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This paper aims to present a requirements analysis for the processing of water-based electrode dispersions in inkjet printing.

A detailed examination of the components and the associated properties of the electrode dispersions has been carried out. The requirements of the printing process and the resulting performance characteristics of the electrode dispersions were analyzed in a top–down approach. The product and process side were compared, and the target specifications of the dispersion components were derived.

Target ranges have been identified for the main component properties, balancing the partly conflicting goals between the product and the process requirements.

The findings are expected to assist with the formulation of electrode dispersions as printing inks.

Little knowledge is available regarding the particular requirements arising from the systematic qualification of aqueous electrode dispersions for inkjet printing. This paper addresses these requirements, covering both product and process specifications.

This paper aims to solve the parameter identification problem to estimate the parameters in electrochemical models of the lithium-ion battery.

The parameter estimation framework is applied to the Doyle-Fuller-Newman (DFN) model containing a total of 44 parameters. The DFN model is fit to experimental data obtained through the cycling of Li-ion cells. The parameter estimation is performed by minimizing the least-squares difference between the experimentally measured and numerically computed voltage curves. The minimization is performed using a state-of-the-art hybrid minimization algorithm.

The DFN model parameter estimation is performed within 14 h, which is a significant improvement over previous works. The mean absolute error for the converged parameters is less than 7 mV.

To the best of the authors’ knowledge, application of a hybrid optimization framework is new in the field of electrical modelling of lithium-ion cells. This approach saves much time in parameterization of models with a high number of parameters while achieving a high-quality fit.

The purpose of this paper is to investigate KM practices that may be in place in the higher education institutions (HEIs) and whether the KM practices are made known to the employees for improving the teaching and learning environment provided at the Malaysian higher education institutions.

Data were collected using a personal administrated method made available to private higher education institutions academic members in five states with 30 or more employees. A total of 1,100 survey questionnaires were handed out, out of which 273 were collected and were usable (24.8 per cent response rate). The sample was checked for response and non-response bias. Results were tested using SPSS application and questionnaire tools.

It was essential to establish the knowledge management (KM) capacity in key areas such as the ability to recognise experts within the institution, leadership’s innovation, knowledge sharing and knowledge acquiring work culture, and technology usage. KM tools and techniques would help the institutions to meet their competitive goals; therefore, it is vital for HEIs to create KM awareness among the employees.

Similar to most studies, it is anticipated that the participants’ awareness of KM practices at their HEIs is very high. The samples were collected to evaluate the general view of KM awareness and how participants perceived KM practices. The total samples received for this study were expected; however, they were sufficient to study the impact.

This paper provides support for the importance of KM practices and employees awareness at HEIs to enhance innovation and performance teaching and learning environment.

This paper is one of the first papers to find empirical support for the role of KM practices at HEIs. Further, the positioning of KM practices as a competitive tool can be considered as an influential factor to competitive advantage.

The purpose of this paper is to establish and implement a direct topological reanalysis algorithm for general successive structural modifications, based on the updating matrix triangular factorization (UMTF) method for non-topological modification proposed by Song et al. [Computers and Structures, 143(2014):60-72].

In this method, topological modifications are viewed as a union of symbolic and numerical change of structural matrices. The numerical part is dealt with UMTF by directly updating the matrix triangular factors. For symbolic change, an integral structure which consists of all potential nodes/elements is introduced to avoid side effects on the efficiency during successive modifications. Necessary pre- and post processing are also developed for memory-economic matrix manipulation.

The new reanalysis algorithm is applicable to successive general structural modifications for arbitrary modification amplitudes and locations. It explicitly updates the factor matrices of the modified structure and thus guarantees the accuracy as full direct analysis while greatly enhancing the efficiency.

Examples including evolutionary structural optimization and sequential construction analysis show the capability and efficiency of the algorithm.

This innovative paper makes direct topological reanalysis be applicable for successive structural modifications in many different areas.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

Competency frameworks can support public procurement capacity development and performance. However, literature on connecting professionalisation with national procurement contexts is limited. This paper aims to explain and conceptualise recent Romanian experience with developing bespoke competency frameworks at national level for public procurement that reflect the features of the Romanian public procurement system. The approach used could guide in broad-brush, mutatis mutandis, other (national) public procurement systems with comparable features, mainly those seeking a shift from a rather administrative function of public procurement towards a strategic function.

This case study reflects on the methodology used for analysing the Romanian public procurement environment in EU context to develop bespoke professionalisation instruments, and on ways to integrate competency management approaches in Romanian public procurement culture. That methodological mix has been mainly qualitative and constructionist, within an applied research approach. It combined desk research with empirical research and included legal research in this context.

A principled, methodological and pragmatic approach tailored to the procurement environment in question is essential for developing competency frameworks capable to resonate to and address the specific practical needs of that procurement system.

Competency frameworks can uphold societal objectives through public procurement.

Using valuable insights into the development of the Romanian public procurement competency frameworks, the paper provides a conceptual framework for instilling competency management approaches to public procurement professional development where the latter is governed by a rather distinct, public administration, paradigm. This conceptual framework can guide other public procurement systems and stimulate further research.

1. To improve the causality analysis performance, a novel causality detector based on time-delayed convergent cross mapping (TD-CCM) is proposed in this work. 2. Identify the root cause of plant-wide oscillations in process control system.

A novel causality analysis framework is proposed based on denoising and periodicity-removing TD-CCM (time-delayed convergent cross mapping). We first point out that noise and periodicity have adverse effects on causality detection. Then, the empirical mode decomposition (EMD) and detrended fluctuation analysis (FDA) are combined to achieve denoising. The periodicities are effectively removed through singular spectrum analysis (SSA). Following, the TD-CCM can accurately capture the causalities and locate the root cause by analyzing the filtered signals.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. Simulation studies show that the proposed method is able to improve the causality analysis performance. 3. Industrial case study shows the proposed method can be used to analyze the root cause of plant-wide oscillations in process control system.

1. A novel causality detector based on denoising and periodicity-removing time-delayed convergent cross mapping (TD-CCM) is proposed. 2. The influences of noise and periodicity on causality analysis are investigated. 3. Simulations and industrial case shows that the proposed method can improve the causality analysis performance and can be used to identify the root cause of plant-wide oscillations in process control system.