Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank

A flexible effort estimator model based on ASO algorithm

Accurate estimation of required effort for software development plays an important role in the success of the software project. This is always a challenging issue due to the intangible nature of the software project. Therefore, a large category of researches have been performed to develop accurate tools to estimate the required efforts for software development. According to the presented papers in related works, the adoption of methods to identify the types of relationship between software project features and features affecting the required effort for software development have a significant impact on effort estimation accuracy increment. In addition, the effectiveness of various features on the software development effort estimation is different. So, the feature effectiveness determination is advantageous in increasing the effort estimation accuracy. This paper presents a new model consisting of sub-models for project features analyzing and it uses a new and accurate heuristic algorithm called Atom Search Optimization (ASO) Algorithm to configure tools and data modeling methods. The presented model in this article is designed in multiple layers and the sub-models are organized in separate layers. The organizations of sub-models are in such a way to increase performance of other layers and ultimately increase the final estimate accuracy. In accuracy evaluation of the proposed model, 3 data sets from real projects are used and the comparisons of the results with different methods are presented. Based on the results, the proposed model leads to significant improvement of final effort estimation accuracy

Prediction of the Punching Load Strength of SCS Slabs with Stud-Bolt Shear Connectors Using Numerical Modeling and GEP Algorithm

Using bolt shear connectors in Steel-Concrete-Steel (SCS) slabs is very important due to producing a complete steel plates connection and adjusting the sandwich thickness desirably. Therefore, in the present research, a numerical study is conducted on the flexural behavior of SCS sandwich slabs with stud-bolt shear connectors under the effect of the quasi-static concentrated load. For this purpose, first, the experimental specimens extracted from the previously published study were numerically modeled and quasi-statically analyzed using explicit dynamic analysis. Then based on the tests, the models were validated. Subsequently, the effect of the parameters, including the thickness of steel plates, stud-bolts diameter, the concrete core thickness, center-to-center distance of stud-bolt connectors, and the concrete core strength was evaluated based on the numerical models on the failure modes and the force-displacement curve. Finally, using the experimental setup and gene expression programming (GEP) algorithm, several numerical models were planned to predict the maximum strength of the slabs and a simple relation was proposed. The maximum strength resulting from the proposed relation and numerical models had an acceptable agreement with an error of 11% based on mean absolute percentage error (MAPE)

Distributed Big Data Analytics Method for the Early Prediction of the Neonatal 5-Minute Apgar Score before or during Birth and Ranking the Risk Factors from a National Dataset

One-minute and five-minute Apgar scores are good measures to assess the health status of newborns. A five-minute Apgar score can predict the risk of some disorders such as asphyxia, encephalopathy, cerebral palsy and ADHD. The early prediction of Apgar score before or during birth and ranking the risk factors can be helpful to manage and reduce the probability of birth producing low Apgar scores. Therefore, the main aim of this study is the early prediction of the neonate 5-min Apgar score before or during birth and ranking the risk factors for a big national dataset using big data analytics methods. In this study, a big dataset including 60 features describing birth cases registered in Iranian maternal and neonatal (IMAN) registry from 1 April 2016 to 1 January 2017 is collected. A distributed big data analytics method for the early prediction of neonate Apgar score and a distributed big data feature ranking method for ranking the predictors of neonate Apgar score are proposed in this study. The main aim of this study is to provide the ability to predict birth cases with low Apgar scores by analyzing the features that describe prenatal properties before or during birth. The top 14 features were identified in this study and used for training the classifiers. Our proposed stack ensemble outperforms the compared classifiers with an accuracy of 99.37 ± 1.06, precision of 99.37 ± 1.06, recall of 99.50 ± 0.61 and F-score of 99.41 ± 0.70 (for confidence interval of 95%) to predict low, moderate and high 5-min Apgar scores. Among the top predictors, fetal height around the baby’s head and fetal weight denote fetal growth status. Fetal growth restrictions can lead to low or moderate 5-min Apgar score. Moreover, hospital type and medical science university are healthcare system-related factors that can be managed via improving the quality of healthcare services all over the country

