Search results for: genetic algorithm

Authors: Yili Chen, Xiaokun Liang, Zhicheng Zhang, Yaoqin Xie

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In an effort to restore visual perception in retinal diseases, an electronic retinal prosthesis with thousands of electrodes has been developed. The image processing strategies of retinal prosthesis system converts the original images from the camera to the stimulus pattern which can be interpreted by the brain. Practically, the original images are with more high resolution (256x256) than that of the stimulus pattern (such as 25x25), which causes a technical image processing challenge to do large-capacity image information reduction. In this paper, we focus on developing an efficient image processing stimulus pattern extraction algorithm by using a single cue saliency map for extracting salient objects in the image with an optimal trimming threshold. Experimental results showed that the proposed stimulus pattern extraction algorithm performs quite well for different scenes in terms of the stimulus pattern. In the algorithm performance experiment, our proposed SCSPE algorithm have almost five times of the score compared with Boyle’s algorithm. Through experiment s we suggested that when there are salient objects in the scene (such as the blind meet people or talking with people), the trimming threshold should be set around 0.4max, in other situations, the trimming threshold values can be set between 0.2max-0.4max to give the satisfied stimulus pattern.

Keywords: retinal prosthesis, image processing, region of interest, saliency map, trimming threshold selection

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Authors: Rui Wu

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In the volatile modern manufacturing environment, new orders randomly occur at any time, while the pre-emptive methods are infeasible. This leads to a real-time scheduling method that can produce a reasonably good schedule quickly. The dynamic Flexible Job Shop problem is an NP-hard scheduling problem that hybrid the dynamic Job Shop problem with the Parallel Machine problem. A Flexible Job Shop contains different work centres. Each work centre contains parallel machines that can process certain operations. Many algorithms, such as genetic algorithms or simulated annealing, have been proposed to solve the static Flexible Job Shop problems. However, the time efficiency of these methods is low, and these methods are not feasible in a dynamic scheduling problem. Therefore, a dynamic rule selection scheduling system based on the reinforcement learning method is proposed in this research, in which the dynamic Flexible Job Shop problem is divided into several parallel machine problems to decrease the complexity of the dynamic Flexible Job Shop problem. Firstly, the features of jobs, machines, work centres, and flexible job shops are selected to describe the status of the dynamic Flexible Job Shop problem at each decision point in each work centre. Secondly, a framework of reinforcement learning algorithm using a double-layer deep Q-learning network is applied to select proper composite dispatching rules based on the status of each work centre. Then, based on the selected composite dispatching rule, an available operation is selected from the waiting buffer and assigned to an available machine in each work centre. Finally, the proposed algorithm will be compared with well-known dispatching rules on objectives of mean tardiness, mean flow time, mean waiting time, or mean percentage of waiting time in the real-time Flexible Job Shop problem. The result of the simulations proved that the proposed framework has reasonable performance and time efficiency.

Keywords: dynamic scheduling problem, flexible job shop, dispatching rules, deep reinforcement learning

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Authors: John Lorenzo Bautista, Yoon-Joong Kim

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In this paper, a set of Filipino Phonetically Balanced Word list consisting of 250 words (PBW250) were constructed for a phoneme-level ASR system for the Filipino language. The Entropy Maximization is used to obtain phonological balance in the list. Entropy of phonemes in a word is maximized, providing an optimal balance in each word’s phonological distribution using the Add-Delete Method (PBW algorithm) and is compared to the modified PBW algorithm implemented in a dynamic algorithm approach to obtain optimization. The gained entropy score of 4.2791 and 4.2902 for the PBW and modified algorithm respectively. The PBW250 was recorded by 40 respondents, each with 2 sets data. Recordings from 30 respondents were trained to produce an acoustic model that were tested using recordings from 10 respondents using the HMM Toolkit (HTK). The results of test gave the maximum accuracy rate of 97.77% for a speaker dependent test and 89.36% for a speaker independent test.

