Pareto Optimization as the Basic for Selecting Robotic Mechanic Assembly Technologies

The task of selecting robotic mechanic assembly technologies (RMAT) is considered as a multi-criteria optimization task, which in this formulation is solved on the set of previously obtained solutions regarding the selection of RMAT. The purpose of the paper is to increase the efficiency of technological preparation of robotic mechanical assembly production of machine and instrument engineering due to a new approach to the selection of RMAT using Pareto optimization and the peculiarities of the selection task formulation. The novelty consists in the further development of a science-based approach to solving multi-criteria selection task, based on the first proposed formalisms of the specified process, which reflect the peculiarities of the selection task formulation, its meaningful essence and the content of the Pareto optimization method. The practical value of the research lies in the proposed engineering-acceptable approach to solving applied multi-criteria selection tasks on the example of RMAT selection, which is invariant to the statement of the selection task, the dimension of the task, and its meaningful essence. The methods of discrete optimization, fuzzy multi-criteria selection of alternatives, and the Pareto optimization method were used for the research. The main results of this work consist of the development of formalisms and the demonstration of the efficiency of the proposed approach for the applied task of RMAT selection. The peculiarity of the developed approach is the combination of Pareto optimization, performed on a discrete set of local criteria. Directions for further research are presented.

Possible effects of selecting different station distributions in the optimal sequence estimation method

Since the inception of the optimal sequence estimation (OSE) method,various research teams have substantiated its efficacy as the optimal stacking technique for handling array data,leading to its successful application in numerous geoscience studies.Nevertheless,concerns persist regarding the potential impact of aliasing resulting from the choice of distinct station distributions on the outcomes derived from OSE.In this investigation,I employ theoretical deduction and experimental analysis to elucidate the reasons behind the immunity of the Y_(l'm')-related common signal obtained through OSE to variations in station distribution selection.The primary objective of OSE is also underscored,i.e.,to restore/strip a Y_(l'm')-related common periodic signal from various stations.Furthermore,I provide additional clarification that the‘Y_(l'm')-related common signal’and the‘Y_(l'm')-related equivalent excitation sequence’are distinct concepts.These analyses will facilitate the utilization of the OSE technique by other researchers in investigating intriguing geophysical phenomena and attaining sound explanations.

Criteria for Selecting Carbon Subsurface and Ocean Storage Site in Developing Countries: A Review

Important first phases in the process of implementing CO2 subsurface and ocean storage projects include selecting of best possible location(s) for CO2 storage, and site selection evaluation. Sites must fulfill a number of criteria that boil down to the following basics: they must be able to accept the desired volume of CO2 at the rate at which it is supplied from the CO2 source(s);they must as well be safe and reliable;and must comply with regulatory and other societal requirements. They also must have at least public acceptance and be based on sound financial analysis. Site geology;hydrogeological, pressure, and geothermal regimes;land features;location, climate, access, etc. can all be refined from these basic criteria. In addition to aiding in site selection, site characterization is essential for other purposes, such as foreseeing the fate and impacts of the injected CO2, and informing subsequent phases of site development, including design, permitting, operation, monitoring, and eventual abandonment. According to data from the IEA, in 2022, emissions from Africa and Asias emerging markets and developing economies, excluding Chinas, increased by 4.2%, which is equivalent to 206 million tonnes of CO2 and were higher than those from developed economies. Coal-fired power generation was responsible for more than half of the rise in emissions that were recorded in the region. The difficulty of achieving sustainable socio-economic progress in the developing countries is entwined with the work of reducing CO2 emissions, which is a demanding project for the economy. Organisations from developing countries, such as Bangladesh, Cameroon, India, and Nigeria, have formed partnerships with organisations in other countries for lessons learned and investment within the climate change arena. The basaltic rocks, coal seams, depleted oil and gas reservoirs, soils, deep saline aquifers, and sedimentary basins that developing countries (Bangladesh, Cameroon, India, and Nigeria etc.) possess all contribute to the individual countrys significant geological sequestration potential. There are limited or no carbon capture and storage or clean development mechanism projects running in these countries at this time. The site selection and characterization procedure are not complete without an estimate of the storage capacity of a storage location. Estimating storage capacity relies on volumetric estimates because a site must accept the planned volume of CO2 during the active injection period. As more and more applications make use of site characterization, so too does the body of written material on the topic. As the science of CO2 storage develops, regulatory requirements are implemented, field experience grows, and the economics of CO2 capture and storage improve, so too will site selection and characterisation change.

