Search results for: machine tools

Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: semantic links, data mining, linked data, SKOS

Procedia PDF Downloads 157

Authors: Rohan Bhasin

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Agrammatism in non-fluent Aphasia Cases can be defined as a language disorder wherein a patient can only use content words ( nouns, verbs and adjectives ) for communication and their speech is devoid of functional word types like conjunctions and articles, generating speech of with extremely rudimentary grammar . Past approaches involve Speech Therapy of some order with conversation analysis used to analyse pre-therapy speech patterns and qualitative changes in conversational behaviour after therapy. We describe this approach as a novel method to generate functional words (prepositions, articles, ) around content words ( nouns, verbs and adjectives ) using a combination of Natural Language Processing and Deep Learning algorithms. The applications of this approach can be used to assist communication. The approach the paper investigates is : LSTMs or Seq2Seq: A sequence2sequence approach (seq2seq) or LSTM would take in a sequence of inputs and output sequence. This approach needs a significant amount of training data, with each training data containing pairs such as (content words, complete sentence). We generate such data by starting with complete sentences from a text source, removing functional words to get just the content words. However, this approach would require a lot of training data to get a coherent input. The assumptions of this approach is that the content words received in the inputs of both text models are to be preserved, i.e, won't alter after the functional grammar is slotted in. This is a potential limit to cases of severe Agrammatism where such order might not be inherently correct. The applications of this approach can be used to assist communication mild Agrammatism in non-fluent Aphasia Cases. Thus by generating these function words around the content words, we can provide meaningful sentence options to the patient for articulate conversations. Thus our project translates the use case of generating sentences from content-specific words into an assistive technology for non-Fluent Aphasia Patients.

Keywords: aphasia, expressive aphasia, assistive algorithms, neurology, machine learning, natural language processing, language disorder, behaviour disorder, sequence to sequence, LSTM

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Authors: Sastry Y. Kandukuri, Isak Andersen

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Despite recent advances in the manufacturing sector, quality issues remain a frequent occurrence, and can result in fatal accidents, equipment downtime, and loss of life. Adequate quality is of high importance in high-risk industries such as sea-going vessels and offshore installations in which third party quality assurance and product control play an important essential role in ensuring manufacturing quality of critical components. Classification societies play a vital role in mitigating risk in these industries by making sure that all the stakeholders i.e. manufacturers, builders, and end users are provided with adequate rules and standards that effectively ensures components produced at a high level of quality based on the area of application and risk of its failure. Quality issues have also been linked to the lack of competence or negligence of stakeholders in supply value chain. However, continued actions and regulatory reforms through modernization of rules and requirements has provided additional tools for purchasers and manufacturers to confront these issues. Included among these tools are updated ‘approval of manufacturer class programs’ aimed at developing and implementing a set of standardized manufacturing quality metrics for use by the manufacturer and verified by the classification society. The establishment and collection of manufacturing and testing requirements described in these programs could provide various stakeholders – from industry to vessel owners – with greater insight into the state of quality at a given manufacturing facility, and allow stakeholders to anticipate better and address quality issues while simultaneously reducing unnecessary failures that are costly to the industry. The publication introduces, explains and discusses critical manufacturing and testing requirements set in a leading class society’s approval of manufacturer regime and its rationale and some case studies.

Keywords: classification society, manufacturing, materials processing, materials testing, quality control

Procedia PDF Downloads 335

Authors: Reshma Raskar-Phule, Makarand Landge, Saurabh Singh, Vijay Singh, Jash Saparia, Shivam Tripathi