Keywords: entropy maximization, Filipino language, Hidden Markov Model, phonetically balanced words, speech recognition

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Authors: Chiung-Man Tsai, Chia-Jui Weng

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Leptin (LEP) and leptin receptor (LEPR) both play a crucial role in the mediation of physiological reactions and carcinogenesis and may serve as a candidate biomarker of oral cancer. The present case-control study aimed to examine the effects of single nucleotide polymorphisms (SNPs) of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) with or without interacting to environmental carcinogens on the risk for oral squamous cell carcinoma (OSCC). The SNPs of three genetic allele, from 567 patients with oral cancer and 560 healthy controls in Taiwan were analyzed. All of The three genetic polymorphisms exhibited insignificant (P > .05) effects on the risk to have oral cancer. However, the patients with polymorphic allele of LEP -2548 have a significant low risk for the development of clinical stage (A/G, AOR = 0.670, 95% CI = 0.454–0.988, P < .05; A/G+G/G, AOR = 0.676, 95% CI = 0.467–0.978, P < .05) compared to patients with ancestral homozygous A/A genotype. Additionally, an interesting result was found that the impact of LEP -2548 G/A SNP on oral carcinogenesis in subjects without tobacco consumption (A/G, AOR=2.078, 95% CI: 1.161-3.720, p=0.014; A/G+G/G, AOR=2.002, 95% CI: 1.143-3.505, p=0.015) is higher than subjects with tobacco consumption. These results suggest that the genetic polymorphism of LEP -2548 G/A (rs7799039), LEPR K109R (rs1137100), and LEPR Q223R (rs1137101) were not associated with the susceptibility of oral cancer; SNP in LEP -2548 G/A showed a poor clinicopathological development of oral cancer; Population without tobacco consumption and with polymorphic LEP -2548 G/A gene may significantly increase the risk to have oral cancer.

Keywords: carcinogen, leptin, leptin receptor, oral squamous cell carcinoma, single nucleotide polymorphism

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Authors: U. Sabura Banu, S. K. Lakshmanaprabu

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The improvement of meta-heuristic algorithm encourages control engineer to design an optimal controller for industrial process. Most real-world industrial processes are non-linear multivariable process with high interaction. Even in sub-process unit, thousands of loops are available mostly interacting in nature. Optimal controller design for such process are still challenging task. Closed loop controller design by multiloop PID involves a tedious procedure by performing interaction study and then PID auto-tuning the loop with higher interaction. Finally, detuning the controller to accommodate the effects of the other process variables. Fractional order PID controllers are replacing integer order PID controllers recently. Design of Multiloop Fractional Order (MFO) PID controller is still more complicated. Cuckoo algorithm, a swarm intelligence technique is used to optimally tune the MFO PID controller with easiness minimizing Integral Time Absolute Error. The closed loop performance is tested under servo, regulatory and servo-regulatory conditions.

Keywords: Cuckoo algorithm, mutliloop fractional order PID controller, two Interacting conical tank process

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Authors: Mafalda Moreira, Diana Alba, Inês Paiva Ferreira, Rita Calejo, Ana Rita Soares, Leonilde Machado

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Intellectual disability (ID) is characterized by deficits in intellectualfunctioningassociatedwithalterations in the adaptive behaviour, whose onset is inthedevelopmentalperiod. Itaffects 3% of the population, ofwhich 10% have a geneticaetiology. One of those causes isAlwadeiSyndrome, with 3 cases describedworldwide. It results from a homozygous nonsense mutation in theRUSC2 gene andisassociatedwithintellectualdisabilityanddysmorphic facialfeatures. Theauthorsreportthe case of a 5-year-old-boy, born to a healthymotherafter a full-termuneventfulpregnancy, thatwasreferred to Neurodevelopmentalconsultationdue toglobal developmentaldelay. Familyhistoryrevealedlearningdifficulties in the paternal brotherhood. Milddismorphicfeatureswereevidentsuch as darkinfraorbitalregion, low-set ears, beakednose, retrognathism, high-archedpalateandjointhyperlaxity. WechslerIntelligenceScale for Children III fullscaleIQ quoted 61. Karyotypeandchromosomalmicroarrayanalysiswerenormal, as well as the fragile X molecular study. DNA sequencingwasthenperformedandallowedtheidentificationof amutation in the RUSC2 gene. Theetiologicaldiagnosisof ID remains unknown in up to 80% of cases, creatinguncertainty in children’sfamilies. Theadvances in DNA sequencingtechnologieshaveincreasedourknowledgeofthegeneticdiseasesinvolved, as theAlwadeisyndromewasonlydescribedsince 2016. Thegeneticdiagnosisof ID allowsfamilygeneticcounselingandenablesthedevelopmentof target therapeutic approaches.