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.

基于SELECT理念腹腔镜下保留脾脏的胰体尾切除

腹腔镜下胰体尾切除术目前已经成为治疗胰腺体尾部肿瘤的标准术式。而腹腔镜下保留脾脏的胰体尾切除术由于可保留脾脏功能,减少脾切除术后并发症,越来越受到重视。然而,胰腺肿瘤发病隐匿、解剖位置复杂,这些对其诊断和保留脾脏的胰体尾切除术带来了挑战。近年来,我们团队积累了丰富的胰腺肿瘤诊疗经验,创新提出“SELECT”(S-Single-Operator Cholangiopancreatoscopy,E-ERCP,L-Laparoscopy,E-Endoscopic ultrasound,C-Choledochoscopy/Confocal laser endomicroscopy,T-Traditional Chinese medicine)多镜组合中西医微创诊治理念。根据胰腺肿瘤的类型及特点,应用多种内镜和腹腔镜技术,多种不同微创诊疗方法的优选组合,围手术期采用中医药,进行中西医结合加速康复。将SELECT理念充分应用于腹腔镜下保留脾脏的胰体尾切除术,有利于术前精准诊断、术中精准切除、术后并发症预判和治疗,一站式诊治胰腺肿瘤,使病人利益最大化。

Method and standard for selecting elite nut trees of Korean pine

The fruit-bearing quantities of nut Korean pines (Pinus Koraiensis) of natural stands in Changbai Mountain, Xiaoxing'an Mountain, and Wanda Mountain and of artificial forest in Hegang area of Heilongjiang Province were investigated and measured by seed collection of singletree during 1988–1998. In order to evaluate the elite nut tree of fructification, the characteristics of fructification of Korena pine, including, the fruit-bearing quantity, output of seed, quantity of cone, cone size, seed size, the ratio of null seed by solid seed, seed percentage of cone, rate of the cones infested with pest, and fruit-bearing index, etc., were analyzed with the variance analysis, multiple comparison and stepwise regression to obtain the contribution ratio of each fruit-bearing factor to fruit-bearing quantity. The multiple correlation factors and the partial correlation factors for fruit-bearing quantities of Korean pine were determined for different geographical areas, and the cone length, thousand-grain-weight, and the seed percentage of cone were considered as important indices for selection of elite trees. The method of modified weighted coefficients was adopted to select the elite nut trees of Korean pine. Standards for selecting elite nut trees from the natural stands and artificial forest of Korean pine were established. This study could provde selection method and standard of elite nut trees for setting up seed orchard of Korean Pine.

An integrated method of selecting environmental covariates for predictive soil depth mapping

Environmental covariates are the basis of predictive soil mapping.Their selection determines the performance of soil mapping to a great extent,especially in cases where the number of soil samples is limited but soil spatial heterogeneity is high.In this study,we proposed an integrated method to select environmental covariates for predictive soil depth mapping.First,candidate variables that may influence the development of soil depth were selected based on pedogenetic knowledge.Second,three conventional methods(Pearson correlation analysis(PsCA),generalized additive models(GAMs),and Random Forest(RF))were used to generate optimal combinations of environmental covariates.Finally,three optimal combinations were integrated to produce a final combination based on the importance and occurrence frequency of each environmental covariate.We tested this method for soil depth mapping in the upper reaches of the Heihe River Basin in Northwest China.A total of 129 soil sampling sites were collected using a representative sampling strategy,and RF and support vector machine(SVM)models were used to map soil depth.The results showed that compared to the set of environmental covariates selected by the three conventional selection methods,the set of environmental covariates selected by the proposed method achieved higher mapping accuracy.The combination from the proposed method obtained a root mean square error(RMSE)of 11.88 cm,which was 2.25–7.64 cm lower than the other methods,and an R^2 value of 0.76,which was 0.08–0.26 higher than the other methods.The results suggest that our method can be used as an alternative to the conventional methods for soil depth mapping and may also be effective for mapping other soil properties.

Immune Algorithm for Selecting Optimum Services in Web Services Composition

For the problem of dynamic optimization in Web services composition, this paper presents a novel approach for selecting optimum Web services, which is based on the longest path method of weighted multistage graph. We propose and implement an Immune Algorithm for global optimization to construct composed Web services. Results of the experimentation illustrates that the algorithm in this paper has a powerful capability and can greatly improve the efficiency and veracity in service selection.

