Digital Application Objectives and Benefit Analysis of BIM Technology in Large-Scale Comprehensive Development Projects

This paper discusses the digital application and benefit analysis of building information model(BIM)technology in the large-scale comprehensive development project of the Guangxi headquarters base.The project covers a total area of 92,100 square meters,with a total construction area of 379,700 square meters,including a variety of architectural forms.Through three-dimensional modeling and simulation analysis,BIM technology significantly enhances the design quality and efficiency,shortens the design cycle by about 20%,and promotes the collaboration and integration of project management,improving the management efficiency by about 25%.During the construction phase,the collision detection and four-dimensional visual management functions of BIM technology have improved construction efficiency by about 15%and saved the cost by about 10%.In addition,BIM technology has promoted green building and sustainable development,achieved the dual improvement of technical and economic indicators and social and economic benefits,set an example for enterprises in digital transformation,and opened up new market businesses.

Non-Cooperative Behavior Management in Large-Scale Group Decision-Making Considering the Altruistic Behaviors of Experts and Its Application in Emergency Alternative Selection

Emergency decision-making problems usually involve many experts with different professional backgrounds and concerns,leading to non-cooperative behaviors during the consensus-reaching process.Many studies on noncooperative behavior management assumed that the maximumdegree of cooperation of experts is to totally accept the revisions suggested by the moderator,which restricted individuals with altruistic behaviors to make more contributions in the agreement-reaching process.In addition,when grouping a large group into subgroups by clustering methods,existing studies were based on the similarity of evaluation values or trust relationships among experts separately but did not consider them simultaneously.In this study,we introduce a clustering method considering the similarity of evaluation values and the trust relations of experts and then develop a consensusmodel taking into account the altruistic behaviors of experts.First,we cluster experts into subgroups by a constrained Kmeans clustering algorithm according to the opinion similarity and trust relationship of experts.Then,we calculate the weights of experts and clusters based on the centrality degrees of experts.Next,to enhance the quality of consensus reaching,we identify three kinds of non-cooperative behaviors and propose corresponding feedback mechanisms relying on the altruistic behaviors of experts.A numerical example is given to show the effectiveness and practicality of the proposed method in emergency decision-making.The study finds that integrating altruistic behavior analysis in group decision-making can safeguard the interests of experts and ensure the integrity of decision-making information.

The evolution of robotics:research and application progress of dental implant robotic systems

The use of robots to augment human capabilities and assist in work has long been an aspiration.Robotics has been developing since the 1960s when the first industrial robot was introduced.As technology has advanced,robotic-assisted surgery has shown numerous advantages,including more precision,efficiency,minimal invasiveness,and safety than is possible with conventional techniques,which are research hotspots and cutting-edge trends.This article reviewed the history of medical robot development and seminal research papers about current research progress.Taking the autonomous dental implant robotic system as an example,the advantages and prospects of medical robotic systems would be discussed which would provide a reference for future research.

PDBSCAN: Parallel DBSCAN for Large-Scale Clustering Applications

Density-based algorithm for discovering clusters in large spatial databases with noise(DBSCAN) is a classic kind of density-based spatial clustering algorithm and is widely applied in several aspects due to good performance in capturing arbitrary shapes and detecting outliers. However, in practice, datasets are always too massive to fit the serial DBSCAN. And a new parallel algorithm-Parallel DBSCAN(PDBSCAN) was proposed to solve the problem which DBSCAN faced. The proposed parallel algorithm bases on MapReduce mechanism. The usage of parallel mechanism in the algorithm focuses on region query and candidate queue processing which needed substantive computation resources. As a result, PDBSCAN is scalable for large-scale dataset clustering and is extremely suitable for applications in E-Commence, especially for recommendation.

Drilling-based measuring method for the c-φ parameter of rock and its field application

The technology of drilling tests makes it possible to obtain the strength parameter of rock accurately in situ. In this paper, a new rock cutting analysis model that considers the influence of the rock crushing zone(RCZ) is built. The formula for an ultimate cutting force is established based on the limit equilibrium principle. The relationship between digital drilling parameters(DDP) and the c-φ parameter(DDP-cφ formula, where c refers to the cohesion and φ refers to the internal friction angle) is derived, and the response of drilling parameters and cutting ratio to the strength parameters is analyzed. The drillingbased measuring method for the c-φ parameter of rock is constructed. The laboratory verification test is then completed, and the difference in results between the drilling test and the compression test is less than 6%. On this basis, in-situ rock drilling tests in a traffic tunnel and a coal mine roadway are carried out, and the strength parameters of the surrounding rock are effectively tested. The average difference ratio of the results is less than 11%, which verifies the effectiveness of the proposed method for obtaining the strength parameters based on digital drilling. This study provides methodological support for field testing of rock strength parameters.

