Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Microblogs recommendations based on implicit similarity in content social networks

With the development of online social networking applications, microblogs have become a necessary online communication network in daily life. Users are interested in obtaining personalized recommendations related to their tastes and needs. In some microblog systems, tags are not available, or the use of tags is rare. In addition, user-specified social relations are extremely rare. Hence, sparsity is a problem in microblog systems. To address this problem, we propose a new framework called Pblog to alleviate sparsity. Pblog identifies users’ interests via their microblogs and social relations and computes implicit similarity among users using a new algorithm. The experimental results indicated that the use of this algorithm can improve the results. In online social networks, such as Twitter, the number of microblogs in the system is high, and it is constantly increasing. Therefore, providing personalized recommendations to target users requires considerable time. To address this problem, the Pblog framework groups similar users using the analytic hierarchy process (AHP) method. Then, Pblog prunes microblogs of the target user group and recommends microblogs with higher ratings to the target user. In the experimental results section, the Pblog framework was compared with several other frameworks. All of these frameworks were run on two datasets: Twitter and Tumblr. Based on the results of these comparisons, the Pblog framework provides more appropriate recommendations to the target user than previous frameworks.

     

Theoretical Study of Electronic,Magnetic, and Optical Response of Fe-doped ZnS: First-Principle Approach

In this study, we demonstrate Zn_1?x Fe _x S (x = 0.0, 0.25, 0.50, 0.75, and 1.0) device applications by reporting electronic, magnetic, and optical properties, computed with Wien2k software, using density functional theory (DFT). The modified Becke and Johnson (mBJ) potential has been applied to accurately determine the material band gap. The presence of half-metallic ferromagnetism (HMF) is demonstrated. Moreover, the observed ferromagnetism is justified in terms of various splitting energies and the exchange constants. The Fe magnetic moment decreases from 4.0 μ _B due to the strong p ? d hybridization. A complete set of various optical parameters is also presented. The variation in the calculated static dielectric constant, due to Fe doping, is inversely related to the band gap that verifies Penn’s model. Moreover, the band gap of ZnS is tunable by the Fe doping, from ultraviolet to visible regions, depicting that the materials are appropriate for optoelectronic devices.     

Nanomembrane‐Based,Thermal‐Transport Biosensor for Living Cells

Knowledge of materials' thermal‐transport properties, conductivity and diffusivity, is crucial for several applications within areas of biology, material science and engineering. Specifically, a microsized, flexible, biologically integrated thermal transport sensor is beneficial to a plethora of applications, ranging across plants physiological ecology and thermal imaging and treatment of cancerous cells, to thermal dissipation in flexible semiconductors and thermoelectrics. Living cells pose extra challenges, due to their small volumes and irregular curvilinear shapes. Here a novel approach of simultaneously measuring thermal conductivity and diffusivity of different materials and its applicability to single cells is demonstrated. This technique is based on increasing phonon‐boundary‐scattering rate in nanomembranes, having extremely low flexural rigidities, to induce a considerable spectral dependence of the bandgap‐emission over excitation‐laser intensity. It is demonstrated that once in contact with organic or inorganic materials, the nanomembranes' emission spectrally shift based on the material's thermal diffusivity and conductivity. This NM‐based technique is further applied to differentiate between different types and subtypes of cancer cells, based on their thermal‐transport properties. It is anticipated that this novel technique to enable an efficient single‐cell thermal targeting, allow better modeling of cellular thermal distribution and enable novel diagnostic techniques based on variations of single‐cell thermal‐transport properties.     

Evaluating Six Sigma failure rate for inverse Gaussian cycle times

Six Sigma is a quality philosophy and methodology that aims to achieve operational excellence and delighted customers. The cost of poor quality depends on the sigma quality level and its corresponding failure rate. Six Sigma provides a well-defined target of 3.4 defects per million. This failure rate is commonly evaluated under the assumption that the process is normally distributed and its specifications are two-sided. However, these assumptions may lead to implementation of quality-improvement strategies that are based on inaccurate evaluations of quality costs and profits. This paper defines the relationship between failure rate and sigma quality level for inverse Gaussian processes. The inverse Gaussian distribution has considerable applications in describing cycle times, product life, employee service times, and so on. We show that for these processes attaining Six Sigma target failure rate requires higher quality efforts than for normal processes. A generic model is presented to characterise cycle times in manufacturing systems. In this model, the asymptotic production is described by a drifted Brownian motion, and the cycle time is evaluated by using the first passage time theory of a Wiener process to a boundary. The proposed method estimates the right efforts required to reach Six Sigma goals.     

The Use of Acoustic Emission Intensity Analysis for the Assessment of Cover Crack Growth in Corroded Concrete Structures

In this paper, acoustic emission (AE) intensity analysis was utilized to assess the concrete cover cracking due to steel corrosion in reinforced concrete structures. A total of 30 reinforced concrete prism samples were tested under an accelerated corrosion test coupled with continuous AE monitoring using attached AE sensors. The samples were cast with three concrete cover thicknesses (20, 30 and 40 mm) around steel bars and were exposed to five percentages of steel mass loss: 1, 2, 3, 4, and 5 %. The cover cracking was monitored daily by visual inspection to detect and measure crack widths. Different AE signal parameters were continuously recorded during the tests, including number of hits, signal strength, energy, and amplitude. The acquired AE events were subjected to an intensity analysis of signal strength to estimate historic index (H (t)) and severity (\(S_r)\). In addition, a b value analysis was conducted on all AE data and the results were compared to those obtained from the intensity analysis. The results showed that increasing the cover thickness had no significant impact on AE parameters (number of hits, cumulative signal strength, cumulative energy, amplitude, H (t), and \(S_{r})\) at similar values of crack growth. Nonetheless, varying the cover thickness from 20 to 40 mm resulted in lower crack widths and slightly higher b values at the same levels of steel mass loss. It was also found that both H (t) and \(S_r\) showed a more evident correlation with the values of crack growth than did b values, regardless of cover thickness or percentage of steel mass loss. Finally, an intensity classification chart was developed to quantify the cover crack growth based on the values of H (t) and \(S_{r}\).     