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For any construction project, slope calculations are necessary in order to evaluate constructability on the site, such as the slope of parking lots, sidewalks, and ramps, the slope of sanitary sewer lines, slope of roads and highways. When slopes and grades are to be determined, designers are concerned with establishing proper slopes and grades for their projects to assess cut and fill volume calculations and determine inverts of pipes. There are several established instruments commonly used to determine slopes, such as Dumpy level, Abney level or Hand Level, Inclinometer, Tacheometer, Henry method, etc., and surveyors are very familiar with the use of these instruments to calculate slopes. However, they have some other drawbacks which cannot be neglected while major surveying works. Firstly, it requires expert surveyors and skilled staff. The accessibility, visibility, and accommodation to remote hilly terrain with these instruments and surveying teams are difficult. Also, determination of gentle slopes in case of road and sewer drainage constructions in congested urban places with these instruments is not easy. This paper aims to develop a method that requires minimum field work, minimum instruments, no high-end technology or instruments or software, and low cost. It requires basic and handy surveying accessories like a plane table with a fixed weighing machine, standard weights, alidade, tripod, and ranging rods should be able to determine the terrain slope in congested areas as well as in remote hilly terrain. Also, being simple and easy to understand and perform the people of that local rural area can be easily trained for the proposed method. The idea for the proposed method is based on the principle of resolution of weight components. When any object of standard weight ‘W’ is placed on an inclined surface with a weighing machine below it, then its cosine component of weight is presently measured by that weighing machine. The slope can be determined from the relation between the true or actual weight and the apparent weight. A proper procedure is to be followed, which includes site location, centering and sighting work, fixing the whole set at the identified station, and finally taking the readings. A set of experiments for slope determination, mild and moderate slopes, are carried out by the proposed method and by the theodolite instrument in a controlled environment, on the college campus, and uncontrolled environment actual site. The slopes determined by the proposed method were compared with those determined by the established instruments. For example, it was observed that for the same distances for mild slope, the difference in the slope obtained by the proposed method and by the established method ranges from 4’ for a distance of 8m to 2o15’20” for a distance of 16m for an uncontrolled environment. Thus, for mild slopes, the proposed method is suitable for a distance of 8m to 10m. The correlation between the proposed method and the established method shows a good correlation of 0.91 to 0.99 for various combinations, mild and moderate slope, with the controlled and uncontrolled environment.

Keywords: surveying, plane table, weight component, slope determination, hilly terrain, construction

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Authors: Marianna Ciaccia, Gennaro Agrimi, Isabella Pisano, Maurizio Bettiga, Silvia Rapacioli, Giulia Mensa, Monica Marzagalli

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Objective: Untargeted metabolomic analysis of extracellular metabolites is a powerful approach that focuses on comprehensively profiling in the extracellular space. In this study, we applied extracellular metabolomic analysis to investigate the metabolism of two probiotic microorganisms with health benefits that extend far beyond the digestive tract and the immune system. Methods: Analytical techniques employed in extracellular metabolomic analysis encompass various technologies, including mass spectrometry (MS), which enables the identification of metabolites present in the fermentation media, as well as the comparison of metabolic profiles under different experimental conditions. Multivariate statistical analysis techniques like principal component analysis (PCA) or partial least squares-discriminant analysis (PLS-DA) play a crucial role in uncovering metabolic signatures and understanding the dynamics of metabolic networks. Results: Different types of supernatants from fermentation processes, such as dairy-free, not dairy-free media and media with no cells or pasteurized, were subjected to metabolite profiling, which contained a complex mixture of metabolites, including substrates, intermediates, and end-products. This profiling provided insights into the metabolic activity of the microorganisms. The integration of advanced software tools has facilitated the identification and characterization of metabolites in different fermentation conditions and microorganism strains. Conclusions: In conclusion, untargeted extracellular metabolomic analysis, combined with software tools, allowed the study of the metabolites consumed and produced during the fermentation processes of probiotic microorganisms. Ongoing advancements in data analysis methods will further enhance the application of extracellular metabolomic analysis in fermentation research, leading to improved bioproduction and the advancement of sustainable manufacturing processes.

Keywords: biotechnology, metabolomics, lactic bacteria, probiotics, postbiotics

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Authors: S. Cowley, M. Lawrance, D. Bick, R. McCord

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Technology that automates manual handling (musculoskeletal disorder or MSD) risk assessments is increasingly available to ergonomists, engineers, generalist health and safety practitioners alike. The risk assessment process is generally based on the use of wearable motion sensors that capture information about worker movements for real-time or for posthoc analysis. Traditionally, MSD risk assessment is undertaken with the assistance of a checklist such as that from the SafeWork Australia code of practice, the expert assessor observing the task and ideally engaging with the worker in a discussion about the detail. Automation enables the non-expert to complete assessments and does not always require the assessor to be there. This clearly has cost and time benefits for the practitioner but is it an improvement on the assessment by the human. Human risk assessments draw on the knowledge and expertise of the assessor but, like all risk assessments, are highly subjective. The complexity of the checklists and models used in the process can be off-putting and sometimes will lead to the assessment becoming the focus and the end rather than a means to an end; the focus on risk control is lost. Automated risk assessment handles the complexity of the assessment for the assessor and delivers a simple risk score that enables decision-making regarding risk control. Being machine-based, they are objective and will deliver the same each time they assess an identical task. However, the WHS professional needs to know that this emergent technology asks the right questions and delivers the right answers. Whether it improves the risk assessment process and results or simply distances the professional from the task and the worker. They need clarity as to whether automation of manual task risk analysis and reporting leads to risk control or to a focus on the worker. Critically, they need evidence as to whether automation in this area of hazard management leads to better risk control or just a bigger collection of assessments. Practitioner experienced determinants of this automated manual task risk analysis and reporting being a benefit or distraction will address an understanding of emergent risk assessment technology, its use and things to consider when making decisions about adopting and applying these technologies.

