A Two-Layer Encoding Learning Swarm Optimizer Based on Frequent Itemsets for Sparse Large-Scale Multi-Objective Optimization

Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.

Assessment of Wet Season Precipitation in the Central United States by the Regional Climate Simulation of the WRFG Member in NARCCAP and Its Relationship with Large-Scale Circulation Biases

Assessment of past-climate simulations of regional climate models(RCMs)is important for understanding the reliability of RCMs when used to project future regional climate.Here,we assess the performance and discuss possible causes of biases in a WRF-based RCM with a grid spacing of 50 km,named WRFG,from the North American Regional Climate Change Assessment Program(NARCCAP)in simulating wet season precipitation over the Central United States for a period when observational data are available.The RCM reproduces key features of the precipitation distribution characteristics during late spring to early summer,although it tends to underestimate the magnitude of precipitation.This dry bias is partially due to the model’s lack of skill in simulating nocturnal precipitation related to the lack of eastward propagating convective systems in the simulation.Inaccuracy in reproducing large-scale circulation and environmental conditions is another contributing factor.The too weak simulated pressure gradient between the Rocky Mountains and the Gulf of Mexico results in weaker southerly winds in between,leading to a reduction of warm moist air transport from the Gulf to the Central Great Plains.The simulated low-level horizontal convergence fields are less favorable for upward motion than in the NARR and hence,for the development of moist convection as well.Therefore,a careful examination of an RCM’s deficiencies and the identification of the source of errors are important when using the RCM to project precipitation changes in future climate scenarios.

Enhancing Evolutionary Algorithms With Pattern Mining for Sparse Large-Scale Multi-Objective Optimization Problems

Sparse large-scale multi-objective optimization problems(SLMOPs)are common in science and engineering.However,the large-scale problem represents the high dimensionality of the decision space,requiring algorithms to traverse vast expanse with limited computational resources.Furthermore,in the context of sparse,most variables in Pareto optimal solutions are zero,making it difficult for algorithms to identify non-zero variables efficiently.This paper is dedicated to addressing the challenges posed by SLMOPs.To start,we introduce innovative objective functions customized to mine maximum and minimum candidate sets.This substantial enhancement dramatically improves the efficacy of frequent pattern mining.In this way,selecting candidate sets is no longer based on the quantity of nonzero variables they contain but on a higher proportion of nonzero variables within specific dimensions.Additionally,we unveil a novel approach to association rule mining,which delves into the intricate relationships between non-zero variables.This novel methodology aids in identifying sparse distributions that can potentially expedite reductions in the objective function value.We extensively tested our algorithm across eight benchmark problems and four real-world SLMOPs.The results demonstrate that our approach achieves competitive solutions across various challenges.

Large-scale model testing of high-pressure grouting reinforcement for bedding slope with rapid-setting polyurethane

Bedding slope is a typical heterogeneous slope consisting of different soil/rock layers and is likely to slide along the weakest interface.Conventional slope protection methods for bedding slopes,such as retaining walls,stabilizing piles,and anchors,are time-consuming and labor-and energy-intensive.This study proposes an innovative polymer grout method to improve the bearing capacity and reduce the displacement of bedding slopes.A series of large-scale model tests were carried out to verify the effectiveness of polymer grout in protecting bedding slopes.Specifically,load-displacement relationships and failure patterns were analyzed for different testing slopes with various dosages of polymer.Results show the great potential of polymer grout in improving bearing capacity,reducing settlement,and protecting slopes from being crushed under shearing.The polymer-treated slopes remained structurally intact,while the untreated slope exhibited considerable damage when subjected to loads surpassing the bearing capacity.It is also found that polymer-cemented soils concentrate around the injection pipe,forming a fan-shaped sheet-like structure.This study proves the improvement of polymer grouting for bedding slope treatment and will contribute to the development of a fast method to protect bedding slopes from landslides.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Online identification and extraction method of regional large-scale adjustable load-aggregation characteristics

This article introduces the concept of load aggregation,which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems.The online identification method is a computer-involved approach for data collection,processing,and system identification,commonly used for adaptive control and prediction.This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration,aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods.The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics,economic efficiency,and comfort.The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes,the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57,indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term.Overall,the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage.