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.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

Construction of Risk Assessment Application Model of Epidemic Disease in Large-scale Pig Farms

The application model of epidemic disease assessment technology for Web-based large-scale pig farm was expounded from the identification of epidemic disease risk factors, construction of risk assessment model and development of risk assessment system. The assessed pig farm uploaded the epidemic disease risk data information through on-line answering evaluating questionnaire to get the immediate evaluation report. The model could enhance the risk communication between pig farm veterinarian, manager and veterinary experts to help farm system understand and find disease risk factors, assess and report the potential high risk items of the pig farm in the three systems of engineering epidemic disease prevention technology, biological safety and immune monitoring, and promote the improvement and perfection of epidemic disease prevention and control measures.

MXene-Based Elastomer Mimetic StretchableSensors: Design, Properties, and Applications

Flexible sensors based on MXene-polymer composites are highly prospective for next-generation wearable electronics used in human-machine interfaces.One of the motivating factors behind the progress of flexible sensors is the steady arrival of new conductive materials.MXenes,a new family of 2D nanomaterials,have been draw-ing attention since the last decade due to their high electronic conduc-tivity,processability,mechanical robustness and chemical tunability.In this review,we encompass the fabrication of MXene-based polymeric nanocomposites,their structure-property relationship,and applications in the flexible sensor domain.Moreover,our discussion is not only lim-ited to sensor design,their mechanism,and various modes of sensing platform,but also their future perspective and market throughout the world.With our article,we intend to fortify the bond between flexible matrices and MXenes thus promoting the swift advancement of flexible MXene-sensors for wearable technologies.

The Application of Bilayer Artificial Dermis Combined with VSD Technology in Chronic Wounds

Background: Bilayer artificial dermis promotes wound healing and offers a treatment option for chronic wounds. Aim: Examine the clinical efficacy of bilayer artificial dermis combined with Vacuum Sealing Drainage (VSD) technology in the treatment of chronic wounds. Method: From June 2021 to December 2023, our hospital treated 24 patients with chronic skin tissue wounds on their limbs using a novel tissue engineering product, the bilayer artificial dermis, in combination with VSD technology to repair the wounds. The bilayer artificial dermis protects subcutaneous tissue, blood vessels, nerves, muscles, and tendons, and also promotes the growth of granulation tissue and blood vessels to aid in wound healing when used in conjunction with VSD technology for wound dressing changes in chronic wounds. Results: In this study, 24 cases of chronic wounds with exposed bone or tendon larger than 1.0 cm2 were treated with a bilayer artificial skin combined with VSD dressing after wound debridement. The wounds were not suitable for immediate skin grafting. At 2 - 3 weeks post-treatment, good granulation tissue growth was observed. Subsequent procedures included thick skin grafting or wound dressing changes until complete wound healing. Patients were followed up on average for 3 months (range: 1 - 12 months) post-surgery. Comparative analysis of the appearance, function, skin color, elasticity, and sensation of the healed chronic wounds revealed superior outcomes compared to traditional skin fl repairs, resulting in significantly higher satisfaction levels among patients and their families. Conclusion: The application of bilayer artificial dermis combined with VSD technology for the repair of chronic wounds proves to be a viable method, yielding satisfactory therapeutic effects compared to traditional skin flap procedures.

Comprehensive Evaluation of Flower Border Application Value of New and Superior Plants in Hefei Area

An analytic hierarchy process(AHP)was employed to assess the applicability of 18 new and superior varieties of flowers in Hefei City flower border applications.A total of 12 indicators were selected from three distinct aspects of adaptability,ornamental characteristics and use traits,in order to establish a comprehensive evaluation model.The results demonstrate that grade I(J≥2.685)exhibits excellent application value,encompassing six species of plants,such asHydrangeamacrophylla‘Endless Summer’;grade II(2.684≤J≤2.420)is also of notable application value,encompassing five species of plants,such asCallistemonrigidus;grade III(2.419≤J≤2.615)is of average application value,including five species of plants,such asCrocosmiacrocosmiflora;grade IV(J≤2.16)is of relatively poor application value.The evaluation results may be utilized as a theoretical reference for the promotion of new and superior varieties in the flower border of Hefei.

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.

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.

