Sterols stabilize the ripple phase structure in dihexadecylphosphatidylcholine

The presence of various sterols in mixtures with dihexadecylphosphatidylcholine (DHPC) was studied using static X-ray diffraction of temperature equilibrated samples, and real-time X-ray diffraction of samples undergoing temperature scans. It was found that these sterols eliminate the interdigitation of the alkyl chains in the DHPC sub-gel and gel-state bilayers while stabilizing the ripple gel-state at the expense of the gel-state bilayer phase. The ripple-ripple phase transition previously observed for dipalmitoylphosphatidylcholine in the presence of low molar concentrations of sterols (Wolfe et al. (1992) Phys. Rev. Lett. 68, 1085-1088) was also observed for similar DHPC-sterol mixtures. In addition, we show the first evidence that the presence of 5 alpha-cholestane-3 beta,5,6 beta-triol will cause the lipid mixtures to continue to adopt a ripple mesophase structure even after the DHPC alkyl chain becomes disordered.     

提高电源冗余度的热插拔结构

为了提高冗余度,不少使用“或”运算二极管的电源都可接入同一个负载。在维护期间,当你拆去任何一个电源时,希望负载的电源骚动尽可能最小。为了补偿“或”运算二极管两端的电压降,你必须在“或”运算二极管之后,在负载处连接电源反馈线。因此,所有参与电源的反馈连接是通用的(图1)。因为每一个电源都会发生自然变化,所以只有V_(OUT)最大的电源才是有效的。其他检测“高电位”输出的电源都试图降低其输出,从而有效地中止稳压功能。如果从与图1类     

Investigation of surface roughness of aluminium solid mould investment castings

Solid investment moulds are used for precious or non-precious metals and alloys castings, the most common application areas are dental prosthesis and jewellery fabrications. Investment casting is known for high dimensional accuracy and smooth surface finish. Casting of ultra-thin sections and small complex parts require vacuum assisted or centrifugal filling. In this study, solid investment casting of aluminium has been tried and surface quality of the castings was investigated. Specially designed steppedwedge surface roughness specimens were produced with A413 aluminium alloy casting into gypsum bonded solid moulds. Casting operations were carried out in two ways; the first only with gravitational force and the second with vacuum assistance. Surface roughness of as cast specimens were measured, effects of section thickness, vacuum assistance and location of the pattern on the wax tree were determined.     

Application of SEUMRE global optimization algorithm in automotive magnetorheological brake design

In this paper, the superior performance of a novel space exploration and unimodal region elimination global optimization algorithm, SEUMRE, is demonstrated through comparisons with other well known global optimization techniques, including genetic algorithm (GA), simulated annealing (SA), and a highly nonlinear design problem—the optimal design of automotive magnetorheological brake (MRB). Unlike the conventional brakes, an MRB employs the interaction between a magnetorheological fluid and an applied magnetic field to generate the retarding braking torque. The SEUMRE design optimization algorithm was used to maximize the braking torque and minimize the weight of the brake structure. The computation time and optimized design parameters illustrated SEUMRE’s capability to converge to an accurate result faster than the conventional global optimization methods. However, SA provided significantly better optimization results than GA and SEUMRE in terms of the cost function.     

Satellite-based and mesoscale regression modeling of monthly air and soil temperatures over complex terrain in Turkey

Simple regression algorithms were developed to quantify spatio-temporal dynamics of minimum and maximum air temperatures (T_min and T_max, respectively) and soil temperature for a depth of 0-5 cm (T_soil-5cm) across complex terrain in Turkey using Moderate Resolution Imaging Spectroradiometer (MODIS) data at a 500-m resolution. A total of 762 16-day MODIS composites (127 images × 6 bands) between 2000 and 2005 were averaged over a monthly basis to temporally match monthly T_min, T_max, and T_soil-5cm from 83 meteorological stations. A total of 60 (28 temporally averaged plus 32 time series-based) linear regression models of T_min, T_max, and T_soil-5cm were developed using best subsets procedure as a function of a combination of 12 explanatory variables: six MODIS bands of blue, red, near infrared (NIR), middle infrared (MIR), normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI); four geographical variables of latitude, longitude, altitude, and distance to sea (DtS); and two temporal variables of month, and year. The best multiple linear regression models elucidated 65% (RMSE = 5.9 °C), 65% (RMSE = 5.1 °C), and 57% (RMSE = 6.9 °C) of variations in T_min, T_max, and T_soil-5cm, respectively, under a wide range of T_min (−34 to 25 °C), T_max (0.2-47 °C) and T_soil-5cm (−9 to 40 °C) observed at the 83 stations.     

