Search results for: ensemble genetic programming

Authors: Othman E. Othman, Hassan R. Darwish, Amira M. Nowier

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Lactoferrin (LF) is a multifunctional protein involved in economically production traits like milk protein composition and skeletal structure in small ruminants including sheep and goat. So, LF gene - with its genetic polymorphisms associated with production traits - is considered a candidate genetic marker used in marker-assisted selection in goats. This study aimed to identify the different alleles and genotypes of this gene in three Egyptian goat breeds using PCR-SSCP (polymerase chain reaction-single-strand conformation polymorphism) and DNA sequencing. Genomic DNA was extracted from 120 animals belonging to Barki, Zaraibi, and Damascus goat breeds. Using specific primers, PCR amplified 247-bp fragments from exon 2 of LF goat gene. The PCR products were subjected to Single-Strand Conformation Polymorphism (SSCP) technique. The results showed the presence of two genotypes GG and AG in the tested animals. The frequencies of both genotypes varied among the three tested breeds with the highest frequencies of GG genotype in all tested goat breeds. The sequence analysis of PCR products representing these two detected genotypes declared the presence of an SNP (single nucleotide polymorphisms) substitution (G/A) among G and A alleles of this gene. The association between different LF genotypes and milk composition as well as body measurement was estimated. The comparison showed that the animals possess AG genotypes are superior over those with GG genotypes for different parameters of milk protein compositions and skeletal structures. This finding declared that allele A of LF gene is considered the promising marker for the productive traits in goat. In conclusion, the Egyptian goat breeds will be needed to enhance their milk protein composition and growth trait parameters through the increasing of allele A frequency in their herds depending on the superior production traits of this allele in goats.

Keywords: lLactoferrin gene, PCR-SSCP, SNPs, Egyptian goat

Procedia PDF Downloads 155

Authors: Mahnaz Hosseinzadeh, Shaghayegh Rezaee Amiri

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This paper presents a methodology for serial production planning problem in wire and cable manufacturing process that addresses the problem of input-output imbalance in different consecutive stations, hoping to minimize the halt of machines in each stage. To this end, a linear Goal Programming (GP) model is developed, in which four main categories of constraints as per the number of runs per machine, machines’ sequences, acceptable inventories of machines at the end of each period, and the necessity of fulfillment of the customers’ orders are considered. The model is formulated based upon on the real data obtained from IKO TAK Company, an important supplier of wire and cable for oil and gas and automotive industries in Iran. By solving the model in GAMS software the optimal number of runs, end-of-period inventories, and the possible minimum idle time for each machine are calculated. The application of the numerical results in the target company has shown the efficiency of the proposed model and the solution in decreasing the lead time of the end product delivery to the customers by 20%. Accordingly, the developed model could be easily applied in wire and cable companies for the aim of optimal production planning to reduce the halt of machines in manufacturing stages.

Keywords: goal programming approach, GP, production planning, serial manufacturing process, wire and cable industry

Procedia PDF Downloads 160

Authors: D. Leibman, D. Wolf, A. Gal-On

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Viral diseases of tomato crops result in heavy yield losses and may even jeopardize the production of these crops. Classical tomato breeding for disease resistance against Tomato yellow leaf curl virus (TYLCV), leads to partial resistance associated with a number of recessive genes. To author’s best knowledge Pepino mosaic virus (PepMV) genetic resistance is not yet available. The generation of viral resistance by means of genetic engineering was reported and implemented for many crops, including tomato. Transgenic resistance against viruses is based, in most cases, on Post Transcriptional Gene Silencing (PTGS), an endogenous mechanism which destroys the virus genome. In this work, we developed immunity against PepMV and TYLCV in a tomato based on a PTGS mechanism. Tomato plants were transformed with a hairpin-construct-expressed transgene-derived double-strand-RNA (tr-dsRNA). In the case of PepMV, the binary construct harbored three consecutive fragments of the replicase gene from three different PepMV strains (Italian, Spanish and American), to provide resistance against a range of virus strains. In the case of TYLCV, the binary vector included three consecutive fragments of the IR, V2 and C2 viral genes constructed in a hairpin configuration. Selected transgenic lines (T0) showed a high accumulation of transgene siRNA of 21-24 bases, and T1 transgenic lines showed complete immunity to PepMV and TYLCV. Graft inoculation displayed immunity of the transgenic scion against PepMV and TYLCV. The study presents the engineering of resistance in tomato against two serious diseases, which will help in the production of high-quality tomato. However, unfortunately, these resistant plants have not been implemented due to public ignorance and opposition against breeding by genetic engineering.

