Search results for: evolutionary genetic algorithm

Authors: Xu Jie

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As a key network security method, cryptographic services must fully cope with problems such as the wide variety of cryptographic algorithms, high concurrency requirements, random job crossovers, and instantaneous surges in workloads. Its complexity and dynamics also make it difficult for traditional static security policies to cope with the ever-changing situation. Cyber Threats and Environment. Traditional resource scheduling algorithms are inadequate when facing complex decisionmaking problems in dynamic environments. A network cryptographic resource allocation algorithm based on reinforcement learning is proposed, aiming to optimize task energy consumption, migration cost, and fitness of differentiated services (including user, data, and task security). By modeling the multi-job collaborative cryptographic service scheduling problem as a multiobjective optimized job flow scheduling problem, and using a multi-agent reinforcement learning method, efficient scheduling and optimal configuration of cryptographic service resources are achieved. By introducing reinforcement learning, resource allocation strategies can be adjusted in real time in a dynamic environment, improving resource utilization and achieving load balancing. Experimental results show that this algorithm has significant advantages in path planning length, system delay and network load balancing, and effectively solves the problem of complex resource scheduling in cryptographic services.

Keywords: multi-agent reinforcement learning, non-stationary dynamics, multi-agent systems, cooperative and competitive agents

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Authors: Adamu Usman, Grace Stanley Balami

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Pearl millet (Pennisetum glaucum L. R. Br.) is a cereal cultivated in arid and semi-arid areas of the world. It supports more than 100 million people around the world. Parasitic weed (Striga hermonthica Del. Benth) is a major constraint to its production. Estimated yield losses are put at 10 - 95% depending on variety, ecology and cultural practices. Potentials in selection of traits in pearl millets for grain yield have been reported and it depends on genotypic variability and heritability among landraces. Variability and heritability among cultivars could offer opportunities for improvement. The study was conducted to determine the genetic variability among cultivars and estimate broad sense heritability among grain yield and related traits. F1 breeding populations were generated with 9 parental cultivars, viz; Ex-Gubio, Ex-Monguno, Ex-Baga as males and PEO 5984, Super-SOSAT, SOSAT-C88, Ex-Borno and LCIC9702 as females through Line × Tester mating during 2017 dry season at Lushi Irrigation Station, Bauchi Metropolitan in Bauchi State, Nigeria. The F1 population and the parents were evaluated during cropping season of 2018 at Bauchi and Maiduguri. Data collected were subjected to analysis of variance. Results showed significant difference among cultivars and among traits indicating variability. Number of plants at emergence, days to 50% flowering, days to 100% flowering, plant height, panicle length, number of plants at harvest, Striga count at 90 days after sowing, panicle weight and grain yield were significantly different. Significant variability offer opportunity for improvement as superior individuals can be isolated. Genotypic variance estimates of traits were largely greater than environmental variances except in plant height and 1000 seed weight. Environmental variances were low and in some cases negligible. The phenotypic variances of all traits were higher than genotypic variances. Similarly phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV). High heritability was found in days to 50% flowering (90.27%), Striga count at 90 days after sowing (90.07%), number of plants at harvest (87.97%), days to 100% flowering (83.89%), number of plants at emergence (82.19%) and plant height (73.18%). Greater heritability estimates could be due to presence of additive gene. The result revealed wider variability among genotypes and traits. Traits having high heritability could easily respond to selection. High value of GCV, PCV and heritability estimates indicate that selection for these traits are possible and could be effective.

Keywords: variability, heritability, phenotypic, genotypic, striga

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Authors: Alexander Heckman, Rohan Goswami, Zachi Attia, Paul Friedman, Peter Noseworthy, Demilade Adedinsewo, Pablo Moreno-Franco, Rickey Carter, Tathagat Narula

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Background: Postoperative atrial fibrillation (POAF) is associated with adverse health consequences, higher costs, and longer hospital stays. Utilizing existing predictive models that rely on clinical variables and circulating biomarkers, multiple societies have published recommendations on the treatment and prevention of POAF. Although reasonably practical, there is room for improvement and automation to help individualize treatment strategies and reduce associated complications. Methods and Results: In this retrospective cohort study of solid organ transplant recipients, we evaluated the diagnostic utility of a previously developed AI-based ECG prediction for silent AF on the development of POAF within 30 days of transplant. A total of 2261 non-cardiac transplant patients without a preexisting diagnosis of AF were found to have a 5.8% (133/2261) incidence of POAF. While there were no apparent sex differences in POAF incidence (5.8% males vs. 6.0% females, p=.80), there were differences by race and ethnicity (p<0.001 and 0.035, respectively). The incidence in white transplanted patients was 7.2% (117/1628), whereas the incidence in black patients was 1.4% (6/430). Lung transplant recipients had the highest incidence of postoperative AF (17.4%, 37/213), followed by liver (5.6%, 56/1002) and kidney (3.6%, 32/895) recipients. The AUROC in the sample was 0.62 (95% CI: 0.58-0.67). The relatively low discrimination may result from undiagnosed AF in the sample. In particular, 1,177 patients had at least 1 AI-ECG screen for AF pre-transplant above .10, a value slightly higher than the published threshold of 0.08. The incidence of POAF in the 1104 patients without an elevated prediction pre-transplant was lower (3.7% vs. 8.0%; p<0.001). While this supported the hypothesis that potentially undiagnosed AF may have contributed to the diagnosis of POAF, the utility of the existing AI-ECG screening algorithm remained modest. When the prediction for POAF was made using the first postoperative ECG in the sample without an elevated screen pre-transplant (n=1084 on account of n=20 missing postoperative ECG), the AUROC was 0.66 (95% CI: 0.57-0.75). While this discrimination is relatively low, at a threshold of 0.08, the AI-ECG algorithm had a 98% (95% CI: 97 – 99%) negative predictive value at a sensitivity of 66% (95% CI: 49-80%). Conclusions: This study's principal finding is that the incidence of POAF is rare, and a considerable fraction of the POAF cases may be latent and undiagnosed. The high negative predictive value of AI-ECG screening suggests utility for prioritizing monitoring and evaluation on transplant patients with a positive AI-ECG screening. Further development and refinement of a post-transplant-specific algorithm may be warranted further to enhance the diagnostic yield of the ECG-based screening.