Keywords: intellectual disability, genetic aetiology, alwadei syndrome, RUSC2

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Authors: Jiheon Park, Taekwang Kim, Kwang Ryel Ryu

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Electric automated guided vehicles (AGVs) are becoming popular in many automated container terminals nowadays because they are pollution-free and environmentally friendly vehicles for transporting the containers within the terminal. Since efficient operation of AGVs is critical for the productivity of the container terminal, the replacement of batteries of the AGVs must be conducted in a strategic way to minimize undesirable transportation interruptions. While a too frequent replacement may lead to a loss of terminal productivity by delaying container deliveries, missing the right timing of battery replacement can result in a dead AGV that causes a severer productivity loss due to the extra efforts required to finish post treatment. In this paper, we propose a strategy for battery replacement based on a scoring function of multiple criteria taking into account the current battery level, the distances to different battery stations, and the progress of the terminal job operations. The strategy is optimized using a genetic algorithm with the objectives of minimizing the total time spent for battery replacement as well as maximizing the terminal productivity.

Keywords: AGV operation, automated container terminal, battery replacement, electric AGV, strategy optimization

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Authors: Hyung Joo Lee, Jin Young Choi, Gun Soo Park, Kyo Sun Oh, Dong Jun Won

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The fluctuation of the output of the renewable generator caused by weather conditions must be mitigated because it imposes strain on the system and adversely affects power quality. In this paper, we focus on mitigating the output fluctuation of the photovoltaic (PV) using battery energy storage system (BESS). To satisfy tight conditions of system, proposed algorithm is developed. This algorithm focuses on adjusting the integrated output curve considering state of capacity (SOC) of the battery. In this paper, the simulation model is PSCAD / EMTDC software. SOC of the battery and the overall output curve are shown using the simulation results. We also considered losses and battery efficiency.

Keywords: photovoltaic generation, battery energy storage system, renewable integration, power smoothing

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Authors: Saeed Kamarian, Mahmoud Shakeri

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Sandwich structures are used in a variety of engineering applications including aircraft, construction and transportation where strong, stiff and light structures are required. In this paper, frequency maximization of Functionally Graded Sandwich (FGS) beams resting on Pasternak foundations is investigated. A generalized power-law distribution with four parameters is considered for material distribution through the thicknesses of face layers. Since the search space is large, the optimization processes becomes so complicated and too much time consuming. Thus a novel meta–heuristic called Imperialist Competitive Algorithm (ICA) which is a socio-politically motivated global search strategy is implemented to improve the speed of optimization process. Results show the success of applying ICA for engineering problems especially for design optimization of FGM sandwich beams.

Keywords: sandwich beam, functionally graded materials, optimization, imperialist competitive algorithm

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Authors: Yossiri Ariyakul, Piyakiat Insom, Poonyawat Sangiamkulthavorn, Takamichi Nakamoto

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In this paper, the method of optimization algorithm for sensor network comprised of movable sensor nodes which can be used for odor source localization was proposed. A sensor node is composed of an odor sensor, an anemometer, and a wireless communication module. The odor intensity measured from the sensor nodes are sent to the processor to perform the localization based on optimization algorithm by which the odor source localization map is obtained as a result. The map can represent the exact position of the odor source or show the direction toward it remotely. The proposed method was experimentally validated by creating the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed.

Keywords: odor sensor, odor source localization, optimization, sensor network

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Authors: Ayush Shrivastava, Arpit Chaudhary, Devang Kulshreshtha, Vibhav Prakash Singh, Rajeev Srivastava

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Early diagnosis of breast cancer can improve the survival rate by detecting cancer at an early stage. Breast region segmentation is an essential step in the analysis of digital mammograms. Accurate image segmentation leads to better detection of cancer. It aims at separating out Region of Interest (ROI) from rest of the image. The procedure begins with removal of labels, annotations and tags from the mammographic image using morphological opening method. Pectoral Muscle Sliding Window Algorithm (PMSWA) is used for removal of pectoral muscle from mammograms which is necessary as the intensity values of pectoral muscles are similar to that of ROI which makes it difficult to separate out. After removing the pectoral muscle, Dispersed Region Growing Algorithm (DRGA) is used for segmentation of mammogram which disperses seeds in different regions instead of a single bright region. To demonstrate the validity of our segmentation method, 322 mammographic images from Mammographic Image Analysis Society (MIAS) database are used. The dataset contains medio-lateral oblique (MLO) view of mammograms. Experimental results on MIAS dataset show the effectiveness of our proposed method.

