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The edge-cloud computing continuum effectively uses fog and cloud servers to meet the quality of service (QoS) requirements of tasks when edge devices cannot meet those requirements. This paper focuses on the workflow offloading problem in edge-cloud computing and formulates this problem as a nonlinear mathematical programming model. The objective function is to minimize the monetary cost of executing a workflow while satisfying constraints related to data dependency among tasks and QoS requirements, including security and deadlines. Additionally, it presents a genetic algorithm for the workflow offloading problem to find near-optimal solutions with the cost minimization objective. The performance of the proposed mathematical model and genetic algorithm is evaluated on several real-world workflows. Experimental results demonstrate that the proposed genetic algorithm can find admissible solutions comparable to the mathematical model and outperforms particle swarm optimization, bee life algorithm, and a hybrid heuristic-genetic algorithm in terms of workflow execution costs. [ABSTRACT FROM AUTHOR]

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images. [ABSTRACT FROM AUTHOR]

The genetic algorithm with aggressive mutations GAAM, is a specialised algorithm for feature selection. This algorithm is dedicated to the selection of a small number of features and allows the user to specify the maximum number of features desired. A major obstacle to the use of this algorithm is its high computational cost, which increases significantly with the number of dimensions to be retained. To solve this problem, we introduce a surrogate model based on machine learning, which reduces the number of evaluations of the fitness function by an average of 48% on the datasets tested, using the standard parameters specified in the original paper. Additionally, we experimentally demonstrate that eliminating the crossover step in the original algorithm does not result in any visible changes in the algorithm's results. We also demonstrate that the original algorithm uses an artificially complex mutation method that could be replaced by a simpler method without loss of efficiency. The sum of the improvements resulted in an average reduction of 53% in the number of evaluations of the fitness functions. Finally, we have shown that these outcomes apply to parameters beyond those utilized in the initial article, while still achieving a comparable decrease in the count of evaluation function calls. Tests were conducted on 9 datasets of varying dimensions, using two different classifiers. [ABSTRACT FROM AUTHOR]

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

The rationality and timeliness of the comprehensive results of English course learning quality are increasingly important in the process of modern education. There are some problems in the scientific evaluation of English course learning quality and teachers' own English course learning, such as the need for proper adjustment and improvement. Based on the improved network theory of genetic algorithm, this paper takes an online English course learning quality evaluation model and uses MATLAB 7.0 to write the graphical user interface of the neural set network English course learning quality prediction model. The model uses the genetic algorithm of adaptive mutation to optimize the initial weights and values of the neural set network and solves the problems of prediction accuracy and convergence speed of English course learning quality evaluation results. Simulation experiments show that the neural set network has a strong dependence on the initial weights and thresholds. Using the improved genetic algorithm to optimize the initial weights and thresholds of the neural set network reduced the time for the neural set network to find the weights and thresholds that meet the training termination conditions, the prediction accuracy was increased to 0.897, the prediction accuracy was 78.85%, and the level prediction accuracy was 84.62%, which effectively promoted the development of online English course learning in colleges and the continuous improvement of teachers' English course learning level. [ABSTRACT FROM AUTHOR]

This paper proposes a transfer learning method for an object detection model using a genetic algorithm to solve the difficulty of the conventional transfer learning of deep learning-based object detection models. The genetic algorithm of the proposed method can select the re-learning layers automatically in the transfer learning process instead of a trial-and-error selection of the conventional method. Transfer learning was performed using the EfficientDet-d0 model pre-trained on the COCO dataset and the Global Wheat Head Detection (GWHD) dataset, and experiments were conducted to compare fine-tuning and the proposed method. Using the training data and the validation data of the GWHD, we compare the mean average precision (mAP) of the models trained by the conventional and the proposed methods, respectively, on the test data of the GWHD. It is confirmed that the model trained by the proposed method has higher performance than the model trained by the conventional method. The average of mAP of the proposed method, which automatically selects the re-learning layer (≈0.603), is higher than the average of mAP of the conventional method (≈0.594). Furthermore, the standard deviation of results obtained by the proposed method is smaller than that of the conventional method, and it shows the stable learning process of the proposed method. [ABSTRACT FROM AUTHOR]

The fighting fish Betta splendens is a freshwater species from Thailand and other Southeast Asian countries. This fish has been domesticated for 1000 years and bred for fighting, various colours, body size and fin types for 600 years. It is one of the most important fish species cultured for the world ornamental fish market. This fish is easy to culture, highly fecund and displays great morphological diversity. Its biology has been studied for over 100 years. Recently, its compact genome and transcriptomes have been sequenced. Genome editing with CRISPR/cas9 has been applied to knock out genes in this fish. Its diverse phenotypes, including colours, colour patterns, fin types, and aggressive behaviour, are complementary to those of other model animals. Therefore, this fish could be the next important model organism for studying phenotypic variation and aggressive behaviour. In this paper, we synthesized knowledge about its aquaculture, biology, genetics, genomic tools, phenotypes and novel insights on phenotypic variation, sex determination and aggression. We hope that the information described in this paper will facilitate genetic studies on phenotypic variations in aquaculture and aggressive behaviour in other species, including humans. [ABSTRACT FROM AUTHOR]

After my country's economy has entered a new normal, in terms of employment, which has led to the coexistence of the old and new contradictions in employment in our country and the coexistence of employment expansion and stabilization of employment. In this context, it is impossible to achieve full employment and completely eliminate unemployment by relying solely on economic growth. This paper improves traditional machine learning algorithms and builds an entrepreneurial policy analysis model based on improved machine learning to analyze the impact of entrepreneurial policies on employment. Moreover, this paper uses a projection pursuit comprehensive evaluation model optimized by genetic algorithm to conduct empirical research on entrepreneurial environment conditions. In addition, this paper verifies its rationality by regression analysis of empirical results and TEA (Entrepreneurial Activity of All Employees) index, and deeply explores the inherent laws and development characteristics of entrepreneurial environmental conditions from multiple perspectives such as time series and spatial distribution. The research results show that the method proposed in this paper is effective. [ABSTRACT FROM AUTHOR]

