Search results for: genetic identification

Authors: Ambali Alade Odebowale, Andargachew Mekonnen Berhe, Haroldo T. Hattori, Andrey E. Miroshnichenko

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Solar thermophotovoltaic systems (STPV) have emerged as a promising solution to overcome the Shockley-Queisser limit, a significant impediment in the direct conversion of solar radiation into electricity using conventional solar cells. The STPV system comprises essential components such as an optical concentrator, selective emitter, and a thermophotovoltaic (TPV) cell. The pivotal element in achieving high efficiency in an STPV system lies in the design of a spectrally selective emitter or absorber. Traditional methods for designing and optimizing selective emitters are often time-consuming and may not yield highly selective emitters, posing a challenge to the overall system performance. In recent years, the application of machine learning techniques in various scientific disciplines has demonstrated significant advantages. This paper proposes a novel nanostructure composed of four-layered materials (SiC/W/SiO2/W) to function as a selective emitter in the energy conversion process of an STPV system. Unlike conventional approaches widely adopted by researchers, this study employs a machine learning-based approach for the design and optimization of the selective emitter. Specifically, a random forest algorithm (RFA) is employed for the design of the selective emitter, while the optimization process is executed using genetic algorithms. This innovative methodology holds promise in addressing the challenges posed by traditional methods, offering a more efficient and streamlined approach to selective emitter design. The utilization of a machine learning approach brings several advantages to the design and optimization of a selective emitter within the STPV system. Machine learning algorithms, such as the random forest algorithm, have the capability to analyze complex datasets and identify intricate patterns that may not be apparent through traditional methods. This allows for a more comprehensive exploration of the design space, potentially leading to highly efficient emitter configurations. Moreover, the application of genetic algorithms in the optimization process enhances the adaptability and efficiency of the overall system. Genetic algorithms mimic the principles of natural selection, enabling the exploration of a diverse range of emitter configurations and facilitating the identification of optimal solutions. This not only accelerates the design and optimization process but also increases the likelihood of discovering configurations that exhibit superior performance compared to traditional methods. In conclusion, the integration of machine learning techniques in the design and optimization of a selective emitter for solar thermophotovoltaic systems represents a groundbreaking approach. This innovative methodology not only addresses the limitations of traditional methods but also holds the potential to significantly improve the overall performance of STPV systems, paving the way for enhanced solar energy conversion efficiency.

Keywords: emitter, genetic algorithm, radiation, random forest, thermophotovoltaic

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Authors: Roozbeh Aghamajidi

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Shaft spillways are circling spillways used generally for emptying unexpected floods on earth and concrete dams. There are different types of shaft spillways: Stepped and Smooth spillways. Stepped spillways pass more flow discharges through themselves in comparison to smooth spillways. Therefore, awareness of flow behavior of these spillways helps using them better and more efficiently. Moreover, using vortex breaker has great effect on passing flow through shaft spillway. In order to use more efficiently, the risk of flow pressure decreases to less than fluid vapor pressure, called cavitations, should be prevented as far as possible. At this research, it has been tried to study different behavior of spillway with different vortex shapes on spillway crest on flow. From the viewpoint of the effects of flow regime changes on spillway, changes of step dimensions, and the change of type of discharge will be studied effectively. Therefore, two spillway models with three different vortex breakers and three arrangements have been used to assess the hydraulic characteristics of flow. With regard to the inlet discharge to spillway, the parameters of pressure and flow velocity on spillway surface have been measured at several points and after each run. Using these kinds of information leads us to create better design criteria of spillway profile. To achieve these purposes, optimization has important role and genetic algorithm are utilized to study the emptying discharge. As a result, it turned out that the best type of spillway with maximum discharge coefficient is smooth spillway with ogee shapes as vortex breaker and 3 number as arrangement. Besides it has been concluded that the genetic algorithm can be used to optimize the results.

Keywords: shaft spillway, vortex breaker, flow, genetic algorithm

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Authors: Emilie Zimet

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During networking and NCCD moderation meetings, best practices for identifying students who require Literacy Intervention are often discussed. Once these students are identified, consideration is given to the most effective process for prioritising those who have the greatest need for Literacy Support and the allocation of resources, tracking of intervention effectiveness and communicating with teachers/external providers/parents. Through a workshop, the group will investigate best practices to identify students who require literacy support and strategies to communicate and track their progress. In groups, participants will examine what they do in their settings and then compare with other models, including the researcher’s model, to decide the most effective path to identification and communication. Participants will complete a worksheet at the beginning of the session to deeply consider their current approaches. The participants will be asked to critically analyse their own identification processes for Literacy Intervention, ensuring students are not overlooked if they fall into the borderline category. A cut-off for students to access intervention will be considered so as not to place strain on already stretched resources along with the most effective allocation of resources. Furthermore, communicating learning needs and differentiation strategies to staff is paramount to the success of an intervention, and participants will look at the frequency of communication to share such strategies and updates. At the end of the session, the group will look at creating or evolving models that allow for best practices for the identification and communication of Literacy Interventions. The proposed outcome for this research is to develop a model of identification of students requiring Literacy Intervention that incorporates the allocation of resources and communication to key stakeholders. This will be done by pooling information and discussing a variety of models used in the participant's school settings.

