Evolution of green shipping research: themes and methods

Over the past 30 years, there have been growing concerns on theenvironmental impacts of maritime transportation, which have attractedgreat attention from both academia and practitioners. Understandingdevelopments in this area can help guide future research. We conducteda comprehensive review of green shipping research, comprising 213papers published in transportation journals in SSCI of 2017 over theperiod 1988–2017. We find that research on green shipping hasincreased greatly since 2012, accounting for 77.5% of the reviewedpapers. The main focus today on green shipping was on air pollution,and the classification of green shipping practice, such as technical measures,operational options, market-based measures, and recycling andreusing, is becoming clear. According to the existing studies, futureresearch on green shipping must strengthen technology research tonot only solve practical problems, but also to establish a theoreticalgreen shipping system. Moreover, researchers from different countriescould cooperate with each other to give effective suggestions on settingstandards and laws of green shipping. Finally, we identify the futureresearch themes will focus on setting up green shipping system andlegislation and policy

Assessment on the research trend of low-carbon energy technology investment: A bibliometric analysis

Based on databases of Science Citation Index Expanded (1981-present) and Social Sciences Citation Index (2002-present), this paper applies the bibliometric method to analyze the scientific publications of low-carbon energy technology investment. By characterizing the basic information of the publications, we found: the historical development process is clearly divided into two stages; the field of low-carbon energy technology investment has entered a stage of rapid development; the strength of developed countries is far greater than that of developing countries; the comprehensive strength of the United States ranks the first in the field, followed by UK and Denmark and only China and Turkey are developing countries among the top 15 countries; the auctorial collaboration degree in this field shows a clear upward trend, but institutional and national collaboration degrees are steady and relatively low. In addition, distributions of geography, journals and subjects, productive authors and institutions, frequently cited articles, etc. are obtained: articles in this area are mainly distributed in the USA, several countries in Europe and China; the most productive journal, author and institution are Energy Policy, Lund H from Denmark and National Technical University of Athens in Greece; Energy Fuel is the most popular subject among all the outcomes; the most frequently cited article is written by Demirbas published in Energy Policy in 2007. According to the frequency analysis of keywords, it reveals that: “renewable energy” is a kind of keyword used most frequently; “carbon capture and storage technology” is an emerging keyword which is increasingly concerned about; scholars pay widespread attention to electricity issues, especially the feed-in tariff; the policy mainly includes energy policy and climate policy; the real option theory is the most widely used theory; the existing uncertainty is summarized as the cost uncertainty and policy uncertainty. In the end, several suggestions for the future research are given

Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Heat Waves -- a hot topic in climate change research

Research on heat waves (periods of excessively hot weather, which may be accompanied by high humidity) is a newly emerging research topic within the field of climate change research with high relevance for the whole of society. In this study, we analyzed the rapidly growing scientific literature dealing with heat waves. No summarizing overview has been published on this literature hitherto. We developed a suitable search query to retrieve the relevant literature covered by the Web of Science (WoS) as complete as possible and to exclude irrelevant literature (n = 6,569 papers). The time-evolution of the publications shows that research dealing with heat waves is a highly dynamic research topic, doubling within about 5 years. An analysis of the thematic content reveals the most severe heat wave events within the recent decades (1995, 2003, 2010), the cities and countries/regions affected (Australia, United States, and Europe), and the ecological and medical impacts (drought, urban heat islands, excess hospital admissions, and mortality). Risk estimation and future strategies for adaptation to hot weather are major political issues. We identified 83 citation classics which include fundamental early works of research on heat waves and more recent works (which are characterized by a relatively strong connection to climate change).Comment: 38 pages, 2 tables, and 9 figure

Reinforcement Learning Exploration Algorithms for Energy Harvesting Communications Systems

Prolonging the lifetime, and maximizing the throughput are important factors in designing an efficient communications system, especially for energy harvesting-based systems. In this work, the problem of maximizing the throughput of point-to-point energy harvesting communications system, while prolonging its lifetime is investigated. This work considers more real communications system, where this system does not have a priori knowledge about the environment. This system consists of a transmitter and receiver. The transmitter is equipped with an infinite buffer to store data, and energy harvesting capability to harvest renewable energy and store it in a finite battery. The problem of finding an efficient power allocation policy is formulated as a reinforcement learning problem. Two different exploration algorithms are used, which are the convergence-based and the epsilon-greedy algorithms. The first algorithm uses the action-value function convergence error and the exploration time threshold to balance between exploration and exploitation. On the other hand, the second algorithm tries to achieve balancing through the exploration probability (i.e. epsilon). Simulation results show that the convergence-based algorithm outperforms the epsilon-greedy algorithm. Then, the effects of the parameters of each algorithm are investigated

Hybrid system to analyze user's behaviour

The evolution of ambient intelligence systems has allowed for the development of adaptable systems. These systems trace user's habits in an automatic way and act accordingly, resulting in a context aware system. The goal is to make these systems adaptable to the user's environment, without the need for their direct interaction. This paper proposes a system that can learn from users' behavior. In order for the system to perform effectively, an adaptable multi agent system is proposed and the results are compared with the use of several classifiers

Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets