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The robot offers interesting capabilities, but suffers from a lack of stiffness. The proposed solution is to introduce redundancies for the overall improvement of different capabilities. The management of redundancy associated with the definition of a set of kinematic, mechanical and stiffness criteria enables path planning to be optimized.

The resolution method is based on the projection onto the kernel of the Jacobian matrix of the gradient of an objective function constructed by aggregating kinematic, mechanical and stiffness weighted criteria. Optimized redundancy management is applied to the 11-DoF (degrees of freedom) cells to provide an efficient placement of turntable and track. The final part presents the improvement of the various criteria applied to both 9-DoF and 11-DoF robotic cells.

The first application concerns the optimized placement of a turntable and a linear track using 11-DoF architecture. Improved criteria for two 9-DoF robotic cells, a robot with parallelogram closed loop and a Tricept are also presented. Simulation results present the contributions of redundancies and the leading role of the track.

The redundancy-based optimization presented and the associated simulation approach must be completed by the experimental determination of the optimization criteria to take into account each machining strategy.

This work in robotics machining relates to milling operations for automotive and aerospace equipment. The study is carried out within the framework of the RobotEx Equipment of Excellence programme.

The resolution method to optimized path planning is applied to 9- and 11-DoF robotic cells, including a hybrid robot with a parallelogram closed loop and a Tricept PKM.

The authors argue that public project managers do not consider the iron triangle (cost, quality and schedule) primary important in measuring the success of their projects. To investigate which success criteria are important to public project managers, the authors interviewed 26 Dutch project managers who are employed by the government and who are responsible for managing infrastructural projects. The paper aims to discuss these issues.

In this research the Q-methodology is applied. Q-methodology helps to find for correlations between subjects across a sample of variables. Q-factor analysis reduces the individual viewpoints down to a few factors. A factor can be seen as the mathematical representation of an “average” perspective shared by a group of people.

Findings are based on the individual rankings of 19 success criteria; the authors distinguished three common perspectives: the holistic and cooperative leader, the socially engaged, ambiguous manager and the executor of a top-down assignment. In none of the perspectives the iron triangle criteria formed the top three to measure project success.

The research results may have a national character. The way project success is perceived by public project managers may be culture dependent. For this the authors expand the research to other countries in the near future.

This paper contributes to the understanding of the public project manager by their private collaboration partners, like consultants, engineers and contractors. This will help them to understand their client and contribute to better collaboration in projects.

This paper shows that the difference in work attitude and value frame in the public sector leads to a specific view on project success.

This study caries a survey approach using the expert's interview and literature to select the important criteria to select and evaluate the third-party reverse logistics providers (3PRLPs) in manufacturing companies. In total, 16 criteria are selected to evaluate 3PRLPs, and these criteria are classified on the basis of three main elements of sustainable growth, including economic, social and environmental development. Therefore, a hybrid decision-making approach is utilized to evaluate and rank the 3PRLPs in manufacturing companies.

This paper proposes a new decision-making approach using the projection model and entropy method under the interval-valued intuitionistic fuzzy set to assess 3PRLPs based on sustainability perspectives. A survey approach using the literature review and experts' interview is conducted to select the important criteria to select and evaluate 3PRLPs in manufacturing companies. To assess the criteria weight, the entropy method is used. Further, the projection model is applied to prioritize the 3PRLPs option. Sensitivity analysis and comparison process are performed in order to test and validate the developed method.

The presented methodology uses the benefits to determine the former for measuring the parameters considered and the latter for rating the 3PRLPs alternatives. A case study is taken to 3PRLPs in the manufacturing industry to illustrate the efficiency of the introduced hybrid method. The findings of this study indicate that when facing uncertainties of input and qualitative data, the proposed solution delivers more viable performance and therefore is suitable for wider uses.

The conception of the circular economy (CE) comes from the last 4 decades, and in recent years, tremendous attention has been carried out on this concept, partially because of the availability of natural resources in the world and changes in consumption behaviour of developed and developing nations. Remarkably, the sustainable supply chain management concepts are established parallel to the CE foundations, grown in industrial practice and ecology literature for a long time. In fact, to reduce the environmental concerns, sustainable supply chain management seeks to diminish the materials' flow and minimize the unintentional harmful consequences of consumption and production processes. Customers and governments are becoming increasingly aware of the environmental sustainability in the CE era, which allows businesses to concentrate more resources on reverse logistics (RLs). However, most manufacturing enterprises have been inspired to outsource their RL operations to competent 3PRLPs due to limited resources and technological limitations. In RL outsourcing practices, the selection of the best 3PRLP is helpfully valuable due to its potential to increase the economic viability of enterprises and boost their long-term growth.

