Rocky Reef Fishery Level 2 Ecological Risk Assessment

A Scoping Study and Level 1 Ecological Risk Assessment (ERA) for the Rocky Reef Fishery was released in July 2019 (Jacobsen et al. 2019). The Level 1 assessment identified ecological components at higher risk from line fishing activities, and these were progressed to a Level 2 assessment. Level 2 ERAs are focused at the species level with risk evaluations based on a Productivity & Susceptibility Analysis (PSA). The PSA evaluates risk for each species through an assessment of seven biological attributes and up to seven fisheries-specific attributes. This Level 2 ERA examined the risk posed to ten target & byproduct species and one species of shark. Of the target & byproduct species, seven were found to be at high risk and three at medium risk. The shark species (grey nurse shark) was found to be at high risk from line fishing activities. Risk profiles were influenced by data deficiencies, an underdeveloped management regime, and cumulative fishing pressures. For six of the 11 species, final risk ratings are more representative of the potential risk. Management of precautionary risks beyond what is already being undertaken as part of the Queensland Sustainable Fisheries Strategy 2017–2027 is not considered an immediate priority. The Level 2 ERA made a list of recommendations to assist in the management and mitigation of risk in the Rocky Reef Fishery. A number of these measures are already being discussed as part of the Queensland Sustainable Fisheries Strategy 2017–2027, including the development of a harvest strategy. As these reforms are still in development and yet to be fully implemented, the Level 2 ERA only considers management arrangements that are in place at the time of assessment

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises

Algorithms for probabilistic uncertain linguistic multiple attribute group decision making based on the GRA and CRITIC method: application to location planning of electric vehicle charging stations

Electric vehicles (EVs) could be regarded as one of the most innovative and high technologies all over the world to cope with the fossil fuel energy resource crisis and environmental pollution issues. As the initiatory task of EV charging station (EVCS) construction, site selection play an important part throughout the whole life cycle, which is deemed to be multiple attribute group decision making (MAGDM) problem involving many experts and many conflicting attributes. In this paper, a grey relational analysis (GRA) method is investigated to tackle the probabilistic uncertain linguistic MAGDM in which the attribute weights are completely unknown information. Firstly, the definition of the expected value is then employed to objectively derive the attribute weights based on the CRiteria Importance Through Intercriteria Correlation (CRITIC) method. Then, the optimal alternative is chosen by calculating largest relative relational degree from the probabilistic uncertain linguistic positive ideal solution (PULPIS) which considers both the largest grey relational coefficient from the PULPIS and the smallest grey relational coefficient from the probabilistic uncertain linguistic negative ideal solution (PULNIS). Finally, a numerical case for site selection of electric vehicle charging stations (EVCS) is designed to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate

Gulf of Carpentaria Inshore Fishery Level 2 Ecological Risk Assessment [Target & Byproduct Species]

The Queensland Ecological Risk Assessment Guideline (the Guideline) was released in March 2018 as part of the Queensland Sustainable Fisheries Strategy 2017–2027. This Guideline provides an overview of strategy being employed to develop Ecological Risk Assessments (ERAs) for Queensland’s fisheries. The Guideline describes a four-stage framework consisting of a Scoping Study; a Level 1, whole of fishery qualitative assessment; a Level 2, species-specific semi-quantitative or low-data quantitative assessment and; a Level 3 quantitative assessment (if applicable). A Scoping Study and Level 1 ERA for the Gulf of Carpentaria Inshore Fishery was released in December 2019 (Jacobsen et al., 2019). The Level 1 assessment identified ecological components at higher risk from net fishing activities, and these were progressed to a Level 2 assessment. Level 2 ERAs are focused at the species level with risk evaluations based on a Productivity & Susceptibility Analysis (PSA). The PSA evaluates risk for each species through an assessment of seven biological attributes and up to seven fisheries-specific attributes. Based on the outputs of the Level 1 ERA and following a species prioritisation process, the Gulf of Carpentaria Inshore Fishery Level 2 ERA assessed risk for 15 target & byproduct species: eight teleosts and seven sharks. All target & byproduct species were found to be at medium to high risk from net fishing activities. The risk profiles for sharks were heavily influenced by the biological attributes (productivity); particularly those relating to their longevity and reproductive outputs. The Level 2 ERA made a list of recommendations to assist in the management and mitigation of risk in the Gulf of Carpentaria Inshore Fishery. A number of these measures are already being discussed and considered as part of the Queensland Sustainable Fisheries Strategy 2017–2027 and will be progressed through the Gulf of Carpentaria Inshore Fishery Working Group