Keywords: automated, manual-handling, risk-assessment, machine-based

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Authors: Hind Bouami, Patrick Millot

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Medication dispensing system is a life-critical system whose failure may result in preventable adverse events leading to longer patient stays in hospitals or patient death. Automation has led to great improvements in life-critical systems as it increased safety, efficiency, and comfort. However, critical risks related to medical organization complexity and automated solutions integration can threaten drug dispensing security and performance. Knowledge about the system’s complexity aspects and human machine parameters to control for automated equipment’s security and performance will help operators to secure their automation process and to optimize their system’s reliability. In this context, this study aims to document the operator’s situation awareness about automation risks and parameters involved in automation security and performance. Our risk management approach has been deployed in the North Luxembourg hospital center’s pharmacy, which is equipped with automated drug dispensing systems since 2009. With more than 4 million euros of gains generated, North Luxembourg hospital center’s success story was enabled by the management commitment, pharmacy’s involvement in the implementation and improvement of the automation project, and the close collaboration between the pharmacy and Sinteco’s firm to implement the necessary innovation and organizational actions for automated solutions integration security and performance. An analysis of the actions implemented by the hospital and the parameters involved in automated equipment’s integration security and performance has been made. The parameters to control for automated equipment’s integration security and performance are human aspects (6.25%), technical aspects (50%), and human-machine interaction (43.75%). The implementation of an anthropocentric analysis system before automation would have prevented and optimized the control of risks related to automation.

Keywords: Automated drug delivery systems, Hospitals, Human-centered automated system, Risk management

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Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

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Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: Saeed Musaid Alzahrani

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Shared values, beliefs, norms and assumptions within the organisation can affect personal and team effectiveness. Organisational culture can also affect the performance of organisational members. The nature of middle leadership in a primary school is largely influenced by organizational culture. The effectiveness of middle leadership in primary schools and their performance is strongly determined by the circumstances in which they work and can be political or institutional. This study aims to examine the influence of organisational culture and government policy on the performance and effectiveness of middle managers, using the English and Saudi education systems as case studies. To examine how education policy conditions educational discourse, and answer the research questions, there is a need to collect qualitative data on middle manager’s perceptions and experiences in the English and Saudi Arabian contexts. The study involved a qualitative and interpretative approach. In-depth interviews with 6 middle managers and school supervisors in 3 English primary schools and 6 middle managers in 3 Saudi Arabian primary schools were conducted to answer the research questions. The study also included ethnographic tools such as observations of a sample of three primary schools in both England and Saudi Arabia where the researcher observed middle managers’ interactions with their peers. The sample of three enabled the study to identify trends and make comparisons between leadership approaches in both systems based on observations without the bias of prescriptions. The use of ethnographic tools not only makes the study empirical but also increases the reliability and validity of the findings by reducing prescriptive bias. The observations will be triangulated with the results of the interviews to draw comparisons and conclusions on whether middle managers act as leaders or as followers in their respective political contexts.

Keywords: education management, government education policies, middle managers, organisational culture

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Authors: Duncan Wallace, M-Tahar Kechadi

