Search results for: ion torrent personal genome machine (PGM)

Authors: Narin Salehiyan, Ramin Ghasemi Shayan

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In order to investigate coronavirus RNA replication, transcription, recombination, protein processing and transport, virion assembly, the identification of coronavirus-specific cell receptors, and polymerase processing, the manipulation of coronavirus clones and complementary DNAs (cDNAs) of defective-interfering (DI) RNAs is the subject of this chapter. The idea of the Covid genome is nonsegmented, single-abandoned, and positive-sense RNA. When compared to other RNA viruses, its size is significantly greater, ranging from 27 to 32 kb. The quality encoding the enormous surface glycoprotein depends on 4.4 kb, encoding a forcing trimeric, profoundly glycosylated protein. This takes off exactly 20 nm over the virion envelope, giving the infection the appearance-with a little creative mind of a crown or coronet. Covid research has added to the comprehension of numerous parts of atomic science as a general rule, like the component of RNA union, translational control, and protein transport and handling. It stays a fortune equipped for creating startling experiences.

Keywords: covid-19, corona, virus, genome, genetic

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Anli Du Preez, James Bekker

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Data is currently one of the most critical and influential emerging technologies. However, the true potential of data is yet to be exploited since, currently, about 1% of generated data are ever actually analyzed for value creation. There is a data gap where data is not explored due to the lack of data analytics infrastructure and the required data analytics skills. This study developed a decision support framework for data analytics by following Jabareen’s framework development methodology. The study focused on machine learning algorithms, which is a subset of data analytics. The developed framework is designed to assist data analysts with little experience, in choosing the appropriate machine learning algorithm given the purpose of their application.

Keywords: Data analytics, Industrial engineering, Machine learning, Value creation

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Authors: Peter Adekunle, Clinton Aigbavboa, Matthew Ikuabe, Opeoluwa Akinradewo

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The future of machine learning (ML) in building may seem like a distant idea that will take decades to materialize, but it is actually far closer than previously believed. In reality, the built environment has been progressively increasing interest in machine learning. Although it could appear to be a very technical, impersonal approach, it can really make things more personable. Instead of eliminating humans out of the equation, machine learning allows people do their real work more efficiently. It is therefore vital to evaluate the factors influencing the implementation and challenges of implementing machine learning techniques in the South African built environment. The study's design was one of a survey. In South Africa, construction workers and professionals were given a total of one hundred fifty (150) questionnaires, of which one hundred and twenty-four (124) were returned and deemed eligible for study. Utilizing percentage, mean item scores, standard deviation, and Kruskal-Wallis, the collected data was analyzed. The results demonstrate that the top factors influencing the adoption of machine learning are knowledge level and a lack of understanding of its potential benefits. While lack of collaboration among stakeholders and lack of tools and services are the key hurdles to the deployment of machine learning within the South African built environment. The study came to the conclusion that ML adoption should be promoted in order to increase safety, productivity, and service quality within the built environment.

Keywords: machine learning, implementation, built environment, construction stakeholders

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Authors: Susan Marie R. De La Cruz, Bernadette F. De La Cruz, Georgia D. Demavibas

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This study determined the top 10 perceived students’ academic needs, personal needs, personal obstacles in achieving their academic goals, are as they need assistance, and their perceived feelings in math, reading and writing. The North Carolina State College, Student Support Services needs assessment survey was used. The respondents were the randomly chosen122Graduate school students. The top 10 academic needs are as follows: need to improve memory, communication skills, study habits, time management skills, career decisions, vocabulary, math skills, test taking skills, reading comprehension, and the need to reduce math anxiety. Top 10 personal needs are as follows: difficulty meeting deadline, difficulty managing money, inadequate computer skills, afraid of failing graduate school, difficulty participating in class/group discussions, absence from school, anxiety during exams, little or no experience with internet, personal counseling needs, and unsure of university academic procedures. Students’ top 10 perceived personal obstacles were as follows: issues surrounding sickness in family, lack of time management, lack of money, feeling tired, fears to speak in class, poor study habits, problems at home, late in class, too shy, and always feeling sick and easily distracted. Students felt need assistance in areas surrounding personal budget, stress management, motivation, anxiety, depressions, leadership development and goals/decision making. It is recommended that enrichment activities be provided to respond to students’ academic and personal needs. Also, Graduate School Guidance Counselor collaborates with other licensed Guidance Counselors in other colleges to have a well-coordinated and effective delivery of services responsive to students’ needs.