Large-Scale Carbon Dioxide Storage in Salt Caverns:Evaluation of Operation,Safety,and Potential in China

Underground salt cavern CO_(2) storage(SCCS)offers the dual benefits of enabling extensive CO_(2) storage and facilitating the utilization of CO_(2) resources while contributing the regulation of the carbon market.Its economic and operational advantages over traditional carbon capture,utilization,and storage(CCUS)projects make SCCS a more cost-effective and flexible option.Despite the widespread use of salt caverns for storing various substances,differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness,carbon injection,brine extraction control,long-term carbon storage stability,and site selection criteria.These distinctions stem from the unique phase change characteristics of CO_(2) and the application scenarios of SCCS.Therefore,targeted and forward-looking scientific research on SCCS is imperative.This paper introduces the implementation principles and application scenarios of SCCS,emphasizing its connections with carbon emissions,carbon utilization,and renewable energy peak shaving.It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods,and addresses associated scientific challenges.In this paper,we establish a pressure equation for carbon injection and brine extraction,that considers the phase change characteristics of CO_(2),and we analyze the pressure during carbon injection.By comparing the viscosities of CO_(2) and other gases,SCCS’s excellent sealing performance is demonstrated.Building on this,we develop a long-term stability evaluation model and associated indices,which analyze the impact of the injection speed and minimum operating pressure on stability.Field countermeasures to ensure stability are proposed.Site selection criteria for SCCS are established,preliminary salt mine sites suitable for SCCS are identified in China,and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons,utilizing only 20%-30%volume of abandoned salt caverns.This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters,such as the operating pressure,burial depth,and storage scale,and it offers essential guidance for implementing SCCS projects in China.

Factorized Smith Method for A Class of High-Ranked Large-Scale T-Stein Equations

We introduce a factorized Smith method(FSM)for solving large-scale highranked T-Stein equations within the banded-plus-low-rank structure framework.To effectively reduce both computational complexity and storage requirements,we develop techniques including deflation and shift,partial truncation and compression,as well as redesign the residual computation and termination condition.Numerical examples demonstrate that the FSM outperforms the Smith method implemented with a hierarchical HODLR structured toolkit in terms of CPU time.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Numerical and theoretical study of large-scale failure of strata overlying sublevel caving mines with steeply dipping discontinuities

The deformation and fracture evolution mechanisms of the strata overlying mines mined using sublevel caving were studied via numerical simulations.Moreover,an expression for the normal force acting on the side face of a steeply dipping superimposed cantilever beam in the surrounding rock was deduced based on limit equilibrium theory.The results show the following:(1)surface displacement above metal mines with steeply dipping discontinuities shows significant step characteristics,and(2)the behavior of the strata as they fail exhibits superimposition characteristics.Generally,failure first occurs in certain superimposed strata slightly far from the goaf.Subsequently,with the constant downward excavation of the orebody,the superimposed strata become damaged both upwards away from and downwards toward the goaf.This process continues until the deep part of the steeply dipping superimposed strata forms a large-scale deep fracture plane that connects with the goaf.The deep fracture plane generally makes an angle of 12°-20°with the normal to the steeply dipping discontinuities.The effect of the constant outward transfer of strata movement due to the constant outward failure of the superimposed strata in the metal mines with steeply dipping discontinuities causes the scope of the strata movement in these mines to be larger than expected.The strata in the metal mines with steeply dipping discontinuities mainly show flexural toppling failure.However,the steeply dipping structural strata near the goaf mainly exhibit shear slipping failure,in which case the mechanical model used to describe them can be simplified by treating them as steeply dipping superimposed cantilever beams.By taking the steeply dipping superimposed cantilever beam that first experiences failure as the key stratum,the failure scope of the strata(and criteria for the stability of metal mines with steeply dipping discontinuities mined using sublevel caving)can be obtained via iterative computations from the key stratum,moving downward toward and upwards away from the goaf.

