Purpose: Case-based learning (CBL) is now used as a teaching strategy to promote clinical problem-solving ability. The purpose of this study was to determine whether CBL is superior to the traditional teaching method in teaching lung cancer curriculum to oncology students. Methods: This study was a randomized controlled trial, enrolled 80 first-year oncology postgraduates from Bengbu medical college in the past 3 years. They were randomized to divide into 2 groups, had courses with the same lung cancer contents and timing. The experimental group (n ¼ 40) utilized the CBL method while the control group (n ¼ 40) used the traditional lecture-based teaching method. A questionnaire was used to attain the students' learning satisfaction and self-efficacy of the course, and a post-study examination was used to assess end-of-course performance. Results: Complete data were obtained from participating students (n ¼ 40 in CBL; n ¼ 40 in traditional teaching). The CBL group performed significantly better in questionnaire and examination compared to traditional teaching groups. Students showed high levels of satisfaction and problem-solving ability in the CBL group. Conclusion: Compared with the traditional teaching method. The case teaching method is a more effective teaching method to improve the ability of problem-solving for graduate students in medical oncology.

PurposeUsing the logistics service supply chain (LSSC) as a research object, this study focuses on the relationship between integration quality (IQ), value co-creation (VCC), and LSSC resilience. Moreover, it discusses the moderating role of digital technology (DT).Design/methodology/approachBased on data about China, this study used the structural equation model to test the research hypothesis. To verify the validity of each construct, this study used various established scales in the literature to conduct exploratory and confirmatory analysis.FindingsThe results show that IQ is an essential antecedent variable that promotes VCC and LSSC resilience. Moreover, this study confirms that DT has a positive moderating effect on the relationship between IQ, VCC, and resilience.Originality/valueThis study constructs a research framework to examine LSSC resilience and expands the theoretical research on the VCC theory in the supply chain literature. Moreover, this paper studies supply chain integration from a new perspective, that is, IQ, which is more in line with the reality of LSSC.

A novel method to predict accurately the formation of fibrous webs in the melt-blowing process is developed. When an image analysis technique is combined with the Fluent software, the fiber spatial position and consequent landing position on the moving collection screen are predicted. The results include the prediction of fiber deposition patterns in the resulting fibrous web. In addition, to verify the prediction quantitatively, the basis weight distribution and the variation coefficient of the basis weight for both the predicted and experimental fibrous webs are investigated. Comparison of the prediction with the experimental data suggests that the method captures the primary trends rather accurately, not only for the single-orifice experiment but also for commercial multiple-orifice production.

Clinical named entity recognition is an essential task for humans to analyze large-scale electronic medical records efficiently. Traditional rule-based solutions need considerable human effort to build rules and dictionaries; machine learning-based solutions need laborious feature engineering. For the moment, deep learning solutions like Long Short-term Memory with Conditional Random Field (LSTM–CRF) achieved considerable performance in many datasets. In this paper, we developed a multitask attention-based bidirectional LSTM–CRF (Att-biLSTM–CRF) model with pretrained Embeddings from Language Models (ELMo) in order to achieve better performance. In the multitask system, an additional task named entity discovery was designed to enhance the model’s perception of unknown entities. Experiments were conducted on the 2010 Informatics for Integrating Biology & the Bedside/Veterans Affairs (I2B2/VA) dataset. Experimental results show that our model outperforms the state-of-the-art solution both on the single model and ensemble model. Our work proposes an approach to improve the recall in the clinical named entity recognition task based on the multitask mechanism.

Large-scale data annotation is indispensable for many applications, such as machine learning and data integration. However, existing annotation solutions either incur expensive cost for large datasets or produce noisy results. This paper introduces a cost-effective annotation approach, and focuses on the labeling rule generation problem that aims to generate high-quality rules to largely reduce the labeling cost while preserving quality. To address the problem, we first generate candidate rules, and then devise a game-based crowdsourcing approach CrowdGame to select high-quality rules by considering coverage and precision. CrowdGame employs two groups of crowd workers: one group answers rule validation tasks (whether a rule is valid) to play a role of rule generator, while the other group answers tuple checking tasks (whether the annotated label of a data tuple is correct) to play a role of rule refuter. We let the two groups play a two-player game: rule generator identifies high-quality rules with large coverage and precision, while rule refuter tries to refute its opponent rule generator by checking some tuples that provide enough evidence to reject rules covering the tuples. This paper studies the challenges in CrowdGame. The first is to balance the trade-off between coverage and precision. We define the loss of a rule by considering the two factors. The second is rule precision estimation. We utilize Bayesian estimation to combine both rule validation and tuple checking tasks. The third is to select crowdsourcing tasks to fulfill the game-based framework for minimizing the loss.We introduce a minimax strategy and develop efficient task selection algorithms. We conduct experiments on entity matching and relation extraction, and the results show that our method outperforms state-of-the-art solutions.

Basis weight uniformity of melt-blown fibrous webs is attracting considerable interest because it directly affects the application performance of nonwovens. There are numerous studies which introduce factors of processing conditions on the basis weight uniformity based on their final applications. However, theoretical research is still scarce. This paper describes the numerical modeling (bead–viscoelastic element fibrous model) involving fibrous web structure generation and basis weight uniformity evaluation. The effects of four processing conditions, including velocity of air jet and suction, die-to-collector distance, and moving speed of collector, on the basis weight uniformity of the fibrous web were quantitatively analyzed. Additionally, computational fluid dynamics simulation was employed to study the air flow (including the suction) in the melt-blowing process. The simulated results were in good agreement with the experimental data. The numerical model was practical and could better be used to research the problems on fibrous web formation and structures.

of main observation and conclusion Magnetic hydrogels have found extensive applications in fields such as soft robotics, drug delivery and shape morphing. Here a facile method was fabricated to prepare polysaccharide-based magnetic hydrogels. The chitosan-Fe3O4 ferrofluid was obtained by dispersing carboxyl groups modified Fe3O4 nanoparticles uniformly in chitosan matrix. Subsequently, the magnetic polysaccharide hydrogel was obtained by simply mixing cellulose acetoacetate solution with chitosan-Fe3O4 ferrofluid. The structures and properties of the magnetic hydrogel were analyzed using Fourier-transform infrared spectroscopy, rheological recovery, responsiveness, and stability measurements. The results indicated that magnetic polysaccharide hydrogel showed pH responsiveness and excellent self-healing properties, and the hydrogel manifested outstanding stability under physiological conditions (37 °C) for 72 h. In addition, the injectable polysaccharide-based hydrogel exhibited sensitive magnetic responsive and shape-shifting ability under an external magnetic field. Therefore, the strategy for the facile preparation of the magnetic polysaccharide-based hydrogel in this work could provide a benign and versatile method for achieving self-healing, responsive, injectable properties for the application in biomedical fields.

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