Due to the limitation in the prediction of the foundation pit settlement, this paper proposed a new methodology which takes advantage of the grey Verhulst model and a genetic algorithm. In the previous study, excavation times are often the only factor to predict the settlement, which is mainly because the correspondence between real-time excavation depth and the excavation time is hard to determine. To solve this issue, the supporting times are precisely recorded and the excavation depth rate can be obtained through the excavation time length and excavation depth between two adjacent supports. After the correspondence between real-time excavation depth and the excavation time is obtained, the internal friction angle, cohesion, bulk density, Poisson's ratio, void ratio, water level changes, permeability coefficient, number of supports, and excavation depth, which can influence the settlement, are taken to be considered in this study. For the application of the methodology, the settlement monitoring point of D4, which is near the bridge pier of the highway, is studied in this paper. The predicted values of the BP neural network, GA-BP neural network, BP neural network optimized by the grey Verhulst model, and GA-BP neural network optimized by the grey Verhulst model are detailed compared with the measured values. And the evaluation indexes of RMSE, MAE, MSE, MAPE, and R 2 are calculated for these models. The results show that the grey Verhulst model can greatly improve the consistency between predicted values and measured values, while the accuracy and resolution is still low. The genetic algorithm (GA) can greatly improve the accuracy of the predicted values, while the GA-BP neural network shows low reflection to the fluctuation of measured values. The GA-BP neural network optimized by the grey Verhulst model, which has taken the advantages of GA and the grey Verhulst model, has extremely high accuracy and well consistency with the measured values. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to address the issue that the traditional V-shaped ball valve profile shape is limiting the flow control characteristics in a series structure and to optimize the design profile by proposing an open-hole profile. Design/methodology/approach: This paper proposes a Gaussian process regression surrogate model based on the genetic algorithm optimization of swarm intelligence, combined with the Expected Improvement point addition criterion, to optimize and correct the design profile. The flow regulation performance of the optimized V-shaped regulating ball valve is verified through a combination of numerical simulation and experiment. Findings: The results demonstrate that the optimized V-shaped regulating ball valve has higher flow regulation accuracy and a more stable flow regulation process. After optimization, the flow characteristic curve of the spool is closer to the ideal equal percentage characteristic. The simulation results of the flow field are consistent with the experimental results. Originality/value: The proposed method significantly reduces the optimization time, has higher efficiency and solves the problem that traditional optimization methods struggle with, which is ensuring optimal flow regulation performance. Compared to the traditional trial-and-error optimization method, the proposed method is more effective. The feasibility of the method is supported by experimental results. [ABSTRACT FROM AUTHOR]

Obesity is a global epidemic disease representing the fifth leading cause of death in the world. It was shown that it is caused by the interaction between environmental factors and genes including leptin gene (LEP). This paper aimed to analyze the association between the LEP gene polymorphisms rs7799039 and rs11761556 with obesity in Moroccan individuals as well as to perform an update meta-analysis of this genetic association. Both polymorphisms were genotyped in 146 obesity patients and 104 controls using real-time PCR technique. The genetic association analysis and the comparison of quantitative parameters were carried out using the R language. Moreover, a meta-analysis including 20 genetic association studies was performed using Review Manager 5.3 software. No significant association was found between the polymorphisms rs7799039 and rs11761556 and the risk of obesity. The comparison of biochemical and clinical parameters between the genotypes of the rs7799039 polymorphism, showed a significant increased triglycerides levels in carriers of AA or GA genotypes (P value = 0.040). The meta-analysis showed no significant association between the rs7799039 polymorphism and obesity under all genetic models. In conclusion, the case–control study and meta-analysis demonstrated that the LEP gene polymorphisms rs7799039 and rs11761556 cannot be considered as genetic risk factors for obesity. [ABSTRACT FROM AUTHOR]

Background: A relevant part of the genetic architecture of complex traits is still unknown; despite the discovery of many disease-associated common variants. Polygenic risk score (PRS) models are based on the evaluation of the additive effects attributable to common variants and have been successfully implemented to assess the genetic susceptibility for many phenotypes. In contrast, burden tests are often used to identify an enrichment of rare deleterious variants in specific genes. Both kinds of genetic contributions are typically analyzed independently. Many studies suggest that complex phenotypes are influenced by both low effect common variants and high effect rare deleterious variants. The aim of this paper is to integrate the effect of both common and rare functional variants for a more comprehensive genetic risk modeling. Methods: We developed a framework combining gene-based scores based on the enrichment of rare functionally relevant variants with genome-wide PRS based on common variants for association analysis and prediction models. We applied our framework on UK Biobank dataset with genotyping and exome data and considered 28 blood biomarkers levels as target phenotypes. For each biomarker, an association analysis was performed on full cohort using gene-based scores (GBS). The cohort was then split into 3 subsets for PRS construction and feature selection, predictive model training, and independent evaluation, respectively. Prediction models were generated including either PRS, GBS or both (combined). Results: Association analyses of the cohort were able to detect significant genes that were previously known to be associated with different biomarkers. Interestingly, the analyses also revealed heterogeneous effect sizes and directionality highlighting the complexity of the blood biomarkers regulation. However, the combined models for many biomarkers show little or no improvement in prediction accuracy compared to the PRS models. Conclusion: This study shows that rare variants play an important role in the genetic architecture of complex multifactorial traits such as blood biomarkers. However, while rare deleterious variants play a strong role at an individual level, our results indicate that classical common variant based PRS might be more informative to predict the genetic susceptibility at the population level. [ABSTRACT FROM AUTHOR]

Active Queue Management (AQM) methods significantly impact the network performance, as they manage the router queue and facilitate the traffic flow through the network. This paper presents a novel fuzzy-based AQM method developed with a computationally efficient precise fuzzy modeling optimized using the Genetic Algorithm. The proposed method focuses on the concept of symmetry as a means to achieve a more balanced and equitable distribution of the resources and avoid bandwidth wasting resulting from unnecessary packet dropping. The proposed method calculates the dropping probability of each packet using a precise fuzzy model that was created and tuned in advance and based on the previous dropping probability value and the queue length. The tuning process is implemented as an optimization problem formulated for the b0, b1, and b2 variables of the precise rules with an objective function that maximizes the performance results in terms of loss, dropping, and delay. To prove the efficiency of the developed method, the simulation was not limited to the common Bernoulli process simulation; instead, the Markov-modulated Bernoulli process was used to mimic the burstiness nature of the traffic. The simulation is conducted on a machine operated with 64-bit Windows 10 with an Intel Core i7 2.0 GHz processor and 16 GB of RAM. The simulation used Java programming language in Apache NetBeans Integrated Development Environment (IDE) 11.2. The results showed that the proposed method outperformed the existing methods in terms of computational complexity, packet loss, dropping, and delay. As such, in low congested networks, the proposed method maintained no packet loss and dropped 22% of the packets with an average delay of 7.57, compared to the best method, LRED, which dropped 21% of the packets with a delay of 10.74, and FCRED, which dropped 21% of the packets with a delay of 16.54. In highly congested networks, the proposed method also maintained no packet loss and dropped 48% of the packets, with an average delay of 16.23, compared to the best method LRED, which dropped 47% of the packets with a delay of 28.04, and FCRED, which dropped 46% of the packets with a delay of 40.23. [ABSTRACT FROM AUTHOR]