Keywords: academic needs, guidance counsellors, guidance service, needs assessment survey, personal needs, student services

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Authors: Guang Yang, Zhiwei Bai, Bo Sun

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This paper analyzes the influence of failure mode and harmfulness of rotative machine according to FMECA (Failure Mode, Effects, and Criticality Analysis) method, and finds out the weak links that affect the reliability of this equipment. Also in this paper, fault tree analysis software is used for quantitative and qualitative analysis, pointing out the main factors of failure of this equipment. Based on the experimental results, this paper puts forward corresponding measures for prevention and improvement, and fundamentally improves the inherent reliability of this rotative machine, providing the basis for the formulation of technical conditions for the safe operation of industrial applications.

Keywords: rotative machine, reliability test, fault tree analysis, FMECA

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Authors: Benjamen Nathaniel, Eludiora Safiriyu Ijiyemi, Egume Oneme Lucky

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A review on machine translation systems shows that a lot of computational artefacts has been carried out to translate written or spoken texts from a source language to Yorùbá language through Machine Translation systems. However, there are no work on French to Yorùbá language machine translation system; hence, the study investigated the process involved in the translation of French-to-Yorùbá language equivalent with the view to adopting a rule- based MT approach to build a Machine Translation framework from simple sentences administered through questionnaire. Articles and relevant textbooks were reviewed with key speakers of both languages interviewed to find out the processes involved in the translation of French language and their equivalent in Yorùbálanguage simple sentences using home domain terminologies. Achieving this, a model was formulated using phrase grammar structure, re-write rule, parse tree, automata theory- based techniques, designed and implemented respectively with unified modeling language (UML) and python programming language. Analysing the result, it was observed when carrying out the result that, the Machine Translation system performed 18.45% above Experimental Subject Respondent and 2.7% below Linguistics Expert when analysed with word orthography, sentence syntax and semantic correctness of the sentences. And, when compared with Google Machine Translation system, it was noticed that the developed system performed better on lexicons of the target language.

Keywords: machine translation (MT), rule-based, French language, Yoru`ba´ language

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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The widespread use of machine learning applications in production is significantly accelerated by improved computing power and increasing data availability. Predictive quality enables the assurance of product quality by using machine learning models as a basis for decisions on test results. The use of real Bosch production data based on geometric gauge blocks from machining, mating data from assembly and hydraulic measurement data from final testing of directional valves is a promising approach to classifying the quality characteristics of workpieces.

Keywords: machine learning, classification, predictive quality, hydraulics, supervised learning

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Authors: Khaled M. EL-Naggar

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Synchronizing and damping torque coefficients of a synchronous machine can give a quite clear picture for machine behavior during transients. These coefficients are used as a power system transient stability measurement. In this paper, a crow search optimization algorithm is presented and implemented to study the power system stability during transients. The algorithm makes use of the machine responses to perform the stability study in time domain. The problem is formulated as a dynamic estimation problem. An objective function that minimizes the error square in the estimated coefficients is designed. The method is tested using practical system with different study cases. Results are reported and a thorough discussion is presented. The study illustrates that the proposed method can estimate the stability coefficients for the critical stable cases where other methods may fail. The tests proved that the proposed tool is an accurate and reliable tool for estimating the machine coefficients for assessment of power system stability.