Exploring the types and reasons of Internet-triggered academic dishonesty among Turkish undergraduate students: Development of Internet-Triggered Academic Dishonesty Scale (ITADS)

Internet contributes to the development of science and facilitates scientific demeanors while it also serves as a ground for academic misdemeanors. Recent studies indicate that Internet facilitates and spreads academic dishonesty. The purpose of the current study is to investigate the extent of involvement of Turkish university students in academic dishonesty practices facilitated through Internet (i.e. e-dishonesty) and to question the conditions which lead to e-dishonesty. Three hundred and forty nine education faculty students from the most populated state university in Turkey were administered two Likert-Scale questionnaires developed by the researchers. After the reliability and validity conditions were met, two exploratory factor analyses were conducted. The first one revealed the factors constituting common types of e-dishonesty among undergraduate students which were fraudulence, plagiarism, falsification, delinquency, and unauthorized help. The second factor analysis exhibited individual and contextual factors triggering e-dishonesty which were named as individual factors, institutional policies and peer pressure. Results of both analyses are discussed and suggestions for further research are provided.     

Malicious code detection in android: the role of sequence characteristics and disassembling methods

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers.

     

Examining the role of initial trust in user adoption of mobile payment services: an empirical investigation

User adoption of mobile payment (m-payment) is low compared to the adoption of traditional forms of payments. Lack of user trust has been identified as the most significant long-term barrier for the success of mobile finances systems. Motivated by this fact, we proposed and tested an initial trust theoretical model for user adoption of m-payment systems. The model not only theorizes the role of initial trust in m-payment adoption, but also identifies the facilitators and inhibitors for a user’s initial trust formation in m-payment systems. The model is empirically validated via a sample of 851 potential m-payment adopters in Australia. Partial least squares structural equation modelling is used to assess the relationships of the research model. The results indicate that perceived information quality, perceived system quality, and perceived service quality as the initial trust facilitators are positively related to initial trust formation, while perceived uncertainty as the initial trust inhibitor exerts a significant negative effect on initial trust. Perceived asset specificity is found to have insignificant effect. In addition, the results show that initial trust positively affects perceived benefit and perceived convenience, and these three factors together predict usage intention. Perceived convenience of m-payment is also found to have a positive effect on perceived benefit. The findings of this study provide several important implications for m-payment adoption research and practice.     

Coatings of Na-aluminosilicate zeolites prepared using predictions from an artificial neural network method

Predictions made by the Generalized Regression Neural Networks (GRNN) method were used to relate the initial compositions of various reaction mixtures to the types of Na-aluminosilicate zeolites that may be obtained from these compositions. In the light of the predictions made, coatings were prepared on stainless steel plates, which were characterized by X-ray diffraction and scanning electron microscopy prior to and after syntheses. Coatings of zeolites P, X, A, analcime as well as their mixtures could be obtained from a variety of previously unknown clear solution compositions, generally in good accordance with the predictions made by the GRNN method. Different textural properties were obtained for the coatings of the same zeolite, such as P and X, which could be prepared from a relatively wide range of compositions.     

Modeling of gamma-ray energy absorption buildup factors using neural network

This paper presents a new approach based on multilayered perceptrons (MLPs) to compute energy absorption buildup factors. The MLP has been trained by a Levenberg–Marquardt learning algorithm. The model is fast and does not require tremendous computational efforts. The results obtained by using the proposed model are in good agreement with the ANSI/ANS-6.4.3 standard data set.