Open source software maturity model based on linear regression and Bayesian analysis

Open Source Software (OSS) is widely used and is becoming a significant and irreplaceable part of the software engineering community. Today a huge number of OSS exist. This becomes a problem if one needs to choose from such a large pool of OSS candidates in the same category. An OSS maturity model that facilitates the software assessment and helps users to make a decision is needed. A few maturity models have been proposed in the past. However, the parameters in the model are assigned not based on experimental data but on human experiences, feelings and judgments. These models are subjective and can provide only limited guidance for the users at the best. This dissertation has proposed a quantitative and objective model which is built from the statistical perspective. In this model, seven metrics are chosen as criteria for OSS evaluation. A linear multiple-regression model is created to assign a final score based on these seven metrics. This final score provides a convenient and objective way for the users to make a decision. The coefficients in the linear multiple-regression model are calculated from 43 OSS. From the statistical perspective, these coefficients are considered random variables. The joint distribution of the coefficients is discussed based on Bayesian statistics. More importantly, an updating rule is established through Bayesian analysis to improve the joint distribution, and thus the objectivity of the coefficients in the linear multiple-regression model, according to new incoming data. The updating rule provides the model the ability to learn and improve itself continually

Influence of fibre steering on the bearing performance of bolted joints in 3D printed pseudo-woven CFRP composites

Aiming to improve the bearing performance of bolted joints in carbon fibre reinforced polymer (CFRP) composites, this study investigates the impact of steered fibre paths around the hole edge within pseudo-woven (interlaced) composites that are manufactured by 3D printing. The influence of fibre steering on the crack initiation and propagation was examined through double-lap bearing tests performed on four distinct cases. Parallel to the comprehensive experimental study, digital image correlation (DIC) and X-ray computed microtomography (micro-CT) scans were performed to aid in understanding and identifying the various damage mechanisms in each specimen type. Results revealed that different patterns provided varying bearing abilities, with an employed pattern improving the initial bearing strength, initial fracture energy and ultimate fracture energy of the 3D printed pseudo-woven composite by 23.5%, 363.7% and 29.6%, respectively. Consequently, fibre steering in composites is found to be a promising method to tailor the bearing behaviour of bolted joints as required

Secondary structure investigation of bovine serum albumin (BSA) by Fourier transform infrared (FTIR) spectroscopy in the amide III region

Fourier transform infrared spectroscopy is widely used to analyze protein secondary structures. The common strategy in this field is to analyze the conformation sensitive 1700-1600 cm-1 amide I region of protein FTIR spectrum. Though the amide III region of protein is also sensitive to secondary structural changes, its potential for protein secondary structural analysis is largely unexplored. In this paper, we performed a detailed investigation on the second structural analysis of bovine serum albumin by monitoring the spectral variation of the amide III band under a variety of pH conditions by FTIR spectroscopy and FTIR second derivative spectroscopy. Our results show that both acidic and basic conditions have pronounced effects on the overall secondary structures of BSA, suggesting denaturation effects. Furthermore, we observe that the amide III band profiles under acidic and basic conditions appear to be quite different. Our results clearly demonstrate that the amide III region is a promising probing region for protein secondary structural analysis

Open source software maturity model based on linear regression and Bayesian analysis

Open Source Software (OSS) is widely used and is becoming a significant and irreplaceable part of the software engineering community. Today a huge number of OSS exist. This becomes a problem if one needs to choose from such a large pool of OSS candidates in the same category. An OSS maturity model that facilitates the software assessment and helps users to make a decision is needed. A few maturity models have been proposed in the past. However, the parameters in the model are assigned not based on experimental data but on human experiences, feelings and judgments. These models are subjective and can provide only limited guidance for the users at the best. This dissertation has proposed a quantitative and objective model which is built from the statistical perspective. In this model, seven metrics are chosen as criteria for OSS evaluation. A linear multiple-regression model is created to assign a final score based on these seven metrics. This final score provides a convenient and objective way for the users to make a decision. The coefficients in the linear multiple-regression model are calculated from 43 OSS. From the statistical perspective, these coefficients are considered random variables. The joint distribution of the coefficients is discussed based on Bayesian statistics. More importantly, an updating rule is established through Bayesian analysis to improve the joint distribution, and thus the objectivity of the coefficients in the linear multiple-regression model, according to new incoming data. The updating rule provides the model the ability to learn and improve itself continually

Improved interlayer performance of short carbon fiber reinforced composites with bio-inspired structured interfaces

The weak layer interfaces of 3D-printed short carbon fiber (SCF) reinforced polymer composites have remained an issue due to planar layer printing by traditional 3D printers. Recently, multi-axis 3D printing technology which can realize non-planar layer printing has been developed. This study’s aim was to evaluate and compare the bonding performance of non-planar interfaces produced by multi-axis 3D printing with that of planar interfaces. The tested non-planar interfaces were designed as bio-inspired structured interfaces (BISIs) based on microstructural interfacial elements in biological materials. The standard specimens with the 0°/90° and 0° infill line directions were printed by a robotic arm multi-axis 3D printer. Double cantilever beam (DCB) and end-notched flexure (ENF) tests were conducted to obtain Mode Ⅰ and Mode Ⅱ interlaminar toughness of SCF-reinforced composites. Test results showed that the critical energy release rates of the integrally formed BISI were significantly improved compared with the planar interface (PLAI) for both Mode I and Mode II delamination. In particular, the BISI with 0° infill line direction exhibited the greatest increase in critical energy release rate, and the damaged areas were spatially swept through the curved interfaces of the BISI with different infill line directions by scanning electron microscopy (SEM) and computed tomography (CT), which showed that the higher critical energy release rate was always accompanied with a larger damaged area. In addition, the tensile and flexural properties of 0°-infilled PLAI and BISI specimens were also measured. This work provides an in-depth investigation of the PLAI and BISI properties of SCF-reinforced composites, demonstrating the potential benefits of integrally formed BISI by multi-axis 3D printing and fostering new perspectives to enhance layer interfaces of 3D printed composites