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This paper gives a review of the finite element techniques (FE) applied in the area of material processing. The latest trends in metal forming, non‐metal forming, powder metallurgy and composite material processing are briefly discussed. The range of applications of finite elements on these subjects is extremely wide and cannot be presented in a single paper; therefore the aim of the paper is to give FE researchers/users only an encyclopaedic view of the different possibilities that exist today in the various fields mentioned above. An appendix included at the end of the paper presents a bibliography on finite element applications in material processing for 1994‐1996, where 1,370 references are listed. This bibliography is an updating of the paper written by Brannberg and Mackerle which has been published in Engineering Computations, Vol. 11 No. 5, 1994, pp. 413‐55.

The purpose of this paper is to evaluate, analyse and select the best suppliers for Satrec Vitalait Milk Company, operating in Senegal, based on criteria obtained from economic, environmental and social dimensions of sustainable supply chain management, through the application of Deng’s grey relational analysis (GRA) model, absolute GRA model (ADGRA) and a novel second synthetic GRA (SSGRA) model, combined with one decision making under the uncertainty-based model, namely, the Hurwicz criteria.

The research adopts a new synthetic GRA model and highlights its reliability on small sample gathered from four senior experts of the company who administered a total number of 28 specialists operating in four departments of the company, through the employment of a self-administered questionnaire designed based on criteria identified from the literature that were refined via a Q-sort model.

The outcomes of the research methodology designated that all the selected five suppliers present a degree of attaining sustainability due to the fact that supplying unprocessed milk does not require the use of polluting methods for stocking and transportation. The undertaken study specifies that all the socio-environmental criteria play a crucial role in shaping the sustainability level of Satrec Vitalait’s suppliers and demonstrates the accuracy of the results obtained through the second synthetic degree of grey relation analysis for ranking the suppliers. Supplier 2 was found to be the best supplier for the company and, as result, a model for other suppliers to mimic.

Future researchers can replicate the GRA-based supply chain model proposed in the current study in different environments especially in the context of green supply chain. Also, in future the SSGRA model, while using the bidirectional ADGRA instead of the conventional ADGRA, should also be tested, especially when the data sequences associated with different supply chain parameters have inconsistent directions. Also, comparative analysis of SSGRA-based results with that of modern statistical methods like structural equation modelling can also be used for future explorations. Furthermore, the current study is built upon the data associated with the Satrec Vitalait Milk Company (Senegal); therefore, the findings should be generalised with caution.

The study can be seen as a first-stepping stone for gauging and selecting the best sustainable supplier for Satrec Vitalait using grey system theory. For purpose of attaining the research goal, the SSGRA was exploited as an innovative experimental approach to estimate relationships between criteria with regard to the sustainability level of the company’s suppliers. Under this scope, relationships between criteria themselves and their goal were depicted by Deng’s degree of GRA and AGRA, respectively. The research is innovative by means of the framework of its methodology and data analysis.

The collaborative filtering algorithm is a classical and widely used approach in product recommendation systems. However, the existing algorithms rely mostly on common ratings of items and do not consider temporal information about items or user interests. To solve this problem, this study proposes a new user-item composite filtering (UICF) recommendation framework by leveraging temporal semantics.

The UICF framework fully utilizes the time information of item ratings for measuring the similarity of items and takes into account the short-term and long-term interest decay for computing users’ latest interest degrees. For an item to be probably recommended to a user, the interest degrees of the user on all the historically rated items are weighted by their similarities with the item to be recommended and then added up to predict the recommendation degree.

Comprehensive experiments on the MovieLens and KuaiRec datasets for user movie recommendation were conducted to evaluate the performance of the proposed UICF framework. Experimental results show that the UICF outperformed three well-known recommendation algorithms Item-Based Collaborative Filtering (IBCF), User-Based Collaborative Filtering (UBCF) and User-Popularity Composite Filtering (UPCF) in the root mean square error (RMSE), mean absolute error (MAE) and F1 metrics, especially yielding an average decrease of 11.9% in MAE.

A UICF recommendation framework is proposed that combines a time-aware item similarity model and a time-wise user interest degree model. It overcomes the limitations of common rating items and utilizes temporal information in item ratings and user interests effectively, resulting in more accurate and personalized recommendations.

The purpose of this paper is to design a robust tracking algorithm which is suitable for the real-time requirement and solves the mistake labeling issue in the appearance model of trackers with the spare features.

This paper proposes a tracker to select the most discriminative randomly projected ferns and integrates a coarse-to-fine search strategy in this framework. First, the authors exploit multiple instance boosting learning to maximize the bag likelihood and select randomly projected fern from feature pool to degrade the effect of mistake labeling. Second, a coarse-to-fine search approach is first integrated into the framework of multiple instance learning (MIL) for less detections.

The quantitative and qualitative experiments demonstrate that the tracker has shown favorable performance in efficiency and effective among the competitors of tracking algorithms.

The proposed method selects the feature from the compressive domain by MIL AnyBoost and integrates the coarse-to-fine search strategy first to reduce the burden of detection. This paper designs a tracker with high speed and favorable results which is more suitable for real-time scene.