Keywords: identification, student selection, communication, special education, school policy, planning for intervention

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Authors: Ashok Kalagura

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This paper presents a data-mining model for fault-zone identification of flexible AC transmission systems (FACTS)-based transmission line including a thyristor-controlled series compensator (TCSC) and unified power-flow controller (UPFC), using ensemble decision trees. Given the randomness in the ensemble of decision trees stacked inside the random forests model, it provides an effective decision on the fault-zone identification. Half-cycle post-fault current and voltage samples from the fault inception are used as an input vector against target output ‘1’ for the fault after TCSC/UPFC and ‘1’ for the fault before TCSC/UPFC for fault-zone identification. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network, including noisy environment providing a reliability measure of 99% with faster response time (3/4th cycle from fault inception). The results of the presented approach using the RF model indicate the reliable identification of the fault zone in FACTS-based transmission lines.

Keywords: distance relaying, fault-zone identification, random forests, RFs, support vector machine, SVM, thyristor-controlled series compensator, TCSC, unified power-flow controller, UPFC

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Authors: Michel Kulhandjian, Dimitris A. Pados

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The objective of this work is to develop a theoretical framework for reliable digital recording system identification from digital audio files alone, for forensic purposes. A digital recording system consists of a microphone and a digital sound processing card. We view the cascade as a system of unknown transfer function. We expect same manufacturer and model microphone-sound card combinations to have very similar/near identical transfer functions, bar any unique manufacturing defect. Input voice (or other) signals are modeled as non-stationary processes. The technical problem under consideration becomes blind deconvolution with non-stationary inputs as it manifests itself in the specific application of digital audio recording equipment classification.

Keywords: blind system identification, audio fingerprinting, blind deconvolution, blind dereverberation

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Authors: Mirza Mohibulla Baig, Imil Hamda Imran, Tri Bagus Susilo, Sami El Ferik

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The paper deals with system identification and control a nonlinear model of semi-autonomous underwater vehicle (UUV). The input-output data is first generated using the experimental values of the model parameters and then this data is used to compute the estimated parameter values. In this study, we use the semi-autonomous UUV LAURS model, which is developed by the Sensors and Actuators Laboratory in University of Sao Paolo. We applied three methods to identify the parameters: integral method, which is a classical least square method, recursive least square, and weighted recursive least square. In this paper, we also apply three different inputs (step input, sine wave input and random input) to each identification method. After the identification stage, we investigate the control performance of yaw motion of nonlinear semi-autonomous Unmanned Underwater Vehicle (UUV) using feedback linearization-based controller. In addition, we compare the performance of the control with an integral and a non-integral part along with state feedback. Finally, disturbance rejection and resilience of the controller is tested. The results demonstrate the ability of the system to recover from such fault.

Keywords: system identification, underwater vehicle, integral method, recursive least square, weighted recursive least square, feedback linearization, integral error

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Authors: Henrik L. Funke, Lars Ulke-Winter, Sandra Gelbrich, Lothar Kroll

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This work reports about an approach for an automatic adaptation of concrete formulations based on genetic algorithms (GA) to optimize a wide range of different fit-functions. In order to achieve the goal, a method was developed which provides a numerical description of a fibre reinforced concrete (FRC) mixture regarding the production technology and the property spectrum of the concrete. In a first step, the FRC mixture with seven fixed components was characterized by varying amounts of the components. For that purpose, ten concrete mixtures were prepared and tested. The testing procedure comprised flow spread, compressive and bending tensile strength. The analysis and approximation of the determined data was carried out by GAs. The aim was to obtain a closed mathematical expression which best describes the given seven-point cloud of FRC by applying a Gene Expression Programming with Free Coefficients (GEP-FC) strategy. The seven-parametric FRC-mixtures model which is generated according to this method correlated well with the measured data. The developed procedure can be used for concrete mixtures finding closed mathematical expressions, which are based on the measured data.

Keywords: concrete design, fibre reinforced concrete, genetic algorithms, GEP-FC

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Authors: Mukhtiar Singh, Sumeet Nagar

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Vehicle routing problem (VRP) is a combinatorial optimization and nonlinear programming problem aiming to optimize decisions regarding given set of routes for a fleet of vehicles in order to provide cost-effective and efficient delivery of both services and goods to the intended customers. This paper proposes the application of integrated particle swarm optimization (PSO) and genetic optimization algorithm (GA) to address the Vehicle routing problem in sugarcane industry in India. Suger industry is very prominent agro-based industry in India due to its impacts on rural livelihood and estimated to be employing around 5 lakhs workers directly in sugar mills. Due to various inadequacies, inefficiencies and inappropriateness associated with the current vehicle routing model it costs huge money loss to the industry which needs to be addressed in proper context. The proposed algorithm utilizes the crossover operation that originally appears in genetic algorithm (GA) to improve its flexibility and manipulation more readily and avoid being trapped in local optimum, and simultaneously for improving the convergence speed of the algorithm, level set theory is also added to it. We employ the hybrid approach to an example of VRP and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison results indicate that the performance of hybrid algorithm is superior to others, and it will become an effective approach for solving discrete combinatory problems.

