This paper provides and illustrates a design-based learning (DBL) approach for fostering individual sustainability competency in engineering education. We performed two studies with engineering students in typical educational activities. The first study helped students perform a topic-specific design task in the practicum unit of a sensor technology course, which compared the performance of the DBL approach and conventional passive learning approach. The second study guided students to develop innovative projects for participating in the "Internet Plus" Innovation and Entrepreneurship Competition (IPIEC). To validate the proposed approach, stakeholder questionnaires and performance evaluations were implemented. The results show that the DBL approach was viable for sustainability competency teaching in terms of learning demand and teaching procedure. We found that students in the DBL group gave more prominence in the individual competencies, such as system-thinking, multidisciplinary applications, and collaboration. These findings suggest that applying the DBL approach to train sustainability competency in engineering education is beneficial for promoting students’ abilities in dealing with challenges involved in sustainability practice.

High-throughput technology has generated large-scale protein interaction data, which is crucial in our understanding of biological organisms. Many complex identification algorithms have been developed to determine protein complexes. However, these methods are only suitable for dense protein interaction networks, because their capabilities decrease rapidly when applied to sparse protein–protein interaction (PPI) networks. In this study, based on penalized matrix decomposition (PMD), a novel method of penalized matrix decomposition for the identification of protein complexes (i.e., PMD_pc) was developed to detect protein complexes in the human protein interaction network. This method mainly consists of three steps. First, the adjacent matrix of the protein interaction network is normalized. Second, the normalized matrix is decomposed into three factor matrices. The PMD_pc method can detect protein complexes in sparse PPI networks by imposing appropriate constraints on factor matrices. Finally, the results of our method are compared with those of other methods in human PPI network. Experimental results show that our method can not only outperform classical algorithms, such as CFinder, ClusterONE, RRW, HC-PIN, and PCE-FR, but can also achieve an ideal overall performance in terms of a composite score consisting of F-measure, accuracy (ACC), and the maximum matching ratio (MMR).

The conventional engineering robustness optimization approach considering uncertainties is generally based on a probabilistic model. However, a probabilistic model faces obstacles when handling problems with epistemic uncertainty. This paper presents an evidence-theory-based robustness optimization (EBRO) model and a corresponding algorithm, which provide a potential computational tool for engineering problems with multi-source uncertainty. An EBRO model with the twin objectives of performance and robustness is formulated by introducing the performance threshold. After providing multiple target belief measures (Bel), the original model is transformed into a series of sub-problems, which are solved by the proposed iterative strategy driving the robustness analysis and the deterministic optimization alternately. The proposed method is applied to three problems of micro-electromechanical systems (MEMS), including a micro-force sensor, an image sensor, and a capacitive accelerometer. In the applications, finite element simulation models and surrogate models are both given. Numerical results show that the proposed method has good engineering practicality due to comprehensive performance in terms of efficiency, accuracy, and convergence.

In this letter, we propose a method for measuring the pulsewidths at different spatial positions of ultrashort laser pulses. By measuring the pulsewidths varying with spatial positions of chirped and chirped-free femtosecond laser pulses, it is found that pulsewidths at edge positions are longer than that of central positions due to the effect of residual spatial chirp. Then, we measure the temporal evolutions of pulse at the strongest spatial modulation position after small-scale self-focusing and our results show that the pulsewidths become narrower with increasing spatial contrast due to spatiotemporal coupling effect. We find that the method is reliable and feasible.