Having comparison test research of economy and emission characteristics of biodiesel under different fuel supply advance angle on L16-type single cylinder diesel engine. The results show that the diesel engine has good effective thermal efficiency and economy with the fuel supply advance angle at 17°CA-BTDC; CO emissions basically increases with the decrease of fuel supply advance angle; NOx emissions increases with the fuel supply advance angle; THC emissions show not obvious and complex variation trend with the change of fuel supply advance angle, but the magnitude of the overall change is not obvious; with the decrease of fuel supply advance angle, Soot emissions increase first and then decreases.
Emerging events, such as the COVID pandemic and the Ukraine Crisis, require a time-sensitive comprehensive understanding of the situation to allow for appropriate decision-making and effective action response. Automated generation of situation reports can significantly reduce the time, effort, and cost for domain experts when preparing their official human-curated reports. However, AI research toward this goal has been very limited, and no successful trials have yet been conducted to automate such report generation. We propose SmartBook, a novel task formulation targeting situation report generation, which consumes large volumes of news data to produce a structured situation report with multiple hypotheses (claims) summarized and grounded with rich links to factual evidence. We realize SmartBook for the Ukraine-Russia crisis by automatically generating intelligence analysis reports to assist expert analysts. The machine-generated reports are structured in the form of timelines, with each timeline organized by major events (or chapters), corresponding strategic questions (or sections) and their grounded summaries (or section content). Our proposed framework automatically detects real-time event-related strategic questions, which are more directed than manually-crafted analyst questions, which tend to be too complex, hard to parse, vague and high-level. Results from thorough qualitative evaluations show that roughly 82% of the questions in Smartbook have strategic importance, with at least 93% of the sections in the report being tactically useful. Further, experiments show that expert analysts tend to add more information into the SmartBook reports, with only 2.3% of the existing tokens being deleted, meaning SmartBook can serve as a useful foundation for analysts to build upon when creating intelligence reports.
In an increasingly competitive and performance-oriented society, workaholic leadership is becoming increasingly common and is even embraced and supported by many organizations. However, previous studies have not paid sufficient attention to the impact of workaholic leadership on employee psychology and behavior. This study, based on the conservation of resources (COR) theory, explores the effect of workaholic leadership on employee self-presentation. Through an empirical analysis of 256 employees' questionnaires, we found a significant positive impact between workaholic leaders and employee self-presentation. This process was achieved through the partly mediating mechanisms of employee workplace anxiety. Concurrently, segmentation supplies negatively moderated the relationship between workplace anxiety and self-presentation and the overall mediating mechanism. These findings provide important insights into the underlying mechanisms of workaholic leadership and employee behavior, which can be utilized to improve employee wellbeing and provide positive organizational outcomes.
Abstract Currently, the feature richness of text encoding vectors in the bug report classification model based on deep learning is limited by the size of the domain dataset and the quality of the text. However, it is difficult to further enrich the features of text encoding vectors. At the same time, most existing bug report classification methods ignore the submitter's personal information. To solve these problems, we construct nine personal information characteristics of bug report submitters in GitHub by survey. Then, we propose a GitHub bug report classification method named personal information fine‐tuning network (PIFTNet) based on transfer learning and the submitter's personal information. PIFTNet transfers the general text feature vectors in bidirectional encoder representation from transformers (BERT) to the domain of bug report classification by fine‐tuning the pre‐training parameters in BERT. It also combines the text characteristics and the characteristics of the submitter's personal information to construct the classification model. In addition, we propose a two‐stage training method to alleviate the catastrophic changes in the pre‐training parameters and loss of the initially learned knowledge caused by direct training of PIFTNet. We verify the proposed PIFTNet on the dataset extracted from GitHub and empirical results prove the effectiveness of PIFTNet.
We introduce a new task, MultiMedia Event Extraction (M2E2), which aims to extract events and their arguments from multimedia documents. We develop the first benchmark and collect a dataset of 245 multimedia news articles with extensively annotated events and arguments. We propose a novel method, Weakly Aligned Structured Embedding (WASE), that encodes structured representations of semantic information from textual and visual data into a common embedding space. The structures are aligned across modalities by employing a weakly supervised training strategy, which enables exploiting available resources without explicit cross-media annotation. Compared to uni-modal state-of-the-art methods, our approach achieves 4.0% and 9.8% absolute F-score gains on text event argument role labeling and visual event extraction. Compared to state-of-the-art multimedia unstructured representations, we achieve 8.3% and 5.0% absolute F-score gains on multimedia event extraction and argument role labeling, respectively. By utilizing images, we extract 21.4% more event mentions than traditional text-only methods.
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