Search results for: genetic testing

Authors: Aminah Muchdar

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To increase the genetic diversity of soybean in order to adapt to agroecology in Indonesia conducted ways including introduction, cross, mutation and genetic transformation. The purpose of this research is to obtain early maturity soybean mutant lines, large seed tolerant to drought with high yield potential. This study consisted of two stages: the first is sensitivity of gamma rays carried out in the Laboratory BATAN. The genetic variety used is Anjasmoro. The method seeds irradiated with gamma rays at a rate of activity with the old ci 1046.16976 irradiation 0-71 minutes. Irradiation doses of 0, 100, 200, 300, 400, 500, 600, 700, 800, 900 and 1000gy. The results indicated all seeds irradiated with doses of 0 - 1000gy, just a dose of 200 and 300gy are able to show the percentage of germination, plant height, number of leaves, number of normal sprouts and green leaves of the best and can be continued for a second trial in order to assemble and to get mutants which is expected. The result of second stage of soybean M2 Population irradiated with diversity Gamma Irradiation performed that in the form of soybean planting, the seed planted is the first derivative of the M2 irradiated seeds. The result after the age of 30ADP has already showing growth and development of plants that vary when compared to its parent, both in terms of plant height, number of leaves, leaf shape and leaf forage level. In the generative phase, a plant that has been irradiated 200 and 300 gy seen some plants flower form packs, but not formed pods, there is also a form packs of flowers, but few pods produce soybean morphological characters such as plant height, number of branches, pods, days to flowering, harvesting, seed weight and seed number.

Keywords: gamma ray, genetic mutation, irradiation, soybean

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Authors: Aritra Majumdar

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While organizations strive to expand their new customer base, retaining existing relationships is a key aspect of improving overall profitability and also showcasing how successful an organization is in holding on to its customers. It is an experimentally proven fact that the lion’s share of profit always comes from existing customers. Hence seamless management of renewal journeys across different channels goes a long way in improving trust in the brand. From a quality assurance standpoint, time travel testing provides an approach to both business and technology teams to enhance the customer experience when they look to extend their partnership with the organization for a defined phase of time. This whitepaper will focus on key pillars of time travel testing: time travel planning, time travel data preparation, and enterprise automation. Along with that, it will call out some of the best practices and common accelerator implementation ideas which are generic across verticals like healthcare, insurance, etc. In this abstract document, a high-level snapshot of these pillars will be provided. Time Travel Planning: The first step of setting up a time travel testing roadmap is appropriate planning. Planning will include identifying the impacted systems that need to be time traveled backward or forward depending on the business requirement, aligning time travel with other releases, frequency of time travel testing, preparedness for handling renewal issues in production after time travel testing is done and most importantly planning for test automation testing during time travel testing. Time Travel Data Preparation: One of the most complex areas in time travel testing is test data coverage. Aligning test data to cover required customer segments and narrowing it down to multiple offer sequencing based on defined parameters are keys for successful time travel testing. Another aspect is the availability of sufficient data for similar combinations to support activities like defect retesting, regression testing, post-production testing (if required), etc. This section will talk about the necessary steps for suitable data coverage and sufficient data availability from a time travel testing perspective. Enterprise Automation: Time travel testing is never restricted to a single application. The workflow needs to be validated in the downstream applications to ensure consistency across the board. Along with that, the correctness of offers across different digital channels needs to be checked in order to ensure a smooth customer experience. This section will talk about the focus areas of enterprise automation and how automation testing can be leveraged to improve the overall quality without compromising on the project schedule. Along with the above-mentioned items, the white paper will elaborate on the best practices that need to be followed during time travel testing and some ideas pertaining to accelerator implementation. To sum it up, this paper will be written based on the real-time experience author had on time travel testing. While actual customer names and program-related details will not be disclosed, the paper will highlight the key learnings which will help other teams to implement time travel testing successfully.

Keywords: time travel planning, time travel data preparation, enterprise automation, best practices, accelerator implementation ideas

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Authors: Cagri Memis, Muzaffer Kapanoglu

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The school bus routing problem (SBRP) is a variant of the Vehicle Routing Problem (VRP) classified as a location-allocation-routing problem. In this study, the SBRP is decomposed into two sub-problems: (1) bus route generation and (2) bus stop selection to solve large instances of the SBRP in reasonable computational times. To solve the first sub-problem, we propose a genetic algorithm to generate bus routes. Once the routes have been fixed, a sub-problem remains of allocating students to stops considering the capacity of the buses and the walkability constraints of the students. While the exact method solves small-scale problems, treating large-scale problems with the exact method becomes complex due to computational problems, a deficiency that the genetic algorithm can overcome. Results obtained from the proposed approach on 150 instances up to 250 stops show that the matheuristic algorithm provides better solutions in reasonable computational times with respect to benchmark algorithms.