Keywords: CAD, dispersed region growing algorithm (DRGA), image segmentation, mammography, pectoral muscle sliding window algorithm (PMSWA)

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Authors: Jianqin Qu

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This paper proposes a rigid point set matching algorithm in arbitrary dimensions based on the idea of symmetric covariant function. A group of functions of the points in the set are formulated using rigid invariants. Each of these functions computes a pair of correspondence from the given point set. Then the computed correspondences are used to recover the unknown rigid transform parameters. Each computed point can be geometrically interpreted as the weighted mean center of the point set. The algorithm is compact, fast, and dimension free without any optimization process. It either computes the desired transform for noiseless data in linear time, or fails quickly in exceptional cases. Experimental results for synthetic data and 2D/3D real data are provided, which demonstrate potential applications of the algorithm to a wide range of problems.

Keywords: covariant point, point matching, dimension free, rigid registration

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Authors: Chuanbo Xu, Xinying Li, Gejirifu De, Yunna Wu

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Energy Internet (EI) is a new form that deeply integrates the Internet and the entire energy process from production to consumption. The assessment of energy efficient performance is of vital importance for the long-term sustainable development of EI project. Although the newly proposed fuzzy integrated cloud evaluation algorithm considers the randomness of uncertainty, it relies too much on the experience and knowledge of experts. Fortunately, the enrichment of EI data has enabled the utilization of data-driven methods. Therefore, the main purpose of this work is to assess the energy efficient of park-level EI by using a combination of a data-driven method with the fuzzy integrated cloud evaluation algorithm. Firstly, the indicators for the energy efficient are identified through literature review. Secondly, the artificial neural network (ANN)-based data-driven method is employed to cluster the values of indicators. Thirdly, the energy efficient of EI project is calculated through the fuzzy integrated cloud evaluation algorithm. Finally, the applicability of the proposed method is demonstrated by a case study.

Keywords: energy efficient, energy internet, data-driven, fuzzy integrated evaluation, cloud model

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Authors: Fakhrosadat Sajjadian

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In the USA, about 5% of women diagnosed with breast cancer annually are affected by Inflammatory Breast Cancer (IBC), which is a highly aggressive type of Locally Advanced Breast Cancer (LABC). It is a type of LABC that is clinically and pathologically different, known for its rapid growth, invasiveness, and ability to promote the growth of blood vessels. Almost all women are found to have lymph nodes affected upon diagnosis, while around 36% show obvious distant metastases. Even with the latest improvements in multimodality therapies, the outlook for patients with IBC remains bleak, as the average disease-free survival time is less than 2.5 years. Recent research on the genetic factors responsible for the IBC phenotype has resulted in the discovery of genes that play a role in the advancement of this illness. The development of primary human cell lines and animal models has assisted in this research. These advancements offer new possibilities for future actions in identifying and treating IBC.

Keywords: breast cancer, inflammation, diagnosis, IBC, LABC

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Authors: Hee-Choul Kwon, Heeyong Kwon

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Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway network systems play a vital role in disaster response for disaster-damaged areas. Damaged bridges in such network systems can impede disaster response by disrupting transportation of rescue teams or humanitarian supplies. Therefore, emergency inspection and repair of bridges to quickly collect damage information of bridges and recover the functionality of highway networks is of paramount importance to disaster response. A widely used measure of a network’s capability to recover from disasters is resilience. To enhance highway network resilience, plenty of studies have developed various repair scheduling methods for the prioritization of bridge-repair tasks. These methods assume that repair activities are performed after the damage to a highway network is fully understood via inspection, although inspecting all bridges in a regional highway network may take days, leading to the significant delay in repairing bridges. In reality, emergency repair activities can be commenced as soon as the damage data of some bridges that are crucial to emergency response are obtained. Given that emergency bridge inspection and repair (EBIR) activities are executed simultaneously in the response phase, the real-time interactions between these activities can occur – the blockage of highways due to repair activities can affect inspection routes which in turn have an impact on emergency repair scheduling by providing real-time information on bridge damages. However, the impact of such interactions on the optimal emergency inspection routes (EIR) and emergency repair schedules (ERS) has not been discussed in prior studies. To overcome the aforementioned deficiencies, this study develops a routing and scheduling model for EBIR while accounting for real-time inspection-repair interactions to maximize highway network resilience. A stochastic, time-dependent integer program is proposed for the complex and real-time interacting EBIR problem given multiple inspection and repair teams at locations as set post-disaster. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. Computational tests are performed using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that the simultaneous implementation of bridge inspection and repair activities can significantly improve the highway network resilience. Moreover, the deployment of inspection and repair teams should match each other, and the network resilience will not be improved once the unilateral increase in inspection teams or repair teams exceeds a certain level. This study contributes to both knowledge and practice. First, the developed mathematical model makes it possible for capturing the impact of real-time inspection-repair interactions on inspection routing and repair scheduling and efficiently deriving optimal EIR and ERS on a large and complex highway network. Moreover, this study contributes to the organizational dimension of highway network resilience by providing optimal strategies for highway bridge management. With the decision support tool, disaster managers are able to identify the most critical bridges for disaster management and make decisions on proper inspection and repair strategies to improve highway network resilience.