Room-temperature synthesis of full-component APbX_(3)perovskite nanocrystal inks for optoelectronic applications

Lead halide perovskite nanocrystals(PNCs)have received great research interests due to their excellent optoelectronic properties.However,high temperature,inert gas protection and insulating long-chain ligands are used during the conventional hot-injection synthesis of PNCs,which limits their practical applications.In this work,we first develop a simple and scalable polar-solvent-free method for the preparation of full-component APbX_(3)(A=Cs,methylammonium(MA),formamidinium(FA),X=Cl,Br,I)PNCs under ambient condition.Through an exothermic reaction between butylamine(BA)and propionic acid(PA)short ligands,the PbX_(2) precursors could be well dissolved without use of any polar solvent.Meanwhile,the relatively lower growth rate of PNCs in our room-temperature reaction enables us to modulate the synthetic procedure to enhance the scalability(40-fold)and achieve large-scale synthesis.The resultant short ligands passivated PNC inks are compatible with varying solution depositing technique like spray coating for large-area film.Finally,we showcase that adopting the as-prepared MAPbI_(3) PNC inks,a self-powered photodetector is fabricated and shows a high photoresponsivity.These results demonstrate that our ambient-condition synthetic approach can accelerate the preparation of tunable and ready-to-use PNCs towards commercial optoelectronic applications.

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.

Application of water loop variable refrigerant flow air-conditioning system in large-scale buildings in northern China

A water loop variable refrigerant flow（WLVRF）air-conditioning system is designed to be applied in large-scale buildings in northern China.The system is energy saving and it is an integrated system consisting of a variable refrigerant flow（VRF）air-conditioning unit,a water loop and an air source heat pump.The water loop transports energy among different regions in the buildings instead of refrigerant pipes,decreasing the scale of the VRF air-conditioning unit and improving the performance.Previous models for refrigerants and building loads are cited in this investigation.Mathematical models of major equipment and other elements of the system are established using the lumped parameter method based on the DATAFIT software and the MATLAB software.The performance of the WLVRF system is simulated.The initial investments and the running costs are calculated based on the results of market research.Finally,a contrast is carried out between the WLVRF system and the traditional VRF system.The results show that the WLVRF system has a better working condition and lower running costs than the traditional VRF system.

Heating of nanoparticles and their environment by laser radiation and applications

This review considers the fundamental dynamic processes involved in the laser heating of metal nanoparticles and their subsequent cooling.Of particular interest are the absorption of laser energy by nanoparticles,the heating of a single nanoparticle or an ensemble thereof,and the dissipation of the energy of nanoparticles due to heat exchange with the environment.The goal is to consider the dependences and values of the temperatures of the nanoparticles and the environment,their time scales,and other parameters that describe these processes.Experimental results and analytical studies on the heating of single metal nanoparticles by laser pulses are discussed,including the laser thresholds for initiating subsequent photothermal processes,how temperature influences the optical properties,and the heating of gold nanoparticles by laser pulses.Experimental studies of the heating of an ensemble of nanoparticles and the results of an analytical study of the heating of an ensemble of nanoparticles and the environment by laser radiation are considered.Nanothermometry methods for nanoparticles under laser heating are considered,including changes in the refractive indices of metals and spectral thermometry of optical scattering of nanoparticles,Raman spectroscopy,the thermal distortion of the refractive index of an environment heated by a nanoparticle,and thermochemical phase transitions in lipid bilayers surrounding a heated nanoparticle.Understanding the sequence of events after radiation absorption and their time scales underlies many applications of nanoparticles.The applicationfields for the laser heating of nanoparticles are reviewed,including thermochemical reactions and selective nanophotothermolysis initiated in the environment by laser-heated nanoparticles,thermal radiation emission by nanoparticles and laser-induced incandescence,electron and ion emission of heated nanoparticles,and optothermal chemical catalysis.Applications of the laser heating of nanoparticles in laser nanomedicine are of particular interest.Significant emphasis is given to the proposed analytical approaches to modeling and calculating the heating processes under the action of a laser pulse on metal nanoparticles,taking into account the temperature dependences of the parameters.The proposed models can be used to estimate the parameters of lasers and nanoparticles in the various applicationfields for the laser heating of nanoparticles.

Combined application of CIRCLE Software and Topo-LASIK for SMILE enhancement

Dear Editor,We present a case of combined application of CIRCLE software(Carl Zeiss Meditec AG,Germany)and topography-guided laser-assisted in situ keratomileusis(Topo-LASIK)for small-incision lenticule extraction(SMILE)enhancement.SMILE is a safe,minimally invasive corneal laser surgery using a femtosecond laser to create an extractable stromal lenticule.