Keywords: genetic algorithm, matheuristic, school bus routing problem, vehicle routing problem

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Authors: T. Z. Butt, T. A. Tabish, K. Anjum, H. Hafeez

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A study of martensitic stainless steel surgical grade AISI 420A produced in Japan was carried out in this research work. The sample was already annealed at about 898˚C. The sample were subjected to chemical analysis, hardness, tensile and metallographic tests. These tests were performed on as received annealed and heat treated samples. In the annealed condition the sample showed 0HRC. However, on tensile testing, in annealed condition the sample showed maximum elongation. The heat treatment is carried out in vacuum furnace within temperature range 980-1035°C. The quenching of samples was carried out using liquid nitrogen. After hardening, the samples were subjected to tempering, which was carried out in vacuum tempering furnace at a temperature of 220˚C. The hardened samples were subjected to hardness and tensile testing. In hardness testing, the samples showed maximum hardness values. In tensile testing the sample showed minimum elongation. The sample in annealed state showed coarse plates of martensite structure. Therefore, the studied steels can be used as biomaterials.

Keywords: biomaterials, martensitic steel, microsrtucture, tensile testing, hardening, tempering, bioinstrumentation

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Authors: Bin Mu, Site Li, Shijin Yuan

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Under the circumstance of environment deterioration, people are increasingly concerned about the quality of the environment, especially air quality. As a result, it is of great value to give accurate and timely forecast of AQI (air quality index). In order to simplify influencing factors of air quality in a city, and forecast the city’s AQI tomorrow, this study used MATLAB software and adopted the method of constructing a mathematic model of PCA-GABP to provide a solution. To be specific, this study firstly made principal component analysis (PCA) of influencing factors of AQI tomorrow including aspects of weather, industry waste gas and IAQI data today. Then, we used the back propagation neural network model (BP), which is optimized by genetic algorithm (GA), to give forecast of AQI tomorrow. In order to verify validity and accuracy of PCA-GABP model’s forecast capability. The study uses two statistical indices to evaluate AQI forecast results (normalized mean square error and fractional bias). Eventually, this study reduces mean square error by optimizing individual gene structure in genetic algorithm and adjusting the parameters of back propagation model. To conclude, the performance of the model to forecast AQI is comparatively convincing and the model is expected to take positive effect in AQI forecast in the future.

Keywords: AQI forecast, principal component analysis, genetic algorithm, back propagation neural network model

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Authors: Watchara Songserm, Teeradej Wuttipornpun

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This paper presents a genetic algorithm based permutation and non-permutation scheduling heuristics (GAPNP) to solve a multi-stage finite capacity material requirement planning (FCMRP) problem in automotive assembly flow shop with unrelated parallel machines. In the algorithm, the sequences of orders are iteratively improved by the GA characteristics, whereas the required operations are scheduled based on the presented permutation and non-permutation heuristics. Finally, a linear programming is applied to minimize the total cost. The presented GAPNP algorithm is evaluated by using real datasets from automotive companies. The required parameters for GAPNP are intently tuned to obtain a common parameter setting for all case studies. The results show that GAPNP significantly outperforms the benchmark algorithm about 30% on average.

Keywords: capacitated MRP, genetic algorithm, linear programming, automotive industries, flow shop, application in industry

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Authors: Yue-Tzu Yang, Peng-Jen Chen

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The present study deals with the numerical optimization of wavy channel with the help of genetic algorithm (GA). Three design variables related to the wave amplitude (A), the wavelength (λ) and the channel aspect ratio (α) are chosen and their ranges are decided through preliminary calculations of three-dimensional Navier-stokes and energy equations. A parametric study is also performed to show the effects of different design variables on the overall performance of the wavy channel. Objective functions related to the heat transfer and pressure drop, performance factor (PF) is formulated to analyze the performance of the wavy channel. The numerical results show that the wave amplitude and the channel aspect ratio have significant effects on the thermal performance. It can improve the performance of the wavy channels by increasing wave amplitude or decreasing the channel aspect ratio. Increasing wavelengths have no significant effects on the heat transfer performance.

