mountain, ecological protection redline, cultural relics protection units, population density, road traffic network, and important transportation hub. These eight factors were assigned in the GIS platform, and the suitability evaluation system of each factor was constructed. The analytic hierarchy process (AHP) and entropy weight method were combined to calculate the weight of these eight factors. The raster calculator was used to weight and overlay the suitability evaluation system of each factor, and the comprehensive suitability evaluation map of greenway route selection was obtained. Based on an important transport hub, along with the water body and the road traffic, the final selection greenway route of traditional villages in Xingtai county was formed. The results of the research can provide a certain reference for the greenway route selection at the county scale.

Stage at diagnosis of colorectal cancer through diagnostic route:Who should be screened?

BACKGROUND Colorectal cancer(CRC)is a global health concern,with advanced-stage diagnoses contributing to poor prognoses.The efficacy of CRC screening has been well-established;nevertheless,a significant proportion of patients remain unscreened,with>70%of cases diagnosed outside screening.Although identifying specific subgroups for whom CRC screening should be particularly recommended is crucial owing to limited resources,the association between the diagnostic routes and identification of these subgroups has been less appreciated.In the Japanese cancer registry,the diagnostic routes for groups discovered outside of screening are primarily categorized into those with comorbidities found during hospital visits and those with CRC-related symptoms.AIM To clarify the stage at CRC diagnosis based on diagnostic routes.METHODS We conducted a retrospective observational study using a cancer registry of patients with CRC between January 2016 and December 2019 at two hospitals.The diagnostic routes were primarily classified into three groups:Cancer screening,follow-up,and symptomatic.The early-stage was defined as Stages 0 or I.Multivariate and univariate logistic regressions were exploited to determine the odds of early-stage diagnosis in the symptomatic and cancer screening groups,referencing the follow-up group.The adjusted covariates were age,sex,and tumor location.RESULTS Of the 2083 patients,715(34.4%),1064(51.1%),and 304(14.6%)belonged to the follow-up,symptomatic,and cancer screening groups,respectively.Among the 2083 patients,CRCs diagnosed at an early stage were 57.3%(410 of 715),23.9%(254 of 1064),and 59.5%(181 of 304)in the follow-up,symptomatic,and cancer screening groups,respectively.The symptomatic group exhibited a lower likelihood of early-stage diagnosis than the follow-up group[P<0.001,adjusted odds ratio(aOR),0.23;95%confidence interval(95%CI):0.19-0.29].The likelihood of diagnosis at an early stage was similar between the follow-up and cancer screening groups(P=0.493,aOR for early-stage diagnosis in the cancer screening group vs follow-up group=1.11;95%CI=0.82-1.49).CONCLUSION CRCs detected during hospital visits for comorbidities were diagnosed earlier,similar to cancer screening.CRC screening should be recommended,particularly for patients without periodical hospital visits for comorbidities.

Fundamental Ideas and Basic Concepts of HSR Route Selection in Complicated Urban Areas

Up till now,no systematic studies on railway route selection in urban areas,HSR route selection in particular,have been taken in China.Based on the Shenzhen Railway Terminal Project and the Shenzhen-Shanwei Railway Project,research on HSR route selection in complicated urban areas has been conducted.An optimal route selection plan is determined after studying the local geological and environmental conditions and the complexity of tunnel construction.The research concludes that there are four major concerns in HSR route selection:the match between the new route and the urban planning,the potential economic return for the massive investment,the likely impacts of land expropriation on social stability,and the best synthesis of multiple controlling factors to meet the HSR standard.Moreover,six principles should be followed in railway route selection in complicated urban areas:the new route should be in alignment with the railway deployment;the route should align with the existing passage as much as possible;extensive analysis and in-depth demonstrations should be done to find the most appropriate combination of open and hidden excavation in tunnel construction;geological conditions and tunnel construction complexities are among the priorities;environmental sensitive sites and environmental vibration noise should be avoided as much as possible;special attention should be paid to the relocation of the power supply,television and communication facilities and the rearrangement of tubes and wires.

Secure Path Cycle Selection Method Using Fuzzy Logic System for Improving Energy Efficiency in Statistical En-Route Filtering Based WSNs

Sensor nodes are easily compromised to malicious attackers due to an open environment. A false injected attack which takes place on application layer is elected by the compromised node. If the false report arrives in a base station, a false alarm is occurred, and the energy of the nodes is consumed. To detect the false report, statistical en-route filtering method is proposed. In this paper, we proposed the secure path cycle selection method using fuzzy rule-based system to consume effective energy. The method makes balanced energy consumption of each node. Moreover, the lifetime of the whole network will be increased. The base station determines the path cycle using the fuzzy rule-based system. The performance of the proposed method is demonstrated using simulation studies with the three methods.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Recently,a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks(WSN).In routing,the trust system includes or avoids nodes related to the estimated trust values in the routing function.This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol(EMTA-SRSP)for WSN.The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN.To accomplish this,the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication.For the clustering process,the nodes with maximum residual energy will be considered cluster heads(CHs).In addition,the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy,distance,and trust degree.The experimental validation of the EMTA-SRSP technique is tested,and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.