The paper presents the importance of mineral deposit modeling, based on the elucidation of key elements of its origin, for the development of reliable criteria for prospecting of new deposits and orebodies of this type. On the example of the Kremikovtsi carbonate-hosted IF-barite-polymetallic deposit, it is shown that the adoption of a wellgrounded syngenetic or epigenetic concept for its origin is crucial for successful prospecting activity. Based on the developed model for a sedimentary-exhalative (SEDEX) origin of the deposit (i.e. syngenetic to the carbonate host), the key factors controlling ore formation are specified, and an optimal strategy for future prospecting in the region is proposed. [ABSTRACT FROM AUTHOR]

The analysis of a composite plate by refined plate theories is presented in this paper. The displacement fields of monolayer plate are expressed by means of Carrera unified formulation (CUF), and Taylor-like series expansion is employed along the thickness direction. The governing differential equation of monolayer plate is derived by Hamilton's principle, and the related element stiffness matrix, mass matrix, and load vector are obtained. The element matrix of composite plate is obtained by superimposing single-layer plate elements, and the global matrix is obtained in the finite element framework. Due to the shear locking phenomenon of thin plate, the higher-order model is revised by tensor component mixed interpolation (MITC). The accuracy and reliability of the present model are demonstrated by comparing with classical plate model (classical plate theory and first-order shear deformation theory) and a solid model generated in the commercial software ANSYS. Meanwhile, the geometric parameter optimization of composite plate is studied based on the constructed higher-order model by the multi-objective genetic algorithm. [ABSTRACT FROM AUTHOR]

Contra-rotating fans (CRFs) have garnered significant attention due to their higher power-to-weight ratio compared to traditional fans; however, limited focus has been given to the localization and development of local aerodynamic losses. Furthermore, there is a need for further research on the impact of load distribution along the radius on local entropy production. Therefore, this study aims to investigate a contra-rotating fan as the research subject. An optimal design for load distribution along the radius is achieved by constructing a surrogate model in combination with a genetic algorithm. The effectiveness of this design has been verified through experimentation using a specific test device. In this study, a local entropy production rate (EPR) model adapted to the shear stress transport-detached eddy simulation (SST-DES) technique is constructed to evaluate the loss distribution of the contra-rotating fan. This paper primarily focuses on comparing and analyzing the blade profile and overall performance of the CRFs before and after optimization. The EPR contribution of each interval along the radius is compared to the corresponding blade channel to identify the approximate range of high-EPR regions. Furthermore, an investigation is conducted to examine the distribution of EPR along the streamwise direction in these high-EPR regions. After that, by comparing the development of the flow structure near a stall before and after optimization, combined with the analysis of the EPR contours, the EPR mechanism of this CRF is revealed. [ABSTRACT FROM AUTHOR]

Determination of the key chemical reaction pathways in cold atmospheric plasmas (CAPs) is of great importance not only for understanding the spatiotemporal evolutions of the key plasma parameters during discharges but also for improving the plasma materials processing qualities. In this paper, a novel chemical reaction reduction method (CRRM) is proposed by using the global fluid model coupled with the genetic algorithm and the dynamic programming technique. With the aid of this newly developed CRRM, the key chemical reaction pathways can be automatically screened with a high computational efficiency under a pre-set critical calculation accuracy for the atmospheric pure helium and helium–nitrogen glow discharge plasmas. By comparing the calculated key plasma parameters, e.g., the species number densities, electron temperatures, voltage–current characteristics, based on the simplified models and their corresponding full models with those of the experimentally measured data, the reliability of the CRRM itself and the established key chemical reaction database for the atmospheric pure helium and helium–nitrogen CAPs are validated. This research also provides a general method for screening the key chemical reaction pathways for various low-temperature plasma sources. [ABSTRACT FROM AUTHOR]

In this paper, a method of obtaining parameters of one-column Jansen–Rit model was proposed. Methods present in literature are focused on obtaining parameters in an on-line manner, producing a set of parameters for every point in time. The method described in this paper can provide one set of parameters for a whole, arbitrarily long signal. The procedure consists of obtaining specific frequency features, then minimizing mean square error of those features between the measured signal and the modeled signal, using genetic algorithm. This method produces an 8-element vector, which can be treated as an EEG signal feature vector specific for a person. The parameters which were being obtained are maximum postsynaptic potential amplitude, maximum inhibitory potential amplitude, ratio of the number of connections between particular neuron populations, the shape of a nonlinear function transforming the average membrane potential into the firing rate and the input noise range. The method shows high reproducibility (intraclass correlation coefficient for particular parameters ranging from 0.676 to 0.978) and accuracy (ranging from 0.662 to 0.863). It was additionally verified using EEG signal obtained for a single participant. This signal was measured using Emotiv EPOC+ NeuroHeadset. [ABSTRACT FROM AUTHOR]

This paper introduces another approach to pre-crash velocity determination by the means of genetic algorithm model adjustment (GAMA). This innovative method is based on the standard genetic algorithm approach applied to the problem of determining the dependency of predicted variable on given parameters. The experiment is performed on a database obtained from NHTSA (National Highway Traffic Safety Administration), which contains potent number of data samples regarding frontal vehicle crash tests: i.e. vehicle mass, deformation zone and deformation coefficients C1-C6. Various classes of possible functions are considered and the model for determining the pre-crash speed based on the given parameters is obtained. [ABSTRACT FROM AUTHOR]