Keywords: artificial intelligence, atrial fibrillation, cardiology, transplant, medicine, ECG, machine learning

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Authors: Tanvi Rajesh Sanghavi

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Introduction: Neurodiversity is a concept which captures the different ways in which everyone's brain functions and is considered as part of normal variation. It is a strength-based approach which focuses on the individual's strengths and capabilities and believes in providing support wherever necessary. In many parts of the world, those diagnosed with autism spectrum disorder have been ostracized and ridiculed due to their sensory and communication differences. Hence, it becomes important for the teachers to have knowledge about autism and understand the needs of children with Autism. Need: India is rich in terms of culture, languages and religious diversity. It is important to study neurodiversity in such a population for better understanding of neurodiverse individuals and appropriate intervention. Aim & objectives: This study seeks teachers' knowledge of the causes, traits and educational requirements of children with autism spectrum disorder (ASD). It also aims to find out whether mainstream schools actually provide training programs to the teachers to manage such children along with the necessary accommodations. Method: The current study was a cross-sectional study conducted among school teachers. A total of 30 school teachers were taken for the study. The participants were enrolled after informed consent. The participants were directed to a google form consisting of objective questions. The first part of the questionnaire elicited information about school, teaching experience, qualification, etc. There were specific questions extracting details on attending/conducting sensitization and professional programs in regard to care for autistic children. The second part of the questionnaire consisted of some basic questions on the teacher’s understanding of diagnosis, traits, causes, road to recovery and understanding the educational and communication needs of autistic children from the teacher’s perspective. The responses were tabulated and analyzed descriptively. Results: Most of the teachers had 5–10 years of teaching experience. The majority of the teachers used the term “special child” for autistic children. Around 54.8% (17 teachers) of the total teachers felt that the parents of autistic children should teach their child to learn adaptive skills and 41.9% of the teachers felt that they should take medical intervention. About 50% of the teachers felt that the cause of autism is related to pre-natal maternal factors and about 40% felt that its cause is genetic. Only a small percentage of teachers felt that they were trained to manage the children with autism. More than 50% of the teachers mentioned that their schools do not conduct training programs for managing these children. Discussion & Conclusion: In this study, the knowledge and perspectives of teachers on children with ASD were studied. The most widely held contemporary belief is that genetic factors play a major part in the development of ASD, although the existing evidence is muddled, with numerous opposing perspectives on the nature of this mechanism. It is worth noting that any culture's level of humanity is mirrored in how that society "treats" its vulnerable population.

Keywords: autism, neurodiversity, awareness, education

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Authors: Jianfeng Hu

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Personal authentication based on electroencephalography (EEG) signals is one of the important field for the biometric technology. More and more researchers have used EEG signals as data source for biometric. However, there are some disadvantages for biometrics based on EEG signals. The proposed method employs entropy measures for feature extraction from EEG signals. Four type of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE) and spectral entropy (PE), were deployed as feature set. In a silhouettes calculation, the distance from each data point in a cluster to all another point within the same cluster and to all other data points in the closest cluster are determined. Thus silhouettes provide a measure of how well a data point was classified when it was assigned to a cluster and the separation between them. This feature renders silhouettes potentially well suited for assessing cluster quality in personal authentication methods. In this study, “silhouettes scores” was used for assessing the cluster quality of k-means clustering algorithm is well suited for comparing the performance of each EEG dataset. The main goals of this study are: (1) to represent each target as a tuple of multiple feature sets, (2) to assign a suitable measure to each feature set, (3) to combine different feature sets, (4) to determine the optimal feature weighting. Using precision/recall evaluations, the effectiveness of feature weighting in clustering was analyzed. EEG data from 22 subjects were collected. Results showed that: (1) It is possible to use fewer electrodes (3-4) for personal authentication. (2) There was the difference between each electrode for personal authentication (p<0.01). (3) There is no significant difference for authentication performance among feature sets (except feature PE). Conclusion: The combination of k-means clustering algorithm and silhouette approach proved to be an accurate method for personal authentication based on EEG signals.