Keywords: wavy channel, genetic algorithm, optimization, numerical simulation

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Authors: E. Behmanesh, J. Pannek

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The distribution-allocation problem is known as one of the most comprehensive strategic decision. In real-world cases, it is impossible to solve a distribution-allocation problem in traditional ways with acceptable time. Hence researchers develop efficient non-traditional techniques for the large-term operation of the whole supply chain. These techniques provide near-optimal solutions particularly for large scales test problems. This paper, presents an integrated supply chain model which is flexible in the delivery path. As the solution methodology, we apply a memetic algorithm with a novelty in population presentation. To illustrate the performance of the proposed memetic algorithm, LINGO optimization software serves as a comparison basis for small size problems. In large size cases that we are dealing with in the real world, the Genetic algorithm as the second metaheuristic algorithm is considered to compare the results and show the efficiency of the memetic algorithm.

Keywords: integrated logistics network, flexible path, memetic algorithm, genetic algorithm

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Authors: Zakir Husain

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Main objective of a power transformer design optimization problem requires minimizing the total overall cost and/or mass of the winding and core material by satisfying all possible constraints obligatory by the standards and transformer user requirement. The constraints include appropriate limits on winding fill factor, temperature rise, efficiency, no-load current and voltage regulation. The design optimizations tasks are a constrained minimum cost and/or mass solution by optimally setting the parameters, geometry and require magnetic properties of the transformer. In this paper, present the above design problems have been formulated by using genetic algorithm (GA) and simulated annealing (SA) on the MATLAB platform. The importance of the presented approach is stems for two main features. First, proposed technique provides reliable and efficient solution for the problem of design optimization with several variables. Second, it guaranteed to obtained solution is global optimum. This paper includes a demonstration of the application of the genetic programming GP technique to transformer design.

Keywords: optimization, power transformer, genetic algorithm (GA), simulated annealing technique (SA)

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Authors: Martin Cermak, Tomas Karasek, Jaroslav Rojicek

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The key role in phenomenological modelling of cyclic plasticity is good understanding of stress-strain behaviour of given material. There are many models describing behaviour of materials using numerous parameters and constants. Combination of individual parameters in those material models significantly determines whether observed and predicted results are in compliance. Parameter identification techniques such as random gradient, genetic algorithm, and sensitivity analysis are used for identification of parameters using numerical modelling and simulation. In this paper genetic algorithm and sensitivity analysis are used to study effect of 4 parameters of modified AbdelKarim-Ohno cyclic plasticity model. Results predicted by Finite Element (FE) simulation are compared with experimental data from biaxial ratcheting test with semi-elliptical loading path.

Keywords: genetic algorithm, sensitivity analysis, inverse approach, finite element method, cyclic plasticity, ratcheting

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Authors: Alaa Jawad Hassan, Saad Marza Al-Aaraji, Fadil Abbas Hamad

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The current study was designed to assess some immunological parameters and pedigree analysis for atopic dermatitis patients, as the study included 64 patients (37 males and 27 females) and 24 healthy individuals (12 males and 12 females) with no history of the AD. The cases of this study were divided into two age groups; the first is infant and children (1-10 years), while the second is adolescent and adults (11- 60 years). The number of cases was 51 and 13 in each age group respectively. Sera samples from confirmed AD patients and healthy control were analysed by mean of ELISA for assessment the concentrations of IL-1β, IL-2, IL-4 and IgE. The study showed that a significant increase (P < 0.05) in IL-1β, IL-4 and IgE levels in the patients compared with the control group in both age groups and gender, while there was no significant difference (P < 0.05) in the concentration of IL-2. The study of pedigree analysis shows the genetic tendency in the frequency of disease depending on the genetic history of family, where more patients returning to families in which both parents or one of them infected with AD, whereas the patients were no parents infected with AD they are suffering from asthma and the disease recurs in their uncles.

Keywords: atopic dermatitis, cytokines, IgE, molecular biology

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Authors: Zhao Yang Liu, Jing Tao

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The Asian long-horned beetle, Anoplophora glabripennis (Motschulsky) (Coleoptera: Cerambycidae: Lamiinae), is a wood-borer and polyphagous xylophages native to Asia and killing healthy trees. As it causes serious danger to trees, the beetle has been paid close attention in the world. However, the genetic markers limited, especially microsatellite. In this study, 24 novel simple sequence repeat (SSR) molecular markers, a powerful tool for genetic diversity studies and linkage map construction, were developed and characterized from whole genome shotgun sequences. We developed SSR loci of 2 to 6 repeated and perfect units including 9895 points, the density of SSRs was found one SSR per 56.57 kb and the abundance of SSR was 0.02/kb, besides 140 types of repeats motifs were found. Half of the 48 pairs SSR primers (containing 4 di-, 7 tri-, 2 tetra- and 11 hexamers SSRs) we selected randomly from 1222 pairs of primers were polymorphism. The number of alleles for these markers in 48 individuals varied from 3 to 21 with an average of 7.71, the number of effective alleles ranged from 1.22 to 9.97 with an average of 3.54. Besides this, the polymorphic information content (PIC) ranged from 0.18 to 0.89 with a mean of 0.65, And Shannon's Information index (I) ranged from 0.46 to 2.62 with an average of 1.44. The results suggest that the method for screening of SSR in the whole genome is feasible and efficient. SSR markers developed in this study can be used for population genetic studies of A. glabripennis. Moreover, they may also be helpful for the development of microsatellites for other Coleoptera.

