Search results for: predicted mean vote

Authors: Moulay Driss Mellaoui, Khadija Zaki, Khalid Abbiche, Abdallah Imjjad, Rachid Boutiddar, Abdelouahid Sbai, Aaziz Jmiai, Souad El Issami, Al Mokhtar Lamsabhi, Hanane Zejli

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This study presents a comprehensive analysis of six isoxazolidine and isoxazoline derivatives, leveraging a multifaceted approach that combines Density Functional Theory (DFT), AdmetSAR analysis, and molecular docking simulations to explore their electronic, pharmacokinetic, and anticancer properties. Through DFT analysis, using the B3LYP-D3BJ functional and the 6-311++G(d,p) basis set, we optimized molecular geometries, analyzed vibrational frequencies, and mapped Molecular Electrostatic Potentials (MEP), identifying key sites for electrophilic attacks and hydrogen bonding. Frontier Molecular Orbital (FMO) analysis and Density of States (DOS) plots revealed varying stability levels among the compounds, with 1b, 2b, and 3b showing slightly higher stability. Chemical potential assessments indicated differences in binding affinities, suggesting stronger potential interactions for compounds 1b and 2b. AdmetSAR analysis predicted favorable human intestinal absorption (HIA) rates for all compounds, highlighting compound 3b superior oral effectiveness. Molecular docking and molecular dynamics simulations were conducted on isoxazolidine and 4-isoxazoline derivatives targeting the EGFR receptor (PDB: 1JU6). Molecular docking simulations confirmed the high affinity of these compounds towards the target protein 1JU6, particularly compound 3b, among the isoxazolidine derivatives, compound 3b exhibited the most favorable binding energy, with a g score of -8.50 kcal/mol. Molecular dynamics simulations over 100 nanoseconds demonstrated the stability and potential of compound 3b as a superior candidate for anticancer applications, further supported by structural analyses including RMSD, RMSF, Rg, and SASA values. This study underscores the promising role of compound 3b in anticancer treatments, providing a solid foundation for future drug development and optimization efforts.

Keywords: isoxazolines, DFT, molecular docking, molecular dynamic, ADMET, drugs.

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Authors: Nashaat A. Deiab, Aida Radwan, Mohamed Elnagdy, Mohamed S. Yahiya, Rasha Moustafa

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This study is to evaluate and investigate the dosimetric performance of our institution's 3D treatment planning system, Elekta PrecisePLAN, for open 6MV fields including square, rectangular, variation in SSD, centrally blocked, missing tissue, square MLC and MLC shaped fields guided by the recommended QA tests prescribed in AAPM TG53, NCS report 15 test packages, IAEA TRS 430 and ESTRO booklet no.7. The study was performed for Elekta Precise linear accelerator designed for clinical range of 4, 6 and 15 MV photon beams with asymmetric jaws and fully integrated multileaf collimator that enables high conformance to target with sharp field edges. Seven different tests were done applied on solid water equivalent phantom along with 2D array dose detection system, the calculated doses using 3D treatment planning system PrecisePLAN, compared with measured doses to make sure that the dose calculations are accurate for open fields including square, rectangular, variation in SSD, centrally blocked, missing tissue, square MLC and MLC shaped fields. The QA results showed dosimetric accuracy of the TPS for open fields within the specified tolerance limits. However large square (25cm x 25cm) and rectangular fields (20cm x 5cm) some points were out of tolerance in penumbra region (11.38 % and 10.9 %, respectively). For the test of SSD variation, the large field resulted from SSD 125 cm for 10cm x 10cm filed the results recorded an error of 0.2% at the central axis and 1.01% in penumbra. The results yielded differences within the accepted tolerance level as recommended. Large fields showed variations in penumbra. These differences between dose values predicted by the TPS and the measured values at the same point may result from limitations of the dose calculation, uncertainties in the measurement procedure, or fluctuations in the output of the accelerator.

Keywords: quality assurance, dose calculation, 3D treatment planning system, photon beam

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Authors: Tondani Mudau, Sherine B. Van Wyk, Zuhayr Kafaar, Janan Dietrich

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Female adolescents residing in a patriarchal society such as South Africa are more inclined to embrace feminine ideologies. Internalizing these ideologies may expose female adolescents to mental health challenges such as depression and anxiety. This study explored whether the internalisation of adolescent femininity ideologies namely, objectified relationship with own body (ORB) and inauthentic self in relationships (ISR) predicted anxiety and depression in late female adolescents at Stellenbosch University. The sample of the study consisted of 1451 (18-24) female undergraduate and postgraduate students enrolled at Stellenbosch University. The mean age of the participants was 20 (SD=1.46), and most participants (39.7%) were first-year students. The study employed a cross-sectional quantitative research design. Data was collected through an online self-completion survey, the survey consisted of three sections, the first section asked biographical questions regarding age, gender, race and family background. The second section measured the internalisation of feminine ideologies by using the adolescent femininity ideology scale which has two subscales namely inauthentic self in relationship with others (ISR) and objectified relationship with one’s own body (ORB). The ISR scale had the Cronbach Alpha of 0.76, and the ORB scale had the Cronbach Alpha of 0.83. The third section measured mental health (depression and anxiety) by using the Hopkins Symptoms 25-checklist which had the Cronbach Alpha of 0.93. Data were analysed through multiple linear regression from IBM SPSS (Statistical Package for the Social Sciences Version 24). The overall results of the multiple linear regression showed that The AFIS combination accounted for 14% for anxiety as measured by the Hopkins Symptoms Checklist R² = .142, F (2, 682) = 56.431, p < .001. The combination also accounted for 24% for depression as measured by the Hopkins Symptoms Checklist R² = .239, F (2, 682) = 106.971, p < .0. The findings in this study affirm the objectification and feminist theory contentions that internalising femininity ideologies (ISR and ORB) predict negative mental health in female adolescents.