Through a two-stage survey, this paper examines how researchers judge the quality of answers on ResearchGate Q&A, an academic social networking site.

In the first-stage survey, 15 researchers from Library and Information Science (LIS) judged the quality of 157 answers to 15 questions and reported the criteria that they had used. The content of their reports was analyzed, and the results were merged with relevant criteria from the literature to form the second-stage survey questionnaire. This questionnaire was then completed by researchers recognized as accomplished at identifying high-quality LIS answers on ResearchGate Q&A.

Most of the identified quality criteria for academic answers—such as relevance, completeness, and verifiability—have previously been found applicable to generic answers. The authors also found other criteria, such as comprehensiveness, the answerer's scholarship, and value-added. Providing opinions was found to be the most important criterion, followed by completeness and value-added.

The findings here show the importance of studying the quality of answers on academic social Q&A platforms and reveal unique considerations for the design of such systems.

The purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province.

The clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed.

This paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability.

The model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

This paper aims to demonstrate the capabilities of a diffuse interface free energy lattice Boltzmann method to perform direct numerical simulations of liquid–liquid dispersions in a well-controlled turbulent environment. The goal of this research study is to develop numerical techniques that can visualize and quantify drop interaction with the turbulent vortices. The obtained information will be used for the development of sub-models of drop breakup for multi-scale simulations.

A pure binary liquid system is considered that is subject to fully developed statistically stationary turbulent flow field in a cubic fully periodic box with the edge size of 300 lattice units. Three turbulent flow fields with varying energy input are examined and their coherent structures are visualized using a normalized Q-criterion. The evolution of the liquid–liquid interface is tracked as a function of time. The detailed explanation of the numerical method is provided with a highlight on a choice of the numerical parameters.

Drop breakup mechanisms differ depending on energy input. Drops break due to interaction with the vortices. Quantification of turbulent structures shows that the size of vortices increases with the decrease of energy input. Drop interacts simultaneously with multiple vortices of the size comparable to or smaller than the drop size. Vortices of the size smaller than the drop size disturb drop interface and pinch off the satellites. Vortices of the size comparable to the drop size tend to elongate the drop and tear it apart producing daughter drops and satellites. Addition of the second phase enhances turbulent dissipation at the high wavenumbers. To obtain physically realistic two-phase energy spectra, the multiple-relaxation-time collision operator should be used.

Detailed information of drop breakup in the turbulent flow field is crucial for the development of drop breakup sub-models that are necessary for multi-scale numerical simulations. The improvement of numerical methods that can provide these data and produce reliable results is important. This work made one step towards a better understanding of how drops interact with the turbulent vortices.

Partial least squares structural equation modeling (PLS-SEM) has become popular in the information systems (IS) field for modeling structural relationships between latent variables as measured by manifest variables. However, while researchers using PLS-SEM routinely stress the causal-predictive nature of their analyses, the model evaluation assessment relies exclusively on criteria designed to assess the path model's explanatory power. To take full advantage of the purpose of causal prediction in PLS-SEM, it is imperative for researchers to comprehend the efficacy of various quality criteria, such as traditional PLS-SEM criteria, model fit, PLSpredict, cross-validated predictive ability test (CVPAT) and model selection criteria.

A systematic review was conducted to understand empirical studies employing the use of the causal prediction criteria available for PLS-SEM in the database of Industrial Management and Data Systems (IMDS) and Management Information Systems Quarterly (MISQ). Furthermore, this study discusses the details of each of the procedures for the causal prediction criteria available for PLS-SEM, as well as how these criteria should be interpreted. While the focus of the paper is on demystifying the role of causal prediction modeling in PLS-SEM, the overarching aim is to compare the performance of different quality criteria and to select the appropriate causal-predictive model from a cohort of competing models in the IS field.

The study found that the traditional PLS-SEM criteria (goodness of fit (GoF) by Tenenhaus, R2 and Q2) and model fit have difficulty determining the appropriate causal-predictive model. In contrast, PLSpredict, CVPAT and model selection criteria (i.e. Bayesian information criterion (BIC), BIC weight, Geweke–Meese criterion (GM), GM weight, HQ and HQC) were found to outperform the traditional criteria in determining the appropriate causal-predictive model, because these criteria provided both in-sample and out-of-sample predictions in PLS-SEM.