Keywords: personal authentication, K-mean clustering, electroencephalogram, EEG, silhouettes

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Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

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Authors: Yanping Liao, Congcong He, Ruigang Zhao

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The application of magnetocardiography signals to detect cardiac electrical function is a new technology developed in recent years. The magnetocardiography signal is detected with Superconducting Quantum Interference Devices (SQUID) and has considerable advantages over electrocardiography (ECG). It is difficult to extract Magnetocardiography (MCG) signal which is buried in the noise, which is a critical issue to be resolved in cardiac monitoring system and MCG applications. In order to remove the severe background noise, the Total Variation (TV) regularization method is proposed to denoise MCG signal. The approach transforms the denoising problem into a minimization optimization problem and the Majorization-minimization algorithm is applied to iteratively solve the minimization problem. However, traditional TV regularization method tends to cause step effect and lacks constraint adaptability. In this paper, an improved TV regularization method for denoising MCG signal is proposed to improve the denoising precision. The improvement of this method is mainly divided into three parts. First, high-order TV is applied to reduce the step effect, and the corresponding second derivative matrix is used to substitute the first order. Then, the positions of the non-zero elements in the second order derivative matrix are determined based on the peak positions that are detected by the detection window. Finally, adaptive constraint parameters are defined to eliminate noises and preserve signal peak characteristics. Theoretical analysis and experimental results show that this algorithm can effectively improve the output signal-to-noise ratio and has superior performance.

Keywords: constraint parameters, derivative matrix, magnetocardiography, regular term, total variation

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Authors: Dali Gaganidze, Ekaterine Abashidze

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Potato is one of the main agricultural crops in Georgia. Georgia produces early and late potato varieties in almost all regions. In traditional potato growing regions (Svaneti, Samckhet javaheti and Tsalka), the yield is higher than 30-35 t/ha. Among the plant pests that limit potato production and quality, the potato cyst nematodes (PCN) are harmful around the world. Yield losses caused by PCN are estimated up to 30%. Rout surveys conducted in two geographically distinct regions of Georgia producing potatoes - Samtskhe - Javakheti and Svaneti revealed potato cyst nematode Globodera rostochiensi. The aim of the study was the Phylogenetic analyses of Globodera rostochiensi revealed in Georgia by the amplification and sequencing of 28S gen in the D3 region and intergenic ITS1-15.8S-ITS2 region. Identification of all the samples from the two Globodera populations (Samtskhe - Javakheti and Svaneti), i.e., G. rostochiensis (20 isolates) were confirmed by conventional multiplex PCR with ITS 5 universal and PITSp4, PITSr3 specific primers of the cyst nematodes’ (G. pallida, G. rostochiensis). The size of PCR fragment 434 bp confirms that PCN samples from two populations, Samtskhe- Javakheti and Svaneti, belong to G. rostochiensi . The ITS1–5.8S-ITS2 regions were amplified using prime pairs: rDNA1 ( 5’ -TTGATTACGTCCCTGCCCTTT-3’ and rDNA2( 5’ TTTCACTCGCCGTTACTAAGG-3’), D3 expansion regions were amplified using primer pairs: D3A (5’ GACCCCTCTTGAAACACGGA-3’) and D3B (5’-TCGGAAGGAACCAGCTACTA-3’. PCR products of each region were cleaned up and sequenced using an ABI 3500xL Genetic Analyzer. Obtained sequencing results were analyzed by computer program BLASTN (https://blast.ncbi.nlm.nih.gov/Blast.cg). Phylogenetic analyses to resolve the relationships between the isolates were conducted in MEGA7 using both distance- and character-based methods. Based on analysis of G.rostochiensis isolate`s D3 expansion regions are grouped in three major clades (A, B and C) on the phylogenetic tree. Clade A is divided into three subclades; clade C is divided into two subclades. Isolates from the Samtckhet-javakheti population are in subclade 1 of clade A and isolates in subclade 1 of clade C. Isolates) from Svaneti populations are in subclade 2 of clade A and in clad B. In Clade C, subclade two is presented by three isolates from Svaneti and by one isolate (GL17) from Samckhet-Javakheti. . Based on analysis of G.rostochiensis isolate`s ITS1–5.8S-ITS2 regions are grouped in two main clades, the first contained 20 Georgian isolates of Globodera rostochiensis from Svaneti . The second clade contained 15 isolates of Globodera rostochiensis from Samckhet javakheti. Our investigation showed of high genetic variation of D3 and ITS1–5.8S-ITS2 region of rDNA of the isolates of G. rostochiensis from different geographic origins (Svameti, Samckhet-Javakheti) of Georgia. Acknowledgement: The research has been supported by the Shota Rustaveli National Scientific Foundation of Georgia : Project # FR17_235

Keywords: globodera rostochiensi, PCR, phylogenetic tree, sequencing

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Authors: Yemane Hailu Fissuh, Zhongzhan Zhang

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An estimating equation technique is an alternative method of the widely used maximum likelihood methods, which enables us to ease some complexity due to the complex characteristics of time-varying covariates. In the situations, when both the time-varying covariates and left-truncation are considered in the model, the maximum likelihood estimation procedures become much more burdensome and complex. To ease the complexity, in this study, the modified estimating equations those have been given high attention and considerations in many researchers under semiparametric transformation model was proposed. The purpose of this article was to develop the modified estimating equation under flexible and general class of semiparametric transformation models for left-truncated and right censored survival data with time-varying covariates. Besides the commonly applied Cox proportional hazards model, such kind of problems can be also analyzed with a general class of semiparametric transformation models to estimate the effect of treatment given possibly time-varying covariates on the survival time. The consistency and asymptotic properties of the estimators were intuitively derived via the expectation-maximization (EM) algorithm. The characteristics of the estimators in the finite sample performance for the proposed model were illustrated via simulation studies and Stanford heart transplant real data examples. To sum up the study, the bias for covariates has been adjusted by estimating density function for the truncation time variable. Then the effect of possibly time-varying covariates was evaluated in some special semiparametric transformation models.

