Overall Selection Design Based on the Mega Highway Bridge Project

In the continuous development of the modern highway and bridge engineering industry,the reasonable selection of mega highway bridges and their design is crucial.Based on this,this paper takes the actual bridge project as an example,and analyses the overall selection design of such highway bridges,including the basic overview of the project,the basic selection principle of mega highway bridge project structure and its design strategy,etc.,to provide scientific reference for its selection design.

Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

Machine learning(ML)is increasingly applied for medical image processing with appropriate learning paradigms.These applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for diagnosis.The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification.Most of the extracted image features are irrelevant and lead to an increase in computation time.Therefore,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features.This process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel distributions.The similarity between the pixels over the various distribution patterns with high indexes is recommended for disease diagnosis.Later,the correlation based on intensity and distribution is analyzed to improve the feature selection congruency.Therefore,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the distribution.Now,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of selection.Therefore,the probability of feature selection,regardless of the textures and medical image patterns,is improved.This process enhances the performance of ML applications for different medical image processing.The proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected dataset.The mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.

Risk Management Techniques in Construction Projects:Development and Assessment of an Innovative

Professional and trade skills are required for handling the construction related projects;Construction industries of the present day however lack useful information concerning different practices,patterns and trends involved in risk management.Considering this,the present study focuses on the aforementioned variables of risk management by quantitative analysis specifically in the domain of construction industry.This study has used IBM’s SPSS(Statistical Package for Social Sciences)version 25.0 to analyze the results.This study is an initiative to assess the impact of risk management in the construction sector of Jordan.It will assist the construction sector for exploring the limitations with respect to integrate effective risk management.A sense of competition will be developed through a comparison of risk factors of construction projects among the project stakeholders such as contractors should enhance their risk management practices.

Impact of Land Use Change on Ecosystem Services Values in Danjiangkou Reservoir Area,China in the Context of National Water Network Project Construction

Investigating the ecological impact of land use change in the context of the construction of national water network project is crucial,as it is imperative for achieving the sustainable development goals of the national water network and guaranteeing regional ecological stability.Using the Danjiangkou Reservoir Area(DRA),China as the study area,this paper first examined the spatiotemporal dynamics of natural landscape patterns and ecosystem service values(ESV)in the DRA from 2000 to 2018 and then investigated the spatial clustering characteristics of the ESV using spatial statistical analysis tools.Finally,the patch-generating land use simulation(PLUS)model was used to simulate the natural landscape and future changes in the ESV of the DRA from 2018 to 2028 under four different development scenarios:business as usual(BAU),economic development(ED),ecological protection(EP),and shoreline protection(SP).The results show that:during 2000-2018,the construction of water facilities had a significant impact on regional land use/land cover(LULC)change,with a 24830 ha increase in watershed area.ESV exhibited an increasing trend,with a significant and growing spatial clustering effect.The transformation of farmland to water bodies led to accelerated ESV growth,while the transformation of forest land to farmland led to a decrease in the ESV.Normalized difference vegetation index(NDVI)had the strongest effect on the ESV.ESV exhibited a continuous increase from 2018 to 2028 under all the simulation scenarios.The EP scenario had the greatest increase in ESV,while the ED scenario had the smallest increase.The findings suggest that projected land use patterns under different scenarios have varied impacts on ecosystem services(ESs)and that the management and planning of the DRA should balance social,economic,ecological,and security benefits.nomic,ecological,and security benefits.

Predicting the Construction Quality of Projects by Using Hybrid Soft Computing Techniques

The construction phase of a project is a critical factor that significantly impacts its overall success.The construction environment is characterized by uncertainty and dynamism,involving nonlinear relationships among various factors that affect construction quality.This study utilized 987 construction inspection records from 1993 to 2022,obtained from the Taiwan residents Public Construction Management Information System(PCMIS),to determine the relationships between construction factors and quality.First,fuzzy logic was applied to calculate the weights of 499 defects,and 25 critical construction factors were selected based on these weight values.Next,a deep neural network was used to identify the relationship between the critical construction factors(input variables)and construction quality(output variable).Finally,the prediction model’s performance was evaluated to confirm the impact of these critical construction factors on project outcomes.This study employed an innovative hybrid soft computing technique,com-bining fuzzy logic and an artificial neural network,to effectively predict the relationship between critical construction factors and construction quality,achieving a model accuracy of 96.08%.Project managers can utilize the findings of this study to enhance project management practices and establish effective construction management strategies,thereby improving project construction quality.