Keywords: disaster management, emergency bridge inspection and repair, highway network, resilience, uncertainty

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Authors: Anteneh Getachew Gebrie, Rabian Wangkeeree

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The purpose of this paper is to introduce iterative algorithm solving split system of minimization problem given as a task of finding a common minimizer point of finite family of proper, lower semicontinuous convex functions and whose image under a bounded linear operator is also common minimizer point of another finite family of proper, lower semicontinuous convex functions. We obtain strong convergence of the sequence generated by our algorithm under some suitable conditions on the parameters. The iterative schemes are developed with a way of selecting the step sizes such that the information of operator norm is not necessary. Some applications and numerical experiment is given to analyse the efficiency of our algorithm.

Keywords: Hilbert Space, minimization problems, Moreau-Yosida approximate, split feasibility problem

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Authors: Aran Abd Al Rahman

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Joubert syndrome regards as a congenital cerebellar ataxia caused by autosomal recessive carried on X chromosome. The disease diagnosed by brain imaging—the so-called molar tooth sign. Neurological signs were present from the neonatal period and include hypotonia progressing to ataxia, global developmental delay, ocular motor apraxia, and breathing dysregulation. These signs are variably associated with multiorgan involvement, mainly of the retina, kidneys, skeleton, and liver. 30 causative genes have been identified so far, all of which encode for proteins of the primary cilium or its apparatus, The purpose of our project was to detect the mutant gene (INPP5E gene) which cause Joubert syndrome. There were many methods used for diagnosis such as MRI and CT- scan and molecular diagnosis by doing ARMS PCR for detection of mutant gene that we were used in this research project. In this research for individual family which reported, the two children with parents, the two children were affected and were carrier.

Keywords: Joubert syndrome, genetic disease, Kurdistan region, Sulaimani

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Authors: K. Y. Tseng, C. Y. Wu

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This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.

Keywords: differential evolution, Truss structure optimization, optimal chiller loading, modified binary differential evolution

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Authors: Renkai Wang, Tingcun Wei

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In this paper, a digital control algorithm based on delta-operator is presented for high-frequency digitally-controlled DC-DC switching converters. The stability and the controlling accuracy of the DC-DC switching converters are improved by using the digital control algorithm based on delta-operator without increasing the hardware circuit scale. The design method of voltage compensator in delta-domain using PID (Proportion-Integration- Differentiation) control is given in this paper, and the simulation results based on Simulink platform are provided, which have verified the theoretical analysis results very well. It can be concluded that, the presented control algorithm based on delta-operator has better stability and controlling accuracy, and easier hardware implementation than the existed control algorithms based on z-operator, therefore it can be used for the voltage compensator design in high-frequency digitally- controlled DC-DC switching converters.

Keywords: digitally-controlled DC-DC switching converter, digital voltage compensator, delta-operator, finite word length, stability

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Authors: Sandra Baßler, Peter Dünow, Mathias Marquardt

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A Fourier series based learning control (FSBLC) algorithm for tracking trajectories of mechanical systems with unknown nonlinearities is presented. Two processes are introduced to which the FSBLC with PD controller is applied. One is a simplified service robot capable of climbing stairs due to special wheels and the other is a propeller driven pendulum with nearly the same requirements on control. Additionally to the investigation of learning the feed forward for the desired trajectories some considerations on the implementation of such an algorithm on low cost microcontroller hardware are made. Simulations of the service robot as well as practical experiments on the pendulum show the capability of the used FSBLC algorithm to perform the task of improving control behavior for repetitive task of such mechanical systems.