Effect of UV-B Radiation on Ingesting and Nutritional Selecting Behavior of Rotifer Brachionus urceus

Effect of UV-B radiation on ingesting and nutritional selecting behavior of the roifer Brachionus urceus on 5 species of microlgae were studied under controlled laboratory conditions. Restdts showed that enhanced UV-B radiation significantly inhibited ingesting of the rotifer B. urceus when it was fed with 5 species of micro-algae （p〈0.05）. The ingesting selectivity rate varied with the UV-B radiation enhancement when it was fed with 5 species micro-algal mixture. Results indicated that the enhanced UV-B radiation could affect ingesting and nutritional selectivity of B. urceus.

Selecting suitable solid organ transplant donors: Reducing the risk of donor-transmitted infections

Selection of the appropriate donor is essential to a successful allograft recipient outcome for solid organ transplantation. Multiple infectious diseases have been transmitted from the donor to the recipient via transplantation. Donor-transmitted infections cause increased morbidity and mortality to the recipient. In recent years, a series of high-profile transmissions of infections have occurred in organ recipients prompt-ing increased attention on the process of improving the selection of an appropriate donor that balances the shortage of needed allografts with an approach that mitigates the risk of donor-transmitted infection to the recipient. Important advances focused on improving donor screening diagnostics, using previously excluded high-risk donors, and individualizing the selection of allografts to recipients based on their prior infection history are serving to increase the donor pool and improve outcomes after transplant. This article serves to review the relevant literature surrounding this topic and to provide a suggested approach to the selection of an appropriate solid organ transplant donor.

Selecting between Sequential Zoning and Simultaneous Zoning for Picker-to-parts Order Picking System Based on Order Cluster and Genetic Algorithm

The existing research of sequential zoning system and simultaneous zoning system mainly focuses on some optimization problems such as workload balance,product assignment and simulation for each system separately.But there is little research on comparative study between sequential zoning and simultaneous zoning.In order to help the designers to choose the suitable zoning policy for picker-to-parts system reasonably and quickly,a systemic selection method is presented.Essentially,both zoning and batching are order clustering,so the customer order sheet can be divided into many unit grids.After the time formulation in one-dimensional unit was defined,the time models for each zoning policy in two-dimensional space were established using filling curves and sequence models to link the one-dimensional unit grids.In consideration of ＂U＂ shaped dual tour into consideration,the subtraction value of order picking time between sequential zoning and simultaneous zoning was defined as the objective function to select the suitable zoning policy based on time models.As it is convergent enough,genetic algorithm is adopted to find the optimal value of order picking time.In the experimental study,5 different kinds of order/stock keeping unit（SKU） matrices with different densities d and quantities q following uniform distribution were created in order to test the suitability of sequential zoning and simultaneous zoning to different kinds of orders.After parameters setting,experimental orders inputting and iterative computations,the optimal order picking time for each zoning policy was gotten.By observing whether the delta time between them is greater than 0 or not,the suitability of zoning policies for picker-to-parts system were obtained.The significant effect of batch size b,zone number z and density d on suitability was also found by experimental study.The proposed research provides a new method for selection between sequential zoning and simultaneous zoning for picker-to-parts system,and improves the rationality and efficiency of selection process in practical design.

Fuzzy Assessment Method and Its Application to Selecting Project Managers

Open competition is a new form of the assessment of candidates and selection of project managers. This has many merits compared to the traditional administrative method of appointment. This article introduces a method of fuzzy assessment of project manager candidates. Fuzzy assessment unifies objective qualitative and quantitative appraisal and can be used for improving decision-making in the selection process.

Selecting the best strategy of treatment in newly diagnosed advanced-stage ovarian cancer patients

Although it is assumed that the combination of chemotherapy and radical surgery should be indicated in all newly diagnosed advanced-stage ovarian cancer patients, one of the main raised questions is how to select the best strategy of initial treatment in this group of patients, neoadjuvant chemotherapy followed by interval debulking surgery or primary debulking surgery followed by adjuvant chemotherapy. The selection criteria to offer one strategy over the other as well as a stepwise patient selection for initial treatment are described. Selecting the best strategy of treatment in newly diagnosed advanced stage ovarian cancer patients is a multifactorial and multidisciplinary decision. Several factors should be taken into consideration:(1) the disease factor, related to the extension and localization of the disease as well as tumor biology;(2) the patient factor, associated with patient age, poor performance status, and co-morbidities; and(3) institutional infrastructure factor, related to the lack of prolonged operative time, an appropriate surgical armamentarium, as well as well-equipped intensive care units with well-trained personnel.

Method for optimizing manipulator's geometrical parameters and selecting reducers

A geometrical parameters optimization and reducers selection method was proposed for robotic manipulators design. The Lagrangian approach was employed in deriving the dynamic model of a two-DOF manipulator. The flexibility of links and joints was taken into account in the mechanical structure dimensions optimization and reducers selection, in which Timoshenko model was used to discretize the hollow links. Two criteria, i.e. maximization of fundamental frequency and minimization of self-mass/load ratio, were utilized to optimize the manipulators. The NSGA-II (fast elitist nondominated sorting genetic algorithms) was employed to solve the multi-objective optimization problem. How the joints flexibility affects the manipulators design was analyzed and shown in the numerical analysis example. The results indicate that simultaneous consideration of the joints and the links flexibility is very necessary for manipulators optimal design. Finally, several optimal combinations were provided. The effectiveness of the optimization method was proved by comparing with ADAMS simulation results. The self-mass/load ratio error of the two methods is within 10%. The maximum error of the natural frequency by the two methods is 23.74%. The method proposed in this work provides a fast and effective pathway for manipulator design and reducers selection.

Parameter selecting and quality predicting of spot welding based on artificial neural networks

This paper proposes a procedure for using artificial neural networks (ANN) in spot welding , and establishes spot welding parameter selecting ANN systems and spot welding joint quality predicting ANN systems . It has been proved that the ANN systems have high prediction precision , providing a new way of parameter selecting and quality predicting in spot welding .

A Rough Set and GIS Based Approach for Selecting Suitable Shelters during an Evacuation Process

Humanity suffers an ever-present threat of crises. In the event of a crisis, the population in affected areas will be in danger and will need to be evacuated to a safer in order to protect their lives. One of the difficulties in emergency management is quickly and accurately selecting suitably safe areas of refuge. This paper aims to explain an evacuation shelter selection process that uses rough set theory and a geographical information system (GIS). The proposed approach uses rough set theory concepts to classify shelters and selects suitable shelters on the basis of three factors: distance, capacity, and the availability of life requirements. The preparation of data and reporting of results are performed via the GIS environment. The proposed approach was implemented using Masoura,Egypt, as a case study and the re- sults of this implementation are presented.

Study on Control of Pneumatic Selecting and Shifting Actuators Using Rapid Control Prototyping

A gearbox in-the-loop control platform using dSPACE real-time system is designed for the study on the control technology of pneumatic selecting and shifting actuators based on rapid control prototyping.The operational principle of such actuators was analyzed using dSPACE hardware and software,resulting in a better knowledge of the logical relationship among solenoid valves,gear positions of cylinders and system input/output.Based on these,a control model was developed under the Matlab/Simulink environment and rapidly improved to meet requirements through experiments.Relevant tests have shown that analysis efficiency on selecting and shifting actuators could be raised and development of control strategy facilitated.

LAYERED IMAGING OF PHOTOACOUSTIC MICROSCOPY BY PHASE SELECTING

The theoretical predzctzon and experimental detection for photoacoustic layered imaging by selecting the signal of proper phase angle are described.Using this technique,we can increase the contrast Jof the subsurface features and suppress the surface influence.Some experimental results of layered imaging are presented.

A New method for selecting multi-scale road network objects

Aimed at solving the problems of road network object selection at any unknown scale, the existing methods on object selection are integrated and extended in this paper, and a new object interpolation method is proposed, which reflects the inheritable and transferable characteristics of related information among multi-scale representation objects, and takes the attribute effects into account. Then the basic idea, the overall framework and the technical flow of the interpolation are put forward, at the samet：me synthetical weight function of the interpolation method is defined and described. The method and technical strategies of object selection are extended, and the key problems are solved, including the dejign of the objective quantitative and structural selections based on the weight values, the interpolation experiment strategies and technical flows, the result of the test shows that the object interpolation method not only inherits the objects at smaller scales, but also takes the attribute effect into account when deriving objects from larger scales according to the road importance, which is a guarantee to objective selection of the road objects at middle scales.

基于Core-Selecting机制的物联网安全路由协议

设计安全的路由协议以确保网络与隐私信息安全是物联网面临的一个巨大挑战,提出了一种Core-Selecting机制,并将该机制应用于物联网路由协议设计,在此基础上设计并实现了一种新的物联网安全路由协议PALXC,有助于抵御合谋攻击和选出可信路由.理论分析和仿真实验结果表明了所设计的协议的有效性.