The aim of the present paper was to summarize the gene polymorphisms of beta-lactoglobulin (BLG) gene and its effects on milk yield in 1840 genotyped Indian dairy cows reported in 17 published studies. The meta-analysis was undertaken using gene frequencies of individual studies under random effects model, whereas for association analysis of genotypes with milk yield, standardized mean differences (SMDs) along with 95% confidence interval (CI) were obtained under four genetic models such as additive (AA vs. BB), dominant (AA+AB vs. BB), completely over dominant (AA+BB vs. AB) and recessive (AA vs. AB+BB). The heterogeneity index (I2) was used to determine heterogeneity between studies. The results of meta-analysis suggested that the pooled allelic frequency of allele A was subsidiary as 0.29 (95% CI 0.24, 0.33, I2 = 88.54%) in targeted population, and also, it was non-significantly (P > 0.05) different between Bos indicus (0.28) and Bos taurus/cross cows (0.30). Egger's test indicated no risk of publication bias (P > 0.05). The results also revealed that BLG gene variants have non-significant (P > 0.05) association with milk yield under all genetic models. Although positive effects of SMDs under some models were observed, however, they failed to meet statistical significance (P > 0.05) due to high heterogeneity between studies which lead to conclusion of only uncertain influences of SNP genotypes with milk yield. It was concluded that BLG markers may not be beneficial for improving milk yield in Indian dairy cows. However, it is suggested that the revalidation of the present results should be done by using more number of studies. [ABSTRACT FROM AUTHOR]

The concept of regulation pervades biology, for example in models of genetic regulatory networks and the endocrine system. Regulation has a normative opposite, dysregulation, which figures prominently in biomedical models of disease. The use of normative concepts in biology, however, has been thought to present some challenges for the physicalist view of the world, and various resolutions have been proposed. Up to now the problem of biological normativity has been debated largely in connection with the concept of biological information. In this paper we shift focus to the concept of biological regulation, proposing that it provides a promising new approach to these issues. Models of regulatory systems have several features: they are causal, but they do not deal with the energy exchanges and transformations covered by physics and chemistry; further, and entirely connected, regulatory systems can break down, and this is because they and their causal-regulatory properties are dependent on fragile molecular structures. Biological regulatory systems exhibit normativity, because they are not determined by physical and chemical laws, but their close relationship with physical laws and physicalist ontology is transparent. [ABSTRACT FROM AUTHOR]

With the continuous development of e-commerce, the logistics industry is thriving, and logistics delays have become an issue that deserves more and more attention. Genetic EM algorithm is a genetic EM algorithm that is an iterative optimization strategy algorithm that can be used to solve the high-quality algorithm of travel problems with many nodes. Bayesian network (BN) is a network model based on probabilistic uncertainty. This article aims to study the probability of many factors that cause logistics delays to construct an algorithm model to control or reduce logistics delays. This paper constructs an EY model (That is the abbreviation of BN model based on genetic EM algorithm) based on the genetic EM algorithm, and conducts related simulation experiments based on the model to verify the accuracy and feasibility of the model. The experimental results of this paper show that the calculation efficiency of the EY model is significantly improved, and the actuarial accuracy is as high as 98%, which can effectively control logistics delays. [ABSTRACT FROM AUTHOR]

In their 2005 paper, Li and her colleagues proposed a test response function (TRF) linking method for a two-parameter testlet model and used a genetic algorithm to find minimization solutions for the linking coefficients. In the present paper the linking task for a three-parameter testlet model is formulated from the perspective of bi-factor modeling, and three linking methods for the model are presented: the TRF, mean/least squares (MLS), and item response function (IRF) methods. Simulations are conducted to compare the TRF method using a genetic algorithm with the TRF and IRF methods using a quasi-Newton algorithm and the MLS method. The results indicate that the IRF, MLS, and TRF methods perform very well, well, and poorly, respectively, in estimating the linking coefficients associated with testlet effects, that the use of genetic algorithms offers little improvement to the TRF method, and that the minimization function for the TRF method is not as well-structured as that for the IRF method. [ABSTRACT FROM AUTHOR]

Recently the 1/f signal of human electroencephalography has attracted attention, as it could potentially reveal a quantitative measure of neural excitation and inhibition in the brain, that may be relevant in a clinical setting. The purpose of this short article is to show that the 1/f signal depends on the vigilance state of the brain in both humans and mice. Therefore, proper labelling of the EEG signal is important as improper labelling may obscure disease-related changes in the 1/f signal. We demonstrate this by comparing EEG results from a longitudinal study in a genetic mouse model for synaptic dysfunction in schizophrenia and autism spectrum disorders to results from a large European cohort study with schizophrenia and mild Alzheimer's disease patients. The comparison shows when the 1/f is corrected for vigilance state there is a difference between groups, and this effect disappears when vigilance state is not corrected for. In conclusion, more attention should be paid to the vigilance state during analysis of EEG signals regardless of the species. [ABSTRACT FROM AUTHOR]

The tail wing of box-launched aircraft needs to be folded in the launch box, which can easily cause malfunctions during flight deployment. This article presents a ducted tail wing aircraft that does not require folding of the tail wing. To address the nonlinear problem of lift coefficient in the ducted tail, an aerodynamic optimization method for ducted tails based on the sparrow search algorithm with back-propagation (SSA-BP) neural network approximate model and multi-objective genetic algorithm fusion is proposed, with the goal of improving the lift-to-drag ratio and linearization degree of the lift curve. The linearization degree of the optimized tail lift coefficient curve is significantly improved, and the lift-to-drag ratio is significantly improved under cruising conditions. Based on this optimization result, the shape of the tail wing and fuselage combination was optimized, and the optimal configuration of the ducted tail wing aircraft was selected, providing a reference for the design of ducted tail wing aircraft. [ABSTRACT FROM AUTHOR]

This paper adopts the multi-criterion decision-making model of fuzzy-TODIM and genetic algorithm (GA) for optimal portfolio allocation. We applied Markowitz's portfolio parameters as inputs for the fuzzy TODIM model to rank stocks that are constituents of each index from three different markets. Portfolios are then generated dynamically using three weighting techniques and subject to multi-objective criteria and additional constraints. The results indicate a significant variation in performance metrics between the model-generated portfolios and the market indices. Replication of the procedure produces a similar outcome. Moreover, the out-of-sample tests conducted over 3 years validate the results' robustness, indicating that fuzzy TODIM, combined with GA, can achieve superior performance in dynamic portfolio allocation. [ABSTRACT FROM AUTHOR]

Network function virtualization can increase the versatility of computing and network platforms, enabling them to provide more flexible services. In terms of a softwaredefined network and network function virtualization environment, this paper explores the joint virtual network function (VNF) deployment and traffic routing in multisource multicast for VNF service chain deployment to minimize the deployment overhead, transmission delay. Firstly, a multi-objective optimization model, which minimizes the deployment overhead and transmission delay, is established to tackle this challenge problem. Then, a highly efficient coding scheme, crossover, and mutation operators are designed. Based on these, an improved genetic algorithm in multi-objective evolutionary algorithm based on decomposition (MOEA/D) framework (GA-MOEA/D) is proposed to solve this multi-objective problem. Finally, simulation experiments are conducted using two widely used network topologies in order to demonstrate the performance of the proposed algorithm. The simulation results demonstrate that the proposed algorithm can obtain the smaller deployment overhead, transmission delay, load degree and energy consumption 5.2%-15.61%, 11.2%-26.4%, 8.3%-17.6% and 7.8%-14.4% than that of the compared algorithm, respectively. [ABSTRACT FROM AUTHOR]

