Decision making for contractor insurance deductible using the evolutionary support vector machines inference model

Loss risk during the course of a construction project may be described in terms of frequency (i.e., loss frequency) and severity (i.e., loss severity). This study focused on improving the methodology used to evaluate loss risk. The authors first identified the common attributes of building construction project loss through a review of the literature and interviews with experts. Objective factors adequate to describe loss attributes were selected as model inputs. The loss prediction model was created using the evolutionary support vector machine inference model (ESIM) and deployed to evaluate loss frequency and loss severity. This research combined the deductible efficient frontier curve with the indifference curve of risk versus insurance cost, and developed criteria for optimal insurance deductible decision making.     

The Effect of Mixed-Solvent and Terpenes on Percutaneous Absorption of Meloxicam Gel

The purpose of the present study was to develop the meloxicam transdermal dosage form. The response surface methodology was used to obtain an appropriate mixed-solvent system of pH-7.4 buffer and ethanol for preparing meloxicam hydrogel. The enhancement effects of terpenes on drug precautious absorption were evaluated via in vitro and in vivo study. The result showed that the solubility of meloxicam was dependent on the pH value of buffer solution. The mixed-solvent system of pH-7.4 buffer and ethanol had a synergistic effect on the increase of drug solubility. The highest solubility was obtained in the ratio of 50/50 pH 7.4 buffer/ethanol. A series of terpenes were used as enhancer for improving the penetration rate of meloxicam. The penetration rates were significantly increased by about 70–593 fold and the lag times were shortened from 7.92 to 0.17 hr by enhancer incorporation. Among these terpenes, menthol showed the greatest effect. In vivo penetration study, the AUC_48h was increased by about 1.7 fold by the addition of 5% menthol as enhancer.     

Local estimation of failure probability function and its confidence interval with maximum entropy principle

An approach is developed to locally estimate the failure probability of a system under various design values. Although it seems to require numerous reliability analysis runs to locally estimate the failure probability function, which is a function of the design variables, the approach only requires a single reliability analysis run. The approach can be regarded as an extension of that proposed by Au [Au SK. Reliability-based design sensitivity by efficient simulation. Computers and Structures 2005;83(14):1048–61], but it proposes a better framework in estimating the failure probability function. The key idea is to implement the maximum entropy principle in estimating the failure probability function. The resulting local failure probability function estimate is more robust; moreover, it is possible to find the confidence interval of the failure probability function as well as estimate the gradient of the logarithm of that function with respect to the design variables. The use of the new approach is demonstrated with several simulated examples. The results show that the new approach can effectively locally estimate the failure probability function and the confidence interval with one single Subset Simulation run. Moreover, the new approach is applicable when the dimension of the uncertainties is high and when the system is highly nonlinear. The approach should be valuable for reliability-based optimization and reliability sensitivity analysis.     

Augmenting teacher-student interaction in digital learning through affective computing

Interactions between teachers and students can be effectively enhanced if teachers can capture the spontaneous nonverbal behaviors (e.g., facial expressions and body language) of their students in real time, thereby effectively improving teaching strategies and the learning effectiveness of students. In this study, we implemented an expression–response analysis system (ERAS) to analyze facial expressions. The ERAS employs a web camera to capture the facial images of students. Their facial expressions are analyzed to assess their attitude toward progressively more difficult course content, and to determine the relationship between their social interactions and learning effectiveness. The ERAS identified 10 facial feature points that form 11 facial action units (AUs). Subsequently, the AUs were classified as positive, neutral, and negative social interactions by applying a rule-based expert system, and cognitive load theory was applied to verify the classifications. The experimental results showed that student with high coding abilities could adapt to the multimedia digital learning content, as evidenced by the comparatively higher expression of neutral and positive social interactions, whereas students with low coding abilities reported a higher frequency of negative social interactions resulting from the increase in cognitive load. Simultaneously, the real time detection of social interactions can provide a basis for diagnosing student learning difficulties and assist teachers in adjusting their teaching strategies.     

Selecting the most helpful answers in online health question answering communities

The online question answering (QA) community has been popular in recent years. In this paper, we focus on the online health question answering (HQA) community. The HQA community provides a platform for health consumers to inquire about health information. There are two ways to use this platform. One is to post a question and wait for answers to be provided by authenticated doctors. The other is to search for relevant questions with answers. For the latter, health consumers may prefer an accepted answer marked by the previous health consumer. However, there is a large proportion of questions without an accepted answer and it is inconvenient for people who want to search for relevant questions. To address this issue, we aim to select high-quality answers from the answers without marked accepted answers. We propose a deep learning approach to achieve this goal. To train the model for the prediction of answer quality, we first view the accepted answer as the positive answer and propose a method to label the negative answer. Next, we capture the semantic information on the question and the answer by the deep learning structure. We then combine the information to predict the quality score of the answer. We collect data from one of the biggest Chinese HQA community and divide them into groups by the medical departments for detailed analysis. Finally, we conduct experiments to show the effectiveness of categorization and the labeling method. The results show that our approach outperforms other studies and we further research into the differences among the results of different categories.