Keywords: climbing stairs, FSBLC, ILC, service robot

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Authors: Raziyeh Shamsi

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In this paper, we present a multiple objective linear programming (MOLP) problem in full fuzzy case and find Data Envelopment Analysis(DEA) targets. In the presented model, we are seeking the least inputs and the most outputs in the production possibility set (PPS) with the variable return to scale (VRS) assumption, so that the efficiency projection is obtained for all decision making units (DMUs). Then, we provide an algorithm for finding DEA targets interactively in the full fuzzy case, which solves the full fuzzy problem without defuzzification. Owing to the use of interactive methods, the targets obtained by our algorithm are more applicable, more realistic, and they are according to the wish of the decision maker. Finally, an application of the algorithm in 21 educational institutions is provided.

Keywords: DEA, MOLP, full fuzzy, target

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Authors: I. Nižetić Kosović, T. Jagušt

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Lately, with the increasing number of location-based applications, demand for highly accurate and reliable indoor localization became urgent. This is a challenging problem, due to the measurement variance which is the consequence of various factors like obstacles, equipment properties and environmental changes in complex nature of indoor environments. In this paper we propose low-cost custom-setup infrastructure solution and localization algorithm based on the Weighted Centroid Localization (WCL) method. Localization accuracy is increased by several enhancements: calibration of RSSI values gained from wireless nodes, repetitive measurements of RSSI to exclude deviating values from the position estimation, and by considering orientation of the device according to the wireless nodes. We conducted several experiments to evaluate the proposed algorithm. High accuracy of ~1m was achieved.

Keywords: indoor environment, received signal strength indicator, weighted centroid localization, wireless localization

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Authors: Shahzad Bhattiab, M. Aslamkhana, Sana Abbasbc, Marcella Attimonellid, Kumarasamy Thangaraje, Erica Martinha Silva de Souzaf, Uzay U. Sezen

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The present study was designed to investigate three regions of mitochondrial DNA, HVI, HVII and HVIII, to hold a powwow genetic diversity and affiliations in 115 probands of 6 major ethnic groups, viz., Bijarani, Chandio, Ghallu, Khoso, Nasrani and Solangi, in the province of Sindh of Pakistan. For this purpose 88 haplotypes were scrutinized, defined by particular set of nucleotides (ignoring the C insertions around position 309 and 315). In spite of that 82% sequences were observed once, 12 % twice and 5.2 % thrice. The most common South Asian haplotypes were observed M (42%), N (6.9%) and R (6.9%) whereas west Eurasian haplotypes were J (1.7%), U (23.4%), H (9.5%), W (6.9%) and T (0.86%), in six ethnic groups. A random match probability between two unrelated individuals was found 0.06 %, while genetic diversity was ranged to be 0.991 to 0.999, and nucleotide diversity ranged from 0.0089 to 0.0142 for the whole control region of the population studied.

Keywords: mtDNA haplogroups, control region, Pakistan, Sindh, ethnicity

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

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Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning

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Authors: Simon B. N. Thompson

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Retinoblastoma is a rare type of childhood genetic cancer that affects children worldwide. The diagnosis is often missed due to lack of education and difficulty in presentation of the tumor. Frequently, the tumor on the retina is noticed by photography when the red-eye flash, commonly seen in normal eyes, is not produced. Instead, a yellow or white colored patch is seen or the child has a noticeable strabismus. Early detection can be life-saving though often results in removal of the affected eye. Remaining functioning in the healthy eye when the child is young has resulted in super-vision and high or above-average intelligence. Technological advancement of cameras has helped in early detection. Brain imaging has also made possible early detection of neurological diseases and, together with the monitoring of cortisol levels and yawning frequency, promises to be the next new early diagnostic tool for the detection of neurological diseases where cortisol insufficiency is particularly salient, such as multiple sclerosis and Cushing’s disease.