Projective Hilbert modules and sequential approximations

We show that when A is a separable C^(*)-algebra,every countably generated Hilbert A-module is projective(with bounded module maps as morphisms).We also study the approximate extensions of bounded module maps.In the case where A is aσ-unital simple C^(*)-algebra with strict comparison,and every strictly positive lower semicontinuous affine function on quasitraces can be realized as the rank of an element in the Cuntz semigroup,we show that the Cuntz semigroup is equivalent to unitarily equivalent classes of countably generated Hilbert A-modules if and only if A has stable rank one.

Genomic selection for meat quality traits based on VIS/NIR spectral information

The principle of genomic selection(GS) entails estimating breeding values(BVs) by summing all the SNP polygenic effects. The visible/near-infrared spectroscopy(VIS/NIRS) wavelength and abundance values can directly reflect the concentrations of chemical substances, and the measurement of meat traits by VIS/NIRS is similar to the processing of genomic selection data by summing all ‘polygenic effects' associated with spectral feature peaks. Therefore, it is meaningful to investigate the incorporation of VIS/NIRS information into GS models to establish an efficient and low-cost breeding model. In this study, we measured 6 meat quality traits in 359Duroc×Landrace×Yorkshire pigs from Guangxi Zhuang Autonomous Region, China, and genotyped them with high-density SNP chips. According to the completeness of the information for the target population, we proposed 4breeding strategies applied to different scenarios: Ⅰ, only spectral and genotypic data exist for the target population;Ⅱ, only spectral data exist for the target population;Ⅲ, only spectral and genotypic data but with different prediction processes exist for the target population;and Ⅳ, only spectral and phenotypic data exist for the target population.The 4 scenarios were used to evaluate the genomic estimated breeding value(GEBV) accuracy by increasing the VIS/NIR spectral information. In the results of the 5-fold cross-validation, the genetic algorithm showed remarkable potential for preselection of feature wavelengths. The breeding efficiency of Strategies Ⅱ, Ⅲ, and Ⅳ was superior to that of traditional GS for most traits, and the GEBV prediction accuracy was improved by 32.2, 40.8 and 15.5%, respectively on average. Among them, the prediction accuracy of Strategy Ⅱ for fat(%) even improved by 50.7% compared to traditional GS. The GEBV prediction accuracy of Strategy Ⅰ was nearly identical to that of traditional GS, and the fluctuation range was less than 7%. Moreover, the breeding cost of the 4 strategies was lower than that of traditional GS methods, with Strategy Ⅳ being the lowest as it did not require genotyping.Our findings demonstrate that GS methods based on VIS/NIRS data have significant predictive potential and are worthy of further research to provide a valuable reference for the development of effective and affordable breeding strategies.

Early and accurate diagnosis and selection of appropriate treatment plans are crucial for patients with gastrointestinal hemangiomas

Gastrointestinal hemangioma(GIH)is clinically rare,accounting for 7%-10%of benign gastrointestinal tumors and 0.5%of systemic hemangiomas.GIH can occur as either solitary or multiple lesions,with gastrointestinal bleeding as a significant clinical manifestation.Understanding the clinical and endoscopic features of GIH is essential for improving diagnostic accuracy,particularly through endoscopy and selective arteriography,which are highly effective in diagnosing GIH and preventing misdiagnosis and inappropriate treatment.Upon confirmed diagnosis,it is essential to thoroughly evaluate the patient's condition to determine the most suitable treatment modality—whether surgical,endoscopic,or minimally invasive intervention.The minimally invasive interventional partial embolization therapy using polyvinyl alcohol particles,proposed and implemented by Pospisilova et al,has achieved excellent clinical outcomes.This approach reduces surgical trauma and the inherent risks of traditional surgical treatments.

Multi-objective optimization in highway pavement maintenance and rehabilitation project selection and scheduling:A state-of-the-art review

The motivation for cost-effective management of highway pavements is evidenced not only by the massive expenditures associated with these activities at a national level but also by the consequences of poor pavement condition on road users.This paper presents a state-of-the-art review of multi-objective optimization(MOO)problems that have been formulated and solution techniques that have been used in selecting and scheduling highway pavement rehabilitation and maintenance activities.First,the paper presents a taxonomy and hierarchy for these activities,the role of funding sources,and levels of jurisdiction.The paper then describes how three different decision mechanisms have been used in past research and practice for project selection and scheduling(historical practices,expert opinion,and explicit mathematical optimization)and identifies the pros and cons of each mechanism.The paper then focuses on the optimization mechanism and presents the types of optimization problems,formulations,and objectives that have been used in the literature.Next,the paper examines various solution algorithms and discusses issues related to their implementation.Finally,the paper identifies some barriers to implementing multi-objective optimization in selecting and scheduling highway pavement rehabilitation and maintenance activities,and makes recommendations to overcome some of these barriers.

