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The study develops a two-zone Smoke Filling model to predict how the smoke will descend in a small space, whose area ≤ 200m 2 and a height ≤ 8m. It intends to improve the unrealistic fast smoke descending rate in a small fire compartment by using the method based on “the technical manual of building fire-evacuation performance evaluation.” The model is developed for compartments equipped with mechanical and/or natural de-smoke apparatus. It has been verified by comparing it with the BRI2002, experiments and other references. These comparison results clearly demonstrate the credibility and reliability of the model. In addition, this study further includes the scenarios with the air supply vents in such model to make it more complete. Then, the model is tested by comparing with FDS and the technical manual. It was found that the predicted results by two-zone model are between the ones simulated by FDS and the technical manual, indicating that a new tool is successfully developed for fire safety assessment in a small fire compartment in Taiwan.

In Taiwan, the rapid development of the petrochemical industry over the past four decades has resulted in economic progress and technological advancement. However, this success has been accompanied by pollution and sporadic accidents. Numerous accidents related to crude oil and its derivatives have resulted in a loss of life, employee injuries, environmental and property damage, economic decay, social outcry, and even political turmoil. This study investigated a caprolactam storage tank accident through root cause analysis with a computational approach. The previously described approaches returned findings that indicated causes similar to the real cause of this caprolactam accident. The four summarized descriptions of false conditions of the caprolactam explosion can serve as a reference for other investigations into the causes of petrochemical accidents. Reason of the caprolactam storage tank accident was determined by root cause analysis accompanying with a computational approach.

The genus Croton belongs to the Euphorbiaceae family, which comprises approximately 1300 species. Many Croton species have been used as folk medicines. This review focuses on the chemical constituents from Croton species and their relevant biological activities, covering the period from 2006 to 2018. A total of 399 new compounds, including 339 diterpenoids, were reported. Diterpenoids are characteristic components of the Croton species. These isolated compounds exhibited a broad spectrum of bioactivities, including cytotoxic, anti-inflammatory, antifungal, acetylcholinesterase inhibitory, and neurite outgrowth-promoting properties. The present review provides a significant clue for further research of the chemical constituents from the Croton species as potential medicines.

Expressing knowledge as expert experience and discovering knowledge implied in data are two important ways for knowledge acquisition. Consistent combination of these two kinds of knowledge has attracted much attention due to the potential applications to knowledge fusion and wide requirements of decision support. In this paper, we focus on the probabilistic modeling of expert experience represented as logical predicate formulas, aiming at the effective fusion of logical and probabilistic knowledge. Taking qualitative probabilistic network (QPN) as the underlying framework of probabilistic knowledge implied in data as well as the abstraction of general Bayesian networks (BNs), we are to construct the probabilistic graphical model for both the given predicate formulas and the ultimate result of knowledge fusion. We first propose the concept and the construction algorithm of predicate graph (PG) to describe the dependence relations among predicate formulas, and discuss PG’s probabilistic semantics correspondingly. We then prove that PG is a probability dependency model and has the same semantics with a general probabilistic graphical model. Consequently, we give the method for fusing PG and QPN. Experimental results show the effectiveness of our methods.

Modeling and analysis of time-series data attract much attention in data mining and knowledge discovery community due to their many applications in financial analysis, automation control, etc. In such applications, time-series data usually contain several attributes that may be causally dependent in historical time slices. Dangerous feedback loops of attributes' dependent relationships can make the system collapse due to amplification or oscillation of attribute values. Motivated by efficient analysis of causalities in time-series data, we propose a temporal qualitative probabilistic graphical model in this paper. From given time-series sample data, we construct the structure of the temporal qualitative probabilistic network (TQPN) and derive the corresponding qualitative influences on directed edges. We then present the approach for TQPN reasoning with time-series features. Consequently, positive time-series feedback loops are defined, and the approach to identify them is proposed. Preliminary experiments show that our proposed method is not only feasible but also efficient.