Keywords: adolescents, anxiety, depression, feminine ideologies, inauthentic self, mental health, self-objectification, South Africa

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Authors: Akhila Vasudev, Himanshu Kaushik, Tadikonda Venkata Bharat

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A large number of mine tailings are produced every year as part of the extraction process of phosphates, gold, copper, and other materials. Mine tailings are high in water content and have very slow dewatering behavior. The efficient design of tailings dam and economical disposal of these slurries requires the knowledge of tailings consolidation behavior. The large-strain consolidation theory closely predicts the self-weight consolidation of these slurries as the theory considers the conservation of mass and momentum conservation and considers the hydraulic conductivity as a function of void ratio. Classical laboratory techniques, such as settling column test, seepage consolidation test, etc., are expensive and time-consuming for the estimation of hydraulic conductivity variation with void ratio. Inverse estimation of the constitutive relationships from the measured settlement versus time curves is explored. In this work, inverse analysis based on metaheuristics techniques will be explored for predicting the hydraulic conductivity parameters for mine tailings from the base excess pore water pressure dissipation curve and the initial conditions of the mine tailings. The proposed inverse model uses particle swarm optimization (PSO) algorithm, which is based on the social behavior of animals searching for food sources. The finite-difference numerical solution of the forward analytical model is integrated with the PSO algorithm to solve the inverse problem. The method is tested on synthetic data of base excess pore pressure dissipation curves generated using the finite difference method. The effectiveness of the method is verified using base excess pore pressure dissipation curve obtained from a settling column experiment and further ensured through comparison with available predicted hydraulic conductivity parameters.

Keywords: base excess pore pressure, hydraulic conductivity, large strain consolidation, mine tailings

Procedia PDF Downloads 121

Authors: Nur Aimi Syairah Mohd Abdul Alim, Azilah Ajit, A. Z. Sulaiman

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The present study aimed to investigate the effects of ultrasound-assisted extraction (UAE) on the extraction of Vitexin and Iso-Vitexin from Ficus deltoidea plants. In recent years, ultrasound technology has been found to be a potential herbal extraction technique. The passage of ultrasound energy in a liquid medium generates mechanical agitation and other physical effects due to acoustic cavitation. The main goal is to optimised ultrasonic-assisted extraction condition providing the highest extraction yield with the most desirable antioxidant activity and stability. Thus, a series of experiments has been developed to investigate the effect of ultrasound energy on the vegetal material and the implemented parameters by using HPLC-photodiode array detection. The influences of several experimental parameters on the ultrasonic extraction of Ficus deltoidea leaves were investigated: extraction time (1-8 h), solvent-to-water ratio (1:10 to 1:50), temperature (50–100 °C), duty cycle (10–continuous sonication) and intensity. The extracts at the optimized condition were compared with those obtained by conventional boiling extraction, in terms of bioactive constituents yield and chemical composition. The compounds of interest identified in the extracts were Vitexin and Isovitexin, which possess anti-diabetic, anti-oxidant and anti-cancer properties. Results showed that the main variables affecting the extraction process were temperature and time. Though in less extent, solvent-to-water ratio, duty cycle and intensity are also demonstrated to be important parameters. The experimental values under optimal conditions were in good consistent with the predicted values, which suggested that ultrasonic-assisted extraction (UAE) is more efficient process as compared to conventional boiling extraction. It recommended that ultrasound extraction of Ficus deltoidea plants are feasible to replace the traditional time-consuming and low efficiency preparation procedure in the future modernized and commercialized manufacture of this highly valuable herbal medicine.

Keywords: Ficus, ultrasounds, vitexin, isovitexin

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Authors: Riccardo Cacciotti, Jiri Frankl, Benjamin Wolf, Michael Machacek

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Moisture fluctuations govern the occurrence of fungi-related problems in buildings, which may impose significant health risks for users and even lead to structural failures. Several numerical engineering models attempt to capture the complexity of mold growth on building materials. From real life observations, in cases with suppressed daily variations of boundary conditions, e.g. in crawlspaces, mold growth model predictions well correspond with the observed mold growth. On the other hand, in cases with substantial diurnal variations of boundary conditions, e.g. in the ventilated cavity of a cold flat roof, mold growth predicted by the models is significantly overestimated. This study, founded by the Grant Agency of the Czech Republic (GAČR 20-12941S), aims at gaining a better understanding of mold growth behavior on solid wood, under varying boundary conditions. In particular, the experimental investigation focuses on the response of mold to changing conditions in the boundary layer and its influence on heat and moisture transfer across the surface. The main results include the design and construction at the facilities of ITAM (Prague, Czech Republic) of an innovative device allowing for the simulation of changing environmental conditions in buildings. It consists of a square section closed circuit with rough dimensions 200 × 180 cm and cross section roughly 30 × 30 cm. The circuit is thermally insulated and equipped with an electric fan to control air flow inside the tunnel, a heat and humidity exchange unit to control the internal RH and variations in temperature. Several measuring points, including an anemometer, temperature and humidity sensor, a loading cell in the test section for recording mass changes, are provided to monitor the variations of parameters during the experiments. The research is ongoing and it is expected to provide the final results of the experimental investigation at the end of 2022.

Keywords: moisture, mold growth, testing, wood

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Authors: Daniel Gebreslassie Mekonnen

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Effective leadership has a crucial role in excelling in the overall success of a school. Today there is much attention given to school leadership, without which schools can never be successful. Therefore, the study was aimed at investigating the role of principals’ leadership styles and their emotional intelligence on the work motivation and job performance of teachers in Addis Ababa, Ethiopia. The study, thus, first examined the relationship between work motivation and job performance of the teachers in relation to the perceived leadership styles and emotional intelligence of principals. Second, it assessed the mean differences and the interaction effects of the principals’ leadership styles and emotional intelligence on the work motivation and job performance of the teachers. Finally, the study investigated whether principals’ leadership styles and emotional intelligence variables had significantly predicted the work motivation and job performance of teachers. As a means, a quantitative approach and descriptive research design were employed to conduct the study. Three hundred sixteen teachers were selected using multistage sampling techniques as participants of the study from the eight sub-cities in Addis Ababa. The main data-gathering instruments used in this study were the path-goal leadership questionnaire, emotional competence inventory, multidimensional work motivation scale, and job performance appraisal scale. The quantitative data were analyzed by using the statistical techniques of Pearson–product-moment correlation analysis, two-way analysis of variance, and stepwise multiple regression analysis. Major findings of the study have revealed that the work motivation and job performance of the teachers were significantly correlated with the perceived participative leadership style, achievement-oriented leadership style, and emotional intelligence of principals. Moreover, the emotional intelligence of the principals was found to be the best predictor of the teachers’ work motivation, whereas the achievement-oriented leadership style of the principals was identified as the best predictor of the job performance of the teachers. Furthermore, the interaction effects of all four path-goal leadership styles vis-a-vis the emotional intelligence of the principals have shown differential effects on the work motivation and job performance of teachers. Thus, it is reasonable to conclude that emotional intelligence is the sine qua non of effective school leadership. Hence, this study would be useful for policymakers and educational leaders to come up with policies that would enhance the role of emotional intelligence on school leadership effectiveness. Finally, pertinent recommendations were drawn from the findings and the conclusions of the study.

