The purpose of this article is to ascertain and appraise the ethical issues inherent to the utilisation of preimplantation genetic diagnosis for gender selection in infertile patients anticipating undergoing a medically indicated assisted reproductive technique procedure. Performance of preimplantation genetic diagnosis per request specifically for gender selection by an infertile couple undergoing medically indicated assisted reproductive technique may not breach the principles of ethics, and is unlikely to alter the population balance of sexes.

Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. In the (...) second scenario, a comparable AI model hybridized with genetic algorithm as a robust bioinspired optimization approach for optimizing the related predictors for the PRSC is proposed. Hybridizing AI models with GA as a selector tool is an attempt to acquire the best accuracy of predictions with the fewest possible related parameters. In accordance with quantitative analysis, it can be observed that the GA-DLNN models required only 7 input parameters and yielded the best prediction accuracy with highest correlation coefficient and lowest value root mean square error. However, the other comparable models such as GA-M5Tree, GA-ANN, and GA-SVR required 10 input parameters to obtain a relatively acceptable level of accuracy. Employing GA as a feature parameter selection technique improves the precision of almost all hybrid models by optimally removing redundant variables which decrease the efficiency of the model. (shrink)

Air temperature prediction can play a significant role in studies related to climate change, radiation and heat flux estimation, and weather forecasting. This study applied and compared the outcomes of three advanced fuzzy inference models, i.e., dynamic evolving neural-fuzzy inference system, hybrid neural-fuzzy inference system, and adaptive neurofuzzy inference system for AT prediction. Modelling was done for three stations in North Dakota, USA, i.e., Robinson, Ada, and Hillsboro. The results reveal that FIS type models are well suited when handling highly (...) variable data, such as AT, which shows a high positive correlation with average daily dew point, total solar radiation, and negative correlation with average wind speed. At the Robinson station, DENFIS performed the best with a coefficient of determination of 0.96 and a modified index of agreement of 0.92, followed by ANFIS with R2 of 0.94 and md of 0.89, and HyFIS with R2 of 0.90 and md of 0.84. A similar result was observed for the other two stations, i.e., Ada and Hillsboro stations where DENFIS performed the best with R2: 0.953/0.960, md: 0.903/0.912, then ANFIS with R2: 0.943/0.942, md: 0.888/0.890, and HyFIS with R2: 0.908/0.905, md: 0.845/0.821, respectively. It can be concluded that all three models are capable of predicting AT with high efficiency by only using DP, TSR, and WS as input variables. This makes the application of these models more reliable for a meteorological variable with the need for the least number of input variables. The study can be valuable for the areas where the climatological and seasonal variations are studied and will allow providing excellent prediction results with the least error margin and without a huge expenditure. (shrink)

Deformation of ground during tunnelling projects is one of the complex issues that is required to be monitored carefully to avoid the unexpected damages and human losses. Accurate prediction of ground settlement is a crucial concern for tunnelling problems, and the adequate predictive model can be a vital tool for tunnel designers to simulate the ground settlement accurately. This study proposes relatively new hybrid artificial intelligence models to predict the ground settlement of earth pressure balance shield tunnelling in the Bangkok (...) MRTA project. The predictive models were various nature-inspired frameworks, such as differential evolution, particle swarm optimization, genetic algorithm, and ant colony optimizer to tune the adaptive neuro-fuzzy inference system. To obtain the accurate and reliable results, the modeling procedure is established based on four different dataset scenarios including preprocessed and normalized, preprocessed and nonnormalized, non-preprocessed and normalized, and non-preprocessed and nonnormalized datasets. Results indicated that PPN dataset scenario significantly affected the prediction models in terms of their perdition accuracy. Among all the developed hybrid models, ANOFS-PSO model achieved the best predictability performance. In quantitative terms, PPN-ANFIS-PSO model attained the least root mean square error value of 7.98 and a correlation coefficient value of 0.83. Overall, the attained results confirmed the superiority of the explored hybrid AI models as robust predictive model for ground settlement of earth pressure balance shield tunnelling. (shrink)

The potential of the soil to hold plant nutrients is governed by the cation-exchange capacity of any soil. Estimating soil CEC aids in conventional soil management practices to replenish the soil solution that supports plant growth. In this study, a multiple model integration scheme supervised with a hybrid genetic algorithm-neural network was developed and employed to predict the accuracy of soil CEC in Tabriz plain, an arid region of Iran. The standalone models and extreme learning machine ) were implemented for (...) incorporation into the MM-GANN. In addition, it was tested to enhance the prediction accuracy of the standalone models. The soil parameters such as clay, silt, pH, carbonate calcium equivalent, and soil organic matter were used as model inputs to predict soil CEC. With the use of several evaluation criteria, the results showed that the MM-GANN model involving the predictions of ELM and ANN models calibrated by considering all the soil parameters as inputs provided superior soil CEC estimates with a Nash Sutcliffe Efficiency = 0.87, Root Mean Square Error = 2.885, Mean Absolute Error = 2.249, Mean Absolute Percentage Error = 12.072, and coefficient of determination = 0.884. The proposed MM-GANN model is a reliable intelligence-based approach for the assessment of soil quality parameters intended for sustainability and management prospects. (shrink)

Nowadays, higher education worldwide is affected by the COVID-19 pandemic. It has affected students’ attendance in the universities and causes universities to close down in more than 190 countries. On the other hand, novice engineers studied only a few lectures related to highway engineering. Their lectures have included very little knowledge about asphalt pavement construction as highway engineering consists of many areas that are not studied in detail during their studying years subject to their traditional education. Due to all mentioned, (...) a new drive to promote online learning paves the way to evaluate our future approach to curriculum development and delivery of educational materials for engineering courses. However, experts can offer solutions to these problems using their past experience. Hence, a system that allows experts to share their experience with other engineers after completing a project is needed. Nevertheless, the web-based expert system for maintaining flexible pavement problems in tropical regions designed in this study is a novel concept. Prior to developing this system, the need for such a system was determined through literature review and validated through a questionnaire survey. Experts were interviewed, and a questionnaire survey was conducted to construct the knowledge base of the system. Knowledge was presented as rules and coded in software through PHP programming. Web pages that support the user interface were designed using a framework that consists of CSS, HTML, and J-Query. Furthermore, the system was tested by an array of users engaged in highway engineering, namely, experts, teaching experts, novice engineers, and students. The mean values of the overall system evaluation performed by 20 users using a five-point Likert scale were 4, 4.5, 3.75, 4.25, 5, 4, and 3.5. Expert and user satisfaction prove the effectiveness of the proposed system. (shrink)