Keywords: PepMV, PTGS, TYLCV, tr-dsRNA

Procedia PDF Downloads 133

Authors: Vitalii Yu. Kapitan, Aleksandr V. Perzhu, Konstantin V. Nefedev

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The necessity of studying magnetic multilayer structures is explained by the prospects of their practical application as a technological base for creating new storages medium. Magnetic multilayer films have many unique features that contribute to increasing the density of information recording and the speed of storage devices. Multilayer structures are structures of alternating magnetic and nonmagnetic layers. In frame of the classical Heisenberg model, lattice spin systems with direct short- and long-range exchange interactions were investigated by Monte Carlo methods. The thermodynamic characteristics of multilayer structures, such as the temperature behavior of magnetization, energy, and heat capacity, were investigated. The processes of magnetization reversal of multilayer structures in external magnetic fields were investigated. The developed software is based on the new, promising programming language Rust. Rust is a new experimental programming language developed by Mozilla. The language is positioned as an alternative to C and C++. For the Monte Carlo simulation, the Metropolis algorithm and its parallel implementation using MPI and the Wang-Landau algorithm were used. We are planning to study of magnetic multilayer films with asymmetric Dzyaloshinskii–Moriya (DM) interaction, interfacing effects and skyrmions textures. This work was supported by the state task of the Ministry of Education and Science of the Russia # 3.7383.2017/8.9

Keywords: The Monte Carlo methods, Heisenberg model, multilayer structures, magnetic skyrmion

Procedia PDF Downloads 166

Authors: Adam Gorine, Faten Spondon

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Python is considered the most popular programming language and offers its own ecosystem for archiving and maintaining open-source software packages. This system is called the python package index (PyPI), the repository of this programming language. Unfortunately, one-third of these software packages have vulnerabilities that allow attackers to execute code automatically when a vulnerable or malicious package is installed. This paper contributes to large-scale empirical studies investigating security issues in the python ecosystem by evaluating package vulnerabilities. These provide a series of implications that can help the security of software ecosystems by improving the process of discovering, fixing, and managing package vulnerabilities. The vulnerable dataset is generated using the NVD, the national vulnerability database, and the Snyk vulnerability dataset. In addition, we evaluated 807 vulnerability reports in the NVD and 3900 publicly known security vulnerabilities in Python Package Manager (pip) from the Snyk database from 2002 to 2022. As a result, many Python vulnerabilities appear in high severity, followed by medium severity. The most problematic areas have been improper input validation and denial of service attacks. A hybrid scanning tool that combines the three scanners bandit, snyk and dlint, which provide a clear report of the code vulnerability, is also described.

Keywords: Python vulnerabilities, bandit, Snyk, Dlint, Python package index, ecosystem, static analysis, malicious attacks

Procedia PDF Downloads 139

Authors: Choonoh Lee, Seyeon Park, Dongyun Kang, Jaehyeong Choi, Soojee Kim, Younggeun Kim

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Market Kurly is the first South Korean online grocery retailer that guarantees same-day, overnight shipping. More than 1.6 million customers place an average of 4.7 million orders and add 3 to 14 products into a cart per month. The company has sold almost 30,000 kinds of various products in the past 6 months, including food items, cosmetics, kitchenware, toys for kids/pets, and even flowers. The company is operating and expanding multiple dry, cold, and frozen fulfillment centers in order to store and ship these products. Due to the scale and complexity of the fulfillment, pick-pack-ship processes are planned and operated in batches, and thus, the planning that decides the batch of the customers’ orders is a critical factor in overall productivity. This paper introduces a metaheuristic optimization method that reduces the complexity of batch processing in a fulfillment center. The method is an iterative genetic algorithm with heuristic creation and evolution strategies; it aims to group similar orders into pick-pack-ship batches to minimize the total number of distinct products. With a well-designed approach to create initial genes, the method produces streamlined plans, up to 13.5% less complex than the actual plans carried out in the company’s fulfillment centers in the previous months. Furthermore, our digital-twin simulations show that the optimized plans can reduce 3% of operation time for packing, which is the most complex and time-consuming task in the process. The optimization method implements a multithreading design on the Spring framework to support the company’s warehouse management systems in near real-time, finding a solution for 4,000 orders within 5 to 7 seconds on an AWS c5.2xlarge instance.

Keywords: fulfillment planning, genetic algorithm, online grocery retail, optimization

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Authors: Atsuko Okazaki, Jurg Ott

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Some genetic diseases (‘digenic traits’) are due to the interaction between two DNA variants. For example, certain forms of Retinitis Pigmentosa (a genetic form of blindness) occur in the presence of two mutant variants, one in the ROM1 gene and one in the RDS gene, while the occurrence of only one of these mutant variants leads to a completely normal phenotype. Detecting such digenic traits by genetic methods is difficult. A common approach to finding disease-causing variants is to compare 100,000s of variants between individuals with a trait (cases) and those without the trait (controls). Such genome-wide association studies (GWASs) have been very successful but hinge on genetic effects of single variants, that is, there should be a difference in allele or genotype frequencies between cases and controls at a disease-causing variant. Frequent pattern mining (FPM) methods offer an avenue at detecting digenic traits even in the absence of single-variant effects. The idea is to enumerate pairs of genotypes (genotype patterns) with each of the two genotypes originating from different variants that may be located at very different genomic positions. What is needed is for genotype patterns to be significantly more common in cases than in controls. Let Y = 2 refer to cases and Y = 1 to controls, with X denoting a specific genotype pattern. We are seeking association rules, ‘X → Y’, with high confidence, P(Y = 2|X), significantly higher than the proportion of cases, P(Y = 2) in the study. Clearly, generally available FPM methods are very suitable for detecting disease-associated genotype patterns. We use fpgrowth as the basic FPM algorithm and built a framework around it to enumerate high-frequency digenic genotype patterns and to evaluate their statistical significance by permutation analysis. Application to a published dataset on opioid dependence furnished results that could not be found with classical GWAS methodology. There were 143 cases and 153 healthy controls, each genotyped for 82 variants in eight genes of the opioid system. The aim was to find out whether any of these variants were disease-associated. The single-variant analysis did not lead to significant results. Application of our FPM implementation resulted in one significant (p < 0.01) genotype pattern with both genotypes in the pattern being heterozygous and originating from two variants on different chromosomes. This pattern occurred in 14 cases and none of the controls. Thus, the pattern seems quite specific to this form of substance abuse and is also rather predictive of disease. An algorithm called Multifactor Dimension Reduction (MDR) was developed some 20 years ago and has been in use in human genetics ever since. This and our algorithms share some similar properties, but they are also very different in other respects. The main difference seems to be that our algorithm focuses on patterns of genotypes while the main object of inference in MDR is the 3 × 3 table of genotypes at two variants.