Keywords: SSR markers, Anoplophora glabripennis, genetic diversity, whole genome

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Authors: Sayor Ajfar Aaron, Md. Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

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This paper examines the transformative potential of quantum computing in the field of cybersecurity, with a focus on advanced penetration testing and forensics. It explores how quantum technologies can be leveraged to identify and exploit vulnerabilities more efficiently than traditional methods and how they can enhance the forensic analysis of cyber-attacks. Through theoretical analysis and practical simulations, this study highlights the enhanced capabilities of quantum algorithms in detecting and responding to sophisticated cyber threats, providing a pathway for developing more resilient cybersecurity infrastructures.

Keywords: cybersecurity, cyber forensics, penetration testing, quantum computing

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Authors: Emine Laarouchi, Daniela Cancila, Laurent Soulier, Hakima Chaouchi

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Cyber-Physical Systems as drones proved their efficiency for supporting emergency applications. For these particular applications, travel time and autonomous navigation algorithms are of paramount importance, especially when missions are performed in urban environments with high obstacle density. In this context, however, safety properties are not properly addressed. Our ambition is to optimize the system safety level under autonomous navigation systems, by preserving performance of the CPS. At this aim, we introduce genetic algorithms in the autonomous navigation process of the drone to better infer its trajectory considering the possible obstacles. We first model the wished safety requirements through a cost function and then seek to optimize it though genetics algorithms (GA). The main advantage in the use of GA is to consider different parameters together, for example, the level of battery for navigation system selection. Our tests show that the GA introduction in the autonomous navigation systems minimize the risk of safety lossless. Finally, although our simulation has been tested for autonomous drones, our approach and results could be extended for other autonomous navigation systems such as autonomous cars, robots, etc.

Keywords: safety, unmanned aerial vehicles , CPS, ACPS, drones, path planning, genetic algorithms

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Authors: Sachiko Hosoya

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Objectives: The purpose of this paper is to investigate the social policy of preventive genetic medicine in Iran, by following the legalization process of abortion law and the factors affecting the process in wider Iranian contexts. In this paper, ethical discussions of prenatal diagnosis and selective abortion in Iran will be presented, by exploring Iranian social policy to control genetic diseases, especially a genetic hemoglobin disorder called Thalassemia. The ethical dilemmas in application of genetic medicine into social policy will be focused. Method: In order to examine the role of the policy for prevention of genetic diseases and selective abortion in Iran, various resources have been sutudied, not only academic articles, but also discussion in the Parliament and documents related to a court case, as well as ethnographic data on living situation of Thalassemia patients. Results: Firstly, the discussion on prenatal diagnosis and selective abortion is overviewed from the viewpoints of ethics, disability rights activists, and public policy for lower-resources countries. As a result, it should be noted that the point more important in the discussion on prenatal diagnosis and selective abortion in Iran is the allocation of medical resources. Secondly, the process of implementation of national thalassemia screening program and legalization of ‘Therapeutic Abortion Law’ is analyzed, through scrutinizing documents such as the Majlis record, government documents and related laws and regulations. Although some western academics accuse that Iranian policy of selective abortion seems to be akin to eugenic public policy, Iranian government carefully avoid to distortions of the policy as ‘eugenic’. Thirdly, as a comparative example, discussions on an Iranian court case of patient’s ‘right not to be born’ will be introduced. Along with that, restrictive living environments of people with Thalassemia patients and the carriers are depicted, to understand some disabling social factors for people with genetic diseases in the local contexts of Iran.