Over the last decade, our world exposed to many types of unpredictable disasters (recently Coronavirus). These disasters have clearly shown the uncertainty and vulnerability of supply chain systems. Also, it confirmed that adopting Just-in-Time (JIT) strategy to reduce the logistic chain cost may lead to inbuilt complexity and risks. Efficient tools are therefore needed to make complexity optimized supply chain decisions. Evolutionary algorithms, including genetic algorithms (GA), have proven effective in identifying optimal solutions that address the trade-offs between total supply chain cost and carbon emissions regulatory policy represented by carbon tax charges. These solutions pertain to the design challenges of supply networks exposed to potential disruption risks. However, GA have a set of parameters must be chosen for effective and robust performance of the algorithms. This paper aims to set the most suitable values of these parameters that used via GA – ased optimization cost and risk reduction model in firms using a JIT as a delivery system. The model has been conceptualized for addressing the design complexities of the supply chain, referred to as SCRRJITS (Simultaneous Cost and Risk Reduction in a Just-in-Time System). A complete analysis of the different parameters and operators of the algorithm is carried out using design of experiments approach. The algorithm performance measure used in this study is convergence of solutions. The results show the extent to which the quality of solution can be changed depending on selection of these parameters. [ABSTRACT FROM AUTHOR]

In this article I test whether students' educational expectations respond to prior academic performance and whether this responsiveness varies by socio-economic status (SES). The responsiveness of high-SES students' expectations may be lower as suggested by the compensatory advantage mechanism or higher because of alienation processes of low-SES students. However, the association between achievement and expectations may be in part spurious because of unobserved social and genetic confounders. This issue is largely ignored by previous research. Therefore, in this paper I estimate behavioral genetic twin models that take into account the possible confounding of the responsiveness of expectations to performance by unobserved genetic and social influences. While students' expectations respond to prior performance, this responsiveness is reduced by more than half once unobserved genetic and social confounders are accounted for. Also, SES differences in responsiveness to performance are completely accounted for by high-SES students' expectations being less responsive to prior levels of cognitive ability. So, this study shows the relevance of taking different types of confounding into account when studying the formation of educational expectations. [ABSTRACT FROM AUTHOR]

Background: Maternal effects influence juvenile traits such as body weight and early growth in broilers. Ignoring significant maternal effects leads to reduced accuracy and inflated predicted breeding values. Including genetic and environmental direct-maternal covariances into prediction models in broilers can increase the accuracy and limit inflation of predicted breeding values better than simply adding maternal effects to the model. To test this hypothesis, we applied a model accounting for direct-maternal genetic covariance and direct-maternal environmental covariance to estimate breeding values. Results: This model, and simplified versions of it, were tested using simulated broiler populations and then was applied to a large broiler population for validation. The real population analyzed consisted of a commercial line of broilers, for which body weight at a common slaughter age was recorded for 41 selection rounds. The direct-maternal genetic covariance was negative whereas the direct-maternal environmental covariance was positive. Simulated populations were created to mimic the real population. The predictive ability of the models was assessed by cross-validation, where the validation birds were all from the last five selection rounds. Accuracy of prediction was defined as the correlation between the predicted breeding values estimated without the phenotypic records of the validation population and a predictor. The predictors were the breeding values estimated using all the phenotypic information and the phenotypes corrected for the fixed effects, and for the simulated data, the true breeding values. In the real data, adding the environmental covariance, with or without also adding the genetic covariance, increased the accuracy, or reduced deflation of breeding values compared with a model not including dam–offspring covariance. Nevertheless, in the simulated data, reduction in the inflation of breeding values was possible and was associated with a gain in accuracy of up to 6% compared with a model not including both forms of direct-maternal covariance. Conclusions: In this paper, we propose a simple approach to estimate the environmental direct-maternal covariance using standard software for REML analysis. The genetic covariance between dam and offspring was negative whereas the corresponding environmental covariance was positive. Considering both covariances in models for genetic evaluation increased the accuracy of predicted breeding values. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a new method of fuselage drag optimization that can obtain results faster than the conventional methods based on full computational fluid dynamics (CFD) calculations and can be used to improve the efficiency of preliminary design. Design/methodology/approach: An efficient method for helicopter fuselage shape optimization based on surrogate-based optimization is presented. Two numerical simulation methods are applied in different stages of optimization according to their relative advantages. The fast panel method is used to calculate the sample data to save calculation time for a large number of sample points. The initial solution is obtained by combining the Kriging surrogate model and the multi-island genetic algorithm. Then, the accuracy of the solution is determined by using the infill criteria based on CFD corrections. A parametric model of the fuselage is established by several characteristic sections and guiding curves. Findings: It is demonstrated that this method can greatly reduce the calculation time while ensuring a high accuracy in the XH-59A helicopter example. The drag coefficient of the optimized fuselage is reduced by 13.3%. Because of the use of different calculation methods for samples, this novel method reduces the total calculation time by almost fourfold compared with full CFD calculations. Originality/value: To the best of the authors' knowledge, this is the first study to provide a novel method of fuselage drag optimization by combining different numerical simulation methods. Some suggestions on fuselage shape optimization are given for the XH-59A example. [ABSTRACT FROM AUTHOR]

Most human traits are influenced by the interplay between genetic and environmental factors. Many statistical methods have been proposed to screen for gene-environment interaction (GxE) in the post genome-wide association study era. However, most of the existing methods assume a linear interaction between genetic and environmental factors toward phenotypic variations, which diminishes statistical power in the case of nonlinear GxE. In this paper, we present a flexible statistical procedure to detect GxE regardless of whether the underlying relationship is linear or not. By modeling the joint genetic and GxE effects as a varying-coefficient function of the environmental factor, the proposed model is able to capture dynamic trajectories of GxE. We employ a likelihood ratio test with a fast Monte Carlo algorithm for hypothesis testing. Simulations were conducted to evaluate validity and power of the proposed model in various settings. Real data analysis was performed to illustrate its power, in particular, in the case of nonlinear GxE. [ABSTRACT FROM AUTHOR]