Keywords: fuzzy logic, genetic algorithm, particle swarm optimization, vehicle routing problem

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Authors: Marziyeh Bahrami, Hamed Pahlevan Hsseini, Behnam Ghamami, Arman Alvanpour, Hamed Ezzati, Amir Salar Sadeghi

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One of the ways to increase the efficiency of services in the system of agents and, of course, in the world of cloud computing, is to use virtualization techniques. The aim of this research is to create changes in cloud computing services that will reduce as much as possible the energy consumption related to the migration of virtual machines and, in some way, the energy related to the allocation of resources and reduce the amount of pollution. So far, several methods have been proposed to increase the efficiency of cloud computing services in order to save energy in the cloud environment. The method presented in this article tries to prevent energy consumption by data centers and the subsequent production of carbon and biological pollutants as much as possible by increasing the efficiency of cloud computing services. The results show that the proposed algorithm, using the improvement in virtualization techniques and with the help of a genetic algorithm, improves the efficiency of cloud services in the matter of migrating virtual machines and finally saves consumption. becomes energy.

Keywords: consumption reduction, cloud computing, genetic algorithm, live migration, virtual Machine

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Authors: Carolina V. Viegas, Monique Gonçalves, Gisel Chenard Diaz, Yordanka Reyes Cruz, Donato Alexandre Gomes Aranda

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To develop the massive cultivation of microalgae, it is necessary to isolate and characterize the species, improving genetic tools in search of specific characteristics. Therefore, the detection, identification and quantification of the compounds that compose the Scenedesmus sp. were prerequisites to verify the potential of these microalgae. The main objective of this work was to carry out the characterization of Scenedesmus sp. as to the content of ash, carbohydrates, proteins and lipids as well as the determination of the composition of their lipid classes and main fatty acids. The biomass of Scenedesmus sp, showed 15,29 ± 0,23 % of ash and CaO (36,17 %) was the main component of this fraction, The total protein and carbohydrate content of the biomass was 40,74 ± 1,01 % and 23,37 ± 0,95 %, respectively, proving to be a potential source of proteins as well as carbohydrates for the production of ethanol via fermentation, The lipid contents extracted via Bligh & Dyer and in situ saponification were 8,18 ± 0,13 % and 4,11 ± 0,11 %, respectively. In the lipid extracts obtained via Bligh & Dyer, approximately 50 % of the composition of this fraction consists of fatty compounds, while the other half is composed of an unsaponifiable fraction composed mainly of chlorophylls, phytosterols and carotenes. From the lowest yield, it was possible to obtain a selectivity of 92,14 % for fatty components (fatty acids and fatty esters) confirmed through the infrared spectroscopy technique. The presence of polyunsaturated acids (~45 %) in the lipid extracts indicated the potential of this fraction as a source of nutraceuticals. The results indicate that the biomass of Scenedesmus sp, can become a promising potential source for obtaining polyunsaturated fatty acids, carotenoids and proteins as well as the simultaneous obtainment of different compounds of high commercial value.

Keywords: microalgae, Desmodesmus, lipid classes, fatty acid profile, proteins, carbohydrates

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Authors: Ovidiu Domşa, Nicolae Bold

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Using Information and Communication Technologies (ICT) notions in education and three basic processes of education (teaching, learning and assessment) can bring benefits to the pupils and the professional development of teachers. In this matter, we refer to these notions as concepts taken from the informatics area and apply them to the domain of education. These notions refer to genetic algorithms and arborescent structures, used in the specific process of assessment or evaluation. This paper uses these kinds of notions to generate subtrees from a main tree of tests related between them by their degree of difficulty. These subtrees must contain the highest number of connections between the nodes and the lowest number of missing edges (which are subtrees of the main tree) and, in the particular case of the non-existence of a subtree with no missing edges, the subtrees which have the lowest (minimal) number of missing edges between the nodes, where a node is a test and an edge is a direct connection between two tests which differs by one degree of difficulty. The subtrees are represented as sequences. The tests are the same (a number coding a test represents that test in every sequence) and they are reused for each sequence of tests.

Keywords: chromosome, genetic algorithm, subtree, test

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Authors: Fariborz Ahmadi, Hamid Salehi, Khosrow Karimi

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A sensor network is set of sensor nodes that cooperate together to perform a predefined tasks. The important problem in this network is power consumption. So, in this paper one algorithm based on the eurygaster life is introduced to minimize power consumption by the nodes of these networks. In this method the search space of problem is divided into several partitions and each partition is investigated separately. The evaluation results show that our approach is more efficient in comparison to other evolutionary algorithm like genetic algorithm.