Keywords: emotional intelligence, leadership style, job performance, work motivation

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

Procedia PDF Downloads 139

Authors: Frida Carrillo Morales, Maria Maricela Carrasco Yépez, Saúl Rojas Hernández

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Naegleria fowleri is the causative agent of primary amebic meningoencephalitis, this disease is acute and fulminant that affects humans. It has been reported that despite the existence of therapeutic options against this disease, its mortality rate is 97%. Therefore, the need arises to have vaccines that confer protection against this disease and, in addition to developing adjuvants to enhance the immune response. In this regard, in our work group, we obtained a peptide designed from the membrane protein MP2CL5 of Naegleria fowleri called Smp145 that was shown to be immunogenic; however, it would be of great importance to enhance its immunological response, being able to co-administer it with a non-toxic adjuvant. Therefore, the objective of this work was to carry out the bioinformatic design of a peptide of the Naegleria fowleri membrane protein MP2CL5 conjugated with a non-toxic modified adjuvant from the native A1 structure of Cholera Toxin. For which different bioinformatics tools were used to obtain a model with a modification in amino acid 61 of the A1 subunit of the CT (CTA1), to which the Smp145 peptide was added and both molecules were joined with a 13-glycine linker. As for the results obtained, the modification in CTA1 bound to the peptide produces a reduction in the toxicity of the molecule in in silico experiments, likewise, the prediction in the binding of Smp145 to the receptor of B cells suggests that the molecule is directed in specifically to the BCR receptor, decreasing its native enzymatic activity. The stereochemical evaluation showed that the generated model has a high number of adequately predicted residues. In the ERRAT test, the confidence with which it is possible to reject regions that exceed the error values was evaluated, in the generated model, a high score was obtained, which determines that the model has a good structural resolution. Therefore, the design of the conjugated peptide in this work will allow us to proceed with its chemical synthesis and subsequently be able to use it in the mouse meningitis protection model caused by N. fowleri.

Keywords: immunology, vaccines, pathogens, infectious disease

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Authors: Abdulmalek Ahmed

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Formation pressure is the main function that affects drilling operation economically and efficiently. Knowing the pore pressure and the parameters that affect it will help to reduce the cost of drilling process. Many empirical models reported in the literature were used to calculate the formation pressure based on different parameters. Some of these models used only drilling parameters to estimate pore pressure. Other models predicted the formation pressure based on log data. All of these models required different trends such as normal or abnormal to predict the pore pressure. Few researchers applied artificial intelligence (AI) techniques to predict the formation pressure by only one method or a maximum of two methods of AI. The objective of this research is to predict the pore pressure based on both drilling parameters and log data namely; weight on bit, rotary speed, rate of penetration, mud weight, bulk density, porosity and delta sonic time. A real field data is used to predict the formation pressure using five different artificial intelligence (AI) methods such as; artificial neural networks (ANN), radial basis function (RBF), fuzzy logic (FL), support vector machine (SVM) and functional networks (FN). All AI tools were compared with different empirical models. AI methods estimated the formation pressure by a high accuracy (high correlation coefficient and low average absolute percentage error) and outperformed all previous. The advantage of the new technique is its simplicity, which represented from its estimation of pore pressure without the need of different trends as compared to other models which require a two different trend (normal or abnormal pressure). Moreover, by comparing the AI tools with each other, the results indicate that SVM has the advantage of pore pressure prediction by its fast processing speed and high performance (a high correlation coefficient of 0.997 and a low average absolute percentage error of 0.14%). In the end, a new empirical correlation for formation pressure was developed using ANN method that can estimate pore pressure with a high precision (correlation coefficient of 0.998 and average absolute percentage error of 0.17%).

Keywords: Artificial Intelligence (AI), Formation pressure, Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machine (SVM), Functional Networks (FN), Radial Basis Function (RBF)

Procedia PDF Downloads 137

Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Inna R. Edara, Haw-Lin Wu

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Positive psychology investigates human strengths and virtues and promotes well-being. Relying on this assumption, positive interventions have been continuously designed to build pleasure and happiness, joy and contentment, engagement and meaning, hope and optimism, satisfaction and gratitude, spirituality, and various other positive measures of well-being. In line with this model of positive psychology and interventions, this study investigated certain measures of well-being in a group of 45 students enrolled in an 18-week positive psychology course and simultaneously engaged in service-oriented interventions that they chose for themselves based on the course content and individual interests. Students’ well-being was measured at the beginning and end of the course. The well-being indicators included positive automatic thoughts, optimism and hope, satisfaction with life, and spirituality. A paired-samples t-test conducted to evaluate the impact of class content and service-oriented interventions on students’ scores of well-being indicators indicated statistically significant increase from pre-class to post-class scores. There were also significant gender differences in post-course well-being scores, with females having higher levels of well-being than males. A two-way between groups analysis of variance indicated a significant interaction effect of age by gender on the post-course well-being scores, with females in the age group of 56-65 having the highest scores of well-being in comparison to the males in the same age group. Regression analyses indicated that positive automatic thought significantly predicted hope and satisfaction with life in the pre-course analysis. In the post-course regression analysis, spiritual transcendence made a significant contribution to optimism, and positive automatic thought made a significant contribution to both hope and satisfaction with life. Finally, a significant test between pre-course and post-course regression coefficients indicated that the regression coefficients at pre-course were significantly different from post-course coefficients, suggesting that the positive psychology course and the interventions were helpful in raising the levels of well-being. The overall results suggest a substantial increase in the participants’ well-being scores after engaging in the positive-oriented interventions, implying a need for designing more positive interventions in education to promote well-being.  

Keywords: hope, optimism, positive automatic thoughts, satisfaction with life, spirituality, well-being

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Authors: Laura Vitola, Diana Bajare, Genadijs Sahmenko, Girts Bumanis

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About 8 % of CO2 emission in the world is produced by concrete industry therefore replacement of cement in concrete composition by additives with pozzolanic activity would give a significant impact on the environment. Material which contains silica SiO2 or amorphous silica SiO2 together with aluminum dioxide Al2O3 is called pozzolana type additives in the concrete industry. Pozzolana additives are possible to obtain from recycling industry and different production by-products such as processed bulb boric silicate (DRL type) and lead (LB type) glass, coal combustion bottom ash, utilized brick pieces and biomass ash, thus solving utilization problem which is so important in the world, as well as practically using materials which previously were considered as unusable. In the literature, there is no summarized method which could be used for quick waste-product pozzolana activity evaluation without the performance of wide researches related to the production of innumerable concrete contents and samples in the literature. Besides it is important to understand which parameters should be predicted to characterize the efficiency of waste-products. Simple methods of pozzolana activity increase for different types of waste-products are also determined. The aim of this study is to evaluate effectiveness of the different types of waste materials and industrial by-products (coal combustion bottom ash, biomass ash, waste glass, waste kaolin and calcined illite clays), and determine which parameters have the greatest impact on pozzolanic activity. By using materials, which previously were considered as unusable and landfilled, in concrete industry basic utilization problems will be partially solved. The optimal methods for treatment of waste materials and industrial by–products were detected with the purpose to increase their pozzolanic activity and produce substitutes for cement in the concrete industry. Usage of mentioned pozzolanic allows us to replace of necessary cement amount till 20% without reducing the compressive strength of concrete.