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In recent years, Machine Learning (ML) approaches have been successfully applied to an analysis of patient symptom data in the context of disease diagnosis, at least where such data is well codified. However, much of the data present in Electronic Health Records (EHR) are unlikely to prove suitable for classic ML approaches. Furthermore, as scores of data are widely spread across both hospitals and individuals, a decentralized, computationally scalable methodology is a priority. The focus of this paper is to develop a method to predict outliers in an out-of-hours healthcare provision center (OOHC). In particular, our research is based upon the early identification of patients who have underlying conditions which will cause them to repeatedly require medical attention. OOHC act as an ad-hoc delivery of triage and treatment, where interactions occur without recourse to a full medical history of the patient in question. Medical histories, relating to patients contacting an OOHC, may reside in several distinct EHR systems in multiple hospitals or surgeries, which are unavailable to the OOHC in question. As such, although a local solution is optimal for this problem, it follows that the data under investigation is incomplete, heterogeneous, and comprised mostly of noisy textual notes compiled during routine OOHC activities. Through the use of Deep Learning methodologies, the aim of this paper is to provide the means to identify patient cases, upon initial contact, which are likely to relate to such outliers. To this end, we compare the performance of Long Short-Term Memory, Gated Recurrent Units, and combinations of both with Convolutional Neural Networks. A further aim of this paper is to elucidate the discovery of such outliers by examining the exact terms which provide a strong indication of positive and negative case entries. While free-text is the principal data extracted from EHRs for classification, EHRs also contain normalized features. Although the specific demographical features treated within our corpus are relatively limited in scope, we examine whether it is beneficial to include such features among the inputs to our neural network, or whether these features are more successfully exploited in conjunction with a different form of a classifier. In this section, we compare the performance of randomly generated regression trees and support vector machines and determine the extent to which our classification program can be improved upon by using either of these machine learning approaches in conjunction with the output of our Recurrent Neural Network application. The output of our neural network is also used to help determine the most significant lexemes present within the corpus for determining high-risk patients. By combining the confidence of our classification program in relation to lexemes within true positive and true negative cases, with an inverse document frequency of the lexemes related to these cases, we can determine what features act as the primary indicators of frequent-attender and non-frequent-attender cases, providing a human interpretable appreciation of how our program classifies cases.

Keywords: artificial neural networks, data-mining, machine learning, medical informatics

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Authors: D. Zhigulskaya, V. Anisimov, A. Pikunov, K. Babanova, S. Zuev, A. Latyshkova, K. Сhernozatonskiy, A. Revazov

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Electrophysiology of information processing in reading is certainly a popular research topic. Natural reading, however, has been relatively poorly studied, despite having broad potential applications for learning and education. In the current study, we explore the relationship between text categories and spontaneous electroencephalogram (EEG) while reading. Thirty healthy volunteers (mean age 26,68 ± 1,84) participated in this study. 15 Russian-language texts were used as stimuli. The first text was used for practice and was excluded from the final analysis. The remaining 14 were opposite pairs of texts in one of 7 categories, the most important of which were: interesting/boring, fiction/non-fiction, free reading/reading with an instruction, reading a text/reading a pseudo text (consisting of strings of letters that formed meaningless words). Participants had to read the texts sequentially on an Apple iPad Pro. EEG was recorded from 12 electrodes simultaneously with eye movement data via ARKit Technology by Apple. EEG spectral amplitude was analyzed in Fz for theta-band (4-8 Hz) and in C3, C4, P3, and P4 for alpha-band (8-14 Hz) using the Friedman test. We found that reading an interesting text was accompanied by an increase in theta spectral amplitude in Fz compared to reading a boring text (3,87 µV ± 0,12 and 3,67 µV ± 0,11, respectively). When instructions are given for reading, we see less alpha activity than during free reading of the same text (3,34 µV ± 0,20 and 3,73 µV ± 0,28, respectively, for C4 as the most representative channel). The non-fiction text elicited less activity in the alpha band (C4: 3,60 µV ± 0,25) than the fiction text (C4: 3,66 µV ± 0,26). A significant difference in alpha spectral amplitude was also observed between the regular text (C4: 3,64 µV ± 0,29) and the pseudo text (C4: 3,38 µV ± 0,22). These results suggest that some brain activity we see on EEG is sensitive to particular features of the text. We propose that changes in theta and alpha bands during reading may serve as electrophysiological tools for assessing the reader’s cognitive state as well as his or her attitude to the text and the perceived information. These physiological markers have prospective practical value for developing technological solutions and biofeedback systems for reading in particular and for education in general.

Keywords: EEG, natural reading, reader's cognitive state, theta-rhythm, alpha-rhythm

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Rezvan Ravanfar Haghighi, V. C. Vani, Suresh Perumal, Sabyasachi Chatterjee, Pratik Kumar

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X-ray attenuation coefficient [µ(E)] of any substance, for energy (E), is a sum of the contributions from the Compton scattering [ μCom(E)] and photoelectric effect [µPh(E)]. In terms of the, electron density (ρe) and the effective atomic number (Zeff) we have µCom(E) is proportional to [(ρe)fKN(E)] while µPh(E) is proportional to [(ρeZeffx)/Ey] with fKN(E) being the Klein-Nishina formula, with x and y being the exponents for photoelectric effect. By taking the sample's HU at two different excitation voltages (V=V1, V2) of the CT machine, we can solve for X=ρe, Y=ρeZeffx from these two independent equations, as is attempted in DECT inversion. Since µCom(E) and µPh(E) are both energy dependent, the coefficients of inversion are also dependent on (a) the source spectrum S(E,V) and (b) the detector efficiency D(E) of the CT machine. In the present paper we tabulate these coefficients of inversion for different practical manifestations of S(E,V) and D(E). The HU(V) values from the CT follow: <µ(V)>=<µw(V)>[1+HU(V)/1000] where the subscript 'w' refers to water and the averaging process <….> accounts for the source spectrum S(E,V) and the detector efficiency D(E). Linearity of μ(E) with respect to X and Y implies that (a) <µ(V)> is a linear combination of X and Y and (b) for inversion, X and Y can be written as linear combinations of two independent observations <µ(V1)>, <µ(V2)> with V1≠V2. These coefficients of inversion would naturally depend upon S(E, V) and D(E). We numerically investigate this dependence for some practical cases, by taking V = 100 , 140 kVp, as are used for cardiological investigations. The S(E,V) are generated by using the Boone-Seibert source spectrum, being superposed on aluminium filters of different thickness lAl with 7mm≤lAl≤12mm and the D(E) is considered to be that of a typical Si[Li] solid state and GdOS scintilator detector. In the values of X and Y, found by using the calculated inversion coefficients, errors are below 2% for data with solutions of glycerol, sucrose and glucose. For low Zeff materials like propionic acid, Zeffx is overestimated by 20% with X being within1%. For high Zeffx materials like KOH the value of Zeffx is underestimated by 22% while the error in X is + 15%. These imply that the source may have additional filtering than the aluminium filter specified by the manufacturer. Also it is found that the difference in the values of the inversion coefficients for the two types of detectors is negligible. The type of the detector does not affect on the DECT inversion algorithm to find the unknown chemical characteristic of the scanned materials. The effect of the source should be considered as an important factor to calculate the coefficients of inversion.

Keywords: attenuation coefficient, computed tomography, photoelectric effect, source spectrum

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Authors: Simont Toussi

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Cameroon and the Central African Republic arebound by the provisions of many regional and international charters, which condemn the manipulation of information, obstacles to access reliable information, or the limitation of freedoms of expression and opinion. These two countries also have constitutional guarantees for free speech and access to true and liable information. However, they are yet to define specific policies and regulations for access to information, disinformation, or misinformation. Yet, certain countries’ laws and regulations related to information and communication technologies, to criminal procedures, to terrorism, or intelligence services contain provisions that rather hider human rights by condemning false information. Like many other African countries, Cameroon and the Central African Republic face a profound democratic regression, and governments use multiple methods to stifle online discourse and digital rights. Despite the increased uptake of digital tools for political participation, there is a lack of interactivity and adoption of these tools. This enables a scarcity of information and creates room for the spreading of disinformation in the public space, hamperingdemocracy and the respect for human rights. This research aims to analyse the adequacy of stakeholders’ responses to disinformation in Cameroon and the Central African Republic in periods of political contestation, such as elections and anti-government protests, to highlight the nature, perpetrators, strategies, and channels of disinformation, as well as its effects on democratic actors, including civil society, bloggers, government critics, activists, and other human rights defenders. The study follows a qualitative method with literature review, content analysis, andkey informant’sinterviews with stakeholders’ representatives, emphasized crowdsourcing as a data and information collecting method in the two countries.

Keywords: disinformation, democracy, political manipulation, social media, media, fake news, central Africa, cameroon, misinformation, free speech

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Authors: Laura Álvarez Antón, Joaquín Díaz

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The construction industry is turning towards sustainability. It is a well-known fact that sustainability is based on a balance between environmental, social and economic aspects. In order to achieve sustainability efficiently, these three criteria should be taken into account in the initial project phases, since that is when a project can be influenced most effectively. Thus the aim must be to integrate important tools like BIM and LCA at an early stage in order to make full use of their potential. With the synergies resulting from the integration of BIM and LCA, a wider approach to sustainability becomes possible, covering the three pillars of sustainability.