Keywords: digenic traits, DNA variants, epistasis, statistical genetics

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Authors: Babak Forouraghi

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A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

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Authors: Saule Musabekova

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Achievements of modern science, especially genetics, led to a sharp intensification of the process of proof. Footprints, subjected to destruction-related cause-effect relationships, are sources of evidentiary information on the circumstances it was committed and the persons committed it. Currently, with the overall growth in the number of crimes against sexual inviolability or sexual freedom, and increased the proportion of the crimes where to destroy the traces of the crime perpetrators different detergents are used. A characteristic feature of modern synthetic detergents is the presence of biological additives - enzymes that break down and gradually destroy stains of protein origin. To study the nature of the influence of modern washing powders semen stains were put kinds of fabrics and prepared in advance stained sperm of men of different groups according to ABO system. For research washing machines of known manufacturers of household appliances have been used with different production characteristics, in which the test was performed and the washing of various kinds of fabrics with semen stains. After washing the tissue with spots were tested for the presence of semen stains visually preserved, establishing in them surviving sperm or their elements, we studied the possibilities of the group diagnostics on the system ABO or molecular-genetic identification. The subsequent study of these spots by morphological method showed that 100% detection of morphological sperm cells - sperm is not possible. As a result, in 30% of further studies of these traces gave weakly positive results are obtained with an immunoassay test PSA SEMIQUANT. It is noted that the percentage of positive results obtained in the study of semen traces disposed on natural fiber fabrics is higher than sperm traces disposed on synthetic fabrics. Study traces of semen, confirmed by PSA - test 3% possible to establish a genetic profile of the person and obtain any positive findings of the molecular genetic examination. In other cases, it was not a sufficient amount of material for DNA identification. Results of research and the practical expert study found, in most cases, the conclusions of the identification of sperm traces do not seem possible. This a consequence of exposure to semen traces on the material evidence of biological additives contained in modern detergents and further the influence of other effective methods. Resulting in DNA has undergone irreversible changes (degradation) under the influence of external human factors. Using molecular genetic methods can partially solve the problems arising in the study of unlaundered physical evidence for the disclosure and investigation of crimes.

Keywords: study of sperm, modern detergents, washing powders, forensic medicine

Procedia PDF Downloads 298

Authors: Hyung Jun Ahn

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With the growing importance of social media, it is imperative to analyze it to understand the users. Users share useful information and their experience through social media, where much of what is shared is in the form of texts. This study focused on blogs and aimed to test whether the psycho-linguistic characteristics of blog texts vary with the subject or the type of experience of the texts. For this goal, blog texts about four different types of experience, Go, skiing, reading, and musical were collected through the search API of the Tistory blog service. The analysis of the texts showed that various psycholinguistic characteristics of the texts are different across the four categories of the texts. Moreover, the machine learning experiment using the characteristics for automatic text classification showed significant performance. Specifically, the ensemble method, based on functional tree and bagging appeared to be most effective in classification.

Keywords: blog, social media, text analysis, psycholinguistics

Procedia PDF Downloads 279

Authors: Abubakar Maikudi

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Contrastive analysis is the method of analyzing the structure of any two languages with a view to determining the possible differential aspects of their systems irrespective of their genetic affinity or level of development. Contrastive analysis of two languages becomes useful when it is adequately describing the sound structure and grammatical structure of two languages, with comparative statements giving emphasis to the compatible items in the two systems. This research work uses comparative analysis theory to analyze adjective and adjectival phrases in Hausa and Yorùbá languages. The Hausa language belongs to the Chadic family of the Afro-Asiatic phylum, while the Yorùbá language belongs to the Benue-Congo family of the Niger-Congo phylum. The findings of the research clearly demonstrated that there are significant similarities in the adjectival phrase constructions of the two languages, i.e., nominal (Head) and post-nominal (Post-Head) use of the adjective, predicative function of an adjective, use of the reduplicative adjective, use of the comparative and superlative adjective, etc. However, there are dissimilarities in the adjectival phrase of the two languages in gender/number agreement and pre-nominal (Post-Head) use of adjectives.