Scenario-based approach for project portfolio selection in army engineering and manufacturing development

The decisions concerning portfolio selection for army engineering and manufacturing development projects determine the benefit of those projects to the country concerned.Projects are typically selected based on ex ante estimates of future return values,which are usually difficult to specify or only generated after project launch.A scenario-based approach is presented here to address the problem of selecting a project portfolio under incomplete scenario information and interdependency constraints.In the first stage,the relevant dominance concepts of scenario analysis are studied to handle the incomplete information.Then,a scenario-based programming approach is proposed to handle the interdependencies to obtain the projects,whose return values are multi-criteria with interval data.Finally,an illustrative example of army engineering and manufacturing development shows the feasibility and advantages of the scenario-based multi-objective programming approach.

Uncertainty Theory Based Novel Multi-Objective Optimization Technique Using Embedding Theorem with Application to R &D Project Portfolio Selection

This paper introduces a novice solution methodology for multi-objective optimization problems having the coefficients in the form of uncertain variables. The embedding theorem, which establishes that the set of uncertain variables can be embedded into the Banach space C[0, 1] × C[0, 1] isometrically and isomorphically, is developed. Based on this embedding theorem, each objective with uncertain coefficients can be transformed into two objectives with crisp coefficients. The solution of the original m-objectives optimization problem with uncertain coefficients will be obtained by solving the corresponding 2 m-objectives crisp optimization problem. The R & D project portfolio decision deals with future events and opportunities, much of the information required to make portfolio decisions is uncertain. Here parameters like outcome, risk, and cost are considered as uncertain variables and an uncertain bi-objective optimization problem with some useful constraints is developed. The corresponding crisp tetra-objective optimization model is then developed by embedding theorem. The feasibility and effectiveness of the proposed method is verified by a real case study with the consideration that the uncertain variables are triangular in nature.

Energy Efficient Access Point Selection and Signal Projection for Accurate Indoor Positioning

We propose a method to improve positioning accuracy while reducing energy consumption in an indoor Wireless Local Area Network(WLAN) environment.First,we intelligently and jointly select the subset of Access Points(APs) used in positioning via Maximum Mutual Information(MMI) criterion.Second,we propose Orthogonal Locality Preserving Projection(OLPP) to reduce the redundancy among selected APs.OLPP effectively extracts the intrinsic location features in situations where previous linear signal projection techniques failed to do,while maintaining computational efficiency.Third,we show that the combination of AP selection and OLPP simultaneously exploits their complementary advantages while avoiding the drawbacks.Experimental results indicate that,compared with the widely used weighted K-nearest neighbor and maximum likelihood estimation method,the proposed method leads to 21.8%(0.49 m) positioning accuracy improvement,while decreasing the computation cost by 65.4%.

Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

This paper presents a Game-theoretic optimization via Parallel Min-Max Ant System(PMMAS)algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project management.To this end,we introduce an approach that proposes:(i)A Game-theoretic model of multiround procurement problem(ii)A Nash equilibrium strategy corresponds to multi-round strategy bid(iii)An application of PSO for the determination of global Nash equilibrium.The balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also in terms of future cooperation.As an alternative of procuring entities subjectively,a methodology to support decision making has been studied using Nash equilibrium to create a balance point on benefit in procurement where buyers and suppliers need multiple rounds of bidding.Our goal focus on the balance point in Nash Equilibrium to optimizing bidder selection in multi-round procurement which is the most beneficial for both investors and selected tenderers.Our PMMAS algorithm is implemented based on MPI(message passing interface)to find the approximate optimal solution for the question of how to choose bidders and ensure a path for a win-win relationship of all participants in the procurement process.We also evaluate the speedup ratio and parallel efficiency between our algorithm and other proposed algorithms.As the experiment results,the high feasibility and effectiveness of the PMMAS algorithm are verified.

人工智能背景下Materials Project数据库在计算材料学课程教学中的应用

该文探讨了在人工智能背景下,Materials Project数据库在计算材料学课程教学中的应用和影响。Materials Project数据库是一个集成了AI和大数据技术的开放获取的材料库,能为学生提供海量的材料晶体结构和物性数据,使教学内容更为丰富,让学生能通过亲自操作获取和分析数据,深入理解微观结构与物性之间的关系。这一新兴的教学模式不仅提升了学生的科研能力和创新思维能力,还有助于培养具备计算材料专业知识和多学科交叉的复合型人才。总体来说,人工智能时代下,大数据的引入为计算材料学课程带来新的活力,并对未来教育改革和实践产生了积极影响。

Application Study of Climatic Feasibility Demonstration of Airport Project Site Selection