Keywords: EM algorithm, estimating equation, semiparametric transformation models, time-to-event outcomes, time varying covariate

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Authors: Nina Penkova, Kalin Krumov, Liliana Zashcova, Ivan Kassabov

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The insulating glass units (IGU) are widely used in the advanced and renovated buildings in order to reduce the energy for heating and cooling. Rules for the choice of IGU to ensure energy efficiency and thermal comfort in the indoor space are well known. The existing of internal loads - gage or vacuum pressure in the hermetized gas space, requires additional attention at the design of the facades. The internal loads appear at variations of the altitude, meteorological pressure and gas temperature according to the same at the process of sealing. The gas temperature depends on the presence of coatings, coating position in the transparent multi-layer system, IGU geometry and space orientation, its fixing on the facades and varies with the climate conditions. An algorithm for modeling and numerical simulation of thermal fields and internal pressure in the gas cavity at insulating glass units as function of the meteorological conditions is developed. It includes models of the radiation heat transfer in solar and infrared wave length, indoor and outdoor convection heat transfer and free convection in the hermetized gas space, assuming the gas as compressible. The algorithm allows prediction of temperature and pressure stratification in the gas domain of the IGU at different fixing system. The models are validated by comparison of the numerical results with experimental data obtained by Hot-box testing. Numerical calculations and estimation of 3D temperature, fluid flow fields, thermal performances and internal loads at IGU in window system are implemented.

Keywords: insulating glass units, thermal loads, internal pressure, CFD analysis

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Authors: Somayeh Komeylian

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The direction of arrival (DoA) estimation is the crucial aspect of the radar technologies for detecting and dividing several signal sources. In this scenario, the antenna array output modeling involves numerous parameters including noise samples, signal waveform, signal directions, signal number, and signal to noise ratio (SNR), and thereby the methods of the DoA estimation rely heavily on the generalization characteristic for establishing a large number of the training data sets. Hence, we have analogously represented the two different optimization models of the DoA estimation; (1) the implementation of the decision directed acyclic graph (DDAG) for the multiclass least-squares support vector machine (LS-SVM), and (2) the optimization method of the deep neural network (DNN) radial basis function (RBF). We have rigorously verified that the LS-SVM DDAG algorithm is capable of accurately classifying DoAs for the three classes. However, the accuracy and robustness of the DoA estimation are still highly sensitive to technological imperfections of the antenna arrays such as non-ideal array design and manufacture, array implementation, mutual coupling effect, and background radiation and thereby the method may fail in representing high precision for the DoA estimation. Therefore, this work has a further contribution on developing the DNN-RBF model for the DoA estimation for overcoming the limitations of the non-parametric and data-driven methods in terms of array imperfection and generalization. The numerical results of implementing the DNN-RBF model have confirmed the better performance of the DoA estimation compared with the LS-SVM algorithm. Consequently, we have analogously evaluated the performance of utilizing the two aforementioned optimization methods for the DoA estimation using the concept of the mean squared error (MSE).

Keywords: DoA estimation, Adaptive antenna array, Deep Neural Network, LS-SVM optimization model, Radial basis function, and MSE

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Authors: Ayse Ozge Demir, Nihat Mert

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In this research, Kilis and Honamli goats are used, which are specific local genetic resources of Turkey. The herds were independent, but they had similar care and nutrition circumstances. From each breed 30 samples were taken, in all 120 samples were collected. Erytrocyte, all blood and serum samples were used for hemoglobine (Hb), glutathione (GSH) and Tf with Cp analysis, respectively. In the analysis of this samples, Hb and Tf bands were determined by electrophoresis. However, Cp and GSH levels were analyzed by the spectrophotometer. Three Hb phenotypes (AA, BB, AB) and Six Tf phenotypes (AA, AB, AC, BB, BC, CC) were determined in this study. In addition, both the observed and the expected values of polymorphic characteristic for 2 characters were presented according to the Hardy-Weinberg Equilibrium (HWE). Cp levels were detected as 0.822 ± 0.055 mg/dl and 1.793 ± 0.109 mg/dl in Kilis and Honamli herds, respectively. GSH levels were detected as, 42,486 ± 1,034 mg/dl and 33.515 ± 0.345 mg/dl in these breeds, respectively,. On the other hand, the high and low GSH levels (GSHH and GSHh) of herds were presented.