Keywords: abortion, Iran, prenatal diagnosis, public health ethics, Thalassemia prevention program

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Authors: Sahar El Shair, Laura Greco, William Reay, Murray Cairns

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Background: Rheumatoid arthritis (RA) is a chronic, systematic, inflammatory, autoimmune disease that involves damages to joints and erosions to the associated bones and cartilage, resulting in reduced physical function and disability. RA is a multifactorial disorder influenced by heterogenous genetic and environmental factors. Whilst different medications have proven successful in reducing inflammation associated with RA, they often come with significant side effects and limited efficacy. To address this, the novel pharmagenic enrichment score (PES) algorithm was tested in self-reported RA patients from the UK Biobank (UKBB), which is a cohort of predominantly European ancestry, and identified individuals with a high genetic risk in clinically actionable biological pathways to identify novel opportunities for precision interventions and drug repurposing to treat RA. Methods and materials: Genetic association data for rheumatoid arthritis was derived from publicly available genome-wide association studies (GWAS) summary statistics (N=97173). The PES framework exploits competitive gene set enrichment to identify pathways that are associated with RA to explore novel treatment opportunities. This data is then integrated into WebGestalt, Drug Interaction database (DGIdb) and DrugBank databases to identify existing compounds with existing use or potential for repurposed use. The PES for each of these candidates was then profiled in individuals with RA in the UKBB (Ncases = 3,719, Ncontrols = 333,160). Results A total of 209 pathways with known drug targets after multiple testing correction were identified. Several pathways, including interferon gamma signaling and TID pathway (which relates to a chaperone that modulates interferon signaling), were significantly associated with self-reported RA in the UKBB when adjusting for age, sex, assessment centre month and location, RA polygenic risk and 10 principal components. These pathways have a major role in RA pathogenesis, including autoimmune attacks against certain citrullinated proteins, synovial inflammation, and bone loss. Encouragingly, many also relate to the mechanism of action of existing RA medications. The analyses also revealed statistically significant association between RA polygenic scores and self-reported RA with individual PES scorings, highlighting the potential utility of the PES algorithm in uncovering additional genetic insights that could aid in the identification of individuals at risk for RA and provide opportunities for more targeted interventions. Conclusions In this study, pharmacologically annotated genetic risk was explored through the PES framework to overcome inter-individual heterogeneity and enable precision drug repurposing in RA. The results showed a statistically significant association between RA polygenic scores and self-reported RA and individual PES scorings for 3,719 RA patients. Interestingly, several enriched PES pathways were targeted by already approved RA drugs. In addition, the analysis revealed genetically supported drug repurposing opportunities for future treatment of RA with a relatively safe profile.

Keywords: rheumatoid arthritis, precision medicine, drug repurposing, system biology, bioinformatics

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Authors: A. S. Haruna, M. U. Tambawal, A. A. Salawu

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The psychological impact of peer influence on its individual group members, can make them resist HIV/AIDS counselling and testing. This study investigated the correlate of peer influence and resistance to HIV/AIDS counselling and testing among students in tertiary institutions in Kano state, Nigeria. To achieve this, three null hypotheses were postulated and tested. Cross-Sectional Survey Design was employed in which 1512 sample was selected from a student population of 104,841.Simple Random Sampling was used in the selection. A self-developed 20-item scale called Peer Influence and Psychological Resistance Inventory (PIPRI) was used for data collection. Pearson Product Moment Correlation (PPMCC) via test-retest method was applied to estimate a reliability coefficient of 0.86 for the scale. Data obtained was analyzed using t-test and PPMCC at 0.05 level of confidence. Results reveal 26.3% (397) of the respondents being influenced by their peer group, while 39.8% showed resistance. Also, the t-tests and PPMCC statistics were greater than their respective critical values. This shows that there was a significant gender difference in peer influence and a difference between peer influence and resistance to HIV/AIDS counselling and testing. However, a positive relationship between peer influence and resistance to HIV/AIDS counselling and testing was shown. A major recommendation offered suggests the use of reinforcement and social support for positive attitudes and maintenance of safe behaviour among students who patronize HIV/AIDS counselling.

Keywords: peer group influence, HIV/AIDS counselling and testing, psychological resistance, students

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Authors: Mohammed Alrefaie, Yaser Miaji

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Greenhouses provide a convenient means to grow plants in the best environment. They achieve this by trapping heat from the sunlight and using artificial means to enhance the environment of the greenhouse. This includes controlling factors such as air flow, light intensity and amount of water among others that can have a big impact on plant growth. The aim of the greenhouse is to give maximum yield from plants possible. This report details the development and testing of greenhouse environment control system that can regulate light intensity, airflow and power supply inside the greenhouse. The details of the module development to control these three factors along with results of testing are presented.

