Conflict analysis aims to identify the intrinsic reasons and find a feasible consensus strategy for a conflict situation. Rough set theory was used to study conflict analysis decision-making in the ...late 90s. The basic way to express the attitudes of every agent are against, favorable and neutral for any issue in the original Pawlak conflict analysis model. The notion of three-way decision (3WD) was initially developed as a means to interpret decision rules induced in probabilistic rough sets. In this paper, we first present the framework of three-way decision (3WD) using probabilistic rough set over two universes. With respect to the probabilistic positive, negative and boundary regions over two universes, we build the rules for making a decision of acceptance, rejection and non-commitment, respectively. So, there is an one-to-one correspondence between the three attitudes of every agent for any issue in a conflict situation and the three decisions in the probabilistic rough set over two universes. Based on this, we present an improved Pawlak conflict analysis model by using the principle of three-way decision based on probabilistic rough set over two universes. We construct the conflict decision-making information system under the framework of two universes. Then we define the favorable issues set and against issues set of any agent between the agent set and the dispute set over conflict decision-making information system, respectively. Furthermore, according to the principle of Bayesian risk decision-making process over two universes, we calculate the threshold value parameters used in the lower and upper approximations of a feasible consensus strategy over conflict decision-making information system. Finally, we present the decision rules and the algorithm of finding a feasible consensus strategy for conflict situation based on three-way decision-making with the probabilistic approximations over two universes. Compared with the original Pawlak conflict analysis model, the proposed model not only provides a new perspective and methodology to handle the conflict analysis problems but also overcomes the limitations of the original model. Lastly, we illustrate the idea and basic principles established in this paper by analyzing a conflict decision-making scenario.
Decision-theoretic rough set provides a new perspective to handle decision-making problems under uncertainty and risk. The three-way decision theory proposed by Yao is based on rough set theory and ...is a natural extension of the classical two-way decision approach. In this paper, we introduce the idea of decision-theoretic rough set into multigranulation approximation space and explore the rough approximation of a fuzzy decision object under the framework of two universes. We construct a variable precision multigranulation fuzzy decision-theoretic rough set over two universes by using the concept of an arbitrary binary fuzzy relation class between two different universes and the probability measurement of a fuzzy event. Several interesting properties of the proposed model are addressed and the decision rules are also deduced using the concept of three-way decision-making over two universes. Moreover, two special types of optimistic and pessimistic models are given by using different precision parameters. We then present a new approach to multiple criteria group decision making problems, based on variable precision multigranulation fuzzy decision-theoretic rough set over two universes. Meanwhile, we establish a cost-based method for sorting among all alternatives of group decision-making problems. Finally, an example of handling a medical diagnosis problem illustrates this approach.
•A new multigranulation fuzzy decision-theoretic rough set over two universes was defined.•The relationship between the proposed model with the existing decision-theoretic rough set models was established.•The three-way decision was deduced based on the multigranulation fuzzy decision-theoretic rough set over two universes.•Multigranulation fuzzy decision-theoretic rough set-based three-way group decision making method was established over two universes.
•Support vector machine is employed to identify the flow patterns in packed beds.•Features of different flow patterns are extracted based on different pressure signals.•Feature extraction is ...conducted by the PDF, PSD and WES methods respectively.•Three SVM models are trained and their identification ability is compared.•Identification rate of typical flow patterns is up to 96.08%.
Rapid and accurate identification of two-phase flow patterns in porous media is of great significance to the chemical industry, petroleum and nuclear engineering, etc. Based on the different pressure signals of gas-liquid two-phase flow in a porous bed, the present work proposes an intelligent recognition method to identify the two-phase flow patterns in porous media by the technologies of feature extraction and support vector machine (SVM). The analysis techniques, including time domain (PDF), frequency domain (PSD) and time-frequency domain (Wavelet), are employed to extract and summarize the corresponding characteristics of differential pressure signals of flow patterns. The intelligent recognition models are developed to identify the two-phase flow patterns in porous media by SVM. The models are trained respectively based on the characteristics of time domain + frequency domain (TF-SVM model), time domain + wavelet (TW-SVM model) and frequency domain + wavelet (FW-SVM model). The overall identification accuracy of the optimal model (TW-SVM model) reaches 96.08%.