Keywords: cement substitutes, micro and nano fillers, pozzolanic properties, specific surface area, particle size, waste products

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Authors: Lizel Bertie, Kim Johnston

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Parental emotion regulation plays a central role in the socialisation of emotion, especially when teaching young children to cope with negative emotions. Despite evidence which shows non-supportive parental responses to children’s expression of negative emotions has implications for the social and emotional development of the child, few studies have investigated risk factors which impact parental emotion socialisation processes. The current study aimed to explore the extent to which parental stress contributes to both difficulties in parental emotion regulation and non-supportive parental responses to children’s expression of negative emotions. In addition, the study examined whether parental use of expressive suppression as an emotion regulation strategy facilitates the influence of parental stress on non-supportive responses by testing the relations in a mediation model. A sample of 140 Australian adults, who identified as parents with children aged 5 to 10 years, completed an online questionnaire. The measures explored recent symptoms of depression, anxiety, and stress, the use of expressive suppression as an emotion regulation strategy, and hypothetical parental responses to scenarios related to children’s expression of negative emotions. A mediated regression indicated that parents who reported higher levels of stress also reported higher levels of expressive suppression as an emotion regulation strategy and increased use of non-supportive responses in relation to young children’s expression of negative emotions. These findings suggest that parents who experience heightened symptoms of stress are more likely to both suppress their emotions in parent-child interaction and engage in non-supportive responses. Furthermore, higher use of expressive suppression strongly predicted the use of non-supportive responses, despite the presence of parental stress. Contrary to expectation, no indirect effect of stress on non-supportive responses was observed via expressive suppression. The findings from the study suggest that parental stress may become a more salient manifestation of psychological distress in a sub-clinical population of parents while contributing to impaired parental responses. As such, the study offers support for targeting overarching factors such as difficulties in parental emotion regulation and stress management, not only as an intervention for parental psychological distress, but also the detection and prevention of maladaptive parenting practices.

Keywords: emotion regulation, emotion socialisation, expressive suppression, non-supportive responses, parental stress

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Authors: Irena Maus, Katharina Gabriella Cibis, Andreas Bremges, Yvonne Stolze, Geizecler Tomazetto, Daniel Wibberg, Helmut König, Alfred Pühler, Andreas Schlüter

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Within the agricultural sector, the production of biogas from organic substrates represents an economically attractive technology to generate bioenergy. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. Recently, species belonging to the phylum Thermotogae were detected in thermophilic biogas-production plants utilizing renewable primary products for biomethanation. To analyze adaptive genome features of representative Thermotogae strains, Defluviitoga tunisiensis L3 was isolated from a rural thermophilic biogas plant (54°C) and completely sequenced on an Illumina MiSeq system. Sequencing and assembly of the D. tunisiensis L3 genome yielded a circular chromosome with a size of 2,053,097 bp and a mean GC content of 31.38%. Functional annotation of the complete genome sequence revealed that the thermophilic strain L3 encodes several genes predicted to facilitate growth of this microorganism on arabinose, galactose, maltose, mannose, fructose, raffinose, ribose, cellobiose, lactose, xylose, xylan, lactate and mannitol. Acetate, hydrogen (H2) and carbon dioxide (CO2) are supposed to be end products of the fermentation process. The latter gene products are metabolites for methanogenic archaea, the key players in the final step of the anaerobic digestion process. To determine the degree of relatedness of dominant biogas community members within selected digester systems to D. tunisiensis L3, metagenome sequences from corresponding communities were mapped on the L3 genome. These fragment recruitments revealed that metagenome reads originating from a thermophilic biogas plant covered 95% of D. tunisiensis L3 genome sequence. In conclusion, availability of the D. tunisiensis L3 genome sequence and insights into its metabolic capabilities provide the basis for biotechnological exploitation of genome features involved in thermophilic fermentation processes utilizing renewable primary products.

Keywords: genome sequence, thermophilic biogas plant, Thermotogae, Defluviitoga tunisiensis

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Authors: A. C. Obosi, H. O. Osinowo, L. I. Okeke

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Benign prostatic hyperplasia (BPH) is one among other prostate diseases with an increasing public health concern. The prevalence and increased psychological distress of BPH among men negatively impact on their health-related quality of life (HRQoL). Although several biomedical factors have been implicated in poor HRQoL among people with BPH, there is a dearth of research on the psychosocial factors predicting HRQoL among them especially in developing climes. This study, therefore, examined the psychosocial (knowledge, perceived stigma, depression, anxiety, perceived social support and illness acceptance) predictors of health-related quality of life among persons living with BPH in Ibadan, Nigeria. Biopsychosocial model and Health-related Quality of life guided this study which utilized ex-post facto design. Eighty-seven males living with BPH were purposively selected and actively participated in the study. Participants’ mean age was 61.77 ± 15.80 years. A standardized questionnaire comprising Socio-demographics and measures of health-related quality of life (α = 0.47); knowledge (α = 0.72); psychological distress (α = 0.95); perceived social support (α = 0.96) and Illness acceptance (α = 0.89) scales was utilized in the study. Data were content analysed, while bivariate correlation, hierarchical multiple regression and t-test for independent samples were computed at p < 0.05. Results revealed that 42.5% of the respondents reported poor HRQoL. Furthermore, age, length of illness, perceived stigma, depression, anxiety, knowledge, perceived social support and illness acceptance jointly predicted HRQoL significantly (R2=0.33, F(9,75)=4.05) and accounted for 33% variance in the total observed variance on HRQoL, while Illness acceptance (β=0.43), anxiety (β=-0.54), and perceived social support (β=0.16) had significant independent contributions to the observed variance on HRQoL. Illness acceptance, knowledge, perceived social support and psychological distress such as anxiety, depression and perceived stigma are important predictors of HRQoL. Therefore, it was recommended that urgent psychological intervention targeted at improving the quality of life of these persons be undertaken.

Keywords: benign prostatic hyperplasia, Health-related quality of life, prostate disorders, psychosocial factors

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Authors: Thu Nhi Tran Caliste

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X-ray Bragg diffraction imaging (“topography”)entered into practical use when Lang designed an “easy” technical setup to characterise the defects / distortions in the high perfection crystals produced for the microelectronics industry. The use of this technique extended to all kind of high quality crystals, and deposited layers, and a series of publications explained, starting from the dynamical theory of diffraction, the contrast of the images of the defects. A quantitative version of “monochromatic topography” known as“Rocking Curve Imaging” (RCI) was implemented, by using synchrotron light and taking advantage of the dramatic improvement of the 2D-detectors and computerised image processing. The rough data is constituted by a number (~300) of images recorded along the diffraction (“rocking”) curve. If the quality of the crystal is such that a one-to-onerelation between a pixel of the detector and a voxel within the crystal can be established (this approximation is very well fulfilled if the local mosaic spread of the voxel is < 1 mradian), a software we developped provides, from the each rocking curve recorded on each of the pixels of the detector, not only the “voxel” integrated intensity (the only data provided by the previous techniques) but also its “mosaic spread” (FWHM) and peak position. We will show, based on many examples, that this new data, never recorded before, open the field to a highly enhanced characterization of the crystal and deposited layers. These examples include the characterization of dislocations and twins occurring during silicon growth, various growth features in Al203, GaNand CdTe (where the diffraction displays the Borrmannanomalous absorption, which leads to a new type of images), and the characterisation of the defects within deposited layers, or their effect on the substrate. We could also observe (due to the very high sensitivity of the setup installed on BM05, which allows revealing these faint effects) that, when dealing with very perfect crystals, the Kato’s interference fringes predicted by dynamical theory are also associated with very small modifications of the local FWHM and peak position (of the order of the µradian). This rather unexpected (at least for us) result appears to be in keeping with preliminary dynamical theory calculations.

Keywords: rocking curve imaging, X-ray diffraction, defect, distortion

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Authors: Misty Attwood, Helgi Schioth

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Transmembrane proteins are important components in many essential cell processes such as signal transduction, cell-cell signalling, transport of solutes, structural adhesion activities, and protein trafficking. Due to their involvement in diverse critical activities, transmembrane proteins are implicated in different disease pathways and hence are the focus of intense interest in understanding functional activities, their pathogenesis in disease, and their potential as pharmaceutical targets. Further, as the structure and function of proteins are correlated, investigating a group of proteins with the same tertiary structure, i.e., the same number of transmembrane regions, may give understanding about their functional roles and potential as therapeutic targets. In this in silico bioinformatics analysis, we identify and comprehensively characterize the previously unstudied group of proteins with five transmembrane-spanning regions (5TM). We classify nearly 60 5TM proteins in which 31 are members of ten families that contain two or more family members and all members are predicted to contain the 5TM architecture. Furthermore, nine singlet proteins that contain the 5TM architecture without paralogues detected in humans were also identifying, indicating the evolution of single unique proteins with the 5TM structure. Interestingly, more than half of these proteins function in localization activities through movement or tethering of cell components and more than one-third are involved in transport activities, particularly in the mitochondria. Surprisingly, no receptor activity was identified within this family in sharp contrast with other TM families. Three major 5TM families were identified and include the Tweety family, which are pore-forming subunits of the swelling-dependent volume regulated anion channel in astrocytes; the sidoreflexin family that acts as mitochondrial amino acid transporters; and the Yip1 domain family engaged in vesicle budding and intra-Golgi transport. About 30% of the proteins have enhanced expression in the brain, liver, or testis. Importantly, 60% of these proteins are identified as cancer prognostic markers, where they are associated with clinical outcomes of various tumour types, indicating further investigation into the function and expression of these proteins is important. This study provides the first comprehensive analysis of proteins with 5TM regions and provides details of the unique characteristics and application in pharmaceutical development.

Keywords: 5TM, cancer prognostic marker, drug targets, transmembrane protein

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Authors: David Sarpong, Waleed Khursheed, Ernest Danquah, Erin Murphy

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Shigella is a pathogenic bacterium responsible for shigellosis, a severe diarrheal disease that claims the lives of immunocompromised individuals worldwide. To develop therapeutics against this disease, an understanding of the molecular mechanisms underlying the pathogen’s physiology is crucial. Small non-coding RNAs (sRNAs) have emerged as important regulators of bacterial physiology, including as components of toxin-antitoxin systems. In this study, we investigated the role of RyfA in S. flexneri physiology and virulence. RyfA, originally identified as an sRNA in Escherichia coli, is conserved within the Enterobacteriaceae family, including Shigella. Whereas two copies of ryfA are present in S. dysenteriae, all other Shigella species contain only one copy of the gene. Additionally, we identified a putative open reading frame within the RyfA transcript, suggesting that it may be a dual-functioning gene encoding a small protein in addition to its sRNA function. To study ryfA in vitro, we cloned the gene into an inducible plasmid and observed the effect on bacterial growth. Here, we report that RyfA production inhibits the growth of S. flexneri, and this inhibition is dependent on the contained open reading frame. In-silico analyses have revealed the presence of two divergently transcribed sRNAs, RyfB1 and RyfB2, which share nucleotide complementarity with RyfA and thus are predicted to function as anti-toxins. Our data demonstrate that RyfB2 has a stronger antitoxin effect than RyfB1. This regulatory pattern suggests a novel form of a toxin-antitoxin system in which the activity of a single toxin is inhibited to varying degrees by two sRNA antitoxins. Studies are ongoing to investigate the regulatory mechanism(s) of the antitoxin genes, as well as the downstream targets and mechanism of growth inhibition by the RyfA toxin. This study offers distinct insights into the regulatory mechanisms underlying Shigella physiology and may inform the development of new anti-Shigella therapeutics.

Keywords: sRNA, shigella, toxin-antitoxin, Type 1 toxin antitoxin

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Authors: Lou J. Pino, Mark Campbell, Matthew J. Kennedy, Ashleigh C. Kennedy

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A concussion is complex and nuanced, with cognitive rest being a key component of recovery. Cognitive overexertion during rehabilitation from a concussion is associated with delayed recovery. However, daily living imposes cognitive demands that may be unavoidable and difficult to quantify. Therefore, a portable tool capable of alerting patients before cognitive overexertion occurs could allow patients to maintain their quality of life while preventing symptoms and recovery setbacks. EEG allows for a sensitive measure of cognitive exertion. Clinical 32-lead EEG headsets are not practical for day-to-day concussion rehabilitation management. However, there are now commercially available and affordable portable EEG headsets. Thus, these headsets can potentially be used to continuously monitor cognitive exertion during mental tasks to alert the wearer of overexertion, with the aim of preventing the occurrence of symptoms to speed recovery times. The objective of this study was to test an algorithm for predicting cognitive exertion from EEG data collected from a portable headset. EEG data were acquired from 10 participants (5 males, 5 females). Each participant wore a portable 4 channel EEG headband while completing 10 tasks: rest (eyes closed), rest (eyes open), three levels of the increasing difficulty of logic puzzles, three levels of increasing difficulty in multiplication questions, rest (eyes open), and rest (eyes closed). After each task, the participant was asked to report their perceived level of cognitive exertion using the NASA Task Load Index (TLX). Each participant then completed a second session on a different day. A customized machine learning model was created using data from the first session. The performance of each model was then tested using data from the second session. The mean correlation coefficient between TLX scores and predicted cognitive exertion was 0.75 ± 0.16. The results support the efficacy of the algorithm for predicting cognitive exertion. This demonstrates that the algorithms developed in this study used with portable EEG devices have the potential to aid in the concussion recovery process by monitoring and warning patients of cognitive overexertion. Preventing cognitive overexertion during recovery may reduce the number of symptoms a patient experiences and may help speed the recovery process.

Keywords: cognitive activity, EEG, machine learning, personalized recovery

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Authors: Shreya Sara Ittycheria, K. C. Sivakumar, Shijulal Nelson Sathi, Priya Srinivas

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hCG, a heterodimer composed of α and β subunits, is a peptide hormone having numerous biological functions. Although hCG is expressed by placenta during pregnancy, ectopic β-hCG secretion is observed in many non-trophoblastic tumors including that of breast. In-vitro and in-vivo studies done in the lab, have proved that BRCA1 defective cancers express β-hCG and when β-hCG is expressed or supplemented, it promotes tumor progression and exhibits resistance to carboplatin and ABT888, in such cancers but not in BRCA1 wild type cancers. In cancer cells, instead of binding to its regular receptor, LH-CGR, β-hCG binds with Transforming Growth Factor Receptor 2 (TGFβRII) and phosphorylates it resulting in faster tumor progression through the Smad signaling pathway. Targeting β-hCG could be a potential therapeutic strategy for managing BRCA1 defective cancers. Here, molecular docking and dynamic simulation studies were done to identify potential small molecule inhibitors against β-hCG as there are currently no such inhibitors reported. The binding sites of TGFβRII on β-hCG were identified from the top 10 predicted complexes from Z Dock. Virtual screening of selected commercially available small molecules from various libraries such as ZINC, NCI and Life Chemicals amounting to a total of 50,025 molecules were done. Four potential small molecule inhibitors were identified, RgcbPs-1, RgcbPs-2, RgcbPs-3 and RgcbPs-4 with binding affinities -60.778 kcal/mol, -45.447 kcal/mol, -65.2268 kcal/mol and -82.040 kcal/mol respectively. Further, 100ns Molecular Dynamics (MD) simulation showed that these molecules form stable complexes with β-hCG. RgcbPs-1 maintains hydrogen bonds with Q54, L52, Q46, C100, G36, C57, C38 residues, RgcbPs-2 maintains hydrogen bonds with A83 residue, RgcbPs-3 maintains hydrogen bonds with C57, Y58, R94, G101 residues and RgcbPs-4 maintains hydrogen bonds with G36, C38, T40, C57, D99, C100, G101 and L104 residues of β-hCG all of which coincide with the TGFβRII binding site on β-hCG. These results show that these two inhibitors could be used either singly or in combination for inhibiting β-hCG from binding to TGFβRII and thereby directly inhibiting the tumorigenesis pathway.

Keywords: β-hCG, breast cancer, dynamic simulations, molecular docking, small molecule inhibitors, virtual screening.

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Authors: Faiz N. K. Yusufi, Aquil Ahmed, Jamal Ahmad

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Diabetes in India is growing at an alarming rate and the complications caused by it need to be controlled. Coronary heart disease (CHD) is one of the complications that will be discussed for prediction in this study. India has the second most number of diabetes patients in the world. To the best of our knowledge, there is no CHD risk score for Indian type 2 diabetes patients. Any form of CHD has been taken as the event of interest. A sample of 750 was determined and randomly collected from the Rajiv Gandhi Centre for Diabetes and Endocrinology, J.N.M.C., A.M.U., Aligarh, India. Collected variables include patients data such as sex, age, height, weight, body mass index (BMI), blood sugar fasting (BSF), post prandial sugar (PP), glycosylated haemoglobin (HbA1c), diastolic blood pressure (DBP), systolic blood pressure (SBP), smoking, alcohol habits, total cholesterol (TC), triglycerides (TG), high density lipoprotein (HDL), low density lipoprotein (LDL), very low density lipoprotein (VLDL), physical activity, duration of diabetes, diet control, history of antihypertensive drug treatment, family history of diabetes, waist circumference, hip circumference, medications, central obesity and history of CHD. Predictive risk scores of CHD events are designed by cox proportional hazard regression. Model calibration and discrimination is assessed from Hosmer Lemeshow and area under receiver operating characteristic (ROC) curve. Overfitting and underfitting of the model is checked by applying regularization techniques and best method is selected between ridge, lasso and elastic net regression. Youden’s index is used to choose the optimal cut off point from the scores. Five year probability of CHD is predicted by both survival function and Markov chain two state model and the better technique is concluded. The risk scores for CHD developed can be calculated by doctors and patients for self-control of diabetes. Furthermore, the five-year probabilities can be implemented as well to forecast and maintain the condition of patients.