Polygenic risk score (PRS) has been recently developed for predicting complex traits and drug responses. It remains unknown whether multi-trait PRS (mtPRS) methods, by integrating information from multiple genetically correlated traits, can improve prediction accuracy and power for PRS analysis compared with single-trait PRS (stPRS) methods. In this paper, we first review commonly used mtPRS methods and find that they do not directly model the underlying genetic correlations among traits, which has been shown to be useful in guiding multi-trait association analysis in the literature. To overcome this limitation, we propose a mtPRS-PCA method to combine PRSs from multiple traits with weights obtained from performing principal component analysis (PCA) on the genetic correlation matrix. To accommodate various genetic architectures covering different effect directions, signal sparseness and across-trait correlation structures, we further propose an omnibus mtPRS method (mtPRS-O) by combining P values from mtPRS-PCA, mtPRS-ML (mtPRS based on machine learning) and stPRSs using Cauchy Combination Test. Our extensive simulation studies show that mtPRS-PCA outperforms other mtPRS methods in both disease and pharmacogenomics (PGx) genome-wide association studies (GWAS) contexts when traits are similarly correlated, with dense signal effects and in similar effect directions, and mtPRS-O is consistently superior to most other methods due to its robustness under various genetic architectures. We further apply mtPRS-PCA, mtPRS-O and other methods to PGx GWAS data from a randomized clinical trial in the cardiovascular domain and demonstrate performance improvement of mtPRS-PCA in both prediction accuracy and patient stratification as well as the robustness of mtPRS-O in PRS association test. [ABSTRACT FROM AUTHOR]

Background: This paper highlights the relationships between economic weights, genetic progress, and phenotypic progress in genomic breeding programs that aim at generating genetic progress in complex, i.e., multi-trait, breeding objectives via a combination of estimated breeding values for different trait complexes. Results: Based on classical selection index theory in combination with quantitative genetic models, we provide a methodological framework for calculating expected genetic and phenotypic progress for all components of a complex breeding objective. We further provide an approach to study the sensitivity of the system to modifications, e.g. to changes in the economic weights. We propose a novel approach to derive the covariance structure of the stochastic errors of estimated breeding values from the observed correlations of estimated breeding values. We define 'realized economic weights' as those weights that would coincide with the observed composition of the genetic trend and show, how they can be calculated. The suggested methodology is illustrated with an index that aims at achieving a breeding goal composed of six trait complexes, that was applied in German Holstein cattle breeding until 2021. Conclusions: Based on the presented results, the main conclusions are (i) the composition of the observed genetic progress matches the expectations well, with predictions being slightly better when the covariance of estimation errors is taken into account; (ii) the composition of the expected phenotypic trend deviates significantly from the expected genetic trend due to the differences in trait heritabilities; and (iii) the realized economic weights derived from the observed genetic trend deviate substantially from the predefined ones, in one case even with a reversed sign. Further results highlight the implications of the change to a modified breeding goal based on the example of a new index comprising eight, partly new, trait complexes, which is used since 2021 in the German Holstein breeding program. The proposed framework and the analytical tools and software provided will be useful to define more rational and generally accepted breeding objectives in the future. [ABSTRACT FROM AUTHOR]

With the popularization of the Internet, network security issues have also emerged. In response to network security issues, there are certain shortcomings in current network intrusion detection technologies. To improve and optimize this technology, a network intrusion detection model based on genetic algorithm and improved negative selection algorithm is designed. The generation of detectors in the selection algorithm is replaced by genetic algorithm, and the non-self spatial distribution of detectors is optimized. This paper proposes a network intrusion detection model using a genetic algorithm-improved negative selection algorithm (GA-INSA) and an improved LeNet-5 CNN. The GA enhances detector generation and distribution in NSA while SMOTE handles class imbalance for CNN. Experiments show GA-INSA has over 9% higher accuracy than NSA, SVM and GA-BP across different data sizes. The improved LeNet-5 demonstrates superior accuracy and recall rates by over 20% over the baseline LeNet-5. However, more comprehensive evaluation on public datasets, design details on architectures, and discussion around limitations are warranted. The data shows that the designed network intrusion detection model has better performance. The model can provide technical support for network intrusion detection in reality and can enrich the content of network intrusion detection technology. [ABSTRACT FROM AUTHOR]

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The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GAALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GAALBP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GAALBP reaches 99.99%, a mean square error of less than 2 × 10−6, and a maximum absolute error of less than 4 × 10−3. The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 °C, the minimum temperature is increased by 0.39 °C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization. [ABSTRACT FROM AUTHOR]

It is important to optimize the structure of inlet due to the increasing demand of ram air. In this paper, structural optimization of the submerged inlet is pursued using a hybrid model by integrating least squares support vector machines (LS-SVM) prediction model and improved genetic algorithm-particle swarm optimization (GA-PSO). Inlet shape is controlled by changing three-dimensional geometric parameters. Ramp angle, width to depth ratio and ramp length play significant parts in this optimization process. Ram efficiency and mass flow are the main objectives of the performance evaluation. Results show that the prediction error of the mass flow and ram efficiency is 2.31% and 0.54%, respectively. Comparison with the original geometry is used to prove the optimization capabilities of the proposed optimization method. The mass flow and ram efficiency are increased by 29.2% and 10.0%, respectively. In addition, the characteristics of the optimized submerged inlet geometry are numerically investigated. The numerical results are compared to the optimization results and indicate that this optimization method has high validity. The error of ram efficiency and mass flow is 0.30% and 0.66%, respectively. Consequently, this optimization method can be valuable to aircraft engineers-by providing a novel approach for the design of the submerged inlet. [ABSTRACT FROM AUTHOR]

Background: Osteoarthritis (OA) is an important health issue in elderly people. Many studies have suggested that genetic factors are important risk factors for OA, of which tumor necrosis factor-α (TNF-α) is one of the most examined genes. Moreover, several studies have investigated the relationship between TNF-α G-308A polymorphisms and OA risk, but consistent results have not been obtained. Objective: This study examines the association between TNF-α G-308A polymorphisms and knee OA. Moreover, meta-analysis and trial sequential analysis (TSA) was used to determine whether this is a susceptibility gene for knee OA. Methods: Between 2015 and 2019, 591 knee OA cases and 536 healthy controls were recruited. The Kellgren–Lawrence grading system was used to identify the knee OA cases. A meta-analysis was conducted including related studies published until 2020 from PubMed, Embase, and previous meta-analysis to improve the evidence level of the current study. The results were expressed as odds ratios (ORs) with corresponding 95% confidence intervals (CI) to evaluate the effect of this polymorphism on knee OA risk. The TSA was used to estimate the sample sizes required in this issue. Results: A nonsignificant association was found between the AA genotype and knee OA [adjusted OR, 0.84; 95% CI, 0.62–1.15) in the recessive model] in the present case–control study, and analysis of other genetic models showed a similar trend. After adding the critical case–control samples for Asians, the TNF-α G-308A, AA genotype exhibited 2.57 times more risk of developing arthritis when compared with the GG + GA genotype (95% CI, 1.56–4.23), and the cumulative samples for TSA (n = 2182) were sufficient to obtain a definite conclusion. Conclusions: The results of this meta-analysis revealed that the TNF-α G-308A, AA genotype is a susceptible genotype for OA in the Asian population. This study integrated all current evidence to arrive at this conclusion, suggesting that future studies on Asians are not required. [ABSTRACT FROM AUTHOR]

