Improving the efficiency of Bayesian Network Based EDAs and their application in Bioinformatics

Estimation of distribution algorithms (EDAs) is a relatively new trend of stochastic optimizers which have received a lot of attention during last decade. In each generation, EDAs build probabilistic models of promising solutions of an optimization problem to guide the search process. New sets of solutions are obtained by sampling the corresponding probability distributions. Using this approach, EDAs are able to provide the user a set of models that reveals the dependencies between variables of the optimization problems while solving them. In order to solve a complex problem, it is necessary to use a probabilistic model which is able to capture the dependencies. Bayesian networks are usually used for modeling multiple dependencies between variables. Learning Bayesian networks, especially for large problems with high degree of dependencies among their variables is highly computationally expensive which makes it the bottleneck of EDAs. Therefore introducing efficient Bayesian learning algorithms in EDAs seems necessary in order to use them for large problems. In this dissertation, after comparing several Bayesian network learning algorithms, we propose an algorithm, called CMSS-BOA, which uses a recently introduced heuristic called max-min parent children (MMPC) in order to constrain the model search space. This algorithm does not consider a fixed and small upper bound on the order of interaction between variables and is able solve problems with large numbers of variables efficiently. We compare the efficiency of CMSS-BOA with the standard Bayesian network based EDA for solving several benchmark problems and finally we use it to build a predictor for predicting the glycation sites in mammalian proteins

Applying Particle Swarm Optimization-Base Decision Tree Classifier for Mental Illnesses

Background: Data mining techniques such as clustering and classification are used to explore patient's data and extract a predictive model. Medical data set are often classified by a large number of irrelevant disease measurements(features). Feature selection is one of the most common tasks which reduces the computational cost by removing insignificant features. Method: This paper presents a graph-based Louvain algorithm for mental illness dataset clustering and a particle swarm optimization combined with a decision tree as the classifier to select the small number of an informative feature from the thousands of features were collected from health centers consist of 1060 people in two groups of 550 patients and 510 healthy. Result: The results show that "aggression" Finding the greatest impact on the diagnosis of mental disorders has been observed in the number of 65. After that, the features such as "prisoner in the family" and "hard labor" with 63 observations had a greater impact on the disease also the third ranking "illiterate" and "elation and euphoria" had 61 and 58 observations. Conclusions: The classification accuracy shows that the proposed method is capable of producing good results with fewer features than the original datasets. Keywords: Mental illness, Graph clustering, Particle swarm optimization, ID3 DOI: 10.7176/JIEA/9-7-03 Publication date: December 31st 2019

Entrepreneurship Education and Experiential Learning in Higher Education

Entrepreneurship education (EE) and experiential learning can be delivered in several ways depending on the program design, the course\u27s purpose, and the learning outcomes. With the distinct stages of doing, observing, thinking, and planning, Kolb\u27s experiential learning theory is favored in EE. Additionally, EE programs and courses can be categorized in the three instructional themes of teaching about, for, or through entrepreneurship. Each theme offers a particular purpose, unique learning objectives, specific teaching methodology, and different student engagement levels. Due to the various references to EE, this exploratory qualitative study presents five selected entrepreneurship project course examples at Southern New Hampshire University (SNHU) using semi-structured interviews. The research objectives aim to (1) share entrepreneurial education teaching practices at SNHU, and (2) collect information on how instructors measure student engagement, course/project impact, reflection, and assessment practices. Common elements and approaches in the for entrepreneurship instructional theme -also known as learning by doing include (a) the learning environment, (b) real-life projects and clients, (c) reflection practices, (d) active student engagement, and (e) subject matter expertise by the instructor

Using the LHeC ERL to generate high-energy photons

The Large Hadron electron Collider (LHeC) is a proposed future particle physics project colliding 60 GeV electrons from a six-pass recirculating energy-recovery linac (ERL) with 7 TeV protons stored in the LHC. The ERL technology allows for much higher beam current and, therefore, higher luminosity than a traditional linac. The high-current, high-energy electron beam can also be used to drive a free electron laser (FEL). In this contribution, we examine how the LHeC ERL can serve as a source of high-energy photons for studies in nuclear physics, high-energy physics, Axion detection, dark energy, and protein crystallography. In the first section, we discuss the performance of the LHeC-based FEL, operated in the SASE mode for generating photon pulses at wavelengths ranging from 200 keV to 600 keV. In the second section, we investigate photon production via Laser Compton scattering (LCS).Comment:

The transition from used fuel container corrosion under oxic conditions to corrosion in an anoxic environment