Keywords: coronary heart disease, cox proportional hazard regression, ROC curve, type 2 diabetes Mellitus

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Authors: Saeed Farhadypour, Majid Azadi, Habibolla Sayyari, Mahmood Mosavi, Shahram Irani, Aliakbar Bidokhti, Omid Alizadeh Choobari, Ziba Hamidi

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In the recent decades, frequencies of dust events have increased significantly in west and south west of Iran. First, a survey on the dust events during the period (1990-2013) is investigated using historical dust data collected at 6 weather stations scattered over west and south-west of Iran. After statistical analysis of the observational data, one of the most severe dust storm event that occurred in the region from 3rd to 6th July 2009, is selected and analyzed. WRF-Chem model is used to simulate the amount of PM10 and how to transport it to the areas. The initial and lateral boundary conditions for model obtained from GFS data with 0.5°×0.5° spatial resolution. In the simulation, two aerosol schemas (GOCART and MADE/SORGAM) with 3 options (chem_opt=106,300 and 303) were evaluated. Results of the statistical analysis of the historical data showed that south west of Iran has high frequency of dust events, so that Bushehr station has the highest frequency between stations and Urmia station has the lowest frequency. Also in the period of 1990 to 2013, the years 2009 and 1998 with the amounts of 3221 and 100 respectively had the highest and lowest dust events and according to the monthly variation, June and July had the highest frequency of dust events and December had the lowest frequency. Besides, model results showed that the MADE / SORGAM scheme has predicted values and trends of PM10 better than the other schemes and has showed the better performance in comparison with the observations. Finally, distribution of PM10 and the wind surface maps obtained from numerical modeling showed that the formation of dust plums formed in Iraq and Syria and also transportation of them to the West and Southwest of Iran. In addition, comparing the MODIS satellite image acquired on 4th July 2009 with model output at the same time showed the good ability of WRF-Chem in simulating spatial distribution of dust.

Keywords: dust storm, MADE/SORGAM scheme, PM10, WRF-Chem

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Authors: Ammar Amsyar Abdul Haddi, Mohd Hasni Jaafar

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Heavy metals are elements naturally presented in the environment that can cause adverse effect to health. But not much literature was found on effects toward lung function, where impairment of lung function may lead to various lung diseases. The objective of the study is to explore the lung function impairment, urinary heavy metal level, and its associated factors among the community in Klang valley, Malaysia. Sampling was done in Kuala Lumpur suburb public and housing areas during community events throughout March 2019 till October 2019. respondents who gave the consent were given a questionnaire to answer and was proceeded with a lung function test. Urine samples were obtained at the end of the session and sent for Inductively coupled plasma mass spectrometry (ICP-MS) analysis for heavy metal cadmium (Cd) and lead (Pb) concentration. A total of 200 samples were analysed, and of all, 52% of respondents were male, Age ranging from 18 years old to 74 years old with a mean age of 38.44. Urinary samples show that 12% of the respondent (n=22) has Cd level above than average, and 1.5 % of the respondent (n=3) has urinary Pb at an above normal level. Bivariate analysis show that there was a positive correlation between urinary Cd and urinary Pb (r= 0.309; p<0.001). Furthermore, there was a negative correlation between urinary Cd level and full vital capacity (FVC) (r=-0.202, p=0.004), Force expiratory volume at 1 second (FEV1) (r = -0.225, p=0.001), and also with Force expiratory flow between 25-75% FVC (FEF25%-75%) (r= -0.187, p=0.008). however, urinary Pb did not show any association with FVC, FEV1, FEV1/FVC, or FEF25%-75%. Multiple linear regression analysis shows that urinary Cd remained significant and negatively affect FVC% (p=0.025) and FEV1% (p=0.004) achieved from the predicted value. On top of that, other factors such as education level (p=0.013) and duration of smoking(p=0.003) may influencing both urinary Cd and performance in lung function as well, suggesting Cd as a potential mediating factor between smoking and impairment of lung function. however, there was no interaction detected between heavy metal or other influencing factor in this study. In short, there is a negative linear relationship detected between urinary Cd and lung function, and urinary Cd is likely to affects lung function in a restrictive pattern. Since smoking is also an influencing factor for urinary Cd and lung function impairment, it is highly suggested that smokers should be screened for lung function and urinary Cd level in the future for early disease prevention.

Keywords: lung function, heavy metals, community

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Authors: Mohamed Moussaoui, Mouna Baassi, Soukayna Baammi, Hatim Soufi, Mohammed Salah, Rachid Daoud, Achraf EL Allali, Mohammed Elalaoui Belghiti, Said Belaaouad

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The present study aims to investigate the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the density functional theory (DFT) method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R²= 0.725, R²ₐ𝒹ⱼ= 0.653, MSE = 0.060, R²ₜₑₛₜ= 0.827, Q²𝒸ᵥ = 0.536). The energy of the highest occupied molecular orbital (EHOMO) orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Additional Thiazole derivatives were then designed and their activities and pharmacokinetic properties were predicted using the validated QSAR model. These designed molecules underwent evaluation through molecular docking (MD) and molecular dynamic (MD) simulations, with binding affinity calculated using the MMPBSA script according to a 100 ns simulation trajectory. This process aimed to study both their affinity and stability towards Cyclin-Dependent Kinase 2 (CDK2), a target protein for cancer disease treatment. The research concluded by identifying four CDK2 inhibitors - A1, A3, A5, and A6 - displaying satisfactory pharmacokinetic properties. MDs results indicated that the designed compound A5 remained stable in the active center of the CDK2 protein, suggesting its potential as an effective inhibitor for the treatment of hepatocellular carcinoma. The findings of this study could contribute significantly to the development of effective CDK2 inhibitors.

Keywords: QSAR, ADMET, Thiazole, anticancer, molecular docking, molecular dynamic simulations, MMPBSA calculation

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Authors: Shireen Ibish

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Physical inactivity and unhealthy diets have contributed to a global burden of disease with increased relation to non-communicable diseases, increased risk of colon and breast cancer, high prevalence of depression, reduced quality of life and early death. The World Health Organisation’s facts on Obesity show a tripling in prevalence across the European Region since the 1980s. This has lead to a huge public health burden, being responsible for and 10-13% of deaths (fourth largest cause of global mortality) and 2-8% of health costs in the Region. In the UK alone, the present cost of physical inactivity has been estimated to be £8.2 billion. In 2002 a paper published in the International Journal of Epidemiology on ‘sedentary’ lifestyle, put into figures the increasingly worrying statistics across European countries. “Percentages of sedentary lifestyles across European countries ranged between 43.3% (Sweden) and 87.8% (Portugal)”. This was especially so amongst obese subjects, less- educated people, and smokers. While in the UK’s “50% of adult population in the UK is predicted to be obese by 2050.” Sports and Exercise Medicine, as a specialty, has a lot to offer in targeting this globally increasing epidemic. The worrying figures and the increasing knowledge of combating and preventing this issue have lead to increased awareness amongst the medical profession and more targeted interventions to reduce the burden of disease. “The public health element of the specialty is critical – this is not simply a specialty for the management of elite athletes’ medical conditions – it is central to the promotion of exercise as a means of disease prevention, to enhance well-being and in the management of disease.” WHO advised on creating National policies, encouraging and providing opportunities for greater physical activity, and improve the affordability, availability and accessibility of healthy foods. In the UK various different movements have been established to target this problem. The Motivate2Move, Move Eat Treat and guidelines advising specialties on targeting and encouraging exercise in the population (Sport and Exercise Medicine A Fresh Approach).

