1,720,992 research outputs found
Green technology upgrading choice in a competitive setting: the effect of environmental tax
No full textThis research considers a supply chain consisting of a regulator and two symmetric firms where the regulator influences the market by imposing a tax on firms’ environmental pollutant emissions. A price competition model is proposed to examine the equilibrium solutions that the two firms can reach in their technology upgrading process, and the effect of the environmental tax is evaluated. The two firms’ Nash equilibrium solutions regarding their technology improvement decision show that there is no asymmetric equilibrium. The decision to upgrade or not upgrade may arise in equilibrium, depending on the technology’s fixed cost. Besides, a prisoner’s dilemma may arise when the two firms do not upgrade their technology, and multiple equilibria may arise when the fixed cost incurred is at a medium level. Technology improvement decision is the dominant strategy when either prisoner’s dilemma arises or multiple equilibria arise for the two firms regardless of whether the environmental tax is exogenous given or not. In addition, firms’ reactions to environmental tax may be non-monotone. However, the role of the tax on firms’ improvement decisions is limited when the regulator further increases the tax. Finally, the optimal tax level for the regulator that can maximise welfare is obtained
Marine fuel refining technology improvement trade-offs: a game theoretic approach
No full textThe implementation of International Maritime Organization (IMO) 2020 sulfur cap requires ship operators to decrease fuels’ sulfur content and this may increase their demand for low-sulfur fuel (LSF). In anticipation, bunker companies can choose to upgrade their refining technology to produce better quality distillate and lighter oil. In this study, we consider a bunker supply chain consisting of bunker companies and a population of ship operators with two main marine fuel products, low-sulfur fuel (LSF) and high-sulfur fuel (HSF). We use Cournot game to model the competition between LSF and HSF under two different market channels (i.e., dual and single channels). The results show that bunker companies’ refining technology upgrading choice is affected by many operational parameters, such as the basic market demand, cost difference between LSF and HSF, market competition, variable and fixed cost for upgrading, the increase of market demand due to upgrading, and so on. Compared with a dual channel bunker company, a single channel bunker company is less likely to implement new refining technology. We further consider the scenario where bunker companies can make decisions after the realization of ship operators’ demand uncertainty. The findings are beneficial for both LSF and HSF bunker companies and may reach a win-win solution for bunker companies
Newbuilding ship price forecasting by parsimonious intelligent model search engine
No full textAsset prices play a significant role in the financial survival and profitability of ship-owning firms. In a highly volatile shipping market, prices of newbuilding ships must be predicted to detect security shortfalls as well as opportunities for temporal arbitration (gaining on high–low pricing). Accordingly, this paper proposes an improved version of the intelligent model search engine (IMSE) by asynchronous time lag selection. The parsimonious IMSE algorithm comprises the essential components such as input and training data size selection by a grid search procedure. In the initial IMSE algorithm, time-lag (memory size) selection is designed such that a serial cluster of memory groups is assigned synchronously for all inputs. By relaxing of lag structures selection, the proposed algorithm estimates unique lead–lag relations for the input of the intended problem set. An extensive benchmark study with several baseline models and the persistence forecast (Naïve I) is performed to observe the out-of-sample accuracy of the proposed approach. The empirical results indicate that second-hand ship prices, scrap values, and orderbook (no. of orders) have predictive features and are selected by the search engine for two ship sizes. Different lag structures are estimated for each input with asynchronous time-lag selection improvement.</p
Echo state neural network-based ensemble deep learning for short-term load forecasting
No full textPrecise electricity load forecasts assist in planning, maintaining, and developing power systems. However, the electricity load's un-stationary and non-linear characteristics impose substantial challenges in anticipating future demand. Recently, a deep echo state network (DESN) with multi-scale features has been proposed for sequential tasks. Inspired by its structure, this paper offers a novel ensemble deep learning algorithm, the ensemble deep ESN (edESN), for load forecasting. First, hierarchical reservoirs are stacked to enforce the deep representation similar to the DESN. Then, instead of computing the readout weights based on the global states, the edESN trains a different readout layer for each scale. Finally, the network combines the outputs from each scale as the final prediction. The edESN is evaluated on twenty publicly available load datasets. This paper compares the edESN with eleven forecasting methods, and the comparative results demonstrate the proposed model's superiority in load forecasting
Bi-directional information fusion-driven deep network for ship trajectory prediction in intelligent transportation systems
No full textAccurate ship trajectory prediction (STP) is crucial to realise the early warning of ship collision and ensure maritime safety. Driven by advancements in artificial intelligence technology, deep learning-based STP has become a predominant approach in the research field of ship collision avoidance. This paper, based on a state-of-the-art survey of the existing STP research progress, aims to develop a new bi-directional information fusion-driven prediction model that enables the achievement of more accurate STP results by addressing the drawbacks of the classical methods in the field. In this context, a cascading network model is developed by combining two bi-directional networks in a specific order. It incorporates the Bi-directional Long Short-Term Memory (BiLSTM) and the Bi-directional Gated Recurrent Unit (BiGRU) neural network into a single three-layer, information-enhanced network. It takes advantage of both networks to realise more accurate prediction of ship trajectories. Furthermore, the performance of the proposed model is comprehensively evaluated using Automatic Identification System (AIS) data from three water areas representing traffic scenarios of different safety concerns. The superiority of the proposed model is verified through comparative analysis with twenty other methods, including the state-of-the-art STP in the literature. The finding reveals that the new model is better than all the benchmarked ones, and thus, the new STP solution in this paper makes new contributions to improving autonomous navigation and maritime safety
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Get PDFElectricity load forecasting is crucial for the power systems' planning and
maintenance. However, its un-stationary and non-linear characteristics impose
significant difficulties in anticipating future demand. This paper proposes a
novel ensemble deep Random Vector Functional Link (edRVFL) network for
electricity load forecasting. The weights of hidden layers are randomly
initialized and kept fixed during the training process. The hidden layers are
stacked to enforce deep representation learning. Then, the model generates the
forecasts by ensembling the outputs of each layer. Moreover, we also propose to
augment the random enhancement features by empirical wavelet transformation
(EWT). The raw load data is decomposed by EWT in a walk-forward fashion, not
introducing future data leakage problems in the decomposition process. Finally,
all the sub-series generated by the EWT, including raw data, are fed into the
edRVFL for forecasting purposes. The proposed model is evaluated on twenty
publicly available time series from the Australian Energy Market Operator of
the year 2020. The simulation results demonstrate the proposed model's superior
performance over eleven forecasting methods in three error metrics and
statistical tests on electricity load forecasting tasks
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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