Used nuclear fuel poses significant risks to human health and the environment, necessitating its safe and permanent disposal. The universally proposed plan for this is to bury the fuel-containing containers in a multi-barrier system known as a deep geological repository (DGR) at least 500 metres underground. These containers, crucial for withstanding long-term mechanical loads and a corrosive environment, vary in design across countries, with some featuring copper (Cu) shells on nodular cast iron insert (Sweden, Finland) or Cu-coated carbon steel vessels (Canada). Upon emplacement, the containers undergo evolving conditions underground, transitioning from warm, humid, and oxidizing to cool, dry, and anoxic environments over time. During the initial oxidizing phase, oxygen entrapped upon sealing the DGR and water-radiolysis will lead to the formation of an oxide/hydroxide film on the Cu container surface. Subsequently, as anoxic conditions prevail, bisulfide (SH−) ions produced by the action of sulfate-reducing bacteria (SRB) remote from the container will become the primary oxidant. While considerable efforts have been devoted to investigating exclusively either the oxic or anoxic periods, the current study has addressed the gap in understanding how early oxide growth impacts later stages, particularly in the presence of SH− ions. In a series of experiments, various methods were employed to create copper oxide/hydroxide layers with known compositions and structures to investigate their role in bisulfide-induced corrosion of the Cu substrate under de‑aerated conditions. The morphology of the oxide film and the concentration of bisulfide species influence potential interaction mechanisms, including chemical conversion, galvanic coupling, and direct corrosion of Cu by bisulfide species. Our findings have shown that regardless of the composition or structure of the oxide film, it underwent partial conversion to copper sulfide via chemical and/or galvanic processes. Moreover, an unreacted remnant of the oxide layer detected on the surface was non-protective and permitted direct Cu corrosion by bisulfide species. Electrochemically- and radiolytically-grown oxides exhibited quick conversion to copper sulfide, whereas hydrothermally-grown oxides, thicker in nature, underwent slower conversion, with regions remaining unreacted. These results highlight the importance of electrochemical pathways in facilitating rapid oxide-to-sulfide conversion, contrasting with slower chemical pathways

Effect of Thermocycling and Type of Restorative Material on Microleakage of Class II Restorations

Objectives: Microleakage is a major cause of failure of dental restorations and results in development of secondary caries, tooth hypersensitivity and pulp pathosis. This study aimed to compare the microleakage of class II cavities filled with two types of composite resins and a compomer and subjected to thermocycling.Methods: In this in vitro experimental study, class II cavities with a gingival margin below the cementoenamel junction (CEJ) and beveled enamel margins were prepared in proximal surfaces of 60 molar teeth. The teeth were randomly divided into three groups of 20 and restored with Spectrum TPH3 and Esthet X composites and Dyract eXtra compomer. Each group was randomly divided into two subgroups (n=10) of control and thermocycling (1000 thermal cycles). Dye penetration in occlusal and cervical margins was scored under a stereomicroscope. Data were analyzed using the Kruskal Wallis test and Mann Whitney U test (P<0.05).Results: No significant difference was noted in microleakage of the three groups neither in the occlusal nor in the cervical margins in presence or absence of thermocycling (P>0.05). But, the microleakage in the cervical margins of compomer restorations was slightly higher than that of other groups especially after thermocycling.Conclusion: Microleakage of composite restorations was not significantly different from that of compomer restorations in the occlusal or gingival margins in presence or absence of thermocycling

Compressive Strength of Bulk-Fill and Conventional Nano-hybrid Composite Resins: An in Vitro Study

Objectives Evaluation of the properties of recently introduced bulk-fill composite resins from different aspects is important. We aimed to evaluate the compressive strength of two bulk-fill composite resins with different viscosities compared with one conventional composite resin. Methods This in vitro study evaluated two different bulk-fill composite resins and one conventional composite resin. Twelve samples were prepared for each group in a mold, measuring 4 mm in diameter and 6 mm in height. In group 1, x-tra fil bulk-fill composite resin was light-cured with 4-mm thickness for 40 seconds. Then, a 2-mm thick increment of composite resin from the same brand was placed over it and light-cured. In group 2, x-tra base composite resin was light-cured with 4 mm thickness. Then, Grandio conventional composite resin was placed over it with 2-mm thickness and light-cured. In group 3, Grandio conventional composite resin was placed in 2-mm thickness using the incremental technique and light-cured. The samples were stored in distilled water at 37°C for 48 hours, followed by the compressive strength test in a universal testing machine at a crosshead speed of 1 mm/minute. The data were analyzed with SPSS 21 using one-way ANOVA and post hoc Tukey’s test. Statistical significance was set at P<0.05. Results There were no significant differences in compressive strength values of the three study groups (P>0.05). Conclusion The bulk-fill composite resins evaluated in the present study exhibited compressive strength values similar to that of the conventional composite resin, indicating favorable compressive strength, with decreased working time

Effects of CPP-ACP and Remin-Pro on Surface Roughness of Bleached Enamel: an Atomic Force Microscopy Study

Objectives Bleaching agents can change the organic and mineral contents of the tooth structure. The aim of this study was to evaluate the effects of two remineralizing agents on surface roughness of bleached enamel. Methods In this experimental study, 24 premolars were collected. The testing area was a window measuring 3 × 4 mm. First the surface roughness of specimens was measured by atomic force microscopy (AFM). Then, the teeth were bleached. Surface roughness was measured again. Specimens were randomly divided into 3 groups. No remineralizing agent was applied in the control group (A). Casein phosphopeptide amorphous calcium phosphate (CPP-ACP) and Remin-Pro were used in groups B and C, respectively. After 15 days, the surface roughness was measured. The changes in surface roughness were analyzed by paired t-test, and comparison between the groups was done by the Welch and Games-Howell post hoc tests. Results The surface roughness increased after bleaching (P<0.000). Surface roughness in groups B (P=0.03) and C (P=0.04) was significantly lower than that in group A. There was no significant difference in the level of surface roughness reduction between groups B and C. The Welch test revealed that the mean change in surface roughness values after remineralization in groups B and C was significantly higher than that in group A (P=0.001 and P=0.002, respectively). The difference between groups B and C was not significant (P=0.97). Conclusion CPP-ACP and Remin-Pro reduce the surface roughness of bleached enamel more effectively than the saliva