Keywords: optimization, estimation, synchronous, machine, crow search

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Authors: Boutheina Ben Abdelmoumen Mardassi, Salim Chibani, Safa Boujemaa, Amaury Vaysse, Julien Guglielmini, Elhem Yacoub

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Background and aim: Mycoplasma hominis (MH) is a pathogenic bacterium belonging to the Mollicutes class. It causes a wide range of gynecological infections and infertility among adults. Recently, we have explored for the first time the phylodistribution of Tunisian M. hominis clinical strains using an expanded MLST. We have demonstrated their distinction into two pure lineages, which each corresponding to a specific pathotype: genital infections and infertility. The aim of this project is to gain further insight into the evolutionary dynamics and the specific genetic factors that distinguish MH pathotypes Methods: Whole genome sequencing of Mycoplasma hominis clinical strains was performed using illumina Miseq. Denovo assembly was performed using a publicly available in-house pipeline. We used prokka to annotate the genomes, panaroo to generate the gene presence matrix and Jolytree to establish the phylogenetic tree. We used treeWAS to identify genetic loci associated with the pathothype of interest from the presence matrix and phylogenetic tree. Results: Our results revealed a clear categorization of the 62 MH clinical strains into two distinct genetic lineages, with each corresponding to a specific pathotype.; gynecological infections and infertility[AV1] . Genome annotation showed that GC content is ranging between 26 and 27%, which is a known characteristic of Mycoplasma genome. Housekeeping genes belonging to the core genome are highly conserved among our strains. TreeWas identified 4 virulence genes associated with the pathotype gynecological infection. encoding for asparagine--tRNA ligase, restriction endonuclease subunit S, Eco47II restriction endonuclease, and transcription regulator XRE (involved in tolerance to oxidative stress). Five genes have been identified that have a statistical association with infertility, tow lipoprotein, one hypothetical protein, a glycosyl transferase involved in capsule synthesis, and pyruvate kinase involved in biofilm formation. All strains harbored an efflux pomp that belongs to the family of multidrug resistance ABC transporter, which confers resistance to a wide range of antibiotics. Indeed many adhesion factors and lipoproteins (p120, p120', p60, p80, Vaa) have been checked and confirmed in our strains with a relatively 99 % to 96 % conserved domain and hypervariable domain that represent 1 to 4 % of the reference sequence extracted from gene bank. Conclusion: In summary, this study led to the identification of specific genetic loci associated with distinct pathotypes in M hominis.

Keywords: mycoplasma hominis, infertility, gynecological infections, virulence genes, antibiotic resistance

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Authors: Tung-Hui Hsu, Wen-Yuh Jywe

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Five-axis computer numerical control (CNC) machine tools (three linear and two rotary axes) are ideally suited to the fabrication of complex work pieces, such as dies, turbo blades, and cams. The locations of the axis average line and centerline of the rotary axes strongly influence the performance of these machines; however, techniques to compensate for eccentric error in the rotary axes remain weak. This paper proposes optical (Non-Bar) techniques capable of calibrating five-axis CNC machine tools and compensating for eccentric error in the rotary axes. This approach employs the measurement path in ISO/CD 10791-6 to determine the eccentric error in two rotary axes, for which compensatory measures can be implemented. Experimental results demonstrate that the proposed techniques can improve the performance of various five-axis CNC machine tools by more than 90%. Finally, a result of the cutting test using a B-type five-axis CNC machine tool confirmed to the usefulness of this proposed compensation technique.

Keywords: calibration, compensation, rotary axis, five-axis computer numerical control (CNC) machine tools, eccentric error, optical calibration system, ISO/CD 10791-6

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Authors: Kgaugelo Lekota, Ayesha Hassim, Henriette Van Heerden

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Bacillus anthracis is the causative agent of anthrax that is composed of three genetic groups, namely A, B, and C. Clade-A is distributed world-wide, while sub-clades B has been identified in Kruger National Park (KNP), South Africa. KNP is one of the endemic anthrax regions in South Africa with distinctive genetic diversity. Genomic surveillance of KNP B. anthracis strains was employed on the historical culture collection isolates (n=67) dated from the 1990’s to 2015 using a whole genome sequencing approach. Whole genome single nucleotide polymorphism (SNPs) and pan-genomics analysis were used to define the B. anthracis genetic population structure. This study showed that KNP has heterologous B. anthracis strains grouping in the A-clade with more prominent ABr.005/006 (Ancient A) SNP lineage. The 2012 and 2015 anthrax isolates are dispersed amongst minor sub-clades that prevail in non-stabilized genetic evolution strains. This was augmented with non-parsimony informative SNPs of the B. anthracis strains across minor sub-clades of the Ancient A clade. Pan-genomics of B. anthracis showed a clear distinction between A and B-clade genomes with 11 374 predicted clusters of protein coding genes. Unique accessory genes of B-clade genomes that included biosynthetic cell wall genes and multidrug resistant of Fosfomycin. South Africa consists of diverse B. anthracis strains with unique defined SNPs. The sequenced B. anthracis strains in this study will serve as a means to further trace the dissemination of B. anthracis outbreaks globally and especially in South Africa.