Keywords: genetic affinity, contrastive analysis, phylum, pre-head, post-head

Procedia PDF Downloads 230

Authors: Zohreh Khojasteh-Ghamari, Takashi Irohara

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One of the efficient ways in supply chain risk management is avoiding the interruption in Supply Chain (SC) before it occurs. Although the majority of the organizations focus on their first-tier suppliers to avoid risk in the SC, studies show that in only 60 percent of the disruption cases the reason is first tier suppliers. In the 40 percent of the SC disruptions, the reason is downstream SC, which is the second tier and lower. Due to the increasing complexity and interrelation of modern supply chains, the SC elements have become difficult to trace. Moreover, studies show that there is a vital need to better understand the integration of risk and visibility, especially in the context of multiple objectives. In this study, we propose a multi-objective programming model to avoid disruption in SC. The objective of this study is evaluating the effect of downstream SCV on managing supply chain risk. We propose a multi-objective mathematical programming model with the objective functions of minimizing the total cost and maximizing the downstream supply chain visibility (SCV). The decision variable is supplier selection. We assume there are several manufacturers and several candidate suppliers. For each manufacturer, our model proposes the best suppliers with the lowest cost and maximum visibility in downstream supply chain. We examine the applicability of the model by numerical examples. We also define several scenarios for datasets and observe the tendency. The results show that minimum visibility in downstream SC is needed to have a safe SC network.

Keywords: downstream supply chain, optimization, supply chain risk, supply chain visibility

Procedia PDF Downloads 244

Authors: Zhong Ma, Zhuping Wang, Congxin Liu, Xiangzeng Liu

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Satellite imagery classification is a challenging problem with many practical applications. In this paper, we designed a deep convolution neural network (DCNN) to classify the satellite imagery. The contributions of this paper are twofold — First, to cope with the large-scale variance in the satellite image, we introduced the inception module, which has multiple filters with different size at the same level, as the building block to build our DCNN model. Second, we proposed a genetic algorithm based method to efficiently search the best hyper-parameters of the DCNN in a large search space. The proposed method is evaluated on the benchmark database. The results of the proposed hyper-parameters search method show it will guide the search towards better regions of the parameter space. Based on the found hyper-parameters, we built our DCNN models, and evaluated its performance on satellite imagery classification, the results show the classification accuracy of proposed models outperform the state of the art method.

Keywords: satellite imagery classification, deep convolution network, genetic algorithm, hyper-parameter optimization

Procedia PDF Downloads 300

Authors: Agnieszka H. Ludwig-Slomczynska, Michal T. Seweryn, Przemyslaw Kapusta, Ewelina Pitera, Katarzyna Cyganek, Urszula Mantaj, Lucja Dobrucka, Ewa Wender-Ozegowska, Maciej T. Malecki, Pawel Wolkow

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Obesity results from an imbalance between energy intake and its expenditure. Genome-Wide Association Study (GWAS) analyses have led to discovery of only about 100 variants influencing body mass index (BMI), which explain only a small portion of genetic variability. Analysis of gene epistasis gives a chance to discover another part. Since it was shown that interaction and communication between nuclear and mitochondrial genome are indispensable for normal cell function, we have looked for epistatic interactions between the two genomes to find their correlation with BMI. Methods: The analysis was performed on 366 T1DM patients using Illumina Infinium OmniExpressExome-8 chip and followed by imputation on Michigan Imputation Server. Only genes which influence mitochondrial functioning (listed in Human MitoCarta 2.0) were included in the analysis – variants of nuclear origin (MAF > 5%) in 1140 genes and 42 mitochondrial variants (MAF > 1%). Gene expression analysis was performed on GTex data. Association analysis between genetic variants and BMI was performed with the use of Linear Mixed Models as implemented in the package 'GENESIS' in R. Analysis of association between mRNA expression and BMI was performed with the use of linear models and standard significance tests in R. Results: Among variants involved in epistasis between mitochondria and nucleus we have identified one in mitochondrial transcription factor, TFB2M (rs6701836). It interacted with mitochondrial variants localized to MT-RNR1 (p=0.0004, MAF=15%), MT-ND2 (p=0.07, MAF=5%) and MT-ND4 (p=0.01, MAF=1.1%). Analysis of the interaction between nuclear variant rs6701836 (nuc) and rs3021088 localized to MT-ND2 mitochondrial gene (mito) has shown that the combination of the two led to BMI decrease (p=0.024). Each of the variants on its own does not correlate with higher BMI [p(nuc)=0.856, p(mito)=0.116)]. Although rs6701836 is intronic, it influences gene expression in the thyroid (p=0.000037). rs3021088 is a missense variant that leads to alanine to threonine substitution in the MT-ND2 gene which belongs to complex I of the electron transport chain. The analysis of the influence of genetic variants on gene expression has confirmed the trend explained above – the interaction of the two genes leads to BMI decrease (p=0.0308). Each of the mRNAs on its own is associated with higher BMI (p(mito)=0.0244 and p(nuc)=0.0269). Conclusıons: Our results show that nuclear-mitochondrial epistasis can influence BMI in T1DM patients. The correlation between transcription factor expression and mitochondrial genetic variants will be subject to further analysis.