Keywords: building information modeling (BIM), construction industry, design phase, life cycle assessment (LCA), sustainability

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Authors: Yanting Cao, Kazumitsu Nawata

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As smartphones upgrading, their software and hardware are getting smarter, so the smartphone-based human activity recognition will be described as more refined, complex, and detailed. In this context, we analyzed a set of experimental data obtained by observing and measuring 30 volunteers with six activities of daily living (ADL). Due to the large sample size, especially a 561-feature vector with time and frequency domain variables, cleaning these intractable features and training a proper model becomes extremely challenging. After a series of feature selection and parameters adjustment, a well-performed SVM classifier has been trained.

Keywords: smart sensors, human activity recognition, artificial intelligence, SVM

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Authors: Ryan Villafuerte Lansangan

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Assessing students’ understanding in the microscopic level of an abstract subject like chemistry poses a challenge to teachers. Literature reveals that the use of representations serves as an essential avenue of measuring the extent of understanding in the discipline as an alternative to traditional assessment methods. This undertaking explored the representational competence profile of high school students from the University of Santo Tomas High School in understanding selected chemical principles and correlate this with their academic profile in chemistry based on their performance in the academic achievement examination in chemistry administered by the Center for Education Measurement (CEM). The common misconceptions of the students on the selected chemistry principles based on their representations were taken into consideration as well as the students’ views regarding their understanding of the role of chemical representations in their learning. The students’ level of representation task instrument consisting of the main lessons in chemistry with a corresponding scoring guide was prepared and utilized in the study. The study revealed that most of the students under study are unanimously rated as Level 2 (symbolic level) in terms of their representational competence in understanding the selected chemical principles through the use of chemical representations. Alternative misrepresentations were most observed on the students’ representations on chemical bonding concepts while the concept of chemical equation appeared to be the most comprehensible topic in chemistry for the students. Data implies that teachers’ representations play an important role in helping the student understand the concept in a microscopic level. Results also showed that the academic achievement in the chemistry of the students based on the standardized CEM examination has a significant association with the students’ representational competence. In addition, the students’ responses on the students’ views in chemical representations questionnaire evidently showed a good understanding of what a chemical representation or a mental model is by drawing a negative response that these tools should be an exact replica. Moreover, the students confirmed a greater appreciation that chemical representations are explanatory tools.

Keywords: chemical representations, representational competence, academic profile in chemistry, secondary students

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Authors: Alexandre D. Ferraz, Dario H. Alliprandini, Mauro Sampaio

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Companies and other institutions are constantly seeking better organizational performance and greater competitiveness. In order to fulfill this purpose, there are many tools, methodologies and models for increasing performance. However, the Lean Management approach seems to be the most effective in terms of achieving a significant improvement in productivity relatively quickly. Although Lean tools are relatively easy to understand and implement in different contexts, many organizations are not able to transform themselves into 'Lean companies'. Most of the efforts in its implementation have shown single benefits, failing to achieve the desired impact on the performance of the overall enterprise system. There is also a growing perception of the importance of management in Lean transformation, but few studies have empirically investigated and described the 'Lean Management'. In order to understand more clearly the ideas that guide Lean Management and its influence on the maturity level of the production system, the objective of this research is analyze the relationship between the Lean Management principles and the Lean maturity level in the organizations. The research also analyzes the principles of Lean Management and its relationship with the 'Lean culture' and the results obtained. The research was developed using the case study methodology. Three manufacturing units of a German multinational company from industrial automation segment, located in different countries were studied, in order to have a better comparison between the practices and the level of maturity in the implementation. The primary source of information was the application of a research questionnaire based on the theoretical review. The research showed that higher the level of Lean Management principles, higher are the Lean maturity level, the Lean culture level, and the level of Lean results obtained in the organization. The research also showed that factors such as time for application of Lean concepts and company size were not determinant for the level of Lean Management principles and, consequently, for the level of Lean maturity in the organization. The characteristics of the production system showed much more influence in different evaluated aspects. The present research also left recommendations for the managers of the plants analyzed and suggestions for future research.