Keywords: greenhouse, control system, light intensity, comprehensive environment

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Authors: Benalia Nadia, Zerzouri Nora, Ben Si Ali Nadia

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Abstract: Among the many different modern heuristic optimization methods, genetic algorithms (GA) and the particle swarm optimization (PSO) technique have been attracting a lot of interest. The GA has gained popularity in academia and business mostly because to its simplicity, ability to solve highly nonlinear mixed integer optimization problems that are typical of complex engineering systems, and intuitiveness. The mechanics of the PSO methodology, a relatively recent heuristic search tool, are modeled after the swarming or cooperative behavior of biological groups. It is suitable to compare the performance of the two techniques since they both aim to solve a particular objective function but make use of distinct computing methods. In this article, PSO and GA optimization approaches are used for the parameter tuning of the power system stabilizer and Proportional integral derivative regulator. Load angle and rotor speed variations in the single machine infinite bus bar system is used to measure the performance of the suggested solution.

Keywords: SMIB, genetic algorithm, PSO, transient stability, power system stabilizer, PID

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Authors: Ashwini Gowda K. S, Archana M. R, Anjaneyappa V

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This paper presents pavement maintenance and management system (PMMS) to obtain optimum pavement maintenance and rehabilitation strategies and maintenance scheduling for a network using a multi-objective genetic algorithm (MOGA). Optimal pavement maintenance & rehabilitation strategy is to maximize the pavement condition index of the road section in a network with minimum maintenance and rehabilitation cost during the planning period. In this paper, NSGA-II is applied to perform maintenance optimization; this maintenance approach was expected to preserve and improve the existing condition of the highway network in a cost-effective way. The proposed PMMS is applied to a network that assessed pavement based on the pavement condition index (PCI). The minimum and maximum maintenance cost for a planning period of 20 years obtained from the non-dominated solution was found to be 5.190x10¹⁰ ₹ and 4.81x10¹⁰ ₹, respectively.

Keywords: genetic algorithm, maintenance and rehabilitation, optimization technique, pavement condition index

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Authors: Abdelsabour G. A. Khaled, Galal A.R. El-Sherbeny, Ahmed M. Hassanein, Gameel M. G. Aly

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Classical methods of identification, based on agronomical characterization, are not always the most accurate way due to the instability of these characteristics under the influence of the different environments. In order to estimate the genetic diversity, molecular markers provided excellent tools. In this study, Genetic variation of nine Egyptian jojoba shrubs was tested using ISSR (inter simple sequences repeats), RAPD (random amplified polymorphic DNA) markers and based on the morphological characterization. The average of the percentage of polymorphism (%P) ranged between 58.17% and 74.07% for ISSR and RAPD markers, respectively. The range of genetic similarity percents among shrubs based on ISSR and RAPD markers were from 82.9 to 97.9% and from 85.5 to 97.8%, respectively. The average of PIC (polymorphism information content) values were 0.19 (ISSR) and 0.24 (RAPD). In the present study, RAPD markers were more efficient than the ISSR markers. Where the RAPD technique exhibited higher marker index (MI) average (1.26) compared to ISSR one (1.11). There was an insignificant correlation between the ISSR and RAPD data (0.076, P > 0.05). The dendrogram constructed by the combined RAPD and ISSR data gave a relatively different clustering pattern.

Keywords: correlation, molecular markers, polymorphism, marker index

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Authors: Afsaneh Jasmine Majidi

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Language testing is an important part of language teaching experience and language learning process as it presents assessment strategies for teachers to evaluate the efficiency of teaching and for learners to examine their outcomes. However, language testing is demanding and challenging because it should provide the opportunity for proper and objective decision. In addition to all the efforts test designers put to design valid and reliable tests, there are some other determining factors which are even more complex and complicated. These factors affect the educational system, individuals, and society, and the impact of the tests vary according to the scope of the test. Seemingly, the impact of a simple classroom assessment is not the same as that of high stake tests such as International English Language Testing System (IELTS). As the importance of the test increases, it affects wider domain. Accordingly, the impacts of high stake tests are reflected not only in teaching, learning strategies but also in society. Testing experts use the term ‘washback’ or ‘impact’ to define the different effects of a test on teaching, learning, and community. This paper first looks at the theoretical background of ‘washback’ and ‘impact’ in language testing by reviewing of relevant literature in the field and then investigates washback effects of IELTS test of on Iranian IELTS teachers and students. The study found significant relationship between the washback effect of IELTS test and teaching strategies of Iranian IELTS teachers as well as performance of Iranian IELTS candidates and their community.

Keywords: high stake tests, IELTS, Iranian Candidates, language testing, test impact, washback

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Authors: Rebeca Ramirez Acosta, Sebastian Lenhoff

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Decision makers worldwide need to determine if the implementation of a new market mechanism will contribute to the sustainability and resilience of the power system. Due to smart grid technologies, new products in the distribution and transmission system can be traded; however, the impact of changing a market rule will differ between several regions. To test systematically those impacts, a market categorization has been compiled and organized in a smart grid market testing toolbox. This toolbox maps all actual energy products and sets the basis for running a co-simulation test with the new rule to be implemented. It will help to measure the impact of the new rule, based on the sustainable and resilience indicators.