Keywords: electrophoresis, gene resource, goat, spectrophotometer

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Authors: Bokkasam Sasidhar, Ibrahim Aljasser

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The problem of finding optimal schedules for each equipment in a production process is considered, which consists of a single stage of manufacturing and which can handle different types of products, where changeover for handling one type of product to the other type incurs certain costs. The machine capacity is determined by the upper limit for the quantity that can be processed for each of the products in a set up. The changeover costs increase with the number of set ups and hence to minimize the costs associated with the product changeover, the planning should be such that similar types of products should be processed successively so that the total number of changeovers and in turn the associated set up costs are minimized. The problem of cost minimization is equivalent to the problem of minimizing the number of set ups or equivalently maximizing the capacity utilization in between every set up or maximizing the total capacity utilization. Further, the production is usually planned against customers’ orders, and generally different customers’ orders are assigned one of the two priorities – “normal” or “priority” order. The problem of production planning in such a situation can be formulated into a Multiple Arc Network (MAN) model and can be solved sequentially using the algorithm for maximizing flow along a MAN and the algorithm for maximizing flow along a MAN with priority arcs. The model aims to provide optimal production schedule with an objective of maximizing capacity utilization, so that the customer-wise delivery schedules are fulfilled, keeping in view the customer priorities. Algorithms have been presented for solving the MAN formulation of the production planning with customer priorities. The application of the model is demonstrated through numerical examples.

Keywords: scheduling, maximal flow problem, multiple arc network model, optimization

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Authors: Leonard Lipovich, Pattaraporn Thepsuwan, Anton-Scott Goustin, Juan Cai, Donghong Ju, James B. Brown

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Two-thirds of human genes do not encode any known proteins. Aside from long non-coding RNA (lncRNA) genes with recently-discovered functions, the ~40,000 non-protein-coding human genes remain poorly understood, and a role for their transcripts as de-facto unconventional messenger RNAs has not been formally excluded. Ribosome profiling (Riboseq) predicts translational potential, but without independent evidence of proteins from lncRNA open reading frames (ORFs), ribosome binding of lncRNAs does not prove translation. Previously, we mass-spectrometrically documented translation of specific lncRNAs in human K562 and GM12878 cells. We now examined lncRNA translation in human MCF7 cells, integrating strand-specific Illumina RNAseq, Riboseq, and deep mass spectrometry in biological quadruplicates performed at two core facilities (BGI, China; City of Hope, USA). We excluded known-protein matches. UCSC Genome Browser-assisted manual annotation of imperfect (tryptic-digest-peptides)-to-(lncRNA-three-frame-translations) alignments revealed three peptides hypothetically explicable by 'stop-to-nonstop' in-frame replacement of stop codons by amino acids in two ORFs of the lncRNA MMP24-AS1. To search for this phenomenon genomewide, we designed and implemented a novel pipeline, matching tryptic-digest spectra to wildcard-instead-of-stop versions of repeat-masked, six-frame, whole-genome translations. Along with singleton putative stop-to-nonstop events affecting four other lncRNAs, we identified 24 additional peptides with stop-to-nonstop in-frame substitutions from multiple positive-strand MMP24-AS1 ORFs. Only UAG and UGA, never UAA, stop codons were impacted. All MMP24-AS1-matching spectra met the same significance thresholds as high-confidence known-protein signatures. Targeted resequencing of MMP24-AS1 genomic DNA and cDNA from the same samples did not reveal any mutations, polymorphisms, or sequencing-detectable RNA editing. This unprecedented apparent gene-specific violation of the genetic code highlights the importance of matching peptides to whole-genome, not known-genes-only, ORFs in mass-spectrometry workflows, and suggests a new mechanism enhancing the combinatorial complexity of the proteome. Funding: NIH Director’s New Innovator Award 1DP2-CA196375 to LL.

Keywords: genetic code, lncRNA, long non-coding RNA, mass spectrometry, proteogenomics, ribo-seq, ribosome, RNAseq

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Authors: Natalia Buderatskaya, Igor Ilyin, Julia Gontar, Sergey Lavrynenko, Olga Parnitskaya, Ekaterina Ilyina, Eduard Kapustin, Yana Lakhno