Keywords: bacillus anthracis, whole genome single nucleotide polymorphisms, pangenomics, kruger national park

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Authors: Abiodun Olayinka, Daniel Dzidzienyo, Pangirayi Tongoona, Samuel Offei, Edwige Gaby Nkouaya Mbanjo, Chiedozie Egesi, Ismail Yusuf Rabbi

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Cassava (Manihot esculenta Crantz) is a major source of starch for various industrial applications. However, the traditional cultivation and harvesting methods of cassava are labour-intensive and inefficient, limiting the supply of fresh cassava roots for industrial starch production. To achieve improved productivity and quality of fresh cassava roots through mechanized cultivation, cassava cultivars with compact plant architecture and moderate plant height are needed. Plant architecture-related traits, such as plant height, harvest index, stem diameter, branching angle, and lodging tolerance, are critical for crop productivity and suitability for mechanized cultivation. However, the genetics of cassava plant architecture remain poorly understood. This study aimed to identify the genetic bases of the relationships between plant architecture traits and productivity-related traits, particularly starch content. A panel of 453 clones developed at the International Institute of Tropical Agriculture, Nigeria, was genotyped and phenotyped for 18 plant architecture and productivity-related traits at four locations in Nigeria. A genome-wide association study (GWAS) was conducted using the phenotypic data from a panel of 453 clones and 61,238 high-quality Diversity Arrays Technology sequencing (DArTseq) derived Single Nucleotide Polymorphism (SNP) markers that are evenly distributed across the cassava genome. Five significant associations between ten SNPs and three plant architecture component traits were identified through GWAS. We found five SNPs on chromosomes 6 and 16 that were significantly associated with shoot weight, harvest index, and total yield through genome-wide association mapping. We also discovered an essential candidate gene that is co-located with peak SNPs linked to these traits in M. esculenta. A review of the cassava reference genome v7.1 revealed that the SNP on chromosome 6 is in proximity to Manes.06G101600.1, a gene that regulates endodermal differentiation and root development in plants. The findings of this study provide insights into the genetic basis of plant architecture and yield in cassava. Cassava breeders could leverage this knowledge to optimize plant architecture and yield in cassava through marker-assisted selection and targeted manipulation of the candidate gene.

Keywords: manihot esculenta crantz, plant architecture, dartseq, snp markers, genome-wide association study

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Authors: S. Ben Brahim, T. H. Vuong, J. David, R. Bouallegue, M. Pietrzak-David

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Electrical machine monitoring is important to protect motor from unexpected problems. Today, using wireless communication for electrical machines is interesting for both real time monitoring and diagnostic purposes. In this paper, we propose a system based on wireless communication IEEE 802.11 to control electrical machine. IEEE 802.11 standard is recommended for this type of applications because it provides a faster connection, better range from the base station, and better security. Therefore, our contribution is to study a new technique to control and monitor the rotating electrical machines (motors, generators) using wireless communication. The reliability of radio channel inside rotating electrical machine is also discussed. Then, the communication protocol, software and hardware design used for the proposed system are presented in detail and the experimental results of our system are illustrated.

Keywords: control, DFIM machine, electromagnetic field, EMC, IEEE 802.11, monitoring, rotating electrical machines, wireless communication

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Authors: R. Sulakshana, R. Lakshmi

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Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR associate (CRISPR/Cas) is an adaptive immunity system found in bacteria and archaea. It has been modified to serve as a potent gene editing tool. Moreover, it has found widespread use in the field of genome research because of its accessibility and low cost. Several bioinformatics methods have been created to aid in the construction of specific single guide RNA (sgRNA), which is highly active and crucial to CRISPR/Cas performance. Various Cas proteins, including Cas1, Cas2, Cas9, and Cas12, have been used to create genome engineering tools because of their programmable sequence specificity. Class 1 and 2 CRISPR/Cas systems, as well as the processes of all known Cas proteins (including Cas9 and Cas12), are discussed in this review paper. In addition, the various CRISPR methodologies and their tools so far discovered are discussed. Finally, the challenges and issues in the CRISPR system along with future works, are presented.

