Influenced by factors such as gendered masculine culture within the engineering fields, female engineering students are facing increasing mental health issues. However, the effect of gender or engineering identity on the mental health distress of female engineering students was not well explored till now. This study adds to the current body of knowledge of mental health distress in female engineering students by proposing and verifying a moderating model based on social identity theory (SIT). The data were collected in June 2022 using a cross-sectional survey questionnaire distributed at five universities in eastern China (N = 376). A stepwise multiple regression analysis was performed to understand the relation between the tension of interpersonal relationships, the mental health distress female engineering students suffer from, and their gender or engineering identity. In our sample, 13.03%, 15.96%, and 14.36% of the female engineering students self-reported moderate to extremely severe stress, anxiety, and depression, respectively. Meanwhile, our results provide empirical evidence for the significantly positive relationship between the female engineering students’ tension of interpersonal relationships and their mental health distress, including stress, anxiety, and depression. Moreover, we found that gender identity can enhance the positive relationships mentioned above, while engineering identity could weaken these relationships. These findings provide empirical evidence for the role of social identity theory in dealing with mental health problems among engineering students. Broadly, the results of this work inform that social identity and professional role identity should be considered when designing interventions to prevent mental health crises among college students.
Autonomous ships are gaining in importance and are expected to shape the future of the global shipping industry. This evolutionary shift raises serious issues about compliance with the International Regulations for Preventing Collisions at Sea 1972 (COLREGs). This paper reviews the literature on autonomous ships from the perspective of the obligations of good seamanship imposed by COLREGs. The authors conclude that to facilitate the introduction of autonomous ships, the application barriers presented by COLREGs need to be analysed. With this goal, this paper presents a perspective from navigational practice. Four nautical scientists and two deck officers were invited to give their opinions. The analysis indicates that COLREGs require further elaboration and amendments to eliminate uncertainty of interpretation. In particular, the paper highlights the need to amend the ‘look-out’ rule (COLREGs Rule 5) to permit look-out by ‘computer vision’ alone while, at the same time, preserving the distinction between vessels navigating in restricted visibility and in sight of one another.
In recent years, Internet of things (IoT) devices are playing an important role in business, education, medical as well as in other fields. Devices connected to the Internet is much more than the number of world population. However, it may face all kinds of attacks from the Internet easily for its accessibility. As we all know, most attacks against IoT devices are based on Web applications. So protecting the security of Web services can effectively improve the situation of IoT ecosystem. Conventional Web attack detection methods highly rely on samples, and artificial intelligence detection results are uninterpretable. Hence, this article introduced a supervised detection algorithm based on benign samples. Seq2Seq algorithm is been chosen and applied to detect malicious web requests. Meanwhile, the attention mechanism is introduced to label the attack payload and highlight labeling abnormal characters. The results of experiments show that on the premise of training a benign sample, the precision of proposed model is 97.02%, and the recall is 97.60%. It explains that the model can detect Web attack requests effectively. Simultaneously, the model can label attack payload visually and make the model “interpretable.”
In recent years, the navigational capacity of Northwest Passage in Arctic has been improved due to climate change. The Chukchi Sea, located on the north side of the Bering Strait, is a key area of the Northwest Passage and one of the most representative areas of sea ice variability. The sea ice extent was calculated using sea ice concentration data, and then the temporal and spatial variation characteristics of sea ice extent and thickness in Chukchi Sea were analyzed. The results show that the sea ice has significant seasonal and interannual variabilities. The ice began melt in May, reaching the minimum extent in September, and then began to freeze again. Meanwhile, the sea ice extent decreases year by year. By analyzing the monthly average thickness of the sea ice, it is found that the sea ice grows from October to April of the next year, and the growth rate during the period is gradually decreasing. The study provides valuable information for safe Arctic navigation in the Northwest Passage.
Abstract Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this article, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Availability and implementation The source code will be made available at https://github.com/zxy951005/KB-CR upon publication. Data is avaliable at http://2011.bionlp-st.org/ and https://github.com/UCDenver-ccp/CRAFT/releases/tag/v3.1.3. Supplementary information Supplementary data are available at Bioinformatics online.
Unsafe crew acts (UCAs) related to human errors are the main contributors to maritime accidents. The prediction of unsafe crew acts will provide an early warning for maritime accidents, which is significant to shipping companies. However, there exist gaps between the prediction models developed by researchers and those adopted by practitioners in human risk analysis (HRA) of the maritime industry. In addition, most research regarding human factors of maritime safety has concentrated on hazard identification or accident analysis, but not on early warning of UCAs. This paper proposes a Bayesian network (BN) version of the Standardized Plant Analysis Risk–Human Reliability Analysis (SPAR-H) method to predict the probability of seafarers’ unsafe acts. After the identification of performance-shaping factors (PSFs) that influence seafarers’ unsafe acts during navigation, the developed prediction model, which integrates the practicability of SPAR-H and the forward and backward inference functions of BN, is adopted to evaluate the probabilistic risk of unsafe acts and PSFs. The model can also be used when the available information is insufficient. Case studies demonstrate the practicability of the model in quantitatively predicting unsafe crew acts. The method allows evaluating whether a seafarer is capable of fulfilling their responsibility and providing an early warning for decision-makers, thereby avoiding human errors and sequentially preventing maritime accidents. The method can also be considered as a starting point for applying the efforts of HRA researchers to the real world for practitioners.
Abstract The development of Maritime Autonomous Surface Ship (MASS) is progressing rapidly within the maritime industry. Degree Two of MASS (MASS-DoA2), balancing human oversight and autonomous efficiency, will likely gain regulatory approval and industry acceptance. MASS-DoA2 possesses different control modes to adapt to various scenarios. However, the control-switching mechanisms among operators at shore control centres, autonomous navigation systems and number of seafarers onboard remain ambiguous, which poses a new risk that may significantly influence navigation safety. This study focuses on MASS-DoA2 and carries out a systematic review of autonomous ship guidelines. A questionnaire was designed based on the review findings, and a survey was carried out among captains and researchers in related fields. The review identified 11 control-switching scenarios with suggested takeover agents and the switching process and outlined the priority relationship between various takeover agents. Finally, a control-switching framework for MASS – DoA2 is proposed. It can serve as a theoretical framework for research on MASS's dynamic degree of autonomy and provide a reference for maritime regulatory authorities in establishing MASS – DoA2 control-switching mechanisms.
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