Keywords: cortisol, neurological disease, retinoblastoma, Thompson cortisol hypothesis, yawning

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Authors: Jianwei Ma, Diriba Gemechu

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In seismic data processing, attenuation of random noise is the basic step to improve quality of data for further application of seismic data in exploration and development in different gas and oil industries. The signal-to-noise ratio of the data also highly determines quality of seismic data. This factor affects the reliability as well as the accuracy of seismic signal during interpretation for different purposes in different companies. To use seismic data for further application and interpretation, we need to improve the signal-to-noise ration while attenuating random noise effectively. To improve the signal-to-noise ration and attenuating seismic random noise by preserving important features and information about seismic signals, we introduce the concept of anisotropic total fractional order denoising algorithm. The anisotropic total fractional order variation model defined in fractional order bounded variation is proposed as a regularization in seismic denoising. The split Bregman algorithm is employed to solve the minimization problem of the anisotropic total fractional order variation model and the corresponding denoising algorithm for the proposed method is derived. We test the effectiveness of theproposed method for synthetic and real seismic data sets and the denoised result is compared with F-X deconvolution and non-local means denoising algorithm.

Keywords: anisotropic total fractional order variation, fractional order bounded variation, seismic random noise attenuation, split Bregman algorithm

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Authors: Hanaa A. S. Oraby, Amal A. M. Hassan, Mahmoud M. Sakr, Atef A. A. Haiba

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Applying the profiling approach for the assessment of proteomic variations due to genetic modification of the Egyptian tomato cultivar "Edkawy", three complementary approaches were used. These methods are amino acids analysis, gel electrophoresis, and Gas chromatography coupled with mass spectrometry (GC/MS). The results of the present study Show evidence of proteomic variations between both modified tomato and its non-modified counterpart. Amino acids concentrations, and the protein patterns separation on the 1D SDS-PAGE were not similar in the case of transformed tomato compared to that of the non-transformed counterpart. These detected differences are most likely derived from the process of transformation. Results also revealed that the efficiency of GC/MS approach to identify a mixture of unknown proteins is limited. GC/MS analysis was only able to identify few number of protein molecules. Therefore, more advanced and specific technologies like MALDI-TOF-MS are recommended to be employed.

Keywords: GMOs, unintended effects, proteomic variations, 1D SDS-PAGE, GC/MS

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Authors: Anujendra, Parag Kumar Guha Thakurta

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The data transmission between mobile hosts and base stations (BSs) in Mobile networks are often vulnerable to failure. Thus, efficient link connectivity, in terms of the services of both base stations and communication channels of the network, is required in wireless mobile networks to achieve highly reliable data transmission. In addition, it is observed that the number of blocked hosts is increased due to insufficient number of channels during heavy load in the network. Under such scenario, the channels are allocated accordingly to offer a reliable communication at any given time. Therefore, a reliability-based channel allocation model with acceptable system performance is proposed as a MOO problem in this paper. Two conflicting parameters known as Resource Reuse factor (RRF) and the number of blocked calls are optimized under reliability constraint in this problem. The solution to such MOO problem is obtained through NSGA-II (Non-dominated Sorting Genetic Algorithm). The effectiveness of the proposed model in this work is shown with a set of experimental results.

Keywords: base station, channel, GA, pareto-optimal, reliability

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Authors: Mohammad Hossein Mohammadi Sanjani, Ashknaz Oraee, Oriol Gomis Bellmunt, Vinicius Albernaz Lacerda Freitas

Abstract:

The use of distributed and renewable sources in power systems has grown significantly, recently. One the most popular sources are wind farms which have grown massively. However, ¬wind farms are connected to the grid, this can cause problems such as reduced voltage stability, frequency fluctuations and reduced dynamic stability. Variable speed generators (asynchronous) are used due to the uncontrollability of wind speed specially Doubley Fed Induction Generators (DFIG). The most important disadvantage of DFIGs is its sensitivity to voltage drop. In the case of faults, a large volume of reactive power is induced therefore, use of FACTS devices such as SVC and STATCOM are suitable for improving system output performance. They increase the capacity of lines and also passes network fault conditions. In this paper, in addition to modeling the reactive power control system in a DFIG with converter, FACTS devices have been used in a DFIG wind turbine to improve the stability of the power system containing two synchronous sources. In the following paper, recent optimal control systems have been designed to minimize fluctuations caused by system disturbances, for FACTS devices employed. For this purpose, a suitable method for the selection of nine parameters for MPSH-phase-post-phase compensators of reactive power compensators is proposed. The design algorithm is formulated ¬¬as an optimization problem searching for optimal parameters in the controller. Simulation results show that the proposed controller Improves the stability of the network and the fluctuations are at desired speed.

Keywords: renewable energy sources, optimization wind power plant, stability, reactive power compensator, double-feed induction generator, optimal control, genetic algorithm

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