Keywords: gene editing tool, Cas proteins, CRISPR, guideRNA, programmable sequence

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Authors: Farahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii

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A huge revolution has emerged in genome engineering by the discovery of CRISPR (clustered regularly interspaced palindromic repeats) and CRISPR-associated system genes (Cas) in bacteria. The function of type II Streptococcus pyogenes (Sp) CRISPR/Cas9 system has been confirmed in various species. Other S. thermophilus (St) CRISPR-Cas systems, CRISPR1-Cas and CRISPR3-Cas, have been also reported for preventing phage infection. The CRISPR1-Cas system interferes by cleaving foreign dsDNA entering the cell in a length-specific and orientation-dependant manner. The S. thermophilus CRISPR3-Cas system also acts by cleaving phage dsDNA genomes at the same specific position inside the targeted protospacer as observed in the CRISPR1-Cas system. It is worth mentioning, for the effective DNA cleavage activity, RNA-guided Cas9 orthologs require their own specific PAM (protospacer adjacent motif) sequences. Activity levels are based on the sequence of the protospacer and specific combinations of favorable PAM bases. Therefore, based on the specific length and sequence of PAM followed by a constant length of target site for the three orthogonals of Cas9 protein, a well-organized procedure will be required for high-throughput and accurate mining of possible target sites in a large genomic dataset. Consequently, we created a reliable procedure to explore potential gRNA sequences for type I (Streptococcus thermophiles), II (Streptococcus pyogenes), and III (Streptococcus thermophiles) CRISPR/Cas systems. To mine CRISPR target sites, four different searching modes of sgRNA binding to target DNA strand were applied. These searching modes are as follows: i) coding strand searching, ii) anti-coding strand searching, iii) both strand searching, and iv) paired-gRNA searching. The output of such procedure highlights the power of comparative genome mining for different CRISPR/Cas systems. This could yield a repertoire of Cas9 variants with expanded capabilities of gRNA design, and will pave the way for further advance genome and epigenome engineering.

Keywords: CRISPR/Cas systems, gRNA mining, Streptococcus pyogenes, Streptococcus thermophiles

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Authors: Amel Ourici

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An electrical machine failure that is not identified in an initial stage may become catastrophic and it may suffer severe damage. Thus, undetected machine faults may cascade in it failure, which in turn may cause production shutdowns. Such shutdowns are costly in terms of lost production time, maintenance costs, and wasted raw materials. Doubly fed induction generators are used mainly for wind energy conversion in MW power plants. This paper presents a detection of an inter turn stator fault in a doubly fed induction machine whose stator and rotor are supplied by two pulse width modulation (PWM) inverters. The method used in this article to detect this fault, is based on Park’s Vector Approach, using a neural network.

Keywords: doubly fed induction machine, PWM inverter, inter turn stator fault, Park’s vector approach, neural network

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Authors: Maria Jędrzejczak, Patryk Pieniążek

