Search results for: mathematical algorithms of targeting and persecution

Authors: Dan W. Rhodes, Juliane Hey, Magali Karagueuzian, Florianne Martinez, Yael Sandowski, Vanessa Roulet, Mahmoud Badawi, Mohammed-Amine Chakir, Valérie Simon, Jérémie Gautier, Françoise Le Boulaire, Catherine Coignard, Claire Vincent, Sandrine Greaume, Isabelle Voisin

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Background: Beckman Coulter, Inc. (BEC) has recently developed a fully automated second-generation anti-HCV test on a new immunoassay platform. The objective of this multicenter study conducted in Europe was to evaluate the performance of the ACCESS anti-HCV assay on the recently CE-marked DxI 9000 ACCESS Immunoassay Analyzer as an aid in the diagnosis of HCV (Hepatitis C Virus) infection and as a screening test for blood and plasma donors. Methods: The clinical specificity of the ACCESS anti-HCV assay was determined using HCV antibody-negative samples from blood donors and hospitalized patients. Sample antibody status was determined by a CE-marked anti-HCV assay (Abbott ARCHITECTTM anti-HCV assay or Abbott PRISM HCV assay) with an additional confirmation method (Immunoblot testing with INNO-LIATM HCV Score - Fujirebio), if necessary, according to pre-determined testing algorithms. The clinical sensitivity was determined using known HCV antibody-positive samples, identified positive by Immunoblot testing with INNO-LIATM HCV Score - Fujirebio. HCV RNA PCR or genotyping was available on all Immunoblot positive samples for further characterization. The false initial reactive rate was determined on fresh samples from blood donors and hospitalized patients. Thirty (30) commercially available seroconversion panels were tested to assess the sensitivity for early detection of HCV infection. The study was conducted from November 2019 to March 2022. Three (3) external sites and one (1) internal site participated. Results: Clinical specificity (95% CI) was 99.7% (99.6 – 99.8%) on 5852 blood donors and 99.0% (98.4 – 99.4%) on 1527 hospitalized patient samples. There were 15 discrepant samples (positive on ACCESS anti-HCV assay and negative on both ARCHITECT and Immunoblot) observed with hospitalized patient samples, and of note, additional HCV RNA PCR results showed five (5) samples had positive HCV RNA PCR results despite the absence of HCV antibody detection by ARCHITECT and Immunoblot, suggesting a better sensitivity of the ACCESS anti-HCV assay with these five samples compared to the ARCHITECT and Immunoblot anti-HCV assays. Clinical sensitivity (95% CI) on 510 well-characterized, known HCV antibody-positive samples was 100.0% (99.3 – 100.0%), including 353 samples with known HCV genotypes (1 to 6). The overall false initial reactive rate (95% CI) on 6630 patient samples was 0.02% (0.00 – 0.09%). Results obtained on 30 seroconversion panels demonstrated that the ACCESS anti-HCV assay had equivalent sensitivity performances, with an average bleed difference since the first reactive bleed below one (1), compared to the ARCHITECTTM anti-HCV assay. Conclusion: The newly developed ACCESS anti-HCV assay from BEC for use on the DxI 9000 ACCESS Immunoassay Analyzer demonstrated high clinical sensitivity and specificity, equivalent to currently marketed anti-HCV assays, as well as a low false initial reactive rate.

Keywords: DxI 9000 ACCESS Immunoassay Analyzer, HCV, HCV antibody, Hepatitis C virus, immunoassay

Procedia PDF Downloads 92

Authors: Subir S. Rao

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Existing warehouse configurations use non-traditional aisle designs with a central P/D point in their models, which is mathematically simple but less practical. Many warehouses use multiple P/D points to avoid congestion for pickers, and different warehouses have different flow policies and infrastructure for using the P/D points. Many warehouses use multiple P/D points with non-traditional aisle designs in their analytical models. Standard warehouse models introduce one-sided multiple P/D points in a flying-V warehouse and minimize pick distance for a one-way travel between an active P/D point and a pick location with P/D points, assuming uniform flow rates. A simulation of the mathematical model generally uses four fixed configurations of P/D points which are on two different sides of the warehouse. It can be easily proved that if the source and destination P/D points are both chosen randomly, in a uniform way, then minimizing the one-way travel is the same as minimizing the two-way travel. Another warehouse configuration analytically models the warehouse for multiple one-sided P/D points while keeping the angle of the cross-aisles and picking aisles as a decision variable. The minimization of the one-way pick travel distance from the P/D point to the pick location by finding the optimal position/angle of the cross-aisle and picking aisle for warehouses having different numbers of multiple P/D points with variable flow rates is also one of the objectives. Most models of warehouses with multiple P/D points are one-way travel models and we extend these analytical models to minimize the two-way pick travel distance wherein the destination P/D is chosen optimally for the return route, which is not similar to minimizing the one-way travel. In most warehouse models, the return P/D is chosen randomly, but in our research, the return route P/D point is chosen optimally. Such warehouses are common in practice, where the flow rates at the P/D points are flexible and depend totally on the position of the picks. A good warehouse management system is efficient in consolidating orders over multiple P/D points in warehouses where the P/D is flexible in function. In the latter arrangement, pickers and shrink-wrap processes are not assigned to particular P/D points, which ultimately makes the P/D points more flexible and easy to use interchangeably for picking and deposits. The number of P/D points considered in this research uniformly increases from a single-central one to a maximum of each aisle symmetrically having a P/D point below it.