According to the Technical Guide for Climatic Feasibility Demonstration of Airport Project Site Selection,via statistical analysis on historical climate data of reference weather station,climatic background characteristics and meteorological disaster situation of preselected site,and characteristics of seasonal distribution,interannual variation and long-term trend of main meteorological elements and disasters were summarized.The difference of meteorological elements between preselected site and reference weather station and its cause were analyzed,and fine analysis of fog,low visibility event,wind,cloud and other meteorological elements or weather with high influence in preselected site was conducted.Climatic feasibility,advantages and disadvantages of meteorological conditions and possible meteorological risks in preselected site were evaluated.The research could provide decision-making basis for site selection of Lingling Airport and airport engineering design.Moreover,key technology route and research results were extracted,and technical analysis process of demonstration report was integrated,which could provide reference for climate feasibility demonstration on site selection of similar airports in China.

Conductor selection and economic analysis of D.R.congo–guinea±800 kV UHVDC transmission project

Africa is embracing new opportunities featured with industrialization,urbanization and regionalization.Based on co-development of‘Electricity,Mining,Metallurgy,Industry and Trade’and grids interconnection proposed by Global Energy Interconnection Development and Cooperation Organization(GEIDCO),the high-quality hydropower resource of the Congo River can be exploited in large scale under the wide-range interconnected framework of African Energy Interconnection(AEI),forging a new engine for Africa economy.The transmission distance of the Congo River hydropower reaches 6,000 km at its farthest end in North Africa,which brings forth challenges to economics of proposed projects.Under this novel continental energy interconnection scheme in Africa,economics of those projects have not yet been in detail studied.This paper has implemented China’s mature engineering experiences and analytical tools of UHVDC project planning into the AEI structure,through exploring the economic behavior of ultra-long distance UHVDC projects in the scope of conductor selection in the Congo River hydropower transmission for the first time,and has provided concerned parties with a technical and analytical results of their economics comparison.This paper has chosen the D.R.Congo-Guinea±800 kV UHVDC project as a typical example.Its preliminary system planning is introduced and three types of conductor are selected for scheme comparison.Later in this paper,the transmission loss,total investment and equivalent annual cost of the project have been calculated and analyzed.In the final part,sensitivity analysis results of the annual cost to utilization hours,transmission loss,loss tariff and construction cost has been provided.

The application of projection pursuit classification in the process of strategy selection and evaluation based on the real coded accelerating genetic algorithm

During the process of enterprises＇ strategy evaluation and selection, there are many evaluating indicators, and among them there are some potential correlations and conflicts. Thus it poses the problems to the decision-makers how to conduct correct evaluation on a business and how to make strategy adjustment and selection according to the evaluation. Based on the qualitative and quantitative method, the paper introduces the Projection Pursuit Classification （PPC） model based on the Real-coded Accelerating Genetic Algorithm （RAGA） into the process of enterprises＇ strategy evaluation and selection. The characteristic of PPC model is that it ultimately overcomes the influence of the proportion of subjectivity and avoids precocious convergence, thus providing a new objective method for strategy evaluation and selection by pursuing the most objective strategy evaluation to make the relatively sensible strategy portfolio and action.

Tabu Search Solution for Resource Confidence Considered Partner Selection Problem in Cross-Enterprise Project

Cross-enterprise project is the main implementation form in multi enterprises collaborative production environment. Minimizing the risk of failure and tardiness caused by the uncertainty of partner’s resources in partner selection is the key problem to ensure success in Cross-enterprise project. In this paper, considering the factors and constraints of sub-project processing times, precedence of sub-project and project due date, especially the resource confidence, a 0-1 integer programming model was presented with the objective to minimize the risk of failure and the tardiness of the project. A project scheduling algorithm was designed to search and evaluate selection solutions, and the project scheduling algorithm was embedded into a Tabu search algorithm to solve the model. Simulation experiments and comparisons with other algorithms showed that the proposed approach was possible to find the optimal solution with a faster speed and higher probability.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Dominance-Based Rough Set Approach in Selection of Portfolio of Sustainable Development Projects

In our study, the Dominance-based Rough Set Approach (DRSA) has been proposed to assist the Board of Directors of the Community Futures Development Corporations (CFDC), the sub-region of Abitibi-West (Quebec). The CFDC needs a tool for decision support to select the projects that are proposed by the contractors and partners of its territory. In decision making, a balanced set of 22 indicators is considered. These indicators derive from five perspectives: economic, social, demographic, health and wellness. The DRSA proposal is suitable for the data processing with multiple indicators providing on many examples to infer decision rules related to the preference model. In this paper we show that decision rules developed with the use of rough set theory allow us to simplify the process of selecting a portfolio for sustainable development by reducing a number of redundant indicators and identifying the critical values of selected indicators.