The knowledge of goals of the successor, who is preparing to take over the business, is extremely important for the succession process and further operation of a family enterprise. The aim of this study is to structure the goals of Polish family enterprises’ successors and to develop a Synthetic Model of the goals of Polish family enterprises' successors with the application of grey clustering evaluation models.

Research into the specifics of the diagnosis and assessment of the goals set for the successors of the first succession in family businesses in Poland was carried out in the third quarter of 2016 at two stages using two research methods: in-depth group interview and individual interview. The main aim of the first stage was the extraction of subjectively identified goals by family enterprises' successors (based on their succession experience). The statements were open and obtained during two in-depth group interviews (2 FGI) with successors being in the process of succession at its various stages (total, n = 14). The respondents presented their experiences connected with the succession process along with emotions that are associated with it. In one of the interview stages, the respondents were asked to enumerate their individual goals they set for themselves in the context of upcoming changes. Next, the group agreed on the most frequently mentioned goals by creating their verbal interpretation. The obtained list of 20 goals was recorded and discussed, and thanks to the application of the elimination rule in the collective decision-making process, that list was reduced to 10 goals, which was approved by all participating successors.

The results show the developed Synthetic Model of the goals of family enterprises’ successors. The study singled out four groups of successors: (1) successors who do not work in the family enterprise yet, (2) successors holding lower-level positions, (3) successors holding managerial positions, (4) successors who manage the entire company. As a result of the calculations, the developed Synthetic Model of the goals of family enterprises' successors was positively verified for successors working in higher-level positions and successors managing the entire family enterprise.

In order to use the results of clustering, e.g. for conducting studies on large samples with the use of statistical tools, a reduced number of goals should be taken into account. A thorough study of three goals may bring results similar to the study of the original ten successors of Polish family enterprises in the process of succession. The aim of future research is to develop a mathematical model using optimization functions that enable selection of elements representing individual clusters in such a way that it leads to the extraction of the elements with the highest value in relation to the accepted criterion for assessing their value.

In the future, conducting family business research in accordance with the developed methodology requires a look at the proposed list of successor goals obtained during the Focus Group Interview (FGI) as it could be shortened using the Cluster of Grey Incidence method. Shortening the list of goals has its analytic and practical justifications. The study of the full list of goals in subsequent (and numerous further studies) could lead to errors related to, for example, different interpretation of goals among the investigated successors. Furthermore, the full list of goals would increase costs and extend research time.

For more than a decade, there has been an increased focus on the need for accountability and transparency about the value that United States and international higher education institutions add to students' knowledge and skills to help increase their economic productivity and career opportunities. This focus on accountability and transparency within the U.S. dates to 2005 when former US Secretary of Education Margaret Spellings established a Commission on the Future of Higher Education to develop a national strategy for higher education reform. This led to an increased focus on measuring value added within higher education institutions and using value-added scores to make institutional comparisons. This chapter presents a brief history of value added within the United States and presents high-level summaries of initiatives, assessments used to measure value added, and a review of how value added is measured. We also present challenges around methodology and interpretation of results. Lastly, we discuss some of the future directions in evaluating value added in higher education and areas for future research.

Subway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant economic and safety losses to cities. Therefore, it is necessary to analyze the factors affecting the resilience of the subway system to reduce the impact of disaster incidents.

Using the interval type-2 fuzzy linguistic term set and the K-medoids clustering algorithm, this paper improves the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to construct a subway resilience factor analysis model for emergencies. Through comparative analysis, this study confirms the superior performance of the proposed approach in enhancing the precision of the DEMATEL method.

The results indicate that the operation and management level of emergency command organizations is the key resilience factors of subway operations in China. Furthermore, based on real case analyses, the corresponding suggestions and measures are put forward to improve the overall operation resilience level of the subway.

This paper identifies four emergency scenarios and 15 resilience factors affecting subway operations through literature review and expert consultation. The improved fuzzy DEMATEL method is applied to explore the levels of influence and causal mechanisms among the resilience factors of the subway system under the four emergency scenarios.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

A nonclassical method, usually called memory-free approach, has shown promising potential to release arithmetic complexity and meets high memory-storage requirements in solving fractional differential equations. Though many successful applications indicate the validity and effectiveness of memory-free methods, it has been much less understood in the rigorous theoretical basis. This study aims to focus on the theoretical basis of the memory-free Yuan–Agrawal (YA) method [Journal of Vibration and Acoustics 124 (2002), pp. 321-324].

Mathematically, the YA method is based on the validity of two fundamental procedures. The first is to reverse the integration order of an improper quadrature deduced from the Caputo-type fractional derivative. And, the second concerns the passage to the limit under the integral sign of the improper quadrature.

Though it suffices to verify the integration order reversibility, the uniform convergence of the improper integral is proved to be false. Alternatively, this paper proves that the integration order can still be reversed, as the target solution can be expanded as Taylor series on [0, ∞). Once the integration order is reversed, the paper presents a sufficient condition for the passage to the limit under the integral sign such that the target solution is continuous on [0, ∞). Both positive and counter examples are presented to illustrate and validate the theoretical analysis results.

This study presents some useful results for the real performance for the YA and some similar memory-free approaches. In addition, it opens a theoretical question on sufficient and necessary conditions, if any, for the validity of memory-free approaches.