ABSTRACT
Climate change has become mankind's main challenge. Greenhouse gas (GHG) emissions from shipping are not totally irresponsible for this representing, roughly, 3% of the global total;an amount equal to that of Germany's total GHG emissions. The Fourth Greenhouse Gas Study 2020 of the International Maritime Organization (IMO) predicts that the share of GHG emissions from shipping will increase further, as international trade recovers and continues to grow, alongside with the economic development of India, China, and Africa. China and the European Union have proposed to include shipping in their carbon emissions trading systems (ETS). As a result, the study of the relationship between the carbon finance market and the shipping industry, attempted here for the first time, is both important and timely, both for policymakers and shipowners. We use wavelet analysis and the spillover index methods to explore the dynamic dependence and information spillovers between the carbon finance market and shipping. We discover a long-term dependence and information linkages between the two markets, with the carbon finance market being the dominant one. Major events, such as the 2009 global financial crisis;Brexit in 2016;the 2018 China-US trade frictions;and COVID-19 are shown to strengthen the dependence of carbon finance and shipping. We find that the dependence is strongest between the EU carbon finance market and dry bulk shipping, while the link is weaker in the case of tanker shipping. Nonetheless, carbon finance and tanker shipping showed a relatively stronger dependence when OPEC refused to cut production in 2014, and when the China-US trade disputes led to the collapse of oil prices after 2018. We show that information spillovers between carbon finance and shipping are bidirectional and asymmetric, with the carbon finance market being the principal transmitter of information. Our results and their interpretation provide guidance to governments on whether (and how) to include shipping in emissions trading schemes, supporting at the same time the environmental sustainability decisions of shipping companies. © 2023 The Authors
ABSTRACT
It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process. © 2022 Elsevier Ltd
ABSTRACT
The outbreak of Covid-19 challenged the education system and caused more disconnections than ever between instructors, students, and content. Having instructors use personal information relevant to their students within a lesson would create a more personalized lesson that could resonate with the students and facilitate their participation in the classroom. However, teaching is already a complex and challenging job as teachers must multitask in delivering content and fulfilling students' needs. To encourage and support instructors to integrate the personal experiences of students during their lessons, we propose an approach based on a speech-recognition-based personal information retrieval pipeline. We designed and developed PRIS, a personalized, real-time teaching support system, as an exemplar of the approach. This paper presents a small-scale within-subjects study comparing the use of PRIS and typical notecards to assess the impact of the proposed approach on teaching. Results showed that the PRIS condition has better usability, imposes lower cognitive load on the teacher, and leads to more frequent personalized teaching behaviors compared to the notecard condition. We discuss the implications for the design of personalized teaching support systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we developed a system of five categories of video recommendations: 1) Watch new video;2) Review current topic video with a new tutor;3) Review segment of current video with current tutor;4) Review segment of current video with a new tutor;5) Watch next video in curriculum sequence. The category of recommendation was determined by student scores on a quiz and a sensor-free engagement detection model. New video recommendations (i.e., category 1) were selected based on a novel reinforcement learning algorithm that takes input from an item response theory model. The recommendation system was evaluated in a large field experiment, both before and after school closures due to the COVID-19 pandemic. The results show evidence of effectiveness of the video recommendation algorithm during the period of normal school operations, but the effect disappears after school closures. Implications for teacher orchestration of technology for normal classroom use and periods of school closure are discussed. © 2022 ACM.
ABSTRACT
OBJECTIVE: This study aimed to explore the atypical imaging findings of the novel coronavirus pneumonia (COVID-19) and its evolution. MATERIALS AND METHODS: The atypical imaging data of ten patients in our hospital who tested positive for COVID-19 were analyzed retrospectively, and the distribution, morphology, and image evolution of the lesions were analyzed. High-resolution computed tomography (HRCT) was performed in all cases, and the imaging features were analyzed and summarized by two senior radiologists. RESULTS: Of these ten patients, three were male, and seven were female. The age of these patients ranged from 21-53 years, with an average age of 36.3 ± 3.6. The first symptom was fever in nine cases and dry cough in one case. A total of 17 lesions were detected in these ten patients. Five patients had a single lesion, and five patients had multiple lesions, for a total of 12 lesions. Ten lesions (58.82%) were located in the inferior lobe of the right lung, four lesions (23.53%) in the left inferior lobe, two lesions (11.76%) in the left upper lobe, and one lesion (5.88%) in the right middle lobe. Among the five single lesions, two were solid lesions, two were mixed ground-glass lesions, and one was a pure ground-glass lesion. Among the 12 multiple lesions, eight were solid lesions, two were mixed ground-glass lesions, and two were pure ground-glass lesions. Atypical manifestations in image signs: five lesions (29.41%) had single solid and sub-solid nodules, and four lesions (23.53%) had cavitary nodules. Typical manifestation (the presence of "white lung"): three lesions (17.65%) had an air bronchogram, two lesions (11.76%) had crazy-paving signs, two lesions (11.76%) had vascular thickening, and one lesion (5.88%) had halo signs. At reexamination 2-6 days later, 15 lesions (88.24%) had enlarged or increased, and two lesions (11.76%) had decreased or absorbed. CONCLUSIONS: Patients with COVID-19 may have atypical imaging findings. Radiologists should improve their understanding of the novel coronavirus pneumonia to avoid any missed diagnoses.