Keywords: evolutionary computation, genetic algorithm, particle swarm optimization, sensor network optimization

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Authors: Ghaleb Bin Huraib, Fahad Al Harthi, Mohammad Mustafa, Abdulrahman Al-Asmari

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Introduction: Vitiligo is an acquired pigmentary skin disorder with the regional disappearance of melanocytes. Vitiligo affects 0.1 to 2% of the global population, and the incidence varies substantially depending on ethnicity. Glutathione S-transferase (GST) is a multigene family of enzymes that detoxify oxidative stress products. The oxidative stress-related GSTM1/GSTT1 genes deletion may cause epidermal melanocytes destruction and the development of vitiligo. Hence, the present study aimed to investigate the association of GST gene polymorphisms with vitiligo in the Saudi population, if any. Materials and Methods: The present study includes 129 vitiligo cases and 130 age-matched healthy controls. The proportion of male and female patients with vitiligo is almost equal. The multiplex polymerase chain reaction (PCR) method was used for polymorphic analysis. Results: Increased odds of generalized vitiligo was observed with the null genotypes of GSTT1- gene (OR = 1.91, 95% CI = 1.07-3.42, p = 0.019). The possible genetic combinations of GSTM1/GSTT1 and their genotypic distribution showed the frequency of GSTM1+/GSTT1+ 62/130 (47.69%) and GSTM1-/GSTT1+ 52/130 (40.00%) were higher in controls than in cases 44/129 (34.11%), 43/129 (33.34%), respectively while GSTM1+/GSTT1- and GSTM1-/GSTT1- null genotypes were higher 22/129 (17.05%) and 20/129 (15.50%) in vitiligo patients as compared to controls 11/130 (8.46%), 5/130 (3.84%), respectively. The strength of association of different genetic combinations with cases have shown GSTM1+/GSTT1- (OR = 2.81, 95% CI = 1.24-6.40, p = 0.009) and GSTM1-/GSTT1- (OR = 5.63, 95% CI = 1.96 - 16.16, p = 0.0004) were significantly higher in vitiligo cases as compared to controls. We did not observe any significant association of age and gender of patients with GST gene polymorphisms. Conclusions: The GSTT1-, GSTM1+/GSTT1- and GSTM1-/GSTT1- null genotypes were significantly associated with vitiligo. These genetic polymorphisms may be the associative genetic risk factor for vitiligo among Saudis. It could be used as a genetic marker for screening vitiligo patients among Saudis. Further studies on GSTs gene polymorphism in larger sample sizes from different geographical areas and ethnicity are needed to strengthen the present findings.

Keywords: vitiligo, GSTM1, GSTT1, gene polymorphism, oxidative stress

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Authors: Javad Khazaei, Rick Blum

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Distributed control is an efficient and flexible approach for coordination of multi-agent systems. One of the main challenges in designing a distributed controller is identifying the governing dynamics of the dynamical systems. Data-driven system identification is currently undergoing a revolution. With the availability of high-fidelity measurements and historical data, model-free identification of dynamical systems can facilitate the control design without tedious modeling of high-dimensional and/or nonlinear systems. This paper develops a distributed control design using consensus theory for linear and nonlinear dynamical systems using sparse identification of system dynamics. Compared with existing consensus designs that heavily rely on knowing the detailed system dynamics, the proposed model-free design can accurately capture the dynamics of the system with available measurements and input data and provide guaranteed performance in consensus and tracking problems. Heterogeneous damped oscillators are chosen as examples of dynamical system for validation purposes.

Keywords: consensus tracking, distributed control, model-free control, sparse identification of dynamical systems

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Authors: Xiyang Li, Qi Yu, Lun Zhang

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In the face of natural disasters and other emergency situations, determining the optimal location of rescue centers is crucial for improving rescue efficiency and minimizing impact on affected populations. This paper proposes a method that integrates genetic algorithms (GA) and backpropagation neural networks (BPNN) to address the site selection optimization problem for emergency rescue centers. We utilize BPNN to accurately estimate the cost of delivering supplies from rescue centers to each temporary camp. Moreover, a genetic algorithm with a special partially matched crossover (PMX) strategy is employed to ensure that the number of temporary camps assigned to each rescue center adheres to predetermined limits. Using the population distribution data during the 2022 epidemic in Jiading District, Shanghai, as an experimental case, this paper verifies the effectiveness of the proposed method. The experimental results demonstrate that the BPNN-GA method proposed in this study outperforms existing algorithms in terms of computational efficiency and optimization performance. Especially considering the requirements for computational resources and response time in emergency situations, the proposed method shows its ability to achieve rapid convergence and optimal performance in the early and mid-stages. Future research could explore incorporating more real-world conditions and variables into the model to further improve its accuracy and applicability.