Keywords: body mass index, epistasis, mitochondria, type 1 diabetes

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Authors: Dana Gabriková, Martin Mistrík, Jarmila Bernasovská, Iveta Tóthová, Jana Kisková

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The Roma (Gypsies) is a transnational minority with a high degree of consanguineous marriages. Similar to other genetically isolated founder populations, the Roma harbor a number of unique or rare genetic disorders. This paper discusses about a rare form of Charcot-Marie-Tooth disease – type 4G (CMT4G), also called Hereditary Motor and Sensory Neuropathy type Russe, an autosomal recessive disease caused by mutation private to Roma characterized by abnormally increased density of non-myelinated axons. CMT4G was originally found in Bulgarian Roma and in 2009 two putative causative mutations in the HK1 gene were identified. Since then, several cases were reported in Roma families mainly from Bulgaria and Spain. Here we present a Slovak Roma family in which CMT4G was diagnosed on the basis of clinical examination and genetic testing. This case is a further proof of the role of the HK1 gene in pathogenesis of the disease. It confirms that mutation in the HK1 gene is a common cause of autosomal recessive CMT disease in Roma and should be considered as a common part of a diagnostic procedure.

Keywords: gypsies, HK1, HSMN-Russe, rare disease

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Authors: Mafarja Majdi, Salwani Abdullah, Najmeh S. Jaddi

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One of the global combinatorial optimization problems in machine learning is feature selection. It concerned with removing the irrelevant, noisy, and redundant data, along with keeping the original meaning of the original data. Attribute reduction in rough set theory is an important feature selection method. Since attribute reduction is an NP-hard problem, it is necessary to investigate fast and effective approximate algorithms. In this paper, we proposed two feature selection mechanisms based on memetic algorithms (MAs) which combine the genetic algorithm with a fuzzy record to record travel algorithm and a fuzzy controlled great deluge algorithm to identify a good balance between local search and genetic search. In order to verify the proposed approaches, numerical experiments are carried out on thirteen datasets. The results show that the MAs approaches are efficient in solving attribute reduction problems when compared with other meta-heuristic approaches.

Keywords: rough set theory, attribute reduction, fuzzy logic, memetic algorithms, record to record algorithm, great deluge algorithm

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Authors: Wen Li, Zhengyu Bai, Qi Zhang

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The demand for smart scheduling platform in agriculture, particularly in the scheduling process of machinery equipment, is high. With the continuous development of agricultural machinery equipment technology, a large number of agricultural machinery equipment and agricultural machinery cooperative service organizations continue to appear in China. The large area of cultivated land and a large number of agricultural activities in the central and western regions of China have made the demand for smart and efficient agricultural machinery equipment scheduling platforms more intense. In this study, we design and implement a platform for agricultural machinery equipment scheduling to allocate agricultural machinery equipment resources reasonably. With agricultural machinery equipment scheduling platform taken as the research object, we discuss its research significance and value, use the service blueprint technology to analyze and characterize the agricultural machinery equipment schedule workflow, the network analytic method to obtain the demand platform function requirements, and divide the platform functions through the platform function division diagram. Simultaneously, based on the case-based reasoning (CBR) algorithm, the equipment scheduling module of the agricultural machinery equipment scheduling platform is realized; finally, a design scheme of the agricultural machinery equipment scheduling platform architecture is provided, and the visualization interface of the platform is established via VB programming language. It provides design ideas and theoretical support for the construction of a modern agricultural equipment information scheduling platform.

Keywords: case-based reasoning, service blueprint, system design, ANP, VB programming language

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Authors: Seraj Makkawi, Abdulaziz A. Alqarni, Himyan Alghaythee, Suzan Y. Alharbi, Anmar Fatani, Reem Adas, Ahmad R. Abuzinadah

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Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig's disease, is a neurodegenerative disease that involves both the upper and lower motor neurons. Familial ALS, including superoxide dismutase 1 (SOD1) mutation, accounts for 5-10% of all cases of ALS. Typically, the symptoms of ALS are purely motor, though coexistent sensory symptoms have been reported in rare cases. In this report, we describe the case of a 47- year-old man who presented with progressive bilateral lower limb weakness and numbness for the last four years. A nerve conduction study (NCS) showed evidence of coexistent axonal sensorimotor polyneuropathy in addition to the typical findings of ALS in needle electromyography. Genetic testing confirmed the diagnosis of familial ALS secondary to the SOD1 genetic mutation. This report highlights that the presence of sensory symptoms should not exclude the possibility of ALS in an appropriate clinical setting.

Keywords: Saudi Arabia, polyneuropathy, SOD1 gene mutation, familial amyotrophic lateral sclerosis, amyotrophic lateral sclerosis

Procedia PDF Downloads 148

Authors: Paul Toprac, Branden Heng, Harsheni Siddharthan, Allison Tseng, Sarah Abraham, Etienne Vouga

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The goal of this project was to evaluate and document the capabilities and limitations of AI tools for empowering small teams to create high budget, high profile (AAA) 3D games typically developed by large studios. Two teams of novice game developers attempted to create two different games using AI and Unreal Engine 5.3. First, the teams evaluated 60 AI art, design, sound, and programming tools by considering their capability, ease of use, cost, and license restrictions. Then, the teams used a shortlist of 13 AI tools for game development. During this process, the following tools were found to be the most productive: (1) ChatGPT 4.0 for both game and narrative concepting and documentation; (2) Dall-E 3 and OpenArt for concept art; (3) Beatoven for music drafting; (4) Epic PCG for level design; and (5) ChatGPT 4.0 and Github Copilot for generating simple code and to complement human-made tutorials as an additional learning resource. While current generative AI may appear impressive at first glance, the assets they produce fall short of AAA industry standards. Generative AI tools are helpful when brainstorming ideas such as concept art and basic storylines, but they still cannot replace human input or creativity at this time. Regarding programming, AI can only effectively generate simple code and act as an additional learning resource. Thus, generative AI tools are at best tools to enhance developer productivity rather than as a system to replace developers.

Keywords: AAA games, AI, automation tools, game development

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Authors: Anderson S. Fonseca, Italo F. S. Da Silva, Robert D. A. Santos, Mayara G. Da Silva, Pedro H. C. Vieira, Antonio M. S. Sobrinho, Victor H. B. Lemos, Petterson S. Diniz, Anselmo C. Paiva, Eliana M. G. Monteiro

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In Brazil, the National Electric Energy Agency (ANEEL) establishes that electrical energy companies are responsible for measuring and billing their customers. Among these regulations, it’s defined that a company must bill your customers within 27-33 days. If a relocation or a change of period is required, the consumer must be notified in writing, in advance of a billing period. To make it easier to organize a workday’s measurements, these companies create a reading plan. These plans consist of grouping customers into reading groups, which are visited by an employee responsible for measuring consumption and billing. The creation process of a plan efficiently and optimally is a capacitated clustering problem with constraints related to homogeneity and compactness, that is, the employee’s working load and the geographical position of the consuming unit. This process is a work done manually by several experts who have experience in the geographic formation of the region, which takes a large number of days to complete the final planning, and because it’s human activity, there is no guarantee of finding the best optimization for planning. In this paper, the GBKMeans method presents a technique based on K-Means and genetic algorithms for creating a capacitated cluster that respects the constraints established in an efficient and balanced manner, that minimizes the cost of relocating consumer units and the time required for final planning creation. The results obtained by the presented method are compared with the current planning of a real city, showing an improvement of 54.71% in the standard deviation of working load and 11.97% in the compactness of the groups.

Keywords: capacitated clustering, k-means, genetic algorithm, districting problems

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Authors: Olumuyiwa Ojo, Masengo Ilunga

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Artificial neural network (ANN) has broken the bounds of the convention programming, which is actually a function of garbage in garbage out by its ability to mimic the human brain. Its ability to adopt, adapt, adjust, evaluate, learn and recognize the relationship, behavior, and pattern of a series of data set administered to it, is tailored after the human reasoning and learning mechanism. Thus, the study aimed at modeling wastewater treatment process in order to accurately diagnose water control problems for effective treatment. For this study, a stage ANN model development and evaluation methodology were employed. The source data analysis stage involved a statistical analysis of the data used in modeling in the model development stage, candidate ANN architecture development and then evaluated using a historical data set. The model was developed using historical data obtained from Daspoort Wastewater Treatment plant South Africa. The resultant designed dimensions and model for wastewater treatment plant provided good results. Parameters considered were temperature, pH value, colour, turbidity, amount of solids and acidity. Others are total hardness, Ca hardness, Mg hardness, and chloride. This enables the ANN to handle and represent more complex problems that conventional programming is incapable of performing.

Keywords: ANN, artificial neural network, wastewater treatment, model, development

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Authors: Koteswara Rao Pagolu, Raghava Rao Tamanam

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The present study was aimed to find out the prevalence of DD's in Visakhapatnam, one of the north coastal districts of Andhra Pradesh, India during a span of five years. A cross-sectional investigation was held at District early intervention center (DEIC), Visakhapatnam from 2016 to 2020. To identify the pattern and trend of different DD's including seasonal variations, a retrospective analysis of the health center's inpatient database for the past 5 years was done. Male and female children aged 2 months-18 years are included in the study with the prior permission of the concerned medical officer. The screening tool developed by the Ministry of health and family welfare, India, was used for the study. Among 26,423 cases, children with birth defects are 962, 2229 with deficiencies, 7516 with diseases, and 15716 with disabilities were admitted during the study period. From birth defects, congenital deafness occurred in large numbers with 22.66%, and neural tube defect observed in a small number of cases with 0.83% during the period. From the side of deficiencies, severe acute malnutrition has mostly occurred (66.80 %) and a small number of children were affected with goiter (1.70%). Among the diseases, dental carriers (67.97%) are mostly found and these cases were at peak during the years 2016 and 2019. From disabilities, children with vision impairment (20.55%) have mostly approached the center. Over the past 5 years, the admission rate of down's syndrome and congenital deafness cases showed a rising trend up to 2019 and then declined. Hearing impairment, motor delay, and learning disorder showed a steep rise and gradual decline trend, whereas severe anemia, vitamin-D deficiency, otitis media, reactive airway disease, and attention deficit hyperactivity disorder showed a declining trend. However, congenital heart diseases, dental caries, and vision impairment admission rates showed a zigzag pattern over the past 5 years. This center had inadequate diagnostic facilities related to genetic disease management. For advanced confirmation, the cases are referred to a district government hospital or private diagnostic laboratories in the city for genetic tests. Information regarding the overall burden and pattern of admissions in the health center is obtained by the review of DEIC records. Through this study, it is observed that the incidence of birth defects, as well as genetic disease burden, is high in the Visakhapatnam district. Hence there is a need for strengthening of management services for these diseases in this region.