     

Oxidation stability of biodiesel derived from free fatty acids associated with kinetics of antioxidants

Biodiesel derived from free fatty acids (FFAs), which has the advantage of not competing with the edible-oil market, exhibited poor oxidation stability. The induction period (IP) of the FFA-based biodiesel determined by the Rancimat method at 110 °C was 0.20 h. This study investigates the effectiveness of one natural and ten synthetic antioxidants, including α-tocopherol (α-T); butylated hydroxyanisole (BHA); butylated hydroxytoluene (BHT); 2, 5-di-tert-butylhydroquinone (DTBHQ); Ethanox 4740; Ethanox 4760E; 2,2′-methylene-bis-(4-methyl-6-tert-butylphenol) (MBMTBP); N,N′-di-sec-butyl-p-phenylenediamine (PDA); propyl gallate (PG); pyrogallol (PY); and tert-butylhydroquinone (TBHQ), at concentrations between 100 and 1000 ppm to improve the oxidation stability of the FFA-based biodiesel. The order of antioxidant effectiveness with respect to the oxidation stability of the FFA-based biodiesel was PY > Ethanox 4760E > PG > Ethanox 4740 > PDA ~ BHA > BHT > MBMTBP ~ TBHQ > DTBHQ > α-T. The IP of the FFA-based biodiesel increased as the antioxidant concentration was increased and decreased at high test temperatures. Furthermore, the relationship between the IP values associated with the consumption of antioxidants in the FFA-based biodiesel was described by first-order reaction rate kinetics. However, the natural logarithm of IP (ln IP) at various concentrations of Ethanox 4760E showed a linear relation with the test temperature. The IP at ambient temperature was predicted based on the extrapolation method of the temperature dependence relation. After long-term storage at room temperature, the IP and acid value of the original FFA-based biodiesel significantly decreased and increased, respectively, with storage time, while the addition of antioxidants ensured the oxidation stability of the FFA-based biodiesel over 6 months of storage.     

Effective database transformation and efficient support computation for mining sequential patterns

In this paper, we propose a novel algorithm for mining frequent sequences from transaction databases. The transactions of the same customers form a set of customer sequences. A sequence (an ordered list of itemsets) is frequent if the number of customer sequences containing it satisfies the user-specified threshold. The 1-sequence is a special type of sequences because it consists of only a single itemset instead of an ordered list, while the k-sequence is a sequence composed of k itemsets. Compared with the cost of mining frequent k-sequences (k ≥ 2), the cost of mining frequent 1-sequences is negligible. We adopt a two-phase architecture to find the two types of frequent sequences separately in order that the discovery of frequent k-sequences can be well designed and optimized. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one constituted by the symbols. We find that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smaller size. For every k ≥ 2, the customer sequences in the transformed database are scanned to find all the frequent k-sequences. We devise the compact representation for a customer sequence and elaborate the method to enumerate all distinct subsequences from a customer sequence without redundant scans. The soundness of the proposed approach is verified and a number of experiments are performed. The results show that our approach outperforms the previous works in both scalability and execution time.     

Electrical properties and hydrogen flux performance of Sr(Ce0.6Zr0.4)1−xYxO3−δ ceramic proton conductors

In this study, Sr(Ce_0.6Zr_0.4)_1?xY_xO_3?δ (x = 0.1, 0.15, 0.2, 0.25, 0.3, SCZY) perovskite proton-conducting ceramics were synthesized using the citrate-ethylenediaminetetraacetic acid complexing method. The effect of the Y concentration on the crystal structure, electrochemical performance and hydrogen flux of the SCZY samples in air and a H₂ atmosphere were investigated. The sintered SCZY samples showed a main perovskite crystal structure with several SrY₂O₄ impurity phases when x ≥ 0.20. The grain size of SCZY samples when x = 0.25 and 0.30 was smaller than that of other samples. The conductivity of all samples increased as the temperature increased in both air and H₂; however, the conductivity of SCZY in H₂ was lower than that in air. The results of AC impedance, demonstrated that resistance mainly derived from the charge transfer, which was apparently higher than the bulk and grain boundary resistance. Sr(Ce_0.6Zr_0.4)_0.85Y_0.15O_3-δ exhibited low charge transfer resistance in both air and the H₂ atmosphere and had a high hydrogen flux of 0.34 ml min^?1 cm^?2 μm^?1 at 800 °C. The structures of all SCZY samples were stable in 80 °C water.     

Fine-tuning the balance between carbazole and oxadiazole units in bipolar hosts to realize highly efficient green PhOLEDs

A series of new bipolar hosts C1O1, C2O1, and C1O2 comprising hole-transporting carbazole as donor (D) and electron-transporting oxadiazole as acceptor (A) have been designed and synthesized to tune D/A ratio with a view for getting in-depth information about structure–property–performance relationship. The D/A ratio exerts subtle influences on absorption spectra and energy levels. Time-of-flight (TOF) measurements reveal that the increment of donor units effectively increase the hole mobility. The appropriate energy levels and triplet energies along with promising morphological and thermal stability of these molecules bring out them as suitable hosts to realize green PhOLEDs with (PBi)₂Ir(acac) and (PPy)₂Ir(acac) emitters. Green phosphorescent devices hosted by C1O1, C2O1, and C1O2 achieved maximum external quantum efficiencies of 20.7%, 20.4%, and 17.3%, respectively. These results indicate that carbazole/oxadiazole hybrid molecule with one carbazole and oxadiazole each is a better candidate for highly efficient green PhOLEDs.