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The European legal reality is on the eve of significant change. In European Union law, there is talk of a “fourth industrial revolution”, which is driven by massive data resources linked to powerful algorithms and powerful computing capacity. The above is closely linked to technological developments in the area of artificial intelligence, which has prompted an analysis covering both the legal environment as well as the economic and social impact, also from an ethical perspective. The discussion on the regulation of artificial intelligence is one of the most serious yet widely held at both European Union and Member State level. The literature expects legal solutions to guarantee security for fundamental rights, including privacy, in artificial intelligence systems. There is no doubt that personal data have been increasingly processed in recent years. It would be impossible for artificial intelligence to function without processing large amounts of data (both personal and non-personal). The main driving force behind the current development of artificial intelligence is advances in computing, but also the increasing availability of data. High-quality data are crucial to the effectiveness of many artificial intelligence systems, particularly when using techniques involving model training. The use of computers and artificial intelligence technology allows for an increase in the speed and efficiency of the actions taken, but also creates security risks for the data processed of an unprecedented magnitude. The proposed regulation in the field of artificial intelligence requires analysis in terms of its impact on the regulation on personal data protection. It is necessary to determine what the mutual relationship between these regulations is and what areas are particularly important in the personal data protection regulation for processing personal data in artificial intelligence systems. The adopted axis of considerations is a preliminary assessment of two issues: 1) what principles of data protection should be applied in particular during processing personal data in artificial intelligence systems, 2) what regulation on liability for personal data breaches is in such systems. The need to change the regulations regarding the rights and obligations of data subjects and entities processing personal data cannot be excluded. It is possible that changes will be required in the provisions regarding the assignment of liability for a breach of personal data protection processed in artificial intelligence systems. The research process in this case concerns the identification of areas in the field of personal data protection that are particularly important (and may require re-regulation) due to the introduction of the proposed legal regulation regarding artificial intelligence. The main question that the authors want to answer is how the European Union regulation against data protection breaches in artificial intelligence systems is shaping up. The answer to this question will include examples to illustrate the practical implications of these legal regulations.

Keywords: data protection law, personal data, AI law, personal data breach

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Authors: Pawel Twardowski, Maciej Tabaszewski, Jakub Czyżycki

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The main goal of the work was to use machine learning to predict cutting-edge wear. The research was carried out while milling hardened steel with sintered carbide cutters at various cutting speeds. During the tests, cutting-edge wear was measured, and vibration acceleration signals were also measured. Appropriate measures were determined from the vibration signals and served as input data in the machine-learning process. Two approaches were used in this work. The first one involved a two-state classification of the cutting edge - suitable and unfit for further work. In the second approach, prediction of the cutting-edge state based on vibration signals was used. The obtained research results show that the appropriate use of machine learning algorithms gives excellent results related to monitoring cutting edge during the process.

Keywords: milling of hardened steel, tool wear, vibrations, machine learning

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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: Yanhua Ma, Lu Zhai, Xinchen Wang, Hongyu Liang

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In this paper, by understanding the development status of the human-machine interface in today's automobile cab, a subjective and objective evaluation system for evaluating the optimization of human-machine interface matching in automobile cab was established. The man-machine interface of the car cab was divided into a software interface and a hard interface. Objective evaluation method of software human factor analysis is used to evaluate the hard interface matching; The analytic hierarchy process is used to establish the evaluation index system for the software interface matching optimization, and the multi-level fuzzy comprehensive evaluation method is used to evaluate hard interface machine. This article takes Dongfeng Sokon (DFSK) C37 model automobile as an example. The evaluation method given in the paper is used to carry out relevant analysis and evaluation, and corresponding optimization suggestions are given, which have certain reference value for designers.

Keywords: analytic hierarchy process, fuzzy comprehension evaluation method, human-machine interface, matching optimization, software human factor analysis

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Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

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Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

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Authors: Gulderen Saglam

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This study explores the elements of personal motivation which influence professional motivation of in-service English language teachers in Bursa in Turkey. Fifty English language teachers participated in a seminar held on ‘teachers’ motivation’ for the length of six hours in two days, which were organized by the local Ministry of Education. During the seminar, teachers firstly aimed to share cornerstones of their professional motivation. Later, those teachers stated the significance of their personal motivation. Two months’ later, those teachers were given the questionnaire including both closed and open-ended questions involving those two types of motivational acts of teachers. Questionnaire items were tested by Crombah’s Alfa Reliability Statistics. Responses to the questionnaire were analyzed by factor analysis and test of normality. The results were also tested by non-parametric and parametric tests. As a result, it was found that language teachers who were personally motivated reported higher professional motivation of theirs in teaching profession in-service.