Keywords: emergency rescue centers, genetic algorithms, back-propagation neural networks, site selection optimization

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Authors: Mohamed Thani Ibouroi, Ali Cheha, Claudine Montgelard, Veronique Arnal, Dawiyat Massoudi, Guillelme Astruc, Said Ali Ousseni Dhurham, Aurelien Besnard

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The Livingstone's flying fox (Pteropus livingstonii) and the Comorian fruit bat (P.seychellensis comorensis) are two endemic fruit bat species among the mostly threatened animals of the Comoros archipelagos. Despite their role as important ecosystem service providers like all flying fox species as pollinators and seed dispersers, little is known about their ecologies, population genetics and structures making difficult the development of evidence-based conservation strategies. In this study, we assess spatial distribution and ecological niche of both species using Species Distribution Modeling (SDM) based on the recent Ensemble of Small Models (ESMs) approach using presence-only data. Population structure and genetic diversity of the two species were assessed using both mitochondrial and microsatellite markers based on non-invasive genetic samples. Our ESMs highlight a clear niche partitioning of the two sympatric species. Livingstone’s flying fox has a very limited distribution, restricted on steep slope of natural forests at high elevation. On the contrary, the Comorian fruit bat has a relatively large geographic range spread over low elevations in farmlands and villages. Our genetic analysis shows a low genetic diversity for both fruit bats species. They also show that the Livingstone’s flying fox population of the two islands were genetically isolated while no evidence of genetic differentiation was detected for the Comorian fruit bats between islands. Our results support the idea that natural habitat loss, especially the natural forest loss and fragmentation are the important factors impacting the distribution of the Livingstone’s flying fox by limiting its foraging area and reducing its potential roosting sites. On the contrary, the Comorian fruit bats seem to be favored by human activities probably because its diets are less specialized. By this study, we concluded that the Livingstone’s flying fox species and its habitat are of high priority in term of conservation at the Comoros archipelagos scale.

Keywords: Comoros islands, ecological niche, habitat loss, population genetics, fruit bats, conservation biology

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Iman Farasat, Howard M. Salis

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Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

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Authors: J. Bernasovska, I. Boronova, J. Poracova, M. Mydlarova Blascakova, V. Szabadosova, P. Ruzbarsky, E. Petrejcikova, I. Bernasovsky

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The paper presents the results of the molecular genetics analysis in sports research, with special emphasis to use genetic information in diagnosing of motoric predispositions in Roma boys from East Slovakia. The ability and move are the basic characteristics of all living organisms. The phenotypes are influenced by a combination of genetic and environmental factors. Genetic tests differ in principle from the traditional motoric tests, because the DNA of an individual does not change during life. The aim of the presented study was to examine motion abilities and to determine the frequency of ACTN3 (R577X) gene in Roma children. Genotype data were obtained from 138 Roma and 155 Slovak boys from 7 to 15 years old. Children were investigated on physical performance level in association with their genotype. Biological material for genetic analyses comprised samples of buccal swabs. Genotypes were determined using Real Time High resolution melting PCR method (Rotor-Gene 6000 Corbett and Light Cycler 480 Roche). The software allows creating reports of any analysis, where information of the specific analysis, normalized and differential graphs and many information of the samples are shown. Roma children of analyzed group legged to non-Romany children at the same age in all the compared tests. The % distribution of R and X alleles in Roma children was different from controls. The frequency of XX genotype was 9.26%, RX 46.33% and RR was 44.41%. The frequency of XX genotype was 9.26% which is comparable to a frequency of an Indian population. Data were analyzed with the ANOVA test.

Keywords: ACTN3 gene, R577X polymorphism, Roma children, sport performance, Slovakia

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Authors: Rakesh C. Mathad, G. Y. Lokesh, Basavegowda

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In agriculture using quality seeds of improved varieties is very important to ensure higher productivity thereby food security and sustainability. To ensure good productivity, seeds should have characters as described by the breeder. To know whether the characters as described by the breeder are expressing in a variety such as genuineness or genetic purity, field grow out test (GOT) is done. In pigeon pea which is long durational crop, conducting a GOT may take very long time and expensive also. Since in pigeon pea flower character is a most distinguishing character from the contaminants, conducting a field grow out test require 120-130 days or till flower emergence, which may increase cost of storage and seed production. This will also delay the distribution of seed inventory to the pigeon pea growing areas. In this view during 2014-15 with financial support of Govt. of Karnataka, India, a project to develop a molecular genetic test for newly developed variety of pigeon pea cv.TS3R was commissioned at Seed Unit, UAS, Raichur. A molecular test was developed with the help SSR markers to identify pure variety from possible off types in newly released pigeon pea variety TS3R. In the investigation, 44 primer pairs were screened to identify the specific marker associated with this variety. Pigeon pea cv. TS3R could be clearly identified by using the primer CCM 293 based on the banding pattern resolved on gel electrophoresis and PCR reactions. However some of the markers like AHSSR 46, CCM 82 and CCM 57 can be used to test other popular varieties in the region like Asha, GRG-811 and Maruti respectively. Further to develop this in to a lab test, the seed sample size was standardized to 200 seeds and a grow out matrix was developed. This matrix was used to sample 12 days old leaves to extract DNA. The lab test results were validated with actual field GOT test results and found variations within the acceptable limit of 1%. This molecular method can now be employed to test the genetic purity in pigeon pea cv TS3R which reduces the time and can be a cheaper alternative method for field GOT.

Keywords: genuineness, grow-out matrix, molecular genetic purity, SSR markers

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Authors: Mitat Uysal, Aynur Uysal

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Computer engineering and mathematics share a deep and symbiotic relationship, with mathematics providing the foundational theories and models for computer engineering advancements. From algorithm development to optimization techniques, mathematics plays a pivotal role in solving complex computational problems. This paper explores key mathematical principles that underpin computer engineering, illustrating their significance through a case study that demonstrates the application of optimization techniques using Python code. The case study addresses the well-known vehicle routing problem (VRP), an extension of the traveling salesman problem (TSP), and solves it using a genetic algorithm.