The Influence of Information Technology(IT)on Accrual Accounting Adoption:The Case of the Jordanian Public Sector

Jordan is one of many countries that intends to reform its public sector by adopting accrual accounting,which is primarily carried out through the International Public Sector Accounting Standards(IPSAS)implementation plan,scheduled to be completed by 2021.Considering the difficulties of adoption,this process cannot be completed without an appropriate Information Technology(IT)system capable of providing the accounting information required for this reform.Therefore,this study aims to examine the influence of IT on accrual accounting adoption in the Jordanian public sector.The study employed the quantitative survey approach.Based on 331 usable questionnaires,the descriptive findings showed that the use of IT in the Jordanian public sector is relevant to daily accounting transactions in general and to accrual accounting in particular.The regression analysis showed a positive,significant relationship between IT use and accrual accounting practice.The IT specialist should be able to understand the needs of public sector in adopting accrual-based accounting.In line with today’s digitalization age,which integrates advanced technologies and numerous techniques,this study suggests that a combination of accounting and IT experts is a value added to sharpen the competitiveness of public sector reform.This study contributes to the extant literature in public sector,concerning the relationship between IT and accrual accounting adoption in the context of Jordan.

Assessing Financial Management Practices and Accounting Mechanisms in Agricultural Cooperatives:A Case Study From Nepal

The study focuses on assessing the financial management practices and accounting mechanisms in agricultural cooperatives in Tulsipur Sub-Metropolitan,Dang District,Nepal with a focus on understanding their implications for financial performance and organizational effectiveness.The sample size of total cooperatives(n=46)was divided into Savings and Credit Cooperatives(n=18)and Multipurpose Cooperatives(n=28),respectively,with a total number of respondents(n=138)categorized into managing directors,employees,and general members.Using a mixed-methods approach that combines quantitative analysis of financial data with qualitative insights gathered through interviews and surveys,the study emphasizes the importance of modern financial practices,improved reporting mechanisms,and relevant staff training for long-term sustainability.Recommendations include the integration of criteria and evaluation tools to assess cooperative performance,with Hamro Pahunch Multipurpose Cooperative identified as a high performer.Emphasizing the need for robust financial management strategies to navigate the complexity of the agricultural sector,manage risks,and achieve sustainable development,the study notes frequent preparation of financial management reports on a monthly and annual basis,and predominantly annual accounting management.Most cooperatives are using computerized models to present financial positions,but face challenges such as lack of marketing infrastructure,cooperative skills,and technical support.Ultimately,the study advocates for educating policy makers,cooperative leaders,practitioners and stakeholders on the role of effective financial management and accounting in enhancing the resilience,expansion and socio-economic impact of agricultural cooperatives,thereby fostering their long-term prosperity and viability as drivers of rural development and empowerment.

Assessing cutter-rock interaction during TBM tunnelling in granite:Large-scale standing rotary cutting tests and 3D DEM simulations

The widespread utilisation of tunnel boring machines(TBMs)in underground construction engineering requires a detailed investigation of the cutter-rock interaction.In this paper,we conduct a series of largescale standing rotary cutting tests on granite in conjunction with high-fidelity numerical simulations based on a particle-type discrete element method(DEM)to explore the effects of key cutting parameters on the TBM cutter performance and the distribution of cutter-rock contact stresses.The assessment results of cutter performance obtained from the cutting tests and numerical simulations reveal similar dependencies on the key cutting parameters.More specifically,the normal and rolling forces exhibit a positive correlation with penetration but are slightly influenced by the cutting radius.In contrast,the side force decreases as the cutting radius increases.Additionally,the side force shows a positive relationship with the penetration for smaller cutting radii but tends to become negative as the cutting radius increases.The cutter's relative effectiveness in rock breaking is significantly impacted by the penetration but shows little dependency on the cutting radius.Consequently,an optimal penetration is identified,leading to a low boreability index and specific energy.A combined Hertz-Weibull function is developed to fit the cutter-rock contact stress distribution obtained in DEM simulations,whereby an improved CSM(Colorado School of Mines)model is proposed by replacing the original monotonic cutting force distribution with this combined Hertz-Weibull model.The proposed model outperforms the original CSM model as demonstrated by a comparison of the estimated cutting forces with those from the tests/simulations.The findings from this work that advance our understanding of TBM cutter performance have important implications for improving the efficiency and reliability of TBM tunnelling in granite.