Objective: To discuss the effect of edaravone on serum pulmonary surfactant protein A (SP-A) and arterial blood gas (ABG) in patients with thoracoscopic lobectomy. Methods: 40 lung cancer patients with right side of lobectomy were randomly divided into control group (group C, 20 cases) and edaravone group (group E, 20 cases). Group E was treated edaravone (1 mg/kg) between induction and skin incision, dropping within 30 min; group C was treated with equivalent normal saline. The venous and arterial blood were collected in both groups immediately before incision (T0), after 1 h of one-lung ventilation 1 h (T1) and in 1 h after lungs ventilation (T2) for ABG analysis and measurement of serum SP-A level. Results After OLV, serum SP-A levels were significantly increased in both groups (P < 0.05); compared with group C, serum levels of SP-A were reduced (P < 0.05) and ABG was significantly improved in group E. Conclusion: Edaravone can reduce serum SP-A levels in patients with lobectomy and alleviate acute lung injury to a certain extent in surgery.

This paper is to compute a Nash equilibrium in a fuzzy environment, which is represented by a fuzzy approximate Nash equilibrium in a space of discrete mixed strategies. For discrete mixed strategies, the relationship between the discrete degree and the approximate degree is discussed. Based on the fuzzy regret degree, a genetic algorithm for computing a fuzzy Nash equilibrium is given.

A temporal nodes Bayesian network (TNBN) is an alternative graphical representation of temporal probabilistic information.The temporal functional dependency is an important data dependencyin temporal relational databases. In this paper temporal data dependencies are extended into delayed conditions, called asynchronous dependencies. The purpose of this paper is to set up a connection between these asynchronous data dependencies and TNBNs. We show that we can obtain the maximum conditionalin dependencies implied by given temporal functional dependencies. Furthermore, we propose an approach to construct a TNBN from a set of temporal functional dependencies.

Finding similar sequences in time series has received much attention and is a widely studied topic. Most existing approaches in the time series area focus on the efficiency of algorithms but seldom provide a means to handle imprecise data. In this paper, a more general approach is proposed to measure the distance of time sequences containing crisp values, intervals, and fuzzy intervals as well. The concept of distance measurement and its associated dynamic-programming-based algorithms are described. In addition to finding the sequences with similar evolving trends, a means of finding the sequences with opposite evolving tendencies is also proposed, which is usually omitted in current related research but could be of great interest to many users.

This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery.

Various temporal functional dependencies have already been proposed by different researches in the field. The basic idea of these dependencies is to extend the functional dependency by including a time state. It is only the reflection of static relationship among attributes. This paper presents a new dynamic dependency. We give the concept of the stability constraint and established complete axioms for stability constraints. Generalization is an important abstract semantics. In this paper, the relationship between the class and the subclass is regarded as a scheme constraint. We present the concept of the inheritance dependency and show that the inference rules for inheritance dependencies are correct and complete. In this paper, we set up a series of design methods for temporal databases based on the generalization semantics and the stability constraint. The theory of the inheritance dependency and the stability constraint provide the guarantee for the semantic unanimity of the class hierarchy.

This paper presents a new dynamic dependency and a new normal form. We give the concept of the stability constraint and establish complete axioms for stability constraints. In addition, we propose a stability normal form. A temporal third normal form can be losslessly decomposed into a sequence of stability constraints. Using the decomposition, the storage space can be reduced. The stability constraints allow to fill a suitable value into a null value. Thus, this null value therefore turns into a known value.

In this paper, based on the concept of the semantic proximity, a partition method of fuzzy inheritance hierarchies has been given. It is shown that this method is reasonable and effective. Particularly, for a problem with fuzzy concepts, such as systematic botany and zoology, this partition method has especial advantages.

This paper presents a new method for learning Bayesian networks from functional dependencies (FD) and third normal form (3NF) tables in relational databases. The method sets up a linkage between the theory of relational databases and probabilistic reasoning models, which is interesting and useful especially when data are incomplete and inaccurate. The effectiveness and practicability of the proposed method is demonstrated by its implementation in a mobile commerce system.