Keywords: VRP, TSP, genetic algorithm, computer engineering, optimization

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Authors: Young Ik Suh, Taewook Chung, Glaucio Scremin, Tywan Martin

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In May of 2016, the Philadelphia 76ers announced that StubHub, the ticket resale company, will have advertising on the team’s jerseys beginning in the 2017-18 season. The 76ers and National Basketball Association (NBA) became the first team and league which embraced jersey sponsorships in the four major U.S. professional sports. Even though many professional teams and leagues in Europe, Asia, Africa, and South America have adopted jersey sponsorship actively, this phenomenon is relatively new in America. While the jersey sponsorship provides economic gains for the professional leagues and franchises, sport fans can have different points of view for the phenomenon of jersey sponsorship. For instance, since many sport fans in U.S. are not familiar with ads on jerseys, this movement can possibly cause negative reaction such as the decrease in ticket and merchandise sales. They also concern the small size of ads on jersey become bigger ads, like in the English Premier League (EPL). However, some sport fans seem they do not mind too much about jersey sponsorship because the ads on jersey will not affect their loyalty and fanship. Therefore, the assumption of this study was that the sport fans’ reaction about jersey sponsorship can be possibly different, especially based on different levels of the sport fans’ team identification and various sizes of ads on jersey. Unlike general sponsorship in sport industry, jersey sponsorship has received little attention regarding its potential impact on sport fans attitudes and buying intentions. Thus, the current study sought to identify how the various levels of team identification influence brand attitude and buying intention in terms of jersey sponsorship. In particular, this study examined the effect of team identification on brand attitude and buying intention when there are no ads, small size ads, and large size ads on jersey. 3 (large, small, and no ads) X 3 (Team Identification: high, moderate, low) between subject factorial design was conducted on attitude toward the brand and buying intention of jersey sponsorship. The ads on Philadelphia 76ers jersey were used. The sample of this study was selected from message board users provided by different sports websites (i.e., forums.realgm.com and phillysportscentral.com). A total of 275 respondents participated in this study by responding to an online survey questionnaire. The results showed that there were significant differences between fans with high identification and fans with low identification. The findings of this study are expected to have many theoretical and practical contributions and implications by extending the research and literature pertaining to the relationship between team identification and brand strategy based upon different levels of team identification.

Keywords: brand attitude, buying intention, Jersey sponsorship, team identification

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Authors: Abejide Dorcas Ropo, , Falusi Olamide Ahmed, Daudu Oladipupo Abdulazeez Yusuf, Salihu Bolaji Zuluquri Neen, Muhammad Liman Muhammad, Gado Aishatu Adamu

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Water stress is a major factor limiting the productivity of crops in the world today. This study evaluated the morpho-agronomic response of twenty-four (24) Nigerian Bambara groundnut landraces to water stress and genetic diversity of some selected accessions using SSR markers. The studies was carried out in the Botanical garden of the Department of Plant Biology, Federal University of Technology, Minna, Niger State, Nigeria in a randomized complete block design using three replicates. Molecular analysis using SSR primers was carried out at the Centre for Bio- Science, International Institute of Tropical Agriculture (IITA) Ibadan, Nigeria in order to characterize ten selected accessions comprising of the seven most drought tolerant and the three most susceptible accessions detected from the morpho-agronomic studies. Results revealed that water stress decreased morpho-agronomic traits such as plant height, leaf area, number of leaves per plant and seed yield etc. A total of 22 alleles were detected by the SSR markers used with a mean number of 4 allelles. Simple Sequence Repeat (SSR) markers MBamCO33, Primer 65 and G358B2-D15 each detected 4 allelles while Primer 3FR and 4FR detected 5 allelles each. The study revealed significantly high polymorphisms in 10 Loci. The mean value of Polymorpic information content was 0.6997 implying the usefulness of the primers used in identifying genetic similarities and differences among the Bambara groundnut genotypes. The SSR analysis revealed a comparable pattern between genetic diversity and drought tolerance of the genotypes. The Unweighted Paired Group Method with Arithmethic Mean (UPGMA) dendrogram showed that at a genetic distance of 0.1, the accessions were grouped into three groups according to their level of tolerance to drought. The two most drought tolerant accessions were grouped together and the 5th and 6th most drought tolerant accession were also grouped together. This suggests that the genotypes grouped together may be genetically close, may possess similar genes or have a common origin. The degree of genetic variants obtained could be useful in bambara groundnut breeding for drought tolerance. The identified drought tolerant bambara groundnut landraces are important genetic resources for drought stress tolerance breeding programme of bambara groundnut. The genotypes are also useful for germplasm conservation and global implications.