Data science for buildings, a multi-scale approach bridging occupants to smart-city energy planning

In a context of global carbon emission reduction goals, buildings have been identified to detain valuable energy-saving abilities. With the exponential increase of smart, connected building automation systems, massive amounts of data are now accessible for analysis. These coupled with powerful data science methods and machine learning algorithms present a unique opportunity to identify untapped energy-saving potentials from field information, and effectively turn buildings into active assets of the built energy infrastructure.However, the diversity of building occupants, infrastructures, and the disparities in collected information has produced disjointed scales of analytics that make it tedious for approaches to scale and generalize over the building stock.This coupled with the lack of standards in the sector has hindered the broader adoption of data science practices in the field, and engendered the following questioning:How can data science facilitate the scaling of approaches and bridge disconnected spatiotemporal scales of the built environment to deliver enhanced energy-saving strategies?This thesis focuses on addressing this interrogation by investigating data-driven, scalable, interpretable, and multi-scale approaches across varying types of analytical classes. The work particularly explores descriptive, predictive, and prescriptive analytics to connect occupants, buildings, and urban energy planning together for improved energy performances.First, a novel multi-dimensional data-mining framework is developed, producing distinct dimensional outlines supporting systematic methodological approaches and refined knowledge discovery. Second, an automated building heat dynamics identification method is put forward, supporting large-scale thermal performance examination of buildings in a non-intrusive manner. The method produced 64\% of good quality model fits, against 14\% close, and 22\% poor ones out of 225 Dutch residential buildings. %, which were open-sourced in the interest of developing benchmarks. Third, a pioneering hierarchical forecasting method was designed, bridging individual and aggregated building load predictions in a coherent, data-efficient fashion. The approach was evaluated over hierarchies of 37, 140, and 383 nodal elements and showcased improved accuracy and coherency performances against disjointed prediction systems.Finally, building occupants and urban energy planning strategies are investigated under the prism of uncertainty. In a neighborhood of 41 Dutch residential buildings, occupants were determined to significantly impact optimal energy community designs in the context of weather and economic uncertainties.Overall, the thesis demonstrated the added value of multi-scale approaches in all analytical classes while fostering best data-science practices in the sector from benchmarks and open-source implementations

A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17

We present the COMBO-17 object catalogue of the Chandra Deep Field South for public use, covering a field which is 31.5' x 30' in size. This catalogue lists astrometry, photometry in 17 passbands from 350 to 930 nm, and ground-based morphological data for 63,501 objects. The catalogue also contains multi-colour classification into the categories 'Star', 'Galaxy' and 'Quasar' as well as photometric redshifts. We include restframe luminosities in Johnson, SDSS and Bessell passbands and estimated errors. The redshifts are most reliable at R<24, where the sample contains approximately 100 quasars, 1000 stars and 10000 galaxies. We use nearly 1000 spectroscopically identified objects in conjunction with detailed simulations to characterize the performance of COMBO-17. We show that the selection of quasars, more generally type-1 AGN, is nearly complete and minimally contaminated at z=[0.5,5] for luminosities above M_B=-21.7. Their photometric redshifts are accurate to roughly 5000 km/sec. Galaxy redshifts are accurate to 1% in dz/(1+z) at R<21. They degrade in quality for progressively fainter galaxies, reaching accuracies of 2% for galaxies with R~222 and of 10% for galaxies with R>24. The selection of stars is complete to R~23, and deeper for M stars. We also present an updated discussion of our classification technique with maps of survey completeness, and discuss possible failures of the statistical classification in the faint regime at R>24.Comment: submitted to Astronomy & Astrophysics, public data set available at http://www.mpia.de/COMBO/combo_index.htm

A review of application of multi-criteria decision making methods in construction

Construction is an area of study wherein making decisions adequately can mean the difference between success and failure. Moreover, most of the activities belonging to this sector involve taking into account a large number of conflicting aspects, which hinders their management as a whole. Multi-criteria decision making analysis arose to model complex problems like these. This paper reviews the application of 22 different methods belonging to this discipline in various areas of the construction industry clustered in 11 categories. The most significant methods are briefly discussed, pointing out their principal strengths and limitations. Furthermore, the data gathered while performing the paper are statistically analysed to identify different trends concerning the use of these techniques. The review shows their usefulness in characterizing very different decision making environments, highlighting the reliability acquired by the most pragmatic and widespread methods and the emergent tendency to use some of them in combination