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Introduction: It is known that cryopreservation of oocytes has peculiar features due to the complex structure of the oocyte. One of the most important features is that mature oocytes contain meiotic division spindle which is very sensitive even to the slightest variation in temperature. Thus, the main objective of this study is to analyse the resulting euploid embryos obtained from thawed oocytes in comparison with the data of preimplantation genetic screening (PGS) in fresh embryo cycles. Material and Methods: The study was conducted at 'Medical Centre IGR' from January to July 2016. Data were analysed for 908 donor oocytes obtained in 67 cycles of assisted reproductive technologies (ART), of which 693 oocytes were used in the 51 'fresh' cycles (group A), and 215 oocytes - 16 ART programs with vitrification female gametes (group B). The average age of donors in the groups match 27.3±2.9 and 27.8±6.6 years. Stimulation of superovulation was conducted the standard way. Vitrification was performed in 1-2 hours after transvaginal puncture and thawing of oocytes were carried out in accordance with the standard protocol of Cryotech (Japan). Manipulation ICSI was performed 4-5 hours after transvaginal follicle puncture for fresh oocytes, or after defrosting - for vitrified female gametes. For the PGS, an embryonic biopsy was done on the third or on the fifth day after fertilization. Diagnostic procedures were performed using fluorescence in situ hybridization with the study of such chromosomes as 13, 16, 18, 21, 22, X, Y. Only morphologically quality blastocysts were used for the transfer, the estimation of which corresponded to the Gardner criteria. The statistical hypotheses were done using the criteria t, x^2 at a significance levels p<0.05, p<0.01, p<0.001. Results: The mean number of mature oocytes per cycle in group A was 13.58±6.65 and in group B - 13.44±6.68 oocytes for patient. The survival of oocytes after thawing totaled 95.3% (n=205), which indicates a highly effective quality of performed vitrification. The proportion of zygotes in the group A corresponded to 91.1%(n=631), in the group B – 80.5%(n=165), which shows statistically significant difference between the groups (p<0.001) and explained by non-viable oocytes elimination after vitrification. This is confirmed by the fact that on the fifth day of embryos development a statistically significant difference in the number of blastocysts was absent (p>0.05), and constituted respectively 61.6%(n=389) and 63.0%(n=104) in the groups. For the PGS performing 250 embryos analyzed in the group A and 72 embryos - in the group B. The results showed that euploidy in the studied chromosomes were 40.0%(n=100) embryos in the group A and 41.7% (n=30) - in the group B, which shows no statistical significant difference (p>0.05). The indicators of clinical pregnancies in the groups amounted to 64.7% (22 pregnancies per 34 embryo transfers) and 61.5% (8 pregnancies per 13 embryo transfers) respectively, and also had no significant difference between the groups (p>0.05). Conclusions: The results showed that the vitrification does not affect the resulting euploid embryos in assisted reproductive technologies and are not reflected in their morphological characteristics in ART programs.

Keywords: euploid embryos, preimplantation genetic screening, thawing oocytes, vitrification

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: assignment, deadline, greedy approach, Hungarian algorithm, operations research, scheduling

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Authors: A. K. Borah, A. K. Singh

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In this paper we deal building blocks of the computer simulation of the multiphase flows. Whole simulation procedure can be viewed as two super procedures; The implementation of VOF method and the solution of Navier Stoke’s Equation. Moreover, a sequential code for a Navier Stoke’s solver has been studied.

Keywords: Bi-conjugate gradient stabilized (Bi-CGSTAB), ILUT function, krylov subspace, multifluid flows preconditioner, simple algorithm

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Authors: Imen Amari, Kaouther Gasmi, Asma Rebaya, Salem Hasnaoui

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The problem of scheduling and mapping data flow applications on multi-core architectures is notoriously difficult. This difficulty is related to the rapid evaluation of Telecommunication and multimedia systems accompanied by a rapid increase of user requirements in terms of latency, execution time, consumption, energy, etc. Having an optimal scheduling on multi-cores DSP (Digital signal Processors) platforms is a challenging task. In this context, we present a novel technic and algorithm in order to find a valid schedule that optimizes the key performance metrics particularly the Latency. Our contribution is based on Satisfiability Modulo Theories (SMT) solving technologies which is strongly driven by the industrial applications and needs. This paper, describe a scheduling module integrated in our proposed Workflow which is advised to be a successful approach for programming the applications based on NoC-DSP platforms. This workflow transform automatically a Simulink model to a synchronous dataflow (SDF) model. The automatic transformation followed by SMT solver scheduling aim to minimize the final latency and other software/hardware metrics in terms of an optimal schedule. Also, finding the optimal numbers of cores to be used. In fact, our proposed workflow taking as entry point a Simulink file (.mdl or .slx) derived from embedded Matlab functions. We use an approach which is based on the synchronous and hierarchical behavior of both Simulink and SDF. Whence, results of running the scheduler which exist in the Workflow mentioned above using our proposed SMT solver algorithm refinements produce the best possible scheduling in terms of latency and numbers of cores.

Keywords: multi-cores DSP, scheduling, SMT solver, workflow

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Authors: Shafait Hussain Ali

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Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

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Authors: Mubashir Hussain, Mu Lv, Xiaohan Dong, Zhiyang Li, Bin Liu, Nongyue He

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Detection and classification of microbes have a vast range of applications in biomedical engineering especially in detection, characterization, and quantification of bacterial contaminants. For identification of pathogens, different techniques are emerging in the field of biomedical engineering. Latest technology uses light scattering, capable of identifying different pathogens without any need for biochemical processing. Bacterial Pathogens Identification System (BPIS) which uses a laser beam, passes through the sample and light scatters off. An assembly of photodetectors surrounded by the sample at different angles to detect the scattering of light. The algorithm of the system consists of two parts: (a) Library files, and (b) Comparator. Library files contain data of known species of bacterial microbes in the form of binned plots, while comparator compares data of unknown sample with library files. Using collected data of unknown bacterial species, highest voltage values stored in the form of peaks and arranged in 3D histograms to find the frequency of occurrence. Resulting data compared with library files of known bacterial species. If sample data matching with any library file of known bacterial species, sample identified as a matched microbe. An experiment performed to identify three different bacteria particles: Enterococcus faecalis, Pseudomonas aeruginosa, and Escherichia coli. By applying algorithm using library files of given samples, results were compromising. This system is potentially applicable to several biomedical areas, especially those related to cell morphology.