Restrainer-type sliding bearing (RTSB) is a practical seismic isolation system for reducing the excessive displacement of flat sliding bearings under strong earthquakes to ensure the seismic safety of highway bridges. Due to the good corrosion resistance and considerable energy dissipation capability, SMA cables can be used in the RTSB to form the SMA-restrained RTSB to improve its performance. To realize the reliable seismic design of the SMA-restrained RTSB, this paper adopted the seismic resilience-based evaluation method to give the optimum design to minimize the seismic resilience of the bridge system. Firstly, a new index of seismic resilience was defined in the proposed resilience-based methodology to account for the variability of ground intensities. The quantile regression was also proposed to improve the estimation of seismic demand dispersions for seismic fragility analysis. The seismic performance of highway bridges isolated with the SMA-restrained RTSB was compared with different sliding bearings to investigate the effect of slack cables on the seismic performance of the SMA-restrained RTSB. Finally, an extensive parametric study was conducted based on a Gaussian Process Regression (GPR) model together with the uniform design method to investigate the sensitivity of different bearing parameters. The optimal design of the SMA-restrained RTSB bearing was then obtained based on the GPR model and genetic method. It is concluded that the mechanical properties of SMA-restrained RTSB have a significant effect on the seismic resilience of highway bridges. The proposed seismic resilience-based methodology can be adopted to obtain the optimal designs of the SMA-restrained sliding bearings given the target resilience of highway bridges. [ABSTRACT FROM AUTHOR]

Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in awide range of environments. Usingmodels without the reaction normin scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off. [ABSTRACT FROM AUTHOR]

We propose a resampling-based fast variable selection technique for detecting relevant single nucleotide polymorphisms (SNP) in a multi-marker mixed effect model. Due to computational complexity, current practice primarily involves testing the effect of one SNP at a time, commonly termed as 'single SNP association analysis'. Joint modeling of genetic variants within a gene or pathway may have better power to detect associated genetic variants, especially the ones with weak effects. In this paper, we propose a computationally efficient model selection approach—based on the e-values framework—for single SNP detection in families while utilizing information on multiple SNPs simultaneously. To overcome computational bottleneck of traditional model selection methods, our method trains one single model, and utilizes a fast and scalable bootstrap procedure. We illustrate through numerical studies that our proposed method is more effective in detecting SNPs associated with a trait than either single-marker analysis using family data or model selection methods that ignore the familial dependency structure. Further, we perform gene-level analysis in Minnesota Center for Twin and Family Research (MCTFR) dataset using our method to detect several SNPs using this that have been implicated to be associated with alcohol consumption. [ABSTRACT FROM AUTHOR]

Evolutionarily stable strategy (ESS) analysis pioneered by Maynard Smith and Price took off in part because it often does not require explicit assumptions about the genetics and demography of a population in contrast to population genetic models. Though this simplicity is useful, it obscures the degree to which ESS analysis applies to populations with more realistic genetics and demography: for example, how does ESS analysis handle complexities such as kin selection, group selection and variable environments when phenotypes are affected by multiple genes? In this paper, I review the history of the ESS concept and show how early uncertainty about the method lead to important mathematical theory linking ESS analysis to general population genetic models. I use this theory to emphasize the link between ESS analysis and the concept of invasion fitness. I give examples of how invasion fitness can measure kin selection, group selection and the evolution of linked modifier genes in response to variable environments. The ESSs in these examples depend crucially on demographic and genetic parameters, which highlights how ESS analysis will continue to be an important tool in understanding evolutionary patterns as new models address the increasing abundance of genetic and long-term demographic data in natural populations. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'. [ABSTRACT FROM AUTHOR]

This paper addresses the lack of attention paid by the scientific community to the optimization of timber structures, specifically in the context of large-span agro-industrial constructions. The study focuses on the optimization of a three-dimensional roof composed of GL32h glulam regular double-tapered beams and purlins. Firstly, MATLAB was employed to develop a calculation software and then run the proposed optimization model based on genetic algorithms, in order to optimize the different geometries of the construction elements based on cost, as well as the optimum arrangement of the purlins and the number of beams. Moreover, statistical analyses were carried out on more than 200 optimization data points to uncover the influence of different variables in the optimization process. It was found that the snow load and span have a significant influence on the prediction of the height and width of beams and purlins, as well as the purlin spacing; while, as expected, the roof length also influences the optimal number of beams. All these findings could promote the use of timber structures; thus, achieving more sustainable and efficient construction practices. [ABSTRACT FROM AUTHOR]

ALSUntangled reviews alternative and off-label treatments for people with amyotrophic lateral sclerosis (PALS). Here we review glucocorticoids. Neuroinflammation plays a prominent role in amyotrophic lateral sclerosis (ALS) pathogenesis, so some hypothesize that glucocorticoids might be an effective ALS therapy through their immunosuppressive effects. In this paper, we review the available evidence for glucocorticoids in ALS, including one pre-clinical study with a genetic mouse model of ALS, nine case reports (ranging from 1 to 26 patients each), and four clinical trials. We also review the possible side effects (including steroid myopathy) and the costs of therapy. We graded the level of evidence as follows: Mechanism, D; Pre-Clinical, F; Cases, B; Trials, F; Risks, C. Our review of the current evidence concludes that glucocorticoids do not offer clinical benefit in ALS and confer serious risks. Thus, ALSUntangled does not recommend glucocorticoids as a treatment for ALS. [ABSTRACT FROM AUTHOR]