Flexural performance of steel-concrete-steel sandwich beams with lightweight fiber-reinforced concrete and corrugated-strip connectors: Experimental tests and numerical modeling

The present paper experimentally investigates the flexural behavior of steel-concrete-steel (SCS) composite beams with lightweight polypropylene fiber-reinforced concrete core and corrugated-strip shear connectors. For this purpose, the four-point bending tests have been performed on 11 specimens. This aims to investigate the influence of lightweight concrete core on the performance of the beams, and therefore all the parameters in the tested beams are kept constant except the type of concrete core. Based on the results of the performed quasi-static loading tests, the observed failure modes and force-displacement curves are reported and analyzed to achieve a deeper understanding of the influence of lightweight fiber-reinforced concrete on the flexural behavior of SCS beams. It was found that the utilization of polypropylene fiber has an improving influence on the performance of SCS beams, and especially enhances their toughness and ultimate deflection. A positive linear relationship between the fiber content (up to 2 %) with the specimen’s toughness was detected. It was also found that the addition of micro-silica to the lightweight fiber-reinforced concrete core results in considerable improvement in the flexural load capacity of the tested SCS beams. In addition, numerical finite element models including detailed material and geometric modeling of SCS beams are developed using the general software package ABAQUS. The accuracy of the developed models is validated using the attained experimental results. The numerical models can be used to simulate the load-deflection behavior and ultimate strength of SCS sandwich beams

Conditioned medium from amniotic fluid mesenchymal stem cells could modulate Alzheimer's disease-like changes in human neuroblastoma cell line SY-SY5Y in a paracrine manner

Background: Alzheimer's disease is usually diagnosed by significant extracellular deposition of beta-amyloid and intracellular neurofibrillary tangle formation. Here, we investigated the paracrine effect of amniotic fluid-derived mesenchymal stem cells on AD changes in human SH-SY5Y cells. Methods: SH-SY5Y cells were divided into five groups: Control, 0.1 mu g/ml LPS, 10 mu g/ml LPS, 0.1 mu g/ml LPS + conditioned medium, and 10 mu g/ml LPS + conditioned medium. Cells were incubated with 0.1% and 10 mu g/ml LPS for 48 h, followed by incubation with the conditioned medium of amniotic fluid-derived mesenchymal stem cells for the next 24 h. Beta-amyloid plaques were monitored by Congo-red staining. Survival and apoptosis were assessed by the MTT assay and flow cytometric analysis of Annexin-V. ELISA was used to measure the levels of neprilysin, angiotensin-converting enzyme, and Matrix Metallopmteinase-9. A PCR array was used to measure the expression of genes involved in neurogenesis. Results: Bright-field imaging showed beta-amyloid plaques in the group treated with 10 mu g/ml LPS. We found minimal effects in groups receiving 0.1 mu g/ml LPS. The data showed that the reduction in the levels of neprilysin, angiotensin-converting enzyme, and Matrix Metalloproteinase-9 in the LPS-treated cells was attenuated after incubation with the stem cell secretome (p < 0.05). Amniotic fluid stem cell secretome increased the viability of LPS-treated SH-SY5Y cells (p 0.05) and was associated with a decrease in apoptotic changes (p < 0.05). We found the modulation of several genes involved in neurogenesis in the 10 mu g/ml LPS + conditioned medium group compared to cells treated with 10 mu g/ml LPS alone. Conclusion: Amniotic fluid stem cell secretion reduces AD-like pathologies in the human neuronal lineage.Tabriz University of Medical Sciences [IR.TBZMED.REC.1396.639]This study was supported by a grant from Tabriz University of Medical Sciences (IR.TBZMED.REC.1396.639) Grant holder: Dr. Alireza Nourazarian

Evaluating Molecular Evolution of Kerogen by Raman Spectroscopy: Correlation with Optical Microscopy and Rock-Eval Pyrolysis

Vitrinite maturity and programmed pyrolysis are conventional methods to evaluate organic matter (OM) regarding its thermal maturity. Moreover, vitrinite reflectance analysis can be difficult if prepared samples have no primary vitrinite or dispersed widely. Raman spectroscopy is a nondestructive method that has been used in the last decade for maturity evaluation of organic matter by detecting structural transformations, however, it might suffer from fluorescence background in low mature samples. In this study, four samples of different maturities from both shale formations of Bakken (the upper and lower members) Formation were collected and analyzed with Rock-Eval (RE) and Raman spectroscopy. In the next step, portions of the same samples were then used for the isolation of kerogen and analyzed by Raman spectroscopy. Results showed that Raman spectroscopy, by detecting structural information of OM, could reflect thermal maturity parameters that were derived from programmed pyrolysis. Moreover, isolating kerogen will reduce the background noise (fluorescence) in the samples dramatically and yield a better spectrum. The study showed that thermal properties of OM could be precisely reflected in Raman signals

Backtracking to Parent Maceral from Produced Bitumen with Raman Spectroscopy

In order to assess a source rock for economical exploitation purposes, many parameters should be considered; regarding the geochemical aspects, the most important ones are the amount of organic matter (OM) and its quality. Quality refers to the thermal maturity level and the type of OM from which it was formed. The origin of the OM affects the ability of the deposited OM between sediments to generate oil, gas, or both with particular potential after going through thermal maturation. Vitrinite reflectance and programmed pyrolysis (for instance, Rock-Eval) are common methods for evaluating the thermal maturity of the OM and its potential to generate petroleum, but they do not provide us with answers to what extent solid bitumen is oil-prone or gas-prone, as they are bulk geochemical methods. In the present study, Raman spectroscopy (RS), as a powerful tool for studying carbonaceous materials and organic matter, was conducted on shale and coal samples and their individual macerals to show the potential of this technique in kerogen typing and to reveal the parent maceral of the examined bitumen. The proposed methodology, by exhibiting the chemical structure of different organic matters as a major secondary product in unconventional reservoirs, can also detect the behavior of solid bitumen and its hydrocarbon production potential for more accurate petroleum system evaluation

Optic nerve head vessel density in different stages of pseudoexfoliation disease

PurposeTo evaluate the superficial vascular density of the optic nerve head in different stages of pseudoexfoliation disease using optical coherence tomography angiography (OCTA).MethodsIn this cross-sectional study, 57 normal eyes, 41 eyes with pseudoexfoliation syndrome (PXS), 82 eyes with pseudoexfoliation glaucoma (PXG) and 27 non-glaucomatous fellow eyes of PXG (NL-PXG) that had OCTA were included. Circumpapillary RNFL (cpRNFL) thickness and circumpapillary capillary density (cpCD) were compared among the groups after adjusting for confounders using linear-mixed model.ResultsPXG eyes had thinner global RNFL and lower cpCD (74.2±14.3&nbsp;µm and 36.7±10.0%) than control (103.3±8.6&nbsp;µm and 52.5±2.3%), PXS (96.8±8.8&nbsp;µm and 51.5±2.3%), and NL-PXG eyes (96.3±11.1&nbsp;µm and 50.1±3.9%) (p&lt;0.001). After adjustment for age, gender and signal strength index, global cpRNFL thickness was comparable among control, PXS and NL-PXG. NL-PXG had the lowest cpCD (p=0.045) and sectoral cpCD compared to PXS and control eyes. Although cpCD was comparable between control and PXS (p=0.425) eyes, sectoral differences (p=0.009 and 0.004, for inferonasal and temporal-inferior cpCD, respectively) were detectable between the two groups. AUROC for differentiating NL-PXG eyes from normal were better for cpCD (0.78) compared to cpRNLF (0.69).ConclusionsOCTA can detect reduced capillary density before significant changes in cpRNFL in fellow eyes of PXG patients. This can enable earlier detection of glaucomatous loss in pseudoexfoliation disease and enhance management of the disease