Keywords: microbial identification, laser scattering, peak identification, binned plots classification

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Authors: Adam Kurnicki, Bartłomiej Stanczyk, Bartosz Kania

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This paper presents development of the light-weight manipulator with series elastic actuation for medical telediagnostics (USG examination). General structure of realized impedance control algorithm was shown. It was described how to perform force measurements based mainly on elasticity of manipulator links.

Keywords: telediagnostics, elastic manipulator, impedance control, force measurement

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Authors: Gary Miller, Andriy Didenko, David Allison

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The boundary of a region in the plane shrinks according to its curvature. A simple algorithm based upon this motion by curvature performed by a spreadsheet simulates the evaporation/dissipation behavior of oil spill boundaries.

Keywords: mathematical modeling, oil, evaporation, dissipation, boundary

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Authors: S. Jalaja, A. M. Vijaya Prakash

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Recursive combination of an algorithm based on Karatsuba multiplication is exploited to design a generalized transpose and parallel Finite Impulse Response (FIR) Filter. Mid-range Karatsuba multiplication and Carry Save adder based on Karatsuba multiplication reduce time complexity for higher order multiplication implemented up to n-bit. As a result, we design modified N-tap Transpose and Parallel Symmetric FIR Filter Structure using Karatsuba algorithm. The mathematical formulation of the FFA Filter is derived. The proposed architecture involves significantly less area delay product (APD) then the existing block implementation. By adopting retiming technique, hardware cost is reduced further. The filter architecture is designed by using 90 nm technology library and is implemented by using cadence EDA Tool. The synthesized result shows better performance for different word length and block size. The design achieves switching activity reduction and low power consumption by applying with and without retiming for different combination of the circuit. The proposed structure achieves more than a half of the power reduction by adopting with and without retiming techniques compared to the earlier design structure. As a proof of the concept for block size 16 and filter length 64 for CKA method, it achieves a 51% as well as 70% less power by applying retiming technique, and for CSA method it achieves a 57% as well as 77% less power by applying retiming technique compared to the previously proposed design.

Keywords: carry save adder Karatsuba multiplication, mid range Karatsuba multiplication, modified FFA and transposed filter, retiming

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Authors: Prakash Singh

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The question that arises is whether a theory such as the Collegial Leadership Model of Emancipation (COLME) has the potency to introduce leadership change by empowering and emancipating their employees. It is a fallacy to simply assume that experience alone, in the absence of theory, will contribute to this knowledge base to develop collegial leaders. The focus of this study is to therefore ascertain whether the COLME can serve as a conceptual framework to transform traditional bureaucratic management practices (TBMPs) in order to promote shared decision-making in organizations such as schools. All the respondents in this exploratory qualitative study embraced collegiality to transform TBMPs in their organizations. For the positive effects to be sustained, the collegial practices need to be evolutionary and emancipatory in order to evoke the values of collegial leadership as elucidated by the findings of this study. Interviewees affirmed that the COLME provides an astute framework to develop commendable collegial leadership practices as it clearly outlines procedures to develop and use the leadership potential of all the employees in order to foster joint accountability. They acknowledged that when the principles of collegiality are flexibly applied, they contribute to the creation of a holistic milieu in which all employees are able to express themselves freely, without fear of failure, and thus feel that they are part of the democratic decision-making process. Evidently, a conceptual framework such as the COLME can serve as a benchmark for leadership effectiveness because organizational outcomes need to be measured against standards of excellence in meeting both employee and customer expectations.

Keywords: collegial leadership model, employee empowerment, shared decision-making, traditional bureaucratic management practices

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Authors: Benito Minjarez, Jesse Haramati, Yury Rodriguez-Yanez, Florencio Recendiz-Hurtado, Juan-Pedro Luna-Arias, Salvador Mena-Munguia

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During the last decades, the world has experienced a rapid industrialization and an expanding economy favoring a demographic boom. As a consequence, countries around the world have focused on developing new strategies related to the production of different farm products in order to meet future demands. Consequently, different strategies have been developed seeking to improve the major food products for both humans and livestock. Corn, after wheat and rice, is the third most important crop globally and is the primary food source for both humans and livestock in many regions around the globe. In addition, maize (Zea mays) is an important source of protein accounting for up to 60% of the daily human protein supply. Generally, many of the cereal grains have proteins with relatively low nutritional value, when they are compared with proteins from meat. In the case of corn, much of the protein is found in the endosperm (75 to 85%) and is deficient in two essential amino acids, lysine, and tryptophan. This deficiency results in an imbalance of amino acids and low protein content; normal maize varieties have less than half of the recommended amino acids for human nutrition. In addition, studies have shown that this deficiency has been associated with symptoms of growth impairment, anemia, hypoproteinemia, and fatty liver. Due to the fact that most of the presently available maize varieties do not contain the quality and quantity of proteins necessary for a balanced diet, different countries have focused on the research of quality protein maize (QPM). Researchers have characterized QPM noting that these varieties may contain between 70 to 100% more residues of the amino acids essential for animal and human nutrition, lysine, and tryptophan, than common corn. Several countries in Africa, Latin America, as well as China, have incorporated QPM in their agricultural development plan. Large parts of these countries have chosen a specific QPM variety based on their local needs and climate. Reviews have described the breeding methods of maize and have revealed the lack of studies on genetic and proteomic diversity of proteins in QPM varieties, and their genetic relationships with normal maize varieties. Therefore, molecular marker identification using tools such as mass spectrometry may accelerate the selection of plants that carry the desired proteins with high lysine and tryptophan concentration. To date, QPM maize lines have played a very important role in alleviating the malnutrition, and better characterization of these lines would provide a valuable nutritional enhancement for use in the resource-poor regions of the world. Thus, the objectives of this study were to identify proteins in QPM maize in comparison with a common maize line as a control.

Keywords: corn, mass spectrometry, QPM, tryptophan

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Authors: Sheheryar Khan, Weidong Li, Fanqian Meng

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The Urban Heat Island (UHI) effect is one of the foremost problems out of other ecological and socioeconomic issues in urbanization. Due to this phenomenon that human-made urban areas have replaced the rural landscape with the surface that increases thermal conductivity and urban warmth; as a result, the temperature in the city is higher than in the surrounding rural areas. To affect the evidence of this phenomenon in the Zhengzhou city area, an observation of the temperature variations in the urban area is done through a scientific method that has been followed. Landsat 8 satellite images were taken from 2013 to 2015 to calculate the effect of Urban Heat Island (UHI) along with the NPP-VRRIS night-time remote sensing data to analyze the result for a better understanding of the center of the built-up area. To further support the evidence, the correlation between land surface temperatures and the normalized difference vegetation index (NDVI) was calculated using the Red band 4 and Near-infrared band 5 of the Landsat 8 data. Mono-window algorithm was applied to retrieve the land surface temperature (LST) distribution from the Landsat 8 data using Band 10 and 11 accordingly to convert the top-of-atmosphere radiance (TOA) and to convert the satellite brightness temperature. Along with Landsat 8 data, NPP-VIIRS night-light data is preprocessed to get the research area data. The analysis between Landsat 8 data and NPP night-light data was taken to compare the output center of the Built-up area of Zhengzhou city.

Keywords: built-up area, land surface temperature, mono-window algorithm, NDVI, remote sensing, threshold method, Zhengzhou

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: A. Rong, P. B. Luh, R. Lahdelma

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High penetration of intermittent renewable energy sources (RES) such as solar power and wind power into the energy system has caused temporal and spatial imbalance between electric power supply and demand for some countries and regions. This brings about the critical need for coordinating power production and power exchange for different regions. As compared with the power-only systems, the combined heat and power (CHP) systems can provide additional flexibility of utilizing RES by exploiting the interdependence of power and heat production in the CHP plant. In the CHP system, power production can be influenced by adjusting heat production level and electric power can be used to satisfy heat demand by electric boiler or heat pump in conjunction with heat storage, which is much cheaper than electric storage. This paper addresses multi-site CHP systems without considering RES, which lay foundation for handling penetration of RES. The problem under study is the unit commitment (UC) of the transmission-constrained multi-site CHP systems. We solve the problem by combining linear relaxation of ON/OFF states and sequential dynamic programming (DP) techniques, where relaxed states are used to reduce the dimension of the UC problem and DP for improving the solution quality. Numerical results for daily scheduling with realistic models and data show that DP-based algorithm is from a few to a few hundred times faster than CPLEX (standard commercial optimization software) with good solution accuracy (less than 1% relative gap from the optimal solution on the average).

Keywords: dynamic programming, multi-site combined heat and power system, relaxed states, transmission-constrained generation unit commitment

Procedia PDF Downloads 363

Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: Ju-Hong Lee, Yi-Lin Shieh

Abstract:

Two-dimensional (2-D) quadrature mirror filter (QMF) banks have been widely considered for high-quality coding of image and video data at low bit rates. Without implementing subband coding, a 2-D QMF bank is required to have an exactly linear-phase response without magnitude distortion, i.e., the perfect reconstruction (PR) characteristics. The design problem of 2-D QMF banks with the PR characteristics has been considered in the literature for many years. This paper presents a transformation approach for designing 2-D two-channel QMF banks. Under a suitable one-dimensional (1-D) to two-dimensional (2-D) transformation with a specified decimation/interpolation matrix, the analysis and synthesis filters of the QMF bank are composed of 1-D causal and stable digital allpass filters (DAFs) and possess the 2-D doubly complementary half-band (DC-HB) property. This facilitates the design problem of the two-channel QMF banks by finding the real coefficients of the 1-D recursive DAFs. The design problem is formulated based on the minimax phase approximation for the 1-D DAFs. A novel objective function is then derived to obtain an optimization for 1-D minimax phase approximation. As a result, the problem of minimizing the objective function can be simply solved by using the well-known weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The novelty of the proposed design method is that the design procedure is very simple and the designed 2-D QMF bank achieves perfect magnitude response and possesses satisfactory phase response. Simulation results show that the proposed design method provides much better design performance and much less design complexity as compared with the existing techniques.

Keywords: Quincunx QMF bank, doubly complementary filter, digital allpass filter, WLS algorithm

Procedia PDF Downloads 223