Keywords: co-simulation, electricity market, smart grid market, market testing

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Authors: Amir Bastani

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The result of the research in the field of human behavioural genetics demonstrates a genetic contribution of behavioural differences in aggression, violence, drug and substance abuse, antisocial personality disorder and other related traits. As the field of human behavioural genetics progresses and achieves credibility, the criminal accused continue to use its types of evidence into the criminal law. One of the most important genetic factors which controls certain neurotransmitters like dopamine and serotonin is the Monoamine Oxidase Acid A (MAOA) gene, known as the 'warrior gene'. The high-profile study by Caspi and colleagues in 2002 showed that the combination between one type of variation of the MAOA gene and childhood maltreatment noticeably predisposes a person to antisocial behaviour. Moreover, further scientific research shows that individuals with the MAOA gene have to some degree difficulties in controlling their impulses. Based on the evidence of MAOA, some criminal accused claimed difficulties in self-control. In the first case – the famous case of Mobley – the court rejected the MAOA evidence on the ground of the lack of scientific support. In contrast, in other cases after the Mobley trial, courts accepted the evidence of MAOA. In this paper, the issue of lack of impulse control produced by the MAOA gene and cases which relied on the MAOA evidence and successfully being accepted will be reviewed in detail. Finally, the anticipation of the paper for the future use of the MAOA evidence in criminal cases will be presented.

Keywords: genetic defence, criminal responsibility, MAOA, self-control

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Authors: Richard Ji

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Nondestructive deflection testing has been accepted widely as a cost-effective tool for evaluating the structural condition of airfield pavements. Backcalculation of pavement layer moduli can be used to characterize the pavement existing condition in order to compute the load bearing capacity of pavement. This paper presents an improved best-fit backcalculation methodology based on deflection predictions obtained using finite element method (FEM). The best-fit approach is based on minimizing the squared error between falling weight deflectometer (FWD) measured deflections and FEM predicted deflections. Then, concrete elastic modulus and modulus of subgrade reaction were back-calculated using Heavy Weight Deflectometer (HWD) deflections collected at the National Airport Pavement Testing Facility (NAPTF) test site. It is an alternative and more versatile method in considering concrete slab geometry and HWD testing locations compared to methods currently available.

Keywords: nondestructive testing, pavement moduli backcalculation, finite element method, concrete pavements

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Authors: M. Sakthivel, A. Devaki, D. Balasubramanyam, P. Kumarasamy, A. Raja, R. Anilkumar, H. Gopi

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The New Zealand White breed of rabbit is one of the most commonly used, well adapted exotic breeds in India. Earlier studies were limited only to analyze the environmental factors affecting the growth and reproductive performance. In the present study, the population of the New Zealand White rabbits in a closed herd was evaluated for its genetic structure. Data on pedigree information (n=2508) for 18 years (1995-2012) were utilized for the study. Pedigree analysis and the estimates of population genetic parameters based on gene origin probabilities were performed using the software program ENDOG (version 4.8). The analysis revealed that the mean values of generation interval, coefficients of inbreeding and equivalent inbreeding were 1.489 years, 13.233 percent and 17.585 percent, respectively. The proportion of population inbred was 100 percent. The estimated mean values of average relatedness and the individual increase in inbreeding were 22.727 and 3.004 percent, respectively. The percent increase in inbreeding over generations was 1.94, 3.06 and 3.98 estimated through maximum generations, equivalent generations, and complete generations, respectively. The number of ancestors contributing the most of 50% genes (fₐ₅₀) to the gene pool of reference population was 4 which might have led to the reduction in genetic variability and increased amount of inbreeding. The extent of genetic bottleneck assessed by calculating the effective number of founders (fₑ) and the effective number of ancestors (fₐ), as expressed by the fₑ/fₐ ratio was 1.1 which is indicative of the absence of stringent bottlenecks. Up to 5th generation, 71.29 percent pedigree was complete reflecting the well-maintained pedigree records. The maximum known generations were 15 with an average of 7.9 and the average equivalent generations traced were 5.6 indicating of a fairly good depth in pedigree. The realized effective population size was 14.93 which is very critical, and with the increasing trend of inbreeding, the situation has been assessed to be worse in future. The proportion of animals with the genetic conservation index (GCI) greater than 9 was 39.10 percent which can be used as a scale to use such animals with higher GCI to maintain balanced contribution from the founders. From the study, it was evident that the herd was completely inbred with very high inbreeding coefficient and the effective population size was critical. Recommendations were made to reduce the probability of deleterious effects of inbreeding and to improve the genetic variability in the herd. The present study can help in carrying out similar studies to meet the demand for animal protein in developing countries.