Keywords: bambara groundnut, genetic diversity, germplasm, SSR markers, water stress

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Masoud Sheidaei, Mohammad-Reza Kordasti, Fahimeh Koohdar

Abstract:

Calotropis procera (Aiton) W.T.Aiton, (Apocynaceae), is an economically and medicinally important plant species which is an evergreen, perennial shrub growing in arid and semi-arid climates, and can tolerate very low annual rainfall (150 mm) and a dry season. The plant can also tolerate temperature ran off 20 to30°C and is not frost tolerant. This plant species prefers free-draining sandy soils but can grow also in alkaline and saline soils.It is found at a range of altitudes from exposed coastal sites to medium elevations up to 1300 m. Due to morpho-physiological adaptations of C. procera and its ability to tolerate various abiotic stresses. This taxa can compete with desirable pasture species and forms dense thickets that interfere with stock management, particularly mustering activities. Caloteropis procera grows only in southern part of Iran where in comprises a limited number of geographical populations. We used different population genetics and r landscape analysis to produce data on geographical populations of C. procera based on molecular genetic study using SCoT molecular markers. First, we used spatial principal components (sPCA), as it can analyze data in a reduced space and can be used for co-dominant markers as well as presence / absence data as is the case in SCoT molecular markers. This method also carries out Moran I and Mantel tests to reveal spatial autocorrelation and test for the occurrence of Isolation by distance (IBD). We also performed Random Forest analysis to identify the importance of spatial and geographical variables on genetic diversity. Moreover, we used both RDA (Redundency analysis), and LFMM (Latent factor mixed model), to identify the genetic loci significantly associated with geographical variables. A niche modellng analysis was carried our to predict present potential area for distribution of these plants and also the area present by the year 2050. The results obtained will be discussed in this paper.

Keywords: population genetics, landscape genetic, Calotreropis procera, niche modeling, SCoT markers

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Authors: Muhammad Sheeraz, Mehwish Ejaz

Abstract:

This study endeavors to enhance the understanding of the determinants of oppositional brand loyalty, particularly within the context of fans of a sports brand. The primary focus is on investigating how oppositional brand loyalty fosters rivalry among the fans and exploring the interplay between various variables, namely self-congruence, consumer brand identification, consumer brand engagement, and narcissism, in influencing the likelihood of endorsing a rival team. The research adopts a cross-sectional survey methodology, employing a structured questionnaire distributed both online and onsite to gather responses from a representative sample of 460 PSL fans in Pakistan. The data collection process involved obtaining responses from diverse settings, including universities, shopping malls, and other public spaces frequented by PSL enthusiasts. Participants were prompted to indicate their allegiance to a specific PSL team and subsequently respond to the questionnaire based on their preferences. The findings of the study reveal that narcissism, as a moderating factor, exhibits no significant influence on consumer brand identification, consumer brand engagement, and oppositional brand loyalty. However, it does emerge as a significant moderator in the relationship between self-congruence and consumer brand identification. Particularly, consumers express brand identification through self-congruence, elucidating the existence of oppositional sentiments among PSL fans and their counterparts supporting rival teams. The implications of these results underscore the importance for marketers to establish a brand identity that resonates with consumers on a personal level. Such an approach fosters a strong sense of identification with the brand, prompting consumers to vigorously defend and support their favored brands, even in the face of opposition from rival teams. Marketers are encouraged to focus on cultivating long-term consumer loyalty, as it proves pivotal in maintaining a competitive advantage over industry counterparts.

Keywords: oppositional brand loyalty, consumer brand identification, consumer brand engagement, narcissism, self-congruence

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Authors: Carolina Silva, João Nuno Oliveira, Rui Sousa, João Paulo Silva

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This study uses genetic algorithms to solve a job scheduling problem in a weaving factory. The underline problem regards an NP-Hard problem concerning unrelated parallel machines, with sequence-dependent setup times. This research uses real data regarding a weaving industry located in the North of Portugal, with a capacity of 96 looms and a production, on average, of 440000 meters of fabric per month. Besides, this study includes a high level of complexity once most of the real production constraints are applied, and several real data instances are tested. Topics such as data analyses and algorithm performance are addressed and tested, to offer a solution that can generate reliable and due date results. All the approaches will be tested in the operational environment, and the KPIs monitored, to understand the solution's impact on the production, with a particular focus on the total number of weeks of late deliveries to clients. Thus, the main goal of this research is to develop a solution that allows for the production of automatically optimized production plans, aiming to the tardiness minimizing.

Keywords: genetic algorithms, textile industry, job scheduling, optimization

Procedia PDF Downloads 155

Authors: Amanda J. Burridge, Keith J. Edwards, Paul A. Wilkinson, Tom Batstone, Erik H. Murchie, Lorna McAusland, Ana Elizabete Carmo-Silva, Ivan Jauregui, Tracy Lawson, Silvere R. M. Vialet-Chabrand

Abstract:

The rate of genetic progress in wheat production must be improved to meet global food security targets. However, past selection for domestication traits has reduced the genetic variation in modern wheat cultivars, a fact that could severely limit the future rate of genetic gain. The genetic variation in agronomically important traits for the wild relatives and progenitors of wheat is far greater than that of the current domesticated cultivars, but transferring these traits into modern cultivars is not straightforward. Between the elite cultivars of wheat, photosynthetic capacity is a key trait for which there is limited variation. Early screening of wheat wild relative and progenitors has shown differences in photosynthetic capacity and efficiency not only between wild relative species but marked differences between the accessions of each species. By identifying wild relative accessions with improved photosynthetic traits and characterising the genetic variation responsible, it is possible to incorporate these traits into advanced breeding programmes by wide crossing and introgression programmes. To identify the potential variety of photosynthetic capacity and efficiency available in the secondary and tertiary genepool, a wide scale survey was carried out for over 600 accessions from 80 species including those from the genus Aegilops, Triticum, Thinopyrum, Elymus, and Secale. Genotype data were generated for each accession using a ‘Wheat Wild Relative’ Single Nucleotide Polymorphism (SNP) genotyping array composed of 35,000 SNP markers polymorphic between wild relatives and elite hexaploid wheat. This genotype data was combined with phenotypic measurements such as gas exchange (CO₂, H₂O), chlorophyll fluorescence, growth, morphology, and RuBisCO activity to identify potential breeding material with enhanced photosynthetic capacity and efficiency. The data and associated analysis tools presented here will prove useful to anyone interested in increasing the genetic diversity in hexaploid wheat or the application of complex genotyping data to plant breeding.

Keywords: wheat, wild relatives, pre-breeding, genomics, photosynthesis

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Authors: P. M. Sreekanth

Abstract:

Teak (Tectona grandis L. f.) is a tree species indigenous to India and other Southeastern countries. It produces high-value timber and is easily established in plantations. Reforestation requires a constant supply of high quality seeds. Seed Production Areas (SPA) of teak are improved stands used for collection of open-pollinated quality seeds in large quantities. Information on the genetic diversity of major teak SPAs in India is scanty. The genetic structure of four important seed production areas of Kerala State in Southern India was analyzed employing amplified fragment length polymorphism markers using ten selective primer combinations on 80 samples (4 populations X 20 trees). The study revealed that the gene diversity of the SPAs varied from 0.169 (Konni SPA) to 0.203 (Wayanad SPA). The percentage of polymorphic loci ranged from 74.42 (Parambikulam SPA) to 84.06 (Konni SPA). The mean total gene diversity index (HT) of all the four SPAs was 0.2296 ±0.02. A high proportion of genetic diversity was observed within the populations (83%) while diversity between populations was lower (17%) (GST = 0.17). Principal coordinate analysis and STRUCTURE analysis of the genotypes indicated that the pattern of clustering was in accordance with the origin and geographic location of SPAs, indicating specific identity of each population. A UPGMA dendrogram was prepared and showed that all the twenty samples from each of Konni and Parambikulam SPAs clustered into two separate groups, respectively. However, five Nilambur genotypes and one Wayanad genotype intruded into the Konni cluster. The higher gene flow estimated (Nm = 2.4) reflected the inclusion of Konni origin planting stock in the Nilambur and Wayanad plantations. Evidence for population structure investigated using 3D Principal Coordinate Analysis of FAMD software 1.30 indicated that the pattern of clustering was in accordance with the origin of SPAs. The present study showed that assessment of genetic diversity in seed production plantations can be achieved using AFLP markers. The AFLP fingerprinting was also capable of identifying the geographical origin of planting stock and there by revealing the occurrence of the errors in genotype labeling. Molecular marker-based selective culling of genetically similar trees from a stand so as to increase the genetic base of seed production areas could be a new proposition to improve quality of seeds required for raising commercial plantations of teak. The technique can also be used to assess the genetic diversity status of plus trees within provenances during their selection for raising clonal seed orchards for assuring the quality of seeds available for raising future plantations.

Keywords: AFLP, genetic structure, spa, teak

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Authors: Micheale Yifter Weldemichael

Abstract:

Sesame (Sesamum indicum L.) is among the most important oilseed crops for its high edible oil quality and quantity. Sesame is grown for food, medicinal, pharmaceutical, and industrial uses. Sesame is also cultivated as a main cash crop in Asia and Africa by smallholder farmers. Despite the global exponential increase in sesame cultivation area, its production and productivity remain low, mainly due to biotic and abiotic constraints. Notwithstanding the efforts to solve these problems, a low level of genetic variation and inadequate genomic resources hinder the progress of sesame improvement. The objective of this paper is, therefore, to review recent advances in the area of molecular breeding and transformation to overcome major production constraints and could result in enhanced and sustained sesame production. This paper reviews various researches conducted to date on molecular breeding and genetic transformation in sesame focusing on molecular markers used in assessing the available online database resources, genes responsible for key agronomic traits as well as transgenic technology and genome editing. The review concentrates on quantitative and semi-quantitative studies on molecular breeding for key agronomic traits such as improvement of yield components, oil and oil-related traits, disease and insect/pest resistance, and drought, waterlogging and salt tolerance, as well as sesame genetic transformation and genome editing techniques. Pitfalls and limitations of existing studies and methodologies used so far are identified and some priorities for future research directions in sesame genetic improvement are identified in this review.

Keywords: abiotic stress, biotic stress, improvement, molecular breeding, oil, sesame, shattering

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