Research on the Path of Transition from Financial Accounting to Management Accounting Under the Background of Big Data

Driven by the wave of big data,the traditional financial accounting model faces an urgent need for transformation,as it struggles to adapt to the complex requirements of modern enterprise management.This paper aims to explore the feasible path for transitioning enterprise financial accounting to management accounting in the context of big data.It first analyzes the limitations of financial accounting in the era of big data,then highlights the necessity of transitioning to management accounting.Following this,the paper outlines the various challenges that may arise during this transition and,based on the analysis,proposes a series of corresponding transition strategies.These strategies aim to provide theoretical support and practical guidance for enterprises seeking a smooth transition from financial accounting to management accounting.

From EBIT to SEBIT(Sustainable EBIT):Sustainable Performance Accounting(SPA)Using the Example of CO_(2) Accounting

Corporate sustainability reporting has become increasingly important in recent years.However,conventional approaches reach their limits when it comes to quantifying and measuring the actual sustainability performance of a company.This article presents a new approach:Sustainable Performance Accounting(SPA),which is based on an extension of bookkeeping by including ESG bookkeeping.SPA enables companies to systematically measure and manage their sustainability performance.The article provides an overview of the basics of SPA methodology and uses a comprehensive example showing how SPA can be implemented in practice.The article is aimed at interested readers from science and practice as well as decision-makers who are interested in future-oriented sustainability reporting.

Variable and Invariable,Explore the Value-Creation Techniques of Management Accounting by Benchmarking the Financial Management of Large Enterprises

With the development of information technology such as ChatGPT,artificial intelligence,5G,and so on,technological innovation brings about a qualitative change in the economic business.This change is embodied in breakthroughs in the field of science and technology and the iterative upgrading process of products.The traditional business model undoubtedly fails to adapt to the management needs of modern enterprises,while traditional financial practices and knowledge reserves cannot meet these needs.Faced with sudden technological changes and the emergence of financial sharing,financial robots,etc.,there is an urgent need for financial practitioners to transform from traditional financial accounting to value-creating management accounting.In the future,financial professionals with the ability to embrace uncertainty,adapt to environmental changes,and find breakthroughs in uncertain environments will be the most sought-after by enterprises.This paper aims to benchmark the excellent financial management models of large enterprises and conduct thematic research on financial management in large enterprises.It seeks to explore,in the era of big data management accounting,the road and art of creating value in enterprises.The objective is to provide references for colleagues in the financial field and to explore effective ways to generate value for enterprise management ideas,thus serving as a reference point in the financial transformation journey.

Financial Calculation Problems and Countermeasure Analysis of Large-Scale Engineering Construction Projects

The financial aspects of large-scale engineering construction projects profoundly influence their success.Strengthening cost control and establishing a scientific financial evaluation system can enhance the project’s economic benefits,minimize unnecessary costs,and provide decision-makers with a robust financial foundation.Additionally,implementing an effective cash flow control mechanism and conducting a comprehensive assessment of potential project risks can ensure financial stability and mitigate the risk of fund shortages.Developing a practical and feasible fundraising plan,along with stringent fund management practices,can prevent fund wastage and optimize fund utilization efficiency.These measures not only facilitate smooth project progression and improve project management efficiency but also enhance the project’s economic and social outcomes.