Keywords: sedentary lifestyle, obesity, public health burden, medicine

Procedia PDF Downloads 551

Authors: Zafer Yüce, Paşa Yayla, Alev Taşkın

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There are heaps of analytical methods to estimate the crack growth life of a component. Soft computing methods have an increasing trend in predicting fatigue life. Their ability to build complex relationships and capability to handle huge amounts of data are motivating researchers and industry professionals to employ them for challenging problems. This study focuses on soft computing methods, especially random forest regressors and artificial neural networks with hyperparameter optimization algorithms such as grid search and random grid search, to estimate the crack growth life of an aircraft wing splice joint under variable amplitude loading. TensorFlow and Scikit-learn libraries of Python are used to build the machine learning models for this study. The material considered in this work is 7050-T7451 aluminum, which is commonly preferred as a structural element in the aerospace industry, and regarding the crack type; corner crack is used. A finite element model is built for the joint to calculate fastener loads and stresses on the structure. Since finite element model results are validated with analytical calculations, findings of the finite element model are fed to AFGROW software to calculate analytical crack growth lives. Based on Fighter Aircraft Loading Standard for Fatigue (FALSTAFF), 90 unique fatigue loading spectra are developed for various load levels, and then, these spectrums are utilized as inputs to the artificial neural network and random forest regression models for predicting crack growth life. Finally, the crack growth life predictions of the machine learning models are compared with analytical calculations. According to the findings, a good correlation is observed between analytical and predicted crack growth lives.

Keywords: aircraft, fatigue, joint, life, optimization, prediction.

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Authors: Mia Françoise

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This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization.

Keywords: financial stocks, optimized asset allocation, prediction modelling, South Africa

Procedia PDF Downloads 74

Authors: Jonas Gradauskas, Oleksandr Masalskyi, Ihor Zharchenko

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The advancement in improving the efficiency of conventional solar cells toward the Shockley-Queisser limit seems to be slowing down or reaching a point of saturation. The challenges hindering the reduction of this efficiency gap can be categorized into extrinsic and intrinsic losses, with the former being theoretically avoidable. Among the five intrinsic losses, two — the below-Eg loss (resulting from non-absorption of photons with energy below the semiconductor bandgap) and thermalization loss —contribute to approximately 55% of the overall lost fraction of solar radiation at energy bandgap values corresponding to silicon and gallium arsenide. Efforts to minimize the disparity between theoretically predicted and experimentally achieved efficiencies in solar cells necessitate the integration of innovative physical concepts. Hot carriers (HC) present a contemporary approach to addressing this challenge. The significance of hot carriers in photovoltaics is not fully understood. Although their excessive energy is thought to indirectly impact a cell's performance through thermalization loss — where the excess energy heats the lattice, leading to efficiency loss — evidence suggests the presence of hot carriers in solar cells. Despite their exceptionally brief lifespan, tangible benefits arise from their existence. The study highlights direct experimental evidence of hot carrier effect induced by both below- and above-bandgap radiation in a singlejunction solar cell. Photocurrent flowing across silicon and GaAs p-n junctions is analyzed. The photoresponse consists, on the whole, of three components caused by electron-hole pair generation, hot carriers, and lattice heating. The last two components counteract the conventional electron-hole generation-caused current required for successful solar cell operation. Also, a model of the temperature coefficient of the voltage change of the current–voltage characteristic is used to obtain the hot carrier temperature. The distribution of cold and hot carriers is analyzed with regard to the potential barrier height of the p-n junction. These discoveries contribute to a better understanding of hot carrier phenomena in photovoltaic devices and are likely to prompt a reevaluation of intrinsic losses in solar cells.

Keywords: solar cell, hot carriers, intrinsic losses, efficiency, photocurrent

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Authors: Mohd. Ashik Shahrier, Hiromi Wada

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Prenatal ethanol exposure has potential to induce difficulties in the social behavior of rats and can alter pup-dam communication suggesting that deficits in pups could result in altered dam behavior, which in turn could result in more aberrant behavior in the pup. Ultrasonic vocalization (USV) is a sensitive tool for investigating social behavior between rat pups and their dam. Rat pups produce USVs on separation from their dam. This signals the dam to locate her pups and retrieve them back to the nest. In this study, it was predicted that prenatal ethanol exposure cause alterations on the acoustic characteristics of USVs in rat pups. Thirteen pregnant rats were purchased and randomly assigned into three groups: high-ethanol (n = 4), low-ethanol (n = 5), and control (n = 4) groups. Laboratory ethanol (purity = 99.5%) was dissolved in tap water and administered to the high- and low-ethanol groups as drinking water from gestational days (GD) 8-20. Ethanol-containing water was administered to the animals in three stages by gradually increasing the concentration between GDs 8–20. From GDs 8–10, 10% and 5%, from GDs 11–13, 20% and 10%, and from GDs 14–20, 30% and 15% ethanol-containing water (v/v) was administered to the high- and low-ethanol groups, respectively. Tap water without ethanol was given to the control group throughout the experiment. The day of birth of the pups was designated as postnatal day (PND) 0. On PND 4, each litter was culled to four male and four female pups. For the present study, two male and two female pups were randomly sampled from each litter as subjects. Thus, eight male and eight female pups from the high-ethanol and control groups and another 10 male and 10 female pups from the low-ethanol group, were sampled. An ultrasonic microphone and the Sonotrack system version 2.4.0 (Metris, Hoofddorp, The Netherlands) were used to record and analyze USVs of the pups. On postnatal days 4, 8, 12 and 16, the resultant pups were individually isolated from their dams and littermates, and USVs were recorded for 5 min in a sound-proof box. Pups in the high-ethanol group produced greater number of USVs compared with that in both low-ethanol and control groups on PND 12. Rat pups in the high-ethanol group also produced higher mean, minimum, and maximum fundamental frequencies of USVs compared with that in both low-ethanol and control groups. Male pups in the high-ethanol group had higher USV amplitudes than in those in low-ethanol and control groups on PND 12. These results suggest that pups in the high-ethanol group relatively experienced more negative emotionality due to the ethanol-induced neuronal activation in the core limbic system and tegmental structures and accordingly, produced altered USVs as distress calls.

Keywords: emotionality, ethanol, maternal separation, ultrasonic vocalization

Procedia PDF Downloads 112