Objectives: Muscle health is essential for children's physical development and future health. PPARGC1A gene encode the peroxisome proliferator-activated receptor coactivator 1α which coactivates transcription factors that control mediating skeletal muscle fiber type conversion and skeletal muscle fiber formation. The PPARGC1A rs8192678 Gly/Ser (Gly482Ser) polymorphism was associated with the regulation of skeletal muscle fibre type. This paper aims to explore the association between the PPARGC1A rs8192678 (Gly482Ser) polymorphism and muscle fitness in Chinese schoolchildren. Methods: We detected the distribution of the PPARGC1A rs8192678 (Gly482Ser) polymorphism by DNA typing of saliva samples from untrained Southern Chinese Han children aged 7–12 years. Considering that muscle studies in children cannot use invasive sampling, we analyzed the association between alleles and genotypes with high validity tests of muscle fitness assesment in children(handgrip strength, standing long jump, sit-ups and push-ups). Results: The results showed no significant differences in height, weight or body mass index between the sexes. The grip strength indicators were correlated with age in boys and height and weight in girls. Sit-ups were significantly higher in girls with the PPARGC1A Gly/Gly genotype than in boys, and handgrip strength and standing long jump were significantly lower in girls with the PPARGC1A rs8192678 (Gly482Ser) genotype than in boys. Genetic model analysis showed that the Gly482 allele had a dominant genetic effect on the Gly482 allele is hypothesized to influence the expression of type I fibers in skeletal muscle in girls, while the Ser482 allele affects on type II fibers in girls. The two alleles had little genetic effect on boys. Conclusions: The results suggested the possible association of the PPARGC1A rs8192678 (Gly482Ser) polymorphism on myofibril type-related phenotypes in Han Chinese children in southern China, with a particular impact on girls. [ABSTRACT FROM AUTHOR]

Simple Summary: In dairy cattle, a cow's productive life is an essential functional trait with great economic significance and is part of the breeding objectives and comprehensive selection indices. Thus, a highly reliable breeding value prediction is crucial to allow sufficient selection of animals with high genetic potential for survival. Suppose we proceed to evaluate survival in successive periods of an animal's life. In that case, we encounter an unfavourable phenomenon where the earlier an animal is culled, the more the reliability of its breeding values is underestimated. We assume that this underestimation can be corrected by using a weighted analysis that balances the amount of information regardless of the number of survived periods. This paper attempts to show the methodological procedure for determining the weights and estimating the genetic parameters for the weighted analysis. The genetic parameters for the survival of Holstein cows, analysed in nine consecutive time periods during the first three calving intervals, were estimated. The earlier the animals are culled, the more they are informationally underestimated. This undervaluing can be remedied by using a weighted analysis that balances the amount of information. If the method of estimating breeding values changes, the genetic parameters will also change. The Holstein cattle dataset from 2005 to 2017 used in this study included 1,813,636 survival records from 298,290 cows. The pedigree with three generations of ancestors included 660,476 individuals. Linear repeatability models estimated genetic parameters for overall and functional survivability. Due to weights, heritability increased from 0.013 to 0.057. Repeatability with weights was 0.505. The standard deviations of breeding values were 1.75 and 2.18 without weights and 6.04 and 6.20 with weights. Including weights in the calculation increased the additive variance proportion and the breeding values' reliabilities. We conclude that the main contribution of the weighted method we have presented is to compensate for the lack of records in culled individuals with a positive impact on the reliability of the breeding value. [ABSTRACT FROM AUTHOR]

Many efforts have been devoted to the forecasting of the capillary force generated by capillary adsorption between solids, which is fundamental and essential in the fields of micro-object manipulation and particle wetting. In this paper, an artificial neural network (ANN) model optimized by a genetic algorithm (GA-ANN) was proposed to predict the capillary force and contact diameter of the liquid bridge between two plates. The mean square error (MSE) and correlation coefficient (R2) were employed to evaluate the prediction accuracy of the GA-ANN model, theoretical solution method of the Young–Laplace equation and simulation approach based on the minimum energy method. The results showed that the values of MSE of capillary force and contact diameter using GA-ANN were 10.3 and 0.0001, respectively. The values of R2 were 0.9989 and 0.9977 for capillary force and contact diameter in regression analysis, respectively, demonstrating the accuracy of the proposed predictive model. The sensitivity analysis was conducted to investigate the influence of input parameters, including liquid volume and separation distance, on the capillary force and contact diameter. The liquid volume and separation distance played dominant roles in affecting the capillary force and contact diameter. [ABSTRACT FROM AUTHOR]

The task of looking for the optimal allocation of resources in an economy is fraught with a number of severe restrictions. This is manifested in the complexity of the technical implementation of the solution even in the case of a low dimension of the problem. In this paper, we consider two approaches, analytical and numerical, for deriving the dynamical optimal allocation of resources in a three-sector economy and show that the use of modern artificial intelligence (AI) technologies such as genetic algorithms (GA), can be useful for expanding the range of effective tools and new contributions to this problem. [ABSTRACT FROM AUTHOR]

Genetic factors affect an individual’s risk of developing obesity, but in most cases each genetic variant has a small effect. Discovery of genes that regulate obesity may provide clues about its underlying biological processes and point to new ways the disease can be treated. Preclinical animal models facilitate genetic discovery in obesity because environmental factors can be better controlled compared with the human population. We studied inbred mouse strains to identify novel genes affecting obesity and glucose metabolism. BTBR T+ Itpr3tf/J (BTBR) mice are fatter and more glucose intolerant than C57BL/6J (B6) mice. Prior genetic studies of these strains identified an obesity locus on chromosome 2. Using congenic mice, we found that obesity was affected by a ∼316 kb region, with only two known genes, pyruvate dehydrogenase kinase 1 (Pdk1) and integrin α 6 (Itga6). Both genes had mutations affecting their amino acid sequence and reducing mRNA levels. Both genes have known functions that could modulate obesity, lipid metabolism, insulin secretion, and/or glucose homeostasis. We hypothesized that genetic variation in or near Pdk1 or Itga6 causing reduced Pdk1 and Itga6 expression would promote obesity and impaired glucose tolerance. We used knockout mice lacking Pdk1 or Itga6 fed an obesigenic diet to test this hypothesis. Under the conditions we studied, we were unable to detect an individual contribution of either Pdk1 or Itga6 to body weight. During our studies, with conditions outside our control, we were unable to reproduce some of our previous body weight data. However, we identified a previously unknown role for Pdk1 in cardiac cholesterol metabolism providing the basis for future investigations. The studies described in this paper highlight the importance and the challenge using physiological outcomes to study obesity genes in mice. [ABSTRACT FROM AUTHOR]