A group decision-making model for wastewater treatment plans selection based on intuitionistic fuzzy sets

As the need for environmental protection and resource sustainability has increased in recent times, wastewater treatment has become increasingly important. In this paper, a group decision-making model for plans selection in wastewater treatment is proposed. In order to deal with uncertainties and multiple attributes in wastewater treatment, an intuitionistic fuzzy set is employed to evaluate wastewater treatment plans effectively. A distance measure is defined to obtain an objective weight measuring the expert’s judgment. More specifically, experts first use group decision-making on the various plans with an intuitionistic fuzzy set. Meanwhile, Due to the decision-makers psychological behavior, the prospect theory is applied. Next, the various plans are ranked by The Order of Preference by Similarity to Ideal Solution (TOPSIS) method and prospect theory. Finally, an illustrative example of wastewater treatment plans selection is used to verify the proposed model

Deep Learning Based Microatoll Detection From Drone Images

Visual object detection has made significant progress with the advent of deep neural networks and has been extensively applied. This thesis reports a novel application that aims to detect individual microatolls, which are circular coral colonies, from island images captured by drones. We first describe data collection and labelling to create a novel microatoll dataset for the microatoll detection task from drone images. Upon this dataset, the state-of-the-art object detectors are then evaluated for this task. To better integrate a detector with the characteristic of microatolls, we propose a modified detector called Microatoll-Net. It actively extracts features from the surrounding area of a microatoll to differentiate it from distractors to improve detection. Multiple ways to incorporate this information into the detector are designed. The experimental study shows the efficacy of the proposed Microatoll-Net, especially on the most challenging area for detection. Besides, in geographical research, the position of a microatoll is more important than its size. It means that we shall pay more attention to detecting the centre of a microatoll instead of generating its bounding box. Motivated by this, we transform this object detection task into an object centre detection task

Model and Data-Driven System Portfolio Selection Based on Value and Risk

System portfolio selection is a kind of tradeoff analysis and decision-making on multiple systems as a whole to fulfill the overall performance on the perspective of System of Systems (SoS). To avoid the subjectivity of traditional expert experience-dependent models, a model and data-driven approach is proposed to make an advance on the system portfolio selection. Two criteria of value and risk are used to indicate the quality of system portfolios. A capability gap model is employed to determine the value of system portfolios, with the weight information determined by correlation analysis. Then, the risk is represented by the remaining useful life (RUL), which is predicted by analyzing time series of system operational data. Next, based on the value and risk, an optimization model is proposed. Finally, a case with 100 candidate systems is studied under the scenario of anti-missile. By utilizing the Non-dominated Sorting Differential Evolution (NSDE) algorithm, a Pareto set with 200 individuals is obtained. Some characters of the Pareto set are analyzed by discussing the frequency of being selected and the association rules. Through the conclusion of the whole procedures, it can be proved that the proposed model and data-driven approach is feasible and effective for system portfolio selection

A Preference Model for Supplier Selection Based on Hesitant Fuzzy Sets

The supplier selection problem is a widespread concern in the modern commercial economy. Ranking suppliers involves many factors and poses significant difficulties for decision makers. Supplier selection is a multi-criteria and multi-objective problem, which leads to decision makers forming their own preferences. In addition, there are both quantifiable and non-quantifiable attributes related to their preferences. To solve this problem, this paper presents a preference model based on hesitant fuzzy sets (HFS) to select suppliers. The cost and service quality of suppliers are the main considerations in the proposed model. HFS with interactive and multi-criteria decision making are used to evaluate the non-quantifiable attributes of service quality, which include competitive display, qualification ability, suitability and competitiveness of solutions, and relational fitness and dynamics. Finally, a numerical example of supplier selection for a high-end equipment manufacturer is provided to illustrate the applicability of the proposed model. The preferences of a decision maker are then analyzed by altering preference parameters

Deep Sequence Labelling Model for Information Extraction in Micro Learning Service

Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services

Sustainable Queuing-Network Design for Airport Security Based on the Monte Carlo Method

The design of airport queuing networks is a significant research field currently for researchers. Many factors must to be considered in order to achieve the optimized strategies, including the passenger flow volume, boarding time, and boarding order of passengers. Optimizing these factors lead to the sustainable development of the queuing network, which currently faces a few difficulties. In particular, the high variance in checkpoint lines can be extremely costly to passengers as they arrive unduly early or possibly miss their scheduled flights. In this article, the Monte Carlo method is used to design the queuing network so as to achieve sustainable development. Thereafter, a network diagram is used to determine the critical working point, and design a structurally and functionally sustainable network. Finally, a case study for a sustainable queuing-network design in the airport is conducted to verify the efficiency of the proposed model. Specifically, three sustainable queuing-network design solutions are proposed, all of which not only maintain the same standards of security, but also increase checkpoint throughput and reduce passenger waiting time variance

Sustainable Production Line Evaluation Based on Evidential Reasoning

Many production line imbalances have been observed in the pursuit of higher profits. A sustainable production line, also called balanced, leads to lower costs, good production environments, and green manufacturing. A decision analysis method, such as production line evaluation, is often employed to help decision makers make sustainable decisions. In this study, a sustainable decision-making model is proposed for the evaluation of engine manufacturing. To solve uncertainties in manufacturing industries while maintaining lower costs and an efficient production environment, evidential reasoning is used in order to evaluate the sustainable production line effectively. First, uncertainties in the engine production line and deficiencies in the existing methods for evaluating the sustainable production line are analyzed. Then, evidential reasoning evaluation of the sustainable engine production line model is proposed and an example is presented; to be specific, the analysis of three production line plans is conducted using evidential reasoning, and plan P3 is found to be the best. Finally, a FlexSim simulation is used to prove the feasibility of evidential reasoning evaluation, verifying its suitability for achieving sustainable production line evaluation

Potential Antioxidative Activity of Homocysteine in Erythrocytes under Oxidative Stress

Homocysteine is an amino acid containing a free sulfhydryl group, making it probably contribute to the antioxidative capacity in the body. We recently found that plasma total homocysteine (total-Hcy) concentration increased with time when whole blood samples were kept at room temperature. The present study was to elucidate how increased plasma total-Hcy is produced and explore the potential physiological role of homocysteine. Erythrocytes and leukocytes were separated and incubated in vitro; the amount of total-Hcy released by these two kinds of cells was then determined by HPLC-MS. The effects of homocysteine and methionine on reactive oxygen species (ROS) production, osmotic fragility, and methemoglobin formation in erythrocytes under oxidative stress were studied. The reducing activities of homocysteine and methionine were tested by ferryl hemoglobin (Hb) decay assay. As a result, it was discovered that erythrocytes metabolized methionine to homocysteine, which was then oxidized within the cells and released to the plasma. Homocysteine and its precursor methionine could significantly decrease Rosup-induced ROS production in erythrocytes and inhibit Rosup-induced erythrocyte’s osmotic fragility increase and methemoglobin formation. Homocysteine (but not methionine) was demonstrated to enhance ferryl Hb reduction. In conclusion, erythrocytes metabolize methionine to homocysteine, which contributes to the antioxidative capability under oxidative stress and might be a supplementary protective factor for erythrocytes against ROS damage