This research substantiates the use of the PLSpredict, CVPAT and the model selection criteria (i.e. BIC, BIC weight, GM, GM weight, HQ and HQC). It provides IS researchers and practitioners with the knowledge they need to properly assess, report on and interpret PLS-SEM results when the goal is only causal prediction, thereby contributing to safeguarding the goal of using PLS-SEM in IS studies.

The mechanization of the meat cutting companies has become essential due to the lack of skilled workers and to working conditions. This paper deals with the analysis of human gestures in order to improve the performance of a redundant robotic cell. The aim is to define optimization criteria linked to the process and the human gesture analysis to improve the cutting process with a redundant robotic cell.

This paper deals with an optimized path planning of complex tasks based on the human arm analysis. The first part details the operator's manual work. The robotized cutting strategy using bones as a guide associated with an industrial force control leads to the tasks redefinition. Thus, the analysis of the arm during the tasks is presented. With a robotic model, the authors evaluate the relevance of two criteria (kinematic and mechanical) that the operator naturally manages. These criteria are used to improve the robotized cutting process by using redundancy. Simulation work and experimentation are presented to show the enhanced performance.

The paper explains how to define optimization criteria based on human arm analysis to realize cutting operations which require force or dexterity performance. It presents a study on the criteria weighting on a robotic arm model established through human arm analysis. The optimized cutting process clearly shows improvement.

The scalability of the ham implied the definition of iterative trajectories to follow the curvature of the bone. Due to the use of an industrial force control, no online optimization can be achieved. The off-line optimization implies that the boundary of the trajectory space is technically feasible. Nevertheless, more information has to be extracted from the deboning process such as vision data in order to improve cutting quality.

This study was carried out within the framework of several national and European projects (FUI SRDViand, ANR ARMS, FP7 Echord Dexdeb) in collaboration with ADIV (Meat Institute Development Agency). The redundant robotic cell was developed and implemented at ADIV and used for feasibility studies in connection with SME/SMI French sector.

The paper deals with the cutting of soft bodies such as meat and complex human gesture analysis, which constitute an innovative challenge for the coming years in order to help or replace humans in industrial meat companies with difficult working conditions.

Due to such issues as the recent economic recession, low salaries, and an aging society, how people can strengthen their investment performance when managing their personal financial affairs is a critical consideration. The purpose of this paper is to consider the assessment of the performance of individual investment policies and to present an evaluation framework for measuring the degree of workability of investment policies.

The proposed evaluation framework combines the fuzzy analytical hierarchy process and the improved fuzzy technique for order preference by similarity to the ideal solution to measure the efficiency scores of the alternatives (i.e. investment policies) under assessment.

This quantitative framework is formed from the criteria of investment return, taxation, risk, and individual circumstances according to prudent evaluation of private wealth management research, and is applied to appraise the investment performance of individuals in Taiwan. The findings indicate that investment performance, risks, and the investment of mutual funds are the most preferred conditions and investment policy for investors, and can offer some effective suggestions for investors as well as for future academic research.

The efficiency scores are computed based on the fuzzy Mahalanobis distances, taking into account the fuzzy correlations among experts’ criteria. The advantage of adopting the fuzzy Mahalanobis distances over the fuzzy Euclidean distances, which are typically computed in the literature, is that the undulation of the efficiency scores can be reduced.

The food supply chain is exposed to severe environmental and social issues with serious economic consequences. The identification and assessment of risk involved in the food supply chain can help to overcome these challenges. In response, the purpose of this paper is to develop a risk assessment framework for a typical food supply chain.

An integrated methodology of grey analytical hierarchy process and grey technique for order preference by similarity to the ideal solution is proposed for developing a comprehensive risk index. The opinion of the experts is used to illustrate an application of the proposed methodology for the risk assessment of the food supply chain in India.

Valuable insights and recommendations are drawn from the results, which are helpful to the practitioners working at strategic and tactical levels in the food supply chain for minimising the supply chain disruptions.

The risk quantification for the case organisation is primarily based on inputs collected from the experts working for Indian food supply chain, and so the generalisation of the results is limited to the context of developing countries. However, the generalisability of the proposed risk quantification methodology and key insights developed in the food supply chain will assist practitioners in policy making.

The risk priorities established by this research would enable an implementation of systematic risk mitigation strategies and deployment of necessary resources for leveraging the efficiency of food supply chain.

Specifically, this research has delivered a risk quantification framework and strengthened the inquiry of risk management for the food supply chain.