Assessing abrasion performance of self-consolidating concrete containing synthetic fibers using acoustic emission analysis

The key objective of this investigation was to evaluate the abrasion resistance of self-consolidating concrete (SCC) with and without synthetic fibers (SynFs). The abrasion resistance of normal concrete was also investigated in this study for comparison. The abrasion test was performed on concrete specimens according to the rotating-cutter method along with continuous monitoring of acoustic emission (AE) using attached AE sensors. The effects of changing concrete type and incorporating various types (flexible and semi-rigid) and lengths of SynFs on the abrasion behaviour were investigated with the aid of AE analysis. AE signal characteristics such as amplitude, signal strength, number of hits, and duration were gathered during testing. Furthermore, the collected AE data was used to complete b-value analysis as well as intensity analysis resulting in three additional parameters: b-value, severity (S_r), and historic index (H(t)). The results showed that the AE parameters were directly correlated with the abrasion damage in all tested mixtures. Adding SynFs to all SCC mixtures enhanced their abrasion resistance. The flexible fibers variety exhibited better abrasion performance on average than the semi-rigid fibers. Meanwhile, longer fibers showed lower abrasion resistance than the shorter ones with the same type. The results also indicated that AE intensity analysis was able to determine the ranges for H(t) and S_r that identify the extent of damage due to abrasion of SynF-reinforced SCC.     

Green synthesis of silver nanoparticles using flower extract of Malva sylvestris and investigation of their antibacterial activity

High‐quality colloidal silver nanoparticles (AgNP) were synthesised via a green approach by using hydroalcoholic extracts of Malva sylvestris. Silver nitrate was used as a substrate ion while the plant extract successfully played the role of reducing and stabilising agents. The synthesised nanoparticles were carefully characterised by using transmission electron microscopy, atomic‐force microscopy, energy dispersive X‐ray spectroscopy, Fourier transform infrared spectroscopy and UV–vis spectroscopy. The maximum absorption wavelengths of the colloidal solutions synthesised using 70 and 96% ethanol and 100% methanol, as extraction solvents, were 430, 485 and 504 nm, respectively. Interestingly, the size distribution of nanoparticles depended on the used solvent. The best particle size distribution belonged to the nanoparticles synthesised by 70% ethanol extract, which was 20–40 nm. The antibacterial activity of the synthesised nanoparticles was studied on Escherichia coli, Staphylococcus aureus and Streptococcus pyogenes using disk diffusion, minimum inhibitory concentrations and minimum bactericidal concentrations assays. The best antibacterial activity obtained for the AgNPs produced by using 96% ethanolic extract.Inspec keywords: silver, nanoparticles, nanofabrication, antibacterial activity, colloids, particle size, transmission electron microscopy, atomic force microscopy, X‐ray chemical analysis, Fourier transform spectra, infrared spectra, ultraviolet spectra, visible spectra, microorganisms, nanomedicine, biomedical materialsOther keywords: Green synthesis, flower extract, Malva sylvestris, antibacterial activity, high‐quality colloidal silver nanoparticles, hydroalcoholic extracts, plant extract, reducing agents, stabilising agents, transmission electron microscopy, atomic‐force microscopy, energy dispersive X‐ray spectroscopy, Fourier transform infrared spectroscopy, UV– vis spectroscopy, colloidal solutions, particle size distribution, Escherichia coli, Staphylococcus aureus, Streptococcus pyogenes, disk diffusion, minimum inhibitory concentrations, minimum bactericidal concentrations assays, ethanolic extract, size 430 nm, size 485 nm, size 504 nm, size 20 nm to 40 nm, Ag     

Engineering carbon quantum dots for photomediated theranostics

Carbon quantum dots (CQDs) have emerged as potential alternatives to classical metal-based semiconductor quantum dots (QDs) due to the abundance of their precursors, their ease of synthesis, high biocompatibility, low cost, and particularly their strong photoresponsiveness, tunability, and stability. Light is a versatile, tunable stimulus that can provide spatiotemporal control. Its interaction with CQDs elicits interesting responses such as wavelength-dependent optical emissions, charge/electron transfer, and heat generation, processes that are suitable for a range of photomediated bioapplications. The carbogenic core and surface characteristics of CQDs can be tuned through versatile engineering strategies to endow specific optical and physicochemical properties, while conjugation with specific moieties can enable the design of targeted probes. Fundamental approaches to tune the responses of CQDs to photo-interactions and the design of bionanoprobes are presented, which enable biomedical applications involving diagnostics and therapeutics. These strategies represent comprehensive platforms for engineering multifunctional probes for nanomedicine, and the design of QD probes with a range of metal-free and emerging 2D materials.

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