Keywords: child health screening, developmental delays, district early intervention center, genetic disease management, infrastructural facility, Visakhapatnam district

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Authors: L. Abubakar, B. M. Sokoto, I. U. Mohammed, M. S. Na’allah, A. Mohammad, A. N. Garba, T. S. Bubuche

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Onion (Allium cepa var. cepa L.) is the most important species of the Allium group belonging to family Alliaceae and genus Allium. It can be regarded as the single important vegetable species in the world after tomatoes. Despite the similarities, which bring the species together, the genus is a strikingly diverse one, with more than five hundred species, which are perennial and mostly bulbous plants. Out of these, only seven species are in cultivation, and five are the most important species of the cultivated Allium. However, Allium cepa (onion) and Allium sativum (Garlic) are the two major cultivated species grown all over the world of which the onion crop is the most important. North Western Nigeria (Sokoto, Kebbi and Zamfara States) constitute the major onion producing zone in Nigeria, which is primarily during the dry season. However, onion production in the zone is seriously affected by two main factors i.e. diseases and storage losses, in addition to other constraints that limits the cultivation of the crop during the rainy season which include lack of prolonged rainy season to allow for proper maturation of the crop. The major onion disease in this zone is purple blotch caused by a fungus Alternaria porri and currently efforts are on to develop onion hybrids resistant to the disease. Genetic diversity plays an important role in plant breeding either to exploit heterosis or to generate productive recombinants. Assessment of a large number of genotypes for a genetic diversity is the first step in this direction. The objective of this research therefore is to evaluate the genetic potentials of the onion cultivars of North Western Nigeria, with a view of developing new cultivars that address the major production challenges to onion cultivation in North Western, Nigeria. Thirteen onion cultivars were collected during an expedition covering North western Nigeria and Southern part of Niger Republic during 2013, which are areas noted for onion production. The cultivars were evaluated at two locations; Sokoto, in Sokoto State and Jega in Kebbi State all in Nigeria during the 2013/14 onion season (dry season) under irrigation. The objective of the research was to determine the genetic potentials of onion cultivars of north western Nigeria as a basis for breeding purposes. Combined analysis of the results revealed highly significant variation between the cultivars across the locations with respect to plant height, number of leaves/plant, bolting %, bulb height, bulb weight, mean bulb yield and cured bulb weight, with significant variation in terms of bulb diameter. Tasa from Warra Local Government Area of Kebbi State (V4) recorded the greatest mean fresh bulb yield with Jar Albasa (V8) from Illela Local Government Area of Sokoto State recording the least. Similarly Marsa (V5) from Silame Local Government Area recorded the greatest mean cured bulb yield (marketable bulb)with Kiba (V11) from Goronyo Local Government of Sokoto State recording the least. Significant variation was recorded between the locations with respect to all characters, with Sokoto being better in terms of plant height, number of leaves/plant, bolting % and bulb diameter. Jega was better in terms of bulb height, bulb yield and cured bulb weight. Significant variation was therefore observed between the cultivars.

Keywords: evaluation, genetic, onions, North Western Nigeria

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Authors: Yasaman Mohammadi

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Emotion regulation is a complex process that allows individuals to manage and modulate their emotional responses in order to adaptively respond to environmental demands. As individuals age, emotion regulation abilities may decline, leading to an increased vulnerability to mood disorders and other negative health outcomes. Advances in neuroimaging techniques have greatly enhanced our understanding of the neural substrates underlying emotion regulation and age-related changes in these neural systems. Additionally, genetic research has identified several candidate genes that may influence age-related changes in emotion regulation. In this paper, we review recent findings from neuroimaging and genetic research on age-related changes in the neural substrates of emotion regulation, highlighting the mechanisms and consequences of these changes. We also discuss potential interventions, including cognitive and behavioral approaches, that may be effective in mitigating age-related declines in emotion regulation. We propose that a better understanding of the mechanisms underlying age-related changes in emotion regulation may lead to the development of more targeted interventions aimed at promoting healthy emotional functioning in older adults. Overall, this paper highlights the importance of studying age-related changes in emotion regulation and provides a roadmap for future research in this field.

Keywords: emotion regulation, aging, neural substrates, neuroimaging, emotional functioning, healthy aging

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Authors: R. O. Ocaya, J. J. Terblans

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The laws of Newtonian mechanics allow ab-initio molecular dynamics to model and simulate particle trajectories in material science by defining a differentiable potential function. This paper discusses some considerations for the coding of ab-initio programs for simulation on a standalone computer and illustrates the approach by C language codes in the context of embedded metallic atoms in the face-centred cubic structure. The algorithms use velocity-time integration to determine particle parameter evolution for up to several thousands of particles in a thermodynamical ensemble. Such functions are reusable and can be placed in a redistributable header library file. While there are both commercial and free packages available, their heuristic nature prevents dissection. In addition, developing own codes has the obvious advantage of teaching techniques applicable to new problems.