Keywords: lean management, lean principles, lean maturity level, lean manufacturing

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Authors: Lawrence Koech, Ray Everson, Hein Neomagus, Hilary Rutto

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Wet Flue gas desulphurization (FGD) systems are commonly used to remove sulphur dioxide from flue gas by contacting it with limestone in aqueous phase which is obtained by dissolution. Dissolution is important as it affects the overall performance of a wet FGD system. In the present study, effects of pH, stirring speed, solid to liquid ratio and acid concentration on the dissolution of limestone using an organic acid (adipic acid) were investigated. This was investigated using the pH stat apparatus. Calcium ions were analyzed at the end of each experiment using Atomic Absorption (AAS) machine.

Keywords: desulphurization, limestone, dissolution, pH stat apparatus

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Authors: Dona Pamoda W. Jayatunga, Veer Bala Gupta, Eugene Hone, Ralph N. Martins

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Being a neurodegenerative disorder, Alzheimer’s disease (AD) affects a majority of the elderly demented worldwide. The key risk factors for AD are age, metabolic syndrome, allele status of APOE gene, head injuries and lifestyle. The progressive nature of AD is characterized by symptoms of multiple cognitive deficits exacerbated over time, leading to death within a decade from clinical diagnosis. However, it is revealed that AD originates via a prodromal phase that spans from one to few decades before symptoms first manifest. The key pathological hallmarks of AD brains are deposition of amyloid beta (Aβ) plaques and neurofibrillary tangles (NFT). However, the yet unknown etiology of the disease fails to distinguish mitochondrial dysfunction between a cause or an outcome. The absence of early diagnosis tools and definite therapies for AD have permitted recruits of nutraceutical-based approaches aimed at reducing the risk of AD by modulating lifestyle or be used as preventive tools during AD prodromal state before widespread neurodegeneration begins. The objective of the present study was to investigate beneficial effects of luteolin, a plant-based flavone compound, against AD. The neuroprotective effects of luteolin on amyloid beta (Aβ) (1-42)-induced neurotoxicity was measured using cultured human neuroblastoma BE(2)-M17 cells. After exposure to 20μM Aβ (1-42) for 48 h, the neuroblastoma cells exhibited marked apoptotic death. Co-treatment of 20μM Aβ (1-42) with luteolin (0.5-5μM) significantly protected the cells against Aβ (1-42)-induced toxicity, as assessed by the MTS [3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2(4sulfophenyl)-2H-tetrazolium, inner salt; MTS] reduction assay and the lactate dehydrogenase (LDH) cell death assay. The results suggest that luteolin prevents Aβ (1-42)-induced apoptotic neuronal death. However, further studies are underway to determine its protective mechanisms in AD including the activity against tau hyperphosphorylation and mitochondrial dysfunction.

Keywords: Aβ (1-42)-induced toxicity, Alzheimer’s disease, luteolin, neuroblastoma cells

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Authors: Xabier Olaz Moratinos

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Drone navigation is critical, particularly during the initial phases, such as the initial ascension, where pilots may fail due to strong external interferences that could potentially lead to a crash. In this ongoing work, a drone has been successfully trained to perform an ascent of up to 6 meters at speeds with external disturbances pushing it up to 24 mph, with the DQN algorithm managing external forces affecting the system. It has been demonstrated that the system can control its height, position, and stability in all three axes (roll, pitch, and yaw) throughout the process. The learning process is carried out in the Gazebo simulator, which emulates interferences, while ROS is used to communicate with the agent.

Keywords: machine learning, DQN, gazebo, navigation

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Authors: Branci Sarra, Branci Saadia

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Because of the multiplied data growth, many computer science tools have been developed to process and analyze these Big Data. High-performance computing architectures have been designed to meet the treatment needs of Big Data (view transaction processing standpoint, strategic, and tactical analytics). The purpose of this article is to provide a historical and global perspective on the recent trend of high-performance computing architectures especially what has a relation with Analytics and Data Mining.

Keywords: high performance computing, HPC, big data, data analysis

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Abdullah Alenezi, Mane Alsudrawi, Rafat Misak