Keywords: effective population size, genetic structure, pedigree analysis, rabbit genetics

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Authors: Sujit Kumar Pradhan, Anil Kumar, Vijay Kumar

Abstract:

The testing process of the software during the software development time is a crucial step as it makes the software more efficient and dependable. To estimate software’s reliability through the mean value function, many software reliability growth models (SRGMs) were developed under the assumption that operating and testing environments are the same. Practically, it is not true because when the software works in a natural field environment, the reliability of the software differs. This article discussed an SRGM comprising change-point and imperfect debugging in a simulated testing environment. Later on, we extended it in a multi-release direction. Initially, the software was released to the market with few features. According to the market’s demand, the software company upgraded the current version by adding new features as time passed. Therefore, we have proposed a generalized multi-release SRGM where change-point and imperfect debugging concepts have been addressed in a simulated testing environment. The failure-increasing rate concept has been adopted to determine the change point for each software release. Based on nine goodness-of-fit criteria, the proposed model is validated on two real datasets. The results demonstrate that the proposed model fits the datasets better. We have also discussed the optimal release time of the software through a cost model by assuming that the testing and debugging costs are time-dependent.

Keywords: software reliability growth models, non-homogeneous Poisson process, multi-release software, mean value function, change-point, environmental factors

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

Abstract:

In this paper, we develop a supplier selection and order allocation multi-objective model in stochastic environment in which purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. To do so, we use dependent chance programming (DCP) that maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. After transforming the above mentioned stochastic multi-objective programming problem into a stochastic single objective problem using minimum deviation method, we apply a genetic algorithm to get the later single objective problem solved. The employed genetic algorithm performs a simulation process in order to calculate the stochastic objective function as its fitness function. At the end, we explore the impact of stochastic parameters on the given solution via a sensitivity analysis exploiting coefficient of variation. The results show that as stochastic parameters have greater coefficients of variation, the value of objective function in the stochastic single objective programming problem is worsened.

Keywords: dependent chance programming, genetic algorithm, minimum deviation method, order allocation, supplier selection

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Authors: Essa Abrahim Abdulgader Saleem, Thien-My Dao

Abstract:

The main objective of this paper is to optimize series-parallel system reliability using Genetic Algorithm (GA) and statistical analysis; considering system reliability constraints which involve the redundant numbers of selected components, total cost, and total weight. To perform this work, firstly the mathematical model which maximizes system reliability subject to maximum system cost and maximum system weight constraints is presented; secondly, a statistical analysis is used to optimize GA parameters, and thirdly GA is used to optimize series-parallel systems reliability. The objective is to determine the strategy choosing the redundancy level for each subsystem to maximize the overall system reliability subject to total cost and total weight constraints. Finally, the series-parallel system case study reliability optimization results are showed, and comparisons with the other previous results are presented to demonstrate the performance of our GA.

Keywords: reliability, optimization, meta-heuristic, genetic algorithm, redundancy

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Authors: E. Tandis, E. Assareh

Abstract:

Designing the optimal shape of MW wind turbine blades is provided in a number of cases through evolutionary algorithms associated with mathematical modeling (Blade Element Momentum Theory). Evolutionary algorithms, among the optimization methods, enjoy many advantages, particularly in stability. However, they usually need a large number of function evaluations. Since there are a large number of local extremes, the optimization method has to find the global extreme accurately. The present paper introduces a new population-based hybrid algorithm called Genetic-Based Bees Algorithm (GBBA). This algorithm is meant to design the optimal shape for MW wind turbine blades. The current method employs crossover and neighborhood searching operators taken from the respective Genetic Algorithm (GA) and Bees Algorithm (BA) to provide a method with good performance in accuracy and speed convergence. Different blade designs, twenty-one to be exact, were considered based on the chord length, twist angle and tip speed ratio using GA results. They were compared with BA and GBBA optimum design results targeting the power coefficient and solidity. The results suggest that the final shape, obtained by the proposed hybrid algorithm, performs better compared to either BA or GA. Furthermore, the accuracy and speed convergence increases when the GBBA is employed

Keywords: Blade Design, Optimization, Genetic Algorithm, Bees Algorithm, Genetic-Based Bees Algorithm, Large Wind Turbine

Procedia PDF Downloads 282