Keywords: non-traditional warehouse, V cross-aisle, multiple P/D point, pick travel distance

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Authors: Raed KOUTA, José-Alcebiades-Ernesto HLUNGUANE, Eric Châtele

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The search for and exploitation of new fossil energy resources is taking place in the context of the gradual depletion of existing deposits. Despite the adoption of international targets to combat global warming, the demand for fuels continues to grow, contradicting the movement towards an energy-efficient society. The increase in the share of offshore in global hydrocarbon production tends to compensate for the depletion of terrestrial reserves, thus constituting a major challenge for the players in the sector. Through the economic potential it represents, and the energy independence it provides, offshore exploitation is also a challenge for States such as Mozambique, which have large maritime areas and whose environmental wealth must be considered. The exploitation of new reserves on economically viable terms depends on available technologies. The development of deep and ultra-deep offshore requires significant research and development efforts. Progress has also been made in managing the multiple risks inherent in this activity. Our study proposes a reliability approach to develop products and processes designed to live at sea. Indeed, the context of an offshore platform requires highly reliable solutions to overcome the difficulties of access to the system for regular maintenance and quick repairs and which must resist deterioration and degradation processes. One of the characteristics of failures that we consider is the actual conditions of use that are considered 'extreme.' These conditions depend on time and the interactions between the different causes. These are the two factors that give the degradation process its dynamic character, hence the need to develop dynamic reliability models. Our work highlights mathematical models that can explicitly manage interactions between components and process variables. These models are accompanied by numerical resolution methods that help to structure a dynamic reliability approach in a physical and probabilistic context. The application developed makes it possible to evaluate the reliability, availability, and maintainability of a floating storage and unloading platform for liquefied natural gas production.

Keywords: dynamic reliability, offshore plateform, stochastic process, uncertainties

Procedia PDF Downloads 115

Authors: Dileep Maarisetty, Janaki Komandur, Saroj S. Baral

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In the current work, photocatalytic degradation of phenol was carried both in UV and visible light to find the slowest step that is limiting the rate of photo-degradation process. Characterization such as XRD, SEM, FT-IR, TEM, XPS, UV-DRS, PL, BET, UPS, ESR and zeta potential experiments were conducted to assess the credibility of catalysts in boosting the photocatalytic activity. To explore the synergy, TiO₂ was doped with graphene and alumina. The orbital hybridization with alumina doping (mediated by graphene) resulted in higher electron transfer from the conduction band of TiO₂ to alumina surface where oxygen reduction reactions (ORR) occur. Besides, the doping of alumina and graphene introduced defects into Ti lattice and helped in improving the adsorptive properties of modified photo-catalyst. Results showed that these defects promoted the oxygen reduction reactions (ORR) on the catalyst’s surface. ORR activity aims at producing reactive oxygen species (ROS). These ROS species oxidizes the phenol molecules which is adsorbed on the surface of photo-catalysts, thereby driving the photocatalytic reactions. Since mass transfer is considered as rate limiting step, various mathematical models were applied to the experimental data to probe the best fit. By varying the parameters, it was found that intra-particle diffusion was the slowest step in the degradation process. Lagergren model gave the best R² values indicating the nature of rate kinetics. Similarly, different adsorption isotherms were employed and realized that Langmuir isotherm suits the best with tremendous increase in uptake capacity (mg/g) of TiO₂-rGO-Al₂O₃ as compared undoped TiO₂. This further assisted in higher adsorption of phenol molecules. The results obtained from experimental, kinetic modelling and adsorption isotherms; it is concluded that apart from changes in surface, optoelectronic and morphological properties that enhanced the photocatalytic activity, the intra-particle diffusion within the catalyst’s pores serve as rate-limiting step in deciding the fate of photo-catalytic degradation of phenol.

Keywords: ORR, phenol degradation, photo-catalyst, rate kinetics

Procedia PDF Downloads 136

Authors: Vivek Mahadev, Manoj Kumar, Neelu Mathur, Brahm Dutt Pandey

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Perimeter guarding and protection of critical installations require prompt intrusion detection and assessment to take effective countermeasures. Currently, visual and electronic surveillance are the primary methods used for perimeter guarding. These methods can be costly and complicated, requiring careful planning according to the location and terrain. Moreover, these methods often struggle to detect stealthy and camouflaged insurgents. The object of the present work is to devise a surveillance technique using seismic sensors that overcomes the limitations of existing systems. The aim is to improve intrusion detection, assessment, and characterization by utilizing seismic sensors. Most of the similar systems have only two types of intrusion detection capability viz., human or vehicle. In our work we could even categorize further to identify types of intrusion activity such as walking, running, group walking, fence jumping, tunnel digging and vehicular movements. A virtual fence of 60 meters at GCNEP, Bahadurgarh, Haryana, India, was created by installing four underground geophones at a distance of 15 meters each. The signals received from these geophones are then processed to find unique seismic signatures called features. Various feature optimization and selection methodologies, such as LightGBM, Boruta, Random Forest, Logistics, Recursive Feature Elimination, Chi-2 and Pearson Ratio were used to identify the best features for training the machine learning models. The trained models were developed using algorithms such as supervised support vector machine (SVM) classifier, kNN, Decision Tree, Logistic Regression, Naïve Bayes, and Artificial Neural Networks. These models were then used to predict the category of events, employing weighted ensemble voting to analyze and combine their results. The models were trained with 1940 training events and results were evaluated with 831 test events. It was observed that using the weighted ensemble voting increased the efficiency of predictions. In this study we successfully developed and deployed the virtual fence using geophones. Since these sensors are passive, do not radiate any energy and are installed underground, it is impossible for intruders to locate and nullify them. Their flexibility, quick and easy installation, low costs, hidden deployment and unattended surveillance make such systems especially suitable for critical installations and remote facilities with difficult terrain. This work demonstrates the potential of utilizing seismic sensors for creating better perimeter guarding and protection systems using multiple machine learning models in weighted ensemble voting. In this study the virtual fence achieved an intruder detection efficiency of over 97%.

Keywords: geophone, seismic perimeter surveillance, machine learning, weighted ensemble method

Procedia PDF Downloads 70

Authors: Nir Nissim, Erez Shalom, Tomer Lancewiki, Yuval Elovici, Yuval Shahar

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Background: A Medical Device (MD) is an instrument, machine, implant, or similar device that includes a component intended for the purpose of the diagnosis, cure, treatment, or prevention of disease in humans or animals. Medical devices play increasingly important roles in health services eco-systems, including: (1) Patient Diagnostics and Monitoring; Medical Treatment and Surgery; and Patient Life Support Devices and Stabilizers. MDs are part of the medical device eco-system and are connected to the network, sending vital information to the internal medical information systems of medical centers that manage this data. Wireless components (e.g. Wi-Fi) are often embedded within medical devices, enabling doctors and technicians to control and configure them remotely. All these functionalities, roles, and uses of MDs make them attractive targets of cyber-attacks launched for many malicious goals; this trend is likely to significantly increase over the next several years, with increased awareness regarding MD vulnerabilities, the enhancement of potential attackers’ skills, and expanded use of medical devices. Significance: We propose to develop and implement Cyber-Med, a unique collaborative project of Ben-Gurion University of the Negev and the Clalit Health Services Health Maintenance Organization. Cyber-Med focuses on the development of a comprehensive detection framework that relies on a critical attack repository that we aim to create. Cyber-Med will allow researchers and companies to better understand the vulnerabilities and attacks associated with medical devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The Cyber-Med detection framework will consist of two independent, but complementary detection approaches: one for known attacks, and the other for unknown attacks. These modules incorporate novel ideas and algorithms inspired by our team's domains of expertise, including cyber security, biomedical informatics, and advanced machine learning, and temporal data mining techniques. The establishment and maintenance of Cyber-Med’s up-to-date attack repository will strengthen the capabilities of Cyber-Med’s detection framework. Major Findings: Based on our initial survey, we have already found more than 15 types of vulnerabilities and possible attacks aimed at MDs and their eco-system. Many of these attacks target individual patients who use devices such pacemakers and insulin pumps. In addition, such attacks are also aimed at MDs that are widely used by medical centers such as MRIs, CTs, and dialysis engines; the information systems that store patient information; protocols such as DICOM; standards such as HL7; and medical information systems such as PACS. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched against MDs. Very little research has been conducted in order to protect these devices from cyber-attacks, since most of the development and engineering efforts are aimed at the devices’ core medical functionality, the contribution to patients’ healthcare, and the business aspects associated with the medical device.

Keywords: medical device, cyber security, attack, detection, machine learning

Procedia PDF Downloads 352

Authors: Joanna Estkowska

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Sensory integration methods are very useful and improve daily functioning autistic and mentally disabled children. Autism is a neurobiological disorder that impairs one's ability to communicate with and relate to others as well as their sensory system. Children with autism, even highly functioning kids, can find it difficult to process language with surrounding noise or smells. They are hypersensitive to things we can ignore such as sight, sounds and touch. Adolescents with highly functioning autism or Asperger Syndrome can study Science and Math but the social aspect is difficult for them. Nature science is an area of study that attracts many of these kids. It is a systematic field in which the children can focus on a small aspect. If you follow these rules you can come up with an expected result. Sensory integration program and systematic classroom observation are quantitative methods of measuring classroom functioning and behaviors from direct observations. These methods specify both the events and behaviors that are to be observed and how they are to be recorded. Our students with and without autism attended the lessons in the classroom of nature science in the school and in the laboratory of University of Science and Technology in Bydgoszcz. The aim of this study is investigation the effects of sensory integration methods in teaching to students with autism. They were observed during experimental lessons in the classroom and in the laboratory. Their physical characteristics, sensory dysfunction, and behavior in class were taken into consideration by comparing their similarities and differences. In the chemistry classroom, every autistic student is paired with a mentor from their school. In the laboratory, the children are expected to wear goggles, gloves and a lab coat. The chemistry classes in the laboratory were held for four hours with a lunch break, and according to the assistants, the children were engaged the whole time. In classroom of nature science, the students are encouraged to use the interactive exhibition of chemical, physical and mathematical models constructed by the author of this paper. Our students with and without autism attended the lessons in those laboratories. The teacher's goals are: to assist the child in inhibiting and modulating sensory information and support the child in processing a response to sensory stimulation.

Keywords: autism spectrum disorder, science education, sensory integration techniques, student with special educational needs

Procedia PDF Downloads 182

Authors: Aleksandra Jonas, Aleksandra Gronowska, Maciej Ścigacz, Szymon Jadczak

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Exact Data is a smart tool which helps with meaningful marketing content creation. It helps marketers achieve this by analyzing the text of an advertisement before and after its publication on social media sites like Facebook or Instagram. In our research we focus on four areas of natural language processing (NLP): grammar correction, sentiment analysis, irony detection and advertisement interpretation. Our research has identified a considerable lack of NLP tools for the Polish language, which specifically aid online marketers. In light of this, our research team has set out to create a robust and versatile NLP tool for the Polish language. The primary objective of our research is to develop a tool that can perform a range of language processing tasks in this language, such as sentiment analysis, text classification, text correction and text interpretation. Our team has been working diligently to create a tool that is accurate, reliable, and adaptable to the specific linguistic features of Polish, and that can provide valuable insights for a wide range of marketers needs. In addition to the Polish language version, we are also developing an English version of the tool, which will enable us to expand the reach and impact of our research to a wider audience. Another area of focus in our research involves tackling the challenge of the limited availability of linguistically diverse corpora for non-English languages, which presents a significant barrier in the development of NLP applications. One approach we have been pursuing is the translation of existing English corpora, which would enable us to use the wealth of linguistic resources available in English for other languages. Furthermore, we are looking into other methods, such as gathering language samples from social media platforms. By analyzing the language used in social media posts, we can collect a wide range of data that reflects the unique linguistic characteristics of specific regions and communities, which can then be used to enhance the accuracy and performance of NLP algorithms for non-English languages. In doing so, we hope to broaden the scope and capabilities of NLP applications. Our research focuses on several key NLP techniques including sentiment analysis, text classification, text interpretation and text correction. To ensure that we can achieve the best possible performance for these techniques, we are evaluating and comparing different approaches and strategies for implementing them. We are exploring a range of different methods, including transformers and convolutional neural networks (CNNs), to determine which ones are most effective for different types of NLP tasks. By analyzing the strengths and weaknesses of each approach, we can identify the most effective techniques for specific use cases, and further enhance the performance of our tool. Our research aims to create a tool, which can provide a comprehensive analysis of advertising effectiveness, allowing marketers to identify areas for improvement and optimize their advertising strategies. The results of this study suggest that a smart tool for advertisement analysis can provide valuable insights for businesses seeking to create effective advertising campaigns.