Keywords: C language, molecular dynamics, simulation, embedded atom method

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Bekir O. Bartin, Kaan Ozbay, Sandeep Mudigonda, Hong Yang

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The use of microscopic traffic simulation in evaluating the operational and safety conditions at toll plazas is demonstrated. Two toll plazas in New Jersey are selected as case studies and were developed and validated in Paramics traffic simulation software. In order to simulate drivers’ lane selection behavior in Paramics, a utility-based lane selection approach is implemented in Paramics Application Programming Interface (API). For each vehicle approaching the toll plaza, a utility value is assigned to each toll lane by taking into account the factors that are likely to impact drivers’ lane selection behavior, such as approach lane, exit lane and queue lengths. The results demonstrate that similar operational conditions, such as lane-by-lane toll plaza traffic volume can be attained using this approach. In addition, assessment of safety at toll plazas is conducted via a surrogate safety measure. In particular, the crash index (CI), an improved surrogate measure of time-to-collision (TTC), which reflects the severity of a crash is used in the simulation analyses. The results indicate that the spatial and temporal frequency of observed crashes can be simulated using the proposed methodology. Further analyses can be conducted to evaluate and compare various different operational decisions and safety measures using microscopic simulation models.

Keywords: microscopic simulation, toll plaza, surrogate safety, application programming interface

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Authors: Artem Kim, Clara Savary, Christele Dubourg, Wilfrid Carre, Houda Hamdi-Roze, Valerie Dupé, Sylvie Odent, Marie De Tayrac, Veronique David

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Holoprosencephaly (HPE) is a rare congenital brain malformation resulting from the incomplete separation of the two cerebral hemispheres. It is characterized by a wide phenotypic spectrum and a high degree of locus heterogeneity. Genetic defects in 16 genes have already been implicated in HPE, but account for only 30% of cases, suggesting that a large part of genetic factors remains to be discovered. HPE has been recently redefined as a complex multigenic disorder, requiring the joint effect of multiple mutational events in genes belonging to one or several developmental pathways. The onset of HPE may result from accumulation of the effects of multiple rare variants in functionally-related genes, each conferring a moderate increase in the risk of HPE onset. In order to decipher the genetic basis of HPE, unconventional patterns of inheritance involving multiple genetic factors need to be considered. The primary objective of this study was to uncover possible disease causing combinations of multiple rare variants underlying HPE by performing trio-based Whole Exome Sequencing (WES) of familial cases where no molecular diagnosis could be established. 39 families were selected with no fully-penetrant causal mutation in known HPE gene, no chromosomic aberrations/copy number variants and without any implication of environmental factors. As the main challenge was to identify disease-related variants among a large number of nonpathogenic polymorphisms detected by WES classical scheme, a novel variant prioritization approach was established. It combined WES filtering with complementary gene-level approaches: transcriptome-driven (RNA-Seq data) and clinically-driven (public clinical data) strategies. Briefly, a filtering approach was performed to select variants compatible with disease segregation, population frequency and pathogenicity prediction to identify an exhaustive list of rare deleterious variants. The exome search space was then reduced by restricting the analysis to candidate genes identified by either transcriptome-driven strategy (genes sharing highly similar expression patterns with known HPE genes during cerebral development) or clinically-driven strategy (genes associated to phenotypes of interest overlapping with HPE). Deeper analyses of candidate variants were then performed on a family-by-family basis. These included the exploration of clinical information, expression studies, variant characteristics, recurrence of mutated genes and available biological knowledge. A novel bioinformatics pipeline was designed. Applied to the 39 families, this final integrated workflow identified an average of 11 candidate variants per family. Most of candidate variants were inherited from asymptomatic parents suggesting a multigenic inheritance pattern requiring the association of multiple mutational events. The manual analysis highlighted 5 new strong HPE candidate genes showing recurrences in distinct families. Functional validations of these genes are foreseen.

Keywords: complex genetic disorder, holoprosencephaly, multiple rare variants, whole exome sequencing

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Authors: Hans Schumann, Colin Domnauer, Louis Rosenberg

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In this study, machine learning techniques were deployed on real-time human swarm data to forecast the likelihood of outcomes for English Premier League matches in the 2020/21 season. These techniques included ensemble models in combination with neural networks and were tested against an industry standard of Vegas Oddsmakers. Predictions made from the collective intelligence of human swarm participants managed to achieve a positive return on investment over a full season on matches, empirically proving the usefulness of a new artificial intelligence valuing human instinct and intelligence.

Keywords: artificial intelligence, data science, English Premier League, human swarming, machine learning, sports betting, swarm intelligence

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Authors: Amir Reza Talaghat

Abstract:

Since 2015, a new approach and policy regarding energy resources protection and using renewable energies has been started in Iran which was developing new projects. Investigating about the feasibility study of these new projects helped to figure out five steps to prepare an executive feasibility study of the concerned projects, which are proper site selections, authorizations, design and simulation, economic study and programming, respectively. The results were interesting and essential for decision makers and investors to start implementing of these projects in reliable condition. The research is obtained through collection and study of the project's documents as well as recalculation to review conformity of the results with GIS data and the technical information of the bidders. In this paper, it is attempted to describe the result of the performed research by describing the five steps as an executive methodology, for preparing a feasible study of installing a 10 MW – solar farm project. The corresponding results of the research also help decision makers to start similar projects is explained in this paper as follows: selecting the best location for the concerned PV plant, reliable and safe conditions for investment and the required authorizations to start implementing the solar farm project in the concerned region, selecting suitable component to achieve the best possible performance for the plant, economic profit of the investment, proper programming to implement the project on time.

Keywords: solar farm, solar energy, execution of PV power plant PV power plant

Procedia PDF Downloads 179