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.

Enhanced selectivity of catalytic hydrogenation of halogenated nitroaromatics by interfacial effects

The highly selective catalytic hydrogenation of halogenated nitroaromatics was achieved by employing Pd‑based catalysts that were co‑modified with organic and inorganic ligands.It was demonstrated that the catalysts contained Pd species in mixed valence states,with high valence Pd at the metal‑support interface and zero valence Pd at the metal surface.While the strong coordination of triphenylphosphine(PPh3)to Pd0 on the Pd surface prevents the adsorption of halogenated nitroaromatics and thus dehalogenation,the coordination of sodium metavanadate(NaVO3)to high‑valence Pd sites at the interface helps to activate H2 in a heterolytic pathway for the selective hydrogenation of nitro‑groups.The excellent catalytic performance of the interfacial active sites enables the selective hydrogenation of a wide range of halogenated nitroaromatics.

Corrosion resistance and antibacterial properties of Ti−3Cu alloy prepared by selective laser melting

The corrosion resistance and antibacterial properties of Ti−3Cu alloy prepared by selective laser melting were evaluated using electrochemical experiments and a variety of antibacterial characterization.It is found that the charge transfer resistance of Ti−3Cu alloy was 4.89×10^(5)Ω∙cm^(2),which was doubled the data obtained by CP-Ti alloy.The antibacterial rates of Ti−3Cu alloy against S.mutans and P.gingivalis were 45.0%and 54.5%.And the antibacterial rates increased with the prolongation of cultivation time,reaching up to 62.8%and 68.6%,respectively.The in-situ nano Ti_(2)Cu precipitates were homogeneously distributed in the matrix of the Ti−3Cu alloy,which was the key reason of increasing the corrosion resistance.Additionally,the microscale electric fields between theα-Ti matrix and the Ti_(2)Cu was responsible for the enhancement of the antibacterial properties.

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.

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.

Study on Vortex-Induced Motions of A New Type of Deep Draft Multi-Columns FDPSO

A numerical simulation and an experimental study on vortex-induced motion（VIM） of a new type of deep draft multi-columns floating drilling production, storage and offloading（FDPSO） are presented in this paper. The main dimension, the special variable cross-section column and the cabin arrangement of the octagonal pontoon are introduced based on the result. The numerical simulation is adapted to study the effects of current incidence angles and reduced velocities on this platform’s sway motion response. The 300 m water depth equivalent truncated mooring system is adopted for the model tests. The model tests are carried out to check the reliability of numerical simulation. The results consist of surge, sway and yaw motions, as well as motion trajectories. The maximum sway amplitudes for different types of offshore platform is also studied. The main results show that the peak frequencies of sway motion under different current incidence angles and reduced velocities vary around the natural frequency. The analysis result of flow field indicates that the change of distribution of vortex in vertical presents significant influences on the VIM of platform. The trend of sway amplitude ratio curve of this new type FDPSO differs from the other types of platform. Under 45° current incidence angle, the sway amplitude of this new type of FDPSO is much smaller than those of other types of offshore platform at 4.4 ≤ V;≤ 8.9. The typical ‘8’ shape trajectory does not appear in the platform’s motion trajectories.

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 .