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Kuwait is faced with a potentially wide and harmful range of both natural and anthropogenic hazardous events such as dust storms, floods, fires, nuclear accidents, earthquakes, oil spills, tsunamis and other disasters. For Kuwait can be highly vulnerable to these complex environmental risks, an up-to-date and in-depth understanding of their typology, genesis, and impact on the Kuwaiti society is needed. Adequate anticipation and management of environmental crises further require a comprehensive system of decision support to the benefit of decision makers to further bridge the gap between (technical) risk understanding and public action. For that purpose, the Kuwait Institute for Scientific Research (KISR), intends to establish a decision support center for management of the environmental crisis in Kuwait. The center will support policy makers, stakeholders and national committees with technical information that helps them efficiently and effectively assess, monitor to manage environmental disasters using decision support tools. These tools will build on state of the art quantification and visualization techniques, such as remote sensing information, Geographical Information Systems (GIS), simulation and prediction models, early warning systems, etc. The center is conceived as a central facility which will be designed, operated and managed by KISR in coordination with national authorities and decision makers of the country. Our vision is that by 2035 the center will be recognized as a leading national source of scientific advice on national risk management in Kuwait and build unity of effort among Kuwaiti’s institutions, government agencies, public and private organizations through provision and sharing of information. The project team now focuses on capacity building through upgrading some KISR facilities manpower development, build strong collaboration with international alliance.

Keywords: decision support, environment, hazard, Kuwait

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Authors: Andi Ismanto

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Indonesia is the largest palm oil producer in the world. The increasing number of palm oil extensification in Indonesia started on 2000-2011. Based on preliminary figures from the Directorate General of Plantation, palm oil area in Indonesia until 2011 is about 8.91 million hectares.On 2011 production of palm oil CPO reaches 22.51 million tons. In the other hands, the increasing palm oil production has impact to environment. The Empty Bunch of Palm Oil (EBPO)waste was increased to 20 million tons in 2009. Utilization of waste EBPO currently only used as an organic fertilizer for plants. But, it was not a good solution, because TKKS that used as organic compost has high content of carbon and hydrogen compound. The EBPO waste has potential used as fuel by gasification because it has short time of decomposition. So, the process will be more efficient in time. Utilization of urban wastehas been created using an incinerator used as a source of electrical energy for household.Usually, waste burning process by incinerator is using diesel fuel and kerosene. It is certainly less effective and not environment friendly, considering the waste incineration process using Incinerator tools are continuously. Considering biomass is a renewable source of energy and the world's energy system must be switch from an energy based on fossil resources into the energy based on renewable resources, the "Gurza Incinerator": Design Build Powerful Biomass Incinerator Empty Bunch of Palm Oil (EBPO) as Elecrical Biomass Base Development, a renewable future technology. The tools is using EBPO waste as source of burning to burn garbage inside the Incinerator hopper. EBPO waste will be processed by means of gasification. Gasification isa process to produce gases that can be used as fuel for electrical power. Hopefully, this technology could be a renewable future energy and also as starting point of electrical biomass base development.

Keywords: incinerator, biomass, empty bunch palm oil, electrical energy

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 150

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 138

Authors: Walid A. M. Ghoneim, Hamdy A. Ashour, Asmaa E. Abdo

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In this paper, linear analysis of a Switched Reluctance Generator (SRG) model is applied on the most common configurations (4/2, 6/4 and 8/6) for both conventional short-pitched and fully-pitched designs, in order to determine the optimum stator/rotor pole angles at which the maximum output voltage is generated per unit excitation current. This study is focused on SRG analysis and design as a proposed solution for renewable energy applications, such as wind energy conversion systems. The world’s potential to develop the renewable energy technologies through dedicated scientific researches was the motive behind this study due to its positive impact on economy and environment. In addition, the problem of rare earth metals (Permanent magnet) caused by mining limitations, banned export by top producers and environment restrictions leads to the unavailability of materials used for rotating machines manufacturing. This challenge gave authors the opportunity to study, analyze and determine the optimum design of the SRG that has the benefit to be free from permanent magnets, rotor windings, with flexible control system and compatible with any application that requires variable-speed operation. In addition, SRG has been proved to be very efficient and reliable in both low-speed or high-speed applications. Linear analysis was performed using MATLAB simulations based on the (Modified generalized matrix approach) of Switched Reluctance Machine (SRM). About 90 different pole angles combinations and excitation patterns were simulated through this study, and the optimum output results for each case were recorded and presented in detail. This procedure has been proved to be applicable for any SRG configuration, dimension and excitation pattern. The delivered results of this study provide evidence for using the 4-phase 8/6 fully pitched SRG as the main optimum configuration for the same machine dimensions at the same angular speed.

Keywords: generalized matrix approach, linear analysis, renewable applications, switched reluctance generator

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Authors: Manpreet Kaur, Cheol-Hong Min

Abstract:

The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

Procedia PDF Downloads 221