Keywords: influencing factor, in-service-teachers, personal motivation, professional motivation, in-service-teachers, influencing factor

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Authors: Kan Yu, Khui Hung Lee, Eben Afrifa-Yamoah, Jing Guo, Katrina Harrison, Jack Goldblatt, Nicholas Pachter, Jitian Xiao, Guicheng Brad Zhang

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Background and Significance of the Study: Congenital Heart Defects (CHDs) are the most common malformation at birth and one of the leading causes of infant death. Although the exact etiology remains a significant challenge, epigenetic modifications, such as DNA methylation, are thought to contribute to the pathogenesis of congenital heart defects. At present, no existing DNA methylation biomarkers are used for early detection of CHDs. The existing CHD diagnostic techniques are time-consuming and costly and can only be used to diagnose CHDs after an infant was born. The present study employed a machine learning technique to analyse genome-wide methylation data in children with and without CHDs with the aim to find methylation biomarkers for CHDs. Methods: The Illumina Human Methylation EPIC BeadChip was used to screen the genome‐wide DNA methylation profiles of 24 infants diagnosed with congenital heart defects and 24 healthy infants without congenital heart defects. Primary pre-processing was conducted by using RnBeads and limma packages. The methylation levels of top 600 genes with the lowest p-value were selected and further investigated by using a random forest approach. ROC curves were used to analyse the sensitivity and specificity of each biomarker in both training and test sample sets. The functionalities of selected genes with high sensitivity and specificity were then assessed in molecular processes. Major Findings of the Study: Three genes (MIR663, FGF3, and FAM64A) were identified from both training and validating data by random forests with an average sensitivity and specificity of 85% and 95%. GO analyses for the top 600 genes showed that these putative differentially methylated genes were primarily associated with regulation of lipid metabolic process, protein-containing complex localization, and Notch signalling pathway. The present findings highlight that aberrant DNA methylation may play a significant role in the pathogenesis of congenital heart defects.

Keywords: biomarker, congenital heart defects, DNA methylation, random forest

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Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

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This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

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Authors: E. K. A. Ogunshile

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This paper is aimed at creating an Automatic Java X-Machine testing tool for software development. The nature of software development is changing; thus, the type of software testing tools required is also changing. Software is growing increasingly complex and, in part due to commercial impetus for faster software releases with new features and value, increasingly in danger of containing faults. These faults can incur huge cost for software development organisations and users; Cambridge Judge Business School’s research estimated the cost of software bugs to the global economy is $312 billion. Beyond the cost, faster software development methodologies and increasing expectations on developers to become testers is driving demand for faster, automated, and effective tools to prevent potential faults as early as possible in the software development lifecycle. Using X-Machine theory, this paper will explore a new tool to address software complexity, changing expectations on developers, faster development pressures and methodologies, with a view to reducing the huge cost of fixing software bugs.

Keywords: conformance testing, finite state machine, software testing, x-machine

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Authors: Akinfiresoye W. A., Olarewaju O. O., Okunola, Okunola I. O.

Abstract:

An indigenous honey-extracting machine was designed, fabricated and evaluated at the workshop of the department of Agricultural Technology, Federal Polytechnic, Ile-Oluji, Nigeria using locally available materials. It has the extraction unit, the presser, the honey collector and the frame. The harvested honeycomb is placed inside the cylindrical extraction unit with perforated holes. The press plate was then placed on the comb while the hydraulic press of 3 tons was placed on it, supported by the frame. The hydraulic press, which is manually operated, forces the oil out of the extraction chamber through the perforated holes into the honey collector positioned at the lowest part of the extraction chamber. The honey-extracting machine has an average throughput of 2.59 kg/min and an efficiency of about 91%. The cost of producing the honey extracting machine is NGN 31, 700: 00, thirty-one thousand and seven hundred nairas only or $70 at NGN 452.8 to a dollar. This cost is affordable to beekeepers and would-be honey entrepreneurs. The honey-extracting machine is easy to operate and maintain without any complex technical know-how.

