Data-Driven Analysis for Safe Ship Operation in Ports Using Quantile Regression Based on Generalized Additive Models and Deep Neural Network

Marine accidents in ports can cause loss of human life and property and have negative material and environmental impacts. In South Korea, due to a pier collision accident of a large container ship in Busan New Port of South Korea, the need for safe ship operation guidelines in ports emerged. Therefore, to support quantitative safe ship operation guidelines, ship trajectory data based on automatic information system information have been used. However, because this trajectory information is variable and uncertain due to various situations arising during a ship’s navigation, there is a limit to deriving results through traditional regression analysis. Considering the characteristics of these data, we analyzed ship trajectories through quantile regression using two models based on generalized additive models and neural networks corresponding to deep learning. Among the automatic information system information, the speed over ground, course over ground, and ship’s position were analyzed, and the model was evaluated based on quantile loss. Based on this study, it is possible to suggest safe operation guidelines for the position, speed, and course of the ship. In addition, the results of this work can be further developed as a manual for the in-port-autonomous operation of ships in the future.

Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline–alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85 % is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021).

EFL Students’ Strategy in Reading English Text in Perspective Automatic Information Processing (AIP)

Currently, students are asked to be able to understand literacy in reading. However, to understand literacy, of course, it has to go through the right strategy especially to beginner reader students. The purpose of this study is to determine the strategy of students in reading English text through perspective from automatic information processing (AIP) in reading. The method for this study used interviews and observations of one participant conducted in the second-level high school. This study also uses descriptive case study as research design. This study shows 5 things that should be reviewed in reading English text, namely attention, decoding, comprehension, switching in reading and automaticity. However, the five things found in this study are still rarely noticed. Therefore, this study also aims to allow educators and students to know what steps to do as a strategy to be able to read and understand the text of reading in English especially for beginner reader.

Text Mining for Building Biomedical Networks Using Cancer as a Case Study

In the assembly of biological networks it is important to provide reliable interactions in an effort to have the most possible accurate representation of real-life systems. Commonly, the data used to build a network comes from diverse high-throughput essays, however most of the interaction data is available through scientific literature. This has become a challenge with the notable increase in scientific literature being published, as it is hard for human curators to track all recent discoveries without using efficient tools to help them identify these interactions in an automatic way. This can be surpassed by using text mining approaches which are capable of extracting knowledge from scientific documents. One of the most important tasks in text mining for biological network building is relation extraction, which identifies relations between the entities of interest. Many interaction databases already use text mining systems, and the development of these tools will lead to more reliable networks, as well as the possibility to personalize the networks by selecting the desired relations. This review will focus on different approaches of automatic information extraction from biomedical text that can be used to enhance existing networks or create new ones, such as deep learning state-of-the-art approaches, focusing on cancer disease as a case-study.

Remediation Strategies for Performance Anxiety across Sex, Sport and Stage: Identifying Common Approaches and a Unified Cognitive Model

Strategies for addressing anxiety-related decrements in performance have been implemented across a variety of domains, including Sex, Sport, and Stage. In this review, we (1) iterate the dominant anxiety-related remediation strategies within each of these domains; (2) identify over-lapping and domain-specific strategies; and (3) attempt to unify the conceptualization of performance-related anxiety across these three areas under the information-processing framework of the Reflective/deliberative—Impulsive/automatic Model (RIM). Despite both diversity and similarity in remediation approaches across domains, we found that many strategies appear to share the common goal of maintaining a dominant automatic style of information processing in high performance demand situations. We then describe how various remediation strategies might hypothetically fit within the RIM framework and its subcomponents, identifying each intervention as falling into one or more broad categories related to achieving and/or maintaining dominance in automatic information processing. We conclude by affirming the benefit of adopting a unifying information-processing framework for the conceptualization of performance-related anxiety, as a way of both guiding future cross- and inter- disciplinary research and elucidating effective remediation models that share common pathways/mechanisms to improved performance.