Green hydrogen, produced during microalgal photosynthesis, is regarded as one of the most promising sustainable energy sources. It utilizes sunlight and water, which are essentially unlimited, and ...its combustion results in only water as a waste product. In microalgal hydrogen energy production systems, the sensitivity of hydrogenase to O2 poses a significant challenge, limiting sustained photosynthetic H2 production in microalgae. Additionally, efficient photosynthetic H2 production in anaerobic microalgal cells is hindered by impaired electron source (photosystem II) and electron loss through the Calvin‐Benson cycle, cyclic electron transfer around photosystem I, and O2 photoreduction, which are identified as the other key challenges. Over the past eight decades, considerable progress has been made in addressing these challenges and regulating electron transfer to achieve sustainable and efficient photosynthetic H2 production in microalgae. In this review, we discuss a range of regulatory methods for achieving sustainable and efficient photosynthetic H2 production in microalgae. Emphasizing the significant progress made over the past eight decades, we also address current challenges and propose potential future solutions.
Microalgae use sunlight and water to produce green hydrogen with the help of hydrogenase (H2ase), offering a promising, eco‐friendly way for achieving global carbon neutrality. Despite the abundance of sunlight and water, challenges arise due to H2ase sensitivity to oxygen, issues with the electron source (PSII), and electron loss. This review highlights progress over the past eight decades, discusses current challenges, and suggests potential solutions for the future.
In this paper, we discuss the properties of the probabilistic rough set over two universes in detail. We present the parameter dependence or the continuous of the lower and upper approximations on ...parameters for probabilistic rough set over two universes. We also investigate some properties of the uncertainty measure, i.e., the rough degree and the precision, for probabilistic rough set over two universes. Meanwhile, we point out the limitation of the uncertainty measure for the traditional method and then define the general Shannon entropy of covering-based on universe. Then we discuss the uncertainty measure of the knowledge granularity and rough entropy for probabilistic rough set over two universes by the proposed concept. Finally, the validity of the methods and conclusions is tested by a numerical example.
Formaldehyde (HCHO), as one of the main indoor toxic pollutions, presents a great threat to human health. Hence, it is imperative to efficiently remove HCHO and create a good indoor living ...environment for people. Herein, a layered perovskite material SrBi2Ta2O9 (SBT), was studied for the first time and exhibited superior photocatalytic efficiency and stability compared to commercial TiO2 (P25). Furthermore, a unique dark–light tandem catalytic mechanism was constructed. In the dark reaction stage, HCHO (Lewis base) site was adsorbed on the terminal (Bi2O2)2+ layer (Lewis acid) site of SBT in the form of Lewis acid-base complexation and was gradually oxidized to CO32− intermediate (HCHO → DOM (dioxymethylene) → HCOO− → CO32−). Then, in the light reaction stage, CO32− was completely converted into CO2 and H2O (CO32− → CO2). Our study contributes to a thorough comprehension of the photocatalytic oxidation of HCHO and points out its potential for day–night continuous work applications in a natural environment.
NDH-1 is a key component of the cyclic-electron-transfer around photosystem I (PSI CET) pathway, an important antioxidant mechanism for efficient photosynthesis. Here, we report a 3.2-Å-resolution ...cryo-EM structure of the ferredoxin (Fd)-NDH-1L complex from the cyanobacterium Thermosynechococcus elongatus. The structure reveals three β-carotene and fifteen lipid molecules in the membrane arm of NDH-1L. Regulatory oxygenic photosynthesis-specific (OPS) subunits NdhV, NdhS and NdhO are close to the Fd-binding site whilst NdhL is adjacent to the plastoquinone (PQ) cavity, and they play different roles in PSI CET under high-light stress. NdhV assists in the binding of Fd to NDH-1L and accelerates PSI CET in response to short-term high-light exposure. In contrast, prolonged high-light irradiation switches on the expression and assembly of the NDH-1MS complex, which likely contains no NdhO to further accelerate PSI CET and reduce ROS production. We propose that this hierarchical mechanism is necessary for the survival of cyanobacteria in an aerobic environment.
Purpose
– The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology.
...Design/methodology/approach
– In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory.
Findings
– This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events.
Originality/value
– The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.
River sand bodies have complex and changeable characteristics and distribution. In order to improve the accuracy and efficiency of target recognition, this study proposes a target recognition method ...of ultra-deep river sand bodies with improved deep learning under unmanned aerial vehicle (UAV) cluster. By constructing the cooperative target allocation model of UAV group, it is ensured that the targets of ultra-deep and large-area river sand bodies are collected. The gradient histogram is used to extract the image characteristics of ultra-deep river sand body and enhance the target image of ultra-deep river sand body. Bi-directional long short-term memory (Bi-LSTM) network model is constructed by introducing bidirectional recurrent neural network (RNN) to improve deep learning. Bi-LSTM neural network is used to construct the target recognition model of ultra-deep river sand body and complete the target recognition. The experimental results show that this method can extract the target edge completely and recognize the image edge accurately, and the average recognition accuracy under different ambiguities is higher than 95. It is proved that this method has high accuracy in sand body feature extraction and classification and has great application potential in river sand body target recognition.
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