Keywords: honey, extractor, cost, efficiency

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Authors: S. Jagadeesh Kumar

Abstract:

This expose presents a luculent of recent machine learning practices as employed in the modern and as pertinent to prospective automatic speech recognition schemes. The aspiration is to promote additional traverse ablution among the machine learning and automatic speech recognition factions that have transpired in the precedent. The manuscript is structured according to the chief machine learning archetypes that are furthermore trendy by now or have latency for building momentous hand-outs to automatic speech recognition expertise. The standards offered and convoluted in this article embraces adaptive and multi-task learning, active learning, Bayesian learning, discriminative learning, generative learning, supervised and unsupervised learning. These learning archetypes are aggravated and conferred in the perspective of automatic speech recognition tools and functions. This manuscript bequeaths and surveys topical advances of deep learning and learning with sparse depictions; further limelight is on their incessant significance in the evolution of automatic speech recognition.

Keywords: automatic speech recognition, deep learning methods, machine learning archetypes, Bayesian learning, supervised and unsupervised learning

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Authors: Jae Hyun Park, Sangmi Chai, Minkyun Kim

Abstract:

As users spend more time on the Internet, the probability of their personal information being exposed has been growing. This research has a main purpose of investigating factors and examining relationships when Internet users recognize their value of private information with a perspective of an economic asset. The study is targeted on Internet users, and the value of their private information will be converted into economic figures. Moreover, how economic value changes in relation with individual attributes, dealer’s traits, circumstantial properties will be studied. In this research, the changes in factors on private information value responding to different situations will be analyzed in an economic perspective. Additionally, this study examines the associations between users’ perceived risk and value of their personal information. By using the cost-benefit analysis framework, the hypothesis that the user’s sense in private information value can be influenced by individual attributes and situational properties will be tested. Therefore, this research will attempt to provide answers for three research objectives. First, this research will identify factors that affect value recognition of users’ personal information. Second, it provides evidences that there are differences on information system users’ economic value of information responding to personal, trade opponent, and situational attributes. Third, it investigates the impact of those attributes on individuals’ perceived risk. Based on the assumption that personal, trade opponent and situation attributes make an impact on the users’ value recognition on private information, this research will present the understandings on the different impacts of those attributes in recognizing the value of information with the economic perspective and prove the associative relationships between perceived risk and decision on the value of users’ personal information. In order to validate our research model, this research used the regression methodology. Our research results support that information breach experience and information security systems is associated with users’ perceived risk. Information control and uncertainty are also related to users’ perceived risk. Therefore, users’ perceived risk is considered as a significant factor on evaluating the value of personal information. It can be differentiated by trade opponent and situational attributes. This research presents new perspective on evaluating the value of users’ personal information in the context of perceived risk, personal, trade opponent and situational attributes. It fills the gap in the literature by providing how users’ perceived risk are associated with personal, trade opponent and situation attitudes in conducting business transactions with providing personal information. It adds to previous literature that the relationship exists between perceived risk and the value of users’ private information in the economic perspective. It also provides meaningful insights to the managers that in order to minimize the cost of information breach, managers need to recognize the value of individuals’ personal information and decide the proper amount of investments on protecting users’ online information privacy.

Keywords: private information, value, users, perceived risk, online information privacy, attributes

Procedia PDF Downloads 198

Authors: M. Al-Farttoosi, M. Y. Rafiq, J. Summerscales, C. Williams

Abstract:

Structures can be subjected to impact loading from various sources like earthquake, tsunami, missiles and explosions. The impact loading can cause different degrees of damage to concrete structures. The demand for strengthening and rehabilitation of damaged structures is increasing. In recent years, Car0bon Fibre Reinforced Polymer (CFRP) matrix composites has gain more attention for strengthening and repairing these structures. To study the impact behaviour of the reinforced concrete (RC) beams strengthened or repaired using CFRP, a heavy impact test machine was designed and manufactured .The machine included a newly designed support system for beams together with various instrumentation. This paper describes the support design configuration of the impact test machine, instrumentation and dynamic analysis of the concrete beams. To evaluate the efficiency of the new impact test machine, experimental impact tests were conducted on simple supported reinforced concrete beam. Different methods were used to determine the impact force and impact response of the RC beams in terms of inertia force, maximum deflection, reaction force and fracture energy. The manufactured impact test machine was successfully used in testing RC beams under impact loading and used successfully to test the reinforced concrete beams strengthened or repaired using CFRP under impact loading.

Keywords: beam, concrete, impact, machine

Procedia PDF Downloads 390