Keywords: NLP, AI, IT, language, marketing, analysis

Procedia PDF Downloads 75

Authors: Carmen Georgeta Dumitrache

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Measurements of terrestrial science aims to study the totality of operations and computing, which are carried out for the purposes of representation on the plan or map of the land surface in a specific cartographic projection and topographic scale. With the development of society, the metrics have evolved, and they land, being dependent on the achievement of a goal-bound utility of economic activity and of a scientific purpose related to determining the form and dimensions of the Earth. For measurements in the field, data processing and proper representation on drawings and maps of planimetry and landform of the land, using topographic and geodesic instruments, calculation and graphical reporting, which requires a knowledge of theoretical and practical concepts from different areas of science and technology. In order to use properly in practice, topographical and geodetic instruments designed to measure precise angles and distances are required knowledge of geometric optics, precision mechanics, the strength of materials, and more. For processing, the results from field measurements are necessary for calculation methods, based on notions of geometry, trigonometry, algebra, mathematical analysis and computer science. To be able to illustrate topographic measurements was established for the lifting of property located near the locality of Gircov, Romania. We determine this total surface of the plan (T30), parcel/plot, but also in the field trace the coordinates of a parcel. The purpose of the removal of the planimetric consisted of: the exact determination of the bounding surface; analytical calculation of the surface; comparing the surface determined with the one registered in the documents produced; drawing up a plan of location and delineation with closeness and distance contour, as well as highlighting the parcels comprising this property; drawing up a plan of location and delineation with closeness and distance contour for a parcel from Dave; in the field trace outline of plot points from the previous point. The ultimate goal of this work was to determine and represent the surface, but also to tear off a plot of the surface total, while respecting the first surface condition imposed by the Act of the beneficiary's property.

Keywords: topography, surface, coordinate, modeling

Procedia PDF Downloads 251

Authors: J. Ritonja

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Available commercial applications of power system stabilizers assure optimal damping of synchronous generator’s oscillations only in a small part of operating range. Parameters of the power system stabilizer are usually tuned for the selected operating point. Extensive variations of the synchronous generator’s operation result in changed dynamic characteristics. This is the reason that the power system stabilizer tuned for the nominal operating point does not satisfy preferred damping in the overall operation area. The small-signal stability and the transient stability of the synchronous generators have represented an attractive problem for testing different concepts of the modern control theory. Of all the methods, the adaptive control has proved to be the most suitable for the design of the power system stabilizers. The adaptive control has been used in order to assure the optimal damping through the entire synchronous generator’s operating range. The use of the adaptive control is possible because the loading variations and consequently the variations of the synchronous generator’s dynamic characteristics are, in most cases, essentially slower than the adaptation mechanism. The paper shows the development and the application of the self-tuning power system stabilizer based on recursive least square identification method and linear quadratic regulator. Identification method is used to calculate the parameters of the Heffron-Phillips model of the synchronous generator. On the basis of the calculated parameters of the synchronous generator’s mathematical model, the synthesis of the linear quadratic regulator is carried-out. The identification and the synthesis are implemented on-line. In this way, the self-tuning power system stabilizer adapts to the different operating conditions. A purpose of this paper is to contribute to development of the more effective power system stabilizers, which would replace currently used linear stabilizers. The presented self-tuning power system stabilizer makes the tuning of the controller parameters easier and assures damping improvement in the complete operating range. The results of simulations and experiments show essential improvement of the synchronous generator’s damping and power system stability.

Keywords: adaptive control, linear quadratic regulator, power system stabilizer, recursive least square identification

Procedia PDF Downloads 238

Authors: M. S. Maglasang, S. O. Palacio, L. P. Ogdoc

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Business Process Outsourcing has been one of the fastest growing and emerging industry in the Philippines today. Unlike most of the contact service centers, more popularly known as "call centers", The BPO Industry’s primary outsourced service is performing audits of the global clients' logistics. As a service industry, manpower is considered as the most important yet the most expensive resource in the company. Because of this, there is a need to maximize the human resources so people are effectively and efficiently utilized. The main purpose of the study is to optimize the current manpower resources through effective distribution and assignment of different types of bills to the different skill-level of data entry operators. The assignment model parameters include the average observed time matrix gathered from through time study, which incorporates the learning curve concept. Subsequently, a simulation model was made to duplicate the arrival rate of demand which includes the different batches and types of bill per day. Next, a mathematical linear programming model was formulated. Its objective is to minimize direct labor cost per bill by allocating the different types of bills to the different skill-levels of operators. Finally, a hypothesis test was done to validate the model, comparing the actual and simulated results. The analysis of results revealed that the there’s low utilization of effective capacity because of its failure to determine the product-mix, skill-mix, and simulated demand as model parameters. Moreover, failure to consider the effects of learning curve leads to overestimation of labor needs. From 107 current number of operators, the proposed model gives a result of 79 operators. This results to an increase of utilization of effective capacity to 14.94%. It is recommended that the excess 28 operators would be reallocated to the other areas of the department. Finally, a manpower capacity planning model is also recommended in support to management’s decisions on what to do when the current capacity would reach its limit with the expected increasing demand.

Keywords: optimization modelling, linear programming, simulation, time and motion study, capacity planning

Procedia PDF Downloads 504

Authors: Achim Kampker, Heiner H. Heimes, Mathias Ordung, Christoph Lienemann, Ansgar Hollah, Nemanja Sarovic

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Electric cars with their fast innovation cycles and their disruptive character offer a high degree of freedom regarding innovative design for remanufacturing. Remanufacturing increases not only the resource but also the economic efficiency by a prolonged product life time. The reduced power train wear of electric cars combined with high manufacturing costs for batteries allow new business models and even second life applications. Modular and intermountable designed battery packs enable the replacement of defective or outdated battery cells, allow additional cost savings and a prolongation of life time. This paper discusses opportunities for future remanufacturing value chains of electric cars and their battery components and how to address their potentials with elaborate designs. Based on a brief overview of implemented remanufacturing structures in different industries, opportunities of transferability are evaluated. In addition to an analysis of current and upcoming challenges, promising perspectives for a sustainable electric car circular economy enabled by design for remanufacturing are deduced. Two mathematical models describe the feasibility of pursuing a circular economy of lithium ion batteries and evaluate remanufacturing in terms of sustainability and economic efficiency. Taking into consideration not only labor and material cost but also capital costs for equipment and factory facilities to support the remanufacturing process, cost benefit analysis prognosticate that a remanufacturing battery can be produced more cost-efficiently. The ecological benefits were calculated on a broad database from different research projects which focus on the recycling, the second use and the assembly of lithium ion batteries. The results of this calculations show a significant improvement by remanufacturing in all relevant factors especially in the consumption of resources and greenhouse warming potential. Exemplarily suitable design guidelines for future remanufacturing lithium ion batteries, which consider modularity, interfaces and disassembly, are used to illustrate the findings. For one guideline, potential cost improvements were calculated and upcoming challenges are pointed out.

Keywords: circular economy, electric mobility, lithium ion batteries, remanufacturing

Procedia PDF Downloads 347

Authors: Faiza Abdul Latip

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One of the ultimate goals of any nation is to produce competitive manpower and this includes Philippines. Inclination in the field of Mathematics has a significant role in achieving this goal. However, Mathematics, as considered by most people, is the most difficult subject matter along with its topics to learn. This could be manifested from the low performance of students in national and international assessments. Educators have been widely using learning style models in identifying the way students learn. Moreover, it could be the frontline in knowing the difficulties held by each learner in a particular topic specifically concepts pertaining to fractions. However, as what many educators observed, students show difficulties in doing mathematical tasks and in great degree in dealing with fractions most specifically in the district of Datu Odin Sinsuat, Maguindanao. This study focused on the Datu Odin Sinsuat district grade six pupils’ learning styles along with their achievements and difficulties in learning concepts on fractions. Five hundred thirty-two pupils from ten different public elementary schools of the Datu Odin Sinsuat districts were purposively used as the respondents of the study. A descriptive research using the survey method was employed in this study. Quantitative analysis on the pupils’ learning styles on the Kolb’s Learning Style Inventory (KLSI) and scores on the mathematics diagnostic test on fraction concepts were made using this method. The simple frequency and percentage counts were used to analyze the pupils’ learning styles and their achievements on fractions. To determine the pupils’ difficulties in fractions, the index of difficulty on every item was determined. Lastly, the Kruskal-Wallis Test was used in determining the significant difference in the pupils’ achievements on fractions classified by their learning styles. This test was set at 0.05 level of significance. The minimum H-Value of 7.82 was used to determine the significance of the test. The results revealed that the pupils of Datu Odin Sinsuat districts learn fractions in varied ways as they are of different learning styles. However, their achievements in fractions are low regardless of their learning styles. Difficulties in learning fractions were found most in the area of Estimation, Comparing/Ordering, and Division Interpretation of Fractions. Most of the pupils find it very difficult to use fraction as a measure, compare or arrange series of fractions and use the concept of fraction as a quotient.

Keywords: difficulties in fraction, fraction, Kolb's model, learning styles

Procedia PDF Downloads 205

Authors: S. Mani, M. Kafil, E. Asadi

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Condition Monitoring (CM) of electrical equipment has gained remarkable importance during the recent years; due to huge production losses, substantial imposed costs and increases in vulnerability, risk and uncertainty levels. Power cables feed numerous electrical equipment such as transformers, motors, and electric furnaces; thus their condition assessment is of a very great importance. This paper investigates electrical, structural and environmental failure sources, all of which influence cables' performances and limit their uptimes; and provides a comprehensive framework entailing practical CM guidelines for maintenance of cables in industries. The multilevel CM framework presented in this study covers performance indicative features of power cables; with a focus on both online and offline diagnosis and test scenarios, and covers short-term and long-term threats to the operation and longevity of power cables. The study, after concisely overviewing the concept of CM, thoroughly investigates five major areas of power quality, Insulation Quality features of partial discharges, tan delta and voltage withstand capabilities, together with sheath faults, shield currents and environmental features of temperature and humidity; and elaborates interconnections and mutual impacts between those areas; using mathematical formulation and practical guidelines. Detection, location, and severity identification methods for every threat or fault source are also elaborated. Finally, the comprehensive, practical guidelines presented in the study are presented for the specific case of Electric Arc Furnace (EAF) feeder MV power cables in Mobarakeh Steel Company (MSC), the largest steel company in MENA region, in Iran. Specific technical and industrial characteristics and limitations of a harsh industrial environment like MSC EAF feeder cable tunnels are imposed on the presented framework; making the suggested package more practical and tangible.

Keywords: condition monitoring, diagnostics, insulation, maintenance, partial discharge, power cables, power quality

Procedia PDF Downloads 219

Authors: Lakshmi N. Reddi, Akanksha Varma Sagi

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Studies on biomimetics are largely developed, deriving inspiration from natural processes in our objective world to develop novel technologies. Recent studies are diverse in nature, making their categorization quite challenging. Based on an exhaustive survey, we developed categorizations based on either the essential elements of nature - air, water, land, fire, and space, or on form/shape, functionality, and process. Such diverse studies as aircraft wings inspired by bird wings, a self-cleaning coating inspired by a lotus petal, wetsuits inspired by beaver fur, and search algorithms inspired by arboreal ant path networks lend themselves to these categorizations. Our categorizations of biomimetic studies allowed us to define a different dimension of biomimetics. This new dimension is not restricted to inspiration from the objective world. It is based on the premise that the biological processes observed in the objective world find their reflections in our human bodies in a variety of ways. For example, the lungs provide the most efficient example for liquid-gas phase exchange, the heart exemplifies a very efficient pumping and circulatory system, and the kidneys epitomize the most effective cleaning system. The main focus of this paper is to bring out the magnificence of the cerebro-spinal system (CSS) insofar as it relates to our current computer architecture. In particular, the paper uses four key measures to analyze the differences between CSS and human- engineered computational systems. These are adaptability, sustainability, energy efficiency, and resilience. We found that the cerebrospinal system reveals some important challenges in the development and evolution of our current computer architectures. In particular, the myriad ways in which the CSS is integrated with other systems/processes (circulatory, respiration, etc) offer useful insights on how the human-engineered computational systems could be made more sustainable, energy-efficient, resilient, and adaptable. In our paper, we highlight the energy consumption differences between CSS and our current computational designs. Apart from the obvious differences in materials used between the two, the systemic nature of how CSS functions provides clues to enhance life-cycles of our current computational systems. The rapid formation and changes in the physiology of dendritic spines and their synaptic plasticity causing memory changes (ex., long-term potentiation and long-term depression) allowed us to formulate differences in the adaptability and resilience of CSS. In addition, the CSS is sustained by integrative functions of various organs, and its robustness comes from its interdependence with the circulatory system. The paper documents and analyzes quantifiable differences between the two in terms of the four measures. Our analyses point out the possibilities in the development of computational systems that are more adaptable, sustainable, energy efficient, and resilient. It concludes with the potential approaches for technological advancement through creation of more interconnected and interdependent systems to replicate the effective operation of cerebro-spinal system.

Keywords: cerebrospinal system, computer architecture, adaptability, sustainability, resilience, energy efficiency

Procedia PDF Downloads 81

Authors: Fonteyne Lot

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SIMON (Study capacities and Interest Monitor is a freely accessible online self-assessment tool that allows secondary education pupils to evaluate their interests and capacities in order to choose a post-secondary major that maximally suits their potential. The tool consists of two broad domains that correspond with two general questions pupils ask: 'What study fields interest me?' and 'Am I capable to succeed in this field of study?'. The first question is addressed by a RIASEC-type interest inventory that links personal interests to post-secondary majors. Pupils are provided with a personal profile and an overview of majors with their degree of congruence. The output is dynamic: respondents can manipulate their score and they can compare their results to the profile of all fields of study. That way they are stimulated to explore the broad range of majors. To answer whether pupils are capable of succeeding in a preferred major, a battery of tests is provided. This battery comprises a range of factors that are predictive of academic success. Traditional predictors such as (educational) background and cognitive variables (mathematical and verbal skills) are included. Moreover, non-cognitive predictors of academic success (such as 'motivation', 'test anxiety', 'academic self-efficacy' and 'study skills') are assessed. These non-cognitive factors are generally not included in admission decisions although research shows they are incrementally predictive of success and are less discriminating. These tests inform pupils on potential causes of success and failure. More important, pupils receive their personal chances of success per major. These differential probabilities are validated through the underlying research on academic success of students. For example, the research has shown that we can identify 22 % of the failing students in psychology and educational sciences. In this group, our prediction is 95% accurate. SIMON leads more students to a suitable major which in turn alleviates student success and retention. Apart from these benefits, the instrument grants insight into risk factors of academic failure. It also supports and fosters the development of evidence-based remedial interventions and therefore gives way to a more efficient use of means.

Keywords: academic success, online self-assessment, student retention, vocational choice

Procedia PDF Downloads 396

Authors: Ali Kazemi

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The digital evolution of financial services, while offering unprecedented convenience and accessibility, has also escalated the vulnerabilities to fraudulent activities. In this study, we introduce a distinct approach to real-time anomaly detection in financial transactions, aiming to fortify the defenses of banking and financial institutions against such threats. Utilizing unsupervised machine learning algorithms, specifically autoencoders and isolation forests, our research focuses on identifying irregular patterns indicative of fraud within transactional data, thus enabling immediate action to prevent financial loss. The data we used in this study included the monetary value of each transaction. This is a crucial feature as fraudulent transactions may have distributions of different amounts than legitimate ones, such as timestamps indicating when transactions occurred. Analyzing transactions' temporal patterns can reveal anomalies (e.g., unusual activity in the middle of the night). Also, the sector or category of the merchant where the transaction occurred, such as retail, groceries, online services, etc. Specific categories may be more prone to fraud. Moreover, the type of payment used (e.g., credit, debit, online payment systems). Different payment methods have varying risk levels associated with fraud. This dataset, anonymized to ensure privacy, reflects a wide array of transactions typical of a global banking institution, ranging from small-scale retail purchases to large wire transfers, embodying the diverse nature of potentially fraudulent activities. By engineering features that capture the essence of transactions, including normalized amounts and encoded categorical variables, we tailor our data to enhance model sensitivity to anomalies. The autoencoder model leverages its reconstruction error mechanism to flag transactions that deviate significantly from the learned normal pattern, while the isolation forest identifies anomalies based on their susceptibility to isolation from the dataset's majority. Our experimental results, validated through techniques such as k-fold cross-validation, are evaluated using precision, recall, and the F1 score alongside the area under the receiver operating characteristic (ROC) curve. Our models achieved an F1 score of 0.85 and a ROC AUC of 0.93, indicating high accuracy in detecting fraudulent transactions without excessive false positives. This study contributes to the academic discourse on financial fraud detection and provides a practical framework for banking institutions seeking to implement real-time anomaly detection systems. By demonstrating the effectiveness of unsupervised learning techniques in a real-world context, our research offers a pathway to significantly reduce the incidence of financial fraud, thereby enhancing the security and trustworthiness of digital financial services.

Keywords: anomaly detection, financial fraud, machine learning, autoencoders, isolation forest, transactional data analysis

Procedia PDF Downloads 44

Authors: Caglayan Hizal, Kutay Yuceturk, Ertugrul Turker Uzun, Hasan Ceylan, Engin Aktas, Gursoy Turan

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Monitoring concepts for structural systems have been investigated by researchers for decades since such tools are quite convenient to determine intervention planning of structures. Despite the considerable development in this regard, the efficient use of monitoring data in reliability assessment, and prediction models are still in need of improvement in their efficiency. More specifically, reliability-based seismic risk assessment of engineering structures may play a crucial role in the post-earthquake decision-making process for the structures. After an earthquake, professionals could identify heavily damaged structures based on visual observations. Among these, it is hard to identify the ones with minimum signs of damages, even if they would experience considerable structural degradation. Besides, visual observations are open to human interpretations, which make the decision process controversial, and thus, less reliable. In this context, when a continuous monitoring system has been previously installed on the corresponding structure, this decision process might be completed rapidly and with higher confidence by means of the observed data. At this stage, the Structural Health Monitoring (SHM) procedure has an important role since it can make it possible to estimate the system reliability based on a recursively updated mathematical model. Therefore, integrating an SHM procedure into the reliability assessment process comes forward as an important challenge due to the arising uncertainties for the updated model in case of the environmental, material and earthquake induced changes. In this context, this study presents a case study on SHM-integrated reliability assessment of the continuously monitored progressively damaged systems. The objective of this study is to get instant feedback on the current state of the structure after an extreme event, such as earthquakes, by involving the observed data rather than the visual inspections. Thus, the decision-making process after such an event can be carried out on a rational basis. In the near future, this can give wing to the design of self-reported structures which can warn about its current situation after an extreme event.

Keywords: condition assessment, vibration-based SHM, reliability analysis, seismic risk assessment

Procedia PDF Downloads 133

Authors: Ugwu O. C., Mamah R. O., Awudu W. S.

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This work is aimed at enhancing signal reception on a mobile radio network and minimizing outage probability in a mobile radio network using adaptive beamforming antenna arrays. In this research work, an empirical real-time drive measurement was done in a cellular network of Globalcom Nigeria Limited located at Ikeja, the headquarters of Lagos State, Nigeria, with reference base station number KJA 004. The empirical measurement includes Received Signal Strength and Bit Error Rate which were recorded for exact prediction of the signal strength of the network as at the time of carrying out this research work. The Received Signal Strength and Bit Error Rate were measured with a spectrum monitoring Van with the help of a Ray Tracer at an interval of 100 meters up to 700 meters from the transmitting base station. The distance and angular location measurements from the reference network were done with the help Global Positioning System (GPS). The other equipment used were transmitting equipment measurements software (Temsoftware), Laptops and log files, which showed received signal strength with distance from the base station. Results obtained were about 11% from the real-time experiment, which showed that mobile radio networks are prone to signal failure and can be minimized using an Adaptive Beamforming Antenna Array in terms of a significant reduction in Bit Error Rate, which implies improved performance of the mobile radio network. In addition, this work did not only include experiments done through empirical measurement but also enhanced mathematical models that were developed and implemented as a reference model for accurate prediction. The proposed signal models were based on the analysis of continuous time and discrete space, and some other assumptions. These developed (proposed) enhanced models were validated using MATLAB (version 7.6.3.35) program and compared with the conventional antenna for accuracy. These outage models were used to manage the blocked call experience in the mobile radio network. 20% improvement was obtained when the adaptive beamforming antenna arrays were implemented on the wireless mobile radio network.

Keywords: beamforming algorithm, adaptive beamforming, simulink, reception

Procedia PDF Downloads 26

Authors: Amanda T. Badaró, Douglas F. Barbin, Sofia T. Garcia, Maria Teresa P. S. Clerici, Amanda R. Ferreira

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Food fortification is the intentional addition of a nutrient in a food matrix and has been widely used to overcome the lack of nutrients in the diet or increasing the nutritional value of food. Fortified food must meet the demand of the population, taking into account their habits and risks that these foods may cause. Wheat and its by-products, such as semolina, has been strongly indicated to be used as a food vehicle since it is widely consumed and used in the production of other foods. These products have been strategically used to add some nutrients, such as fibers. Methods of analysis and quantification of these kinds of components are destructive and require lengthy sample preparation and analysis. Therefore, the industry has searched for faster and less invasive methods, such as Near-Infrared Spectroscopy (NIR). NIR is a rapid and cost-effective method, however, it is based on indirect measurements, yielding high amount of data. Therefore, NIR spectroscopy requires calibration with mathematical and statistical tools (Chemometrics) to extract analytical information from the corresponding spectra, as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). PCA is well suited for NIR, once it can handle many spectra at a time and be used for non-supervised classification. Advantages of the PCA, which is also a data reduction technique, is that it reduces the data spectra to a smaller number of latent variables for further interpretation. On the other hand, LDA is a supervised method that searches the Canonical Variables (CV) with the maximum separation among different categories. In LDA, the first CV is the direction of maximum ratio between inter and intra-class variances. The present work used a portable infrared spectrometer (NIR) for identification and classification of pure and fiber-fortified semolina samples. The fiber was added to semolina in two different concentrations, and after the spectra acquisition, the data was used for PCA and LDA to identify and discriminate the samples. The results showed that NIR spectroscopy associate to PCA was very effective in identifying pure and fiber-fortified semolina. Additionally, the classification range of the samples using LDA was between 78.3% and 95% for calibration and 75% and 95% for cross-validation. Thus, after the multivariate analysis such as PCA and LDA, it was possible to verify that NIR associated to chemometric methods is able to identify and classify the different samples in a fast and non-destructive way.

Keywords: Chemometrics, fiber, linear discriminant analysis, near-infrared spectroscopy, principal component analysis, semolina

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Authors: Dino Carpentras, Alejandro Dinkelberg, Michael Quayle

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Opinion dynamics models use an agent-based modeling approach to model people’s opinions. Model's properties are usually explored by testing the two 'degrees of freedom': the interaction rule and the network topology. The latter defines the connection, and thus the possible interaction, among agents. The interaction rule, instead, determines how agents select each other and update their own opinion. Here we show the existence of the third degree of freedom. This can be used for turning one model into each other or to change the model’s output up to 100% of its initial value. Opinion dynamics models represent the evolution of real-world opinions parsimoniously. Thus, it is fundamental to know how real-world opinion (e.g., supporting a candidate) could be turned into a number. Specifically, we want to know if, by choosing a different opinion-to-number transformation, the model’s dynamics would be preserved. This transformation is typically not addressed in opinion dynamics literature. However, it has already been studied in psychometrics, a branch of psychology. In this field, real-world opinions are converted into numbers using abstract objects called 'scales.' These scales can be converted one into the other, in the same way as we convert meters to feet. Thus, in our work, we analyze how this scale transformation may affect opinion dynamics models. We perform our analysis both using mathematical modeling and validating it via agent-based simulations. To distinguish between scale transformation and measurement error, we first analyze the case of perfect scales (i.e., no error or noise). Here we show that a scale transformation may change the model’s dynamics up to a qualitative level. Meaning that a researcher may reach a totally different conclusion, even using the same dataset just by slightly changing the way data are pre-processed. Indeed, we quantify that this effect may alter the model’s output by 100%. By using two models from the standard literature, we show that a scale transformation can transform one model into the other. This transformation is exact, and it holds for every result. Lastly, we also test the case of using real-world data (i.e., finite precision). We perform this test using a 7-points Likert scale, showing how even a small scale change may result in different predictions or a number of opinion clusters. Because of this, we think that scale transformation should be considered as a third-degree of freedom for opinion dynamics. Indeed, its properties have a strong impact both on theoretical models and for their application to real-world data.

Keywords: degrees of freedom, empirical validation, opinion scale, opinion dynamics

Procedia PDF Downloads 146

Authors: Sheraz Ahmad, Li Yiming, Li XiangFang, Xia Wei, Zeen Chen

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Holding CO₂ at massive scale in the enclathrated solid matter called hydrate can be perceived as one of the most reliable methods for CO₂ sequestration to take greenhouse gases emission control measures and global warming preventive actions. In this study, a dynamically coupled mass and heat transfer mathematical model is developed which elaborates the unsteady behavior of CO₂ flowing into a porous medium and converting itself into hydrates. The combined numerical model solution by implicit finite difference method is explained and through coupling the mass, momentum and heat conservation relations, an integrated model can be established to analyze the CO₂ hydrate growth within P-T equilibrium conditions. CO₂ phase transition, effect of hydrate nucleation by exothermic heat release and variations of thermo-physical properties has been studied during hydrate nucleation. The results illustrate that formation pressure distribution becomes stable at the early stage of hydrate nucleation process and always remains stable afterward, but formation temperature is unable to keep stable and varies during CO₂ injection and hydrate nucleation process. Initially, the temperature drops due to cold high-pressure CO₂ injection since when the massive hydrate growth triggers and temperature increases under the influence of exothermic heat evolution. Intermittently, it surpasses the initial formation temperature before CO₂ injection initiates. The hydrate growth rate increases by increasing injection pressure in the long formation and it also expands overall hydrate covered length in the same induction period. The results also show that the injection pressure conditions and hydrate growth rate affect other parameters like CO₂ velocity, CO₂ permeability, CO₂ density, CO₂ and H₂O saturation inside the porous medium. In order to enhance the hydrate growth rate and expand hydrate covered length, the injection temperature is reduced, but it did not give satisfactory outcomes. Hence, CO₂ injection in vacated natural gas hydrate porous sediment may form hydrate under low temperature and high-pressure conditions, but it seems very challenging on a huge scale in lengthy formations.

Keywords: CO₂ hydrates, CO₂ injection, CO₂ Phase transition, CO₂ sequestration

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Authors: Mohanad Al-Behadili, Xiang Song, Djamila Ouelhadj, Alex Fraess-Ehrfeld

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In off-shore wind farms, turbine blade inspection accessibility under various sea states is very challenging and greatly affects the downtime of wind turbines. Maintenance of any offshore system is not an easy task due to the restricted logistics and accessibility. The multirotor unmanned helicopter is of increasing interest in inspection applications due to its manoeuvrability and payload capacity. These advantages increase when many of them are deployed simultaneously in a swarm. Hence this paper proposes a drone swarm framework for inspecting offshore wind turbine blades and nacelles so as to reduce downtime. One of the big challenges of this task is that when operating a drone swarm, an individual drone may not have enough power to fly and communicate during missions and it has no capability of refueling due to its small size. Once the drone power is drained, there are no signals transmitted and the links become intermittent. Vessels equipped with 5G masts and small power units are utilised as platforms for drones to recharge/swap batteries. The research work aims at designing a smart energy management system, which provides automated vessel and drone routing and recharging plans. To achieve this goal, a novel mathematical optimisation model is developed with the main objective of minimising the number of drones and vessels, which carry the charging stations, and the downtime of the wind turbines. There are a number of constraints to be considered, such as each wind turbine must be inspected once and only once by one drone; each drone can inspect at most one wind turbine after recharging, then fly back to the charging station; collision should be avoided during the drone flying; all wind turbines in the wind farm should be inspected within the given time window. We have developed a real-time Ant Colony Optimisation (ACO) algorithm to generate real-time and near-optimal solutions to the drone swarm routing problem. The schedule will generate efficient and real-time solutions to indicate the inspection tasks, time windows, and the optimal routes of the drones to access the turbines. Experiments are conducted to evaluate the quality of the solutions generated by ACO.

Keywords: drone swarm, routing, scheduling, optimisation model, ant colony optimisation

Procedia PDF Downloads 245

Authors: M. Hossain, A. Abdkader, C. Cherif, A. Berger, M. Sparing, R. Hühne, L. Schultz, K. Nielsch

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Due to the best quality of yarn and the flexibility of the machine, the ring spinning process is the most widely used spinning method for short staple yarn production. However, the productivity of these machines is still much lower in comparison to other spinning systems such as rotor or air-jet spinning process. The main reason for this limitation lies on the twisting mechanism of the ring spinning process. In the ring/traveler twisting system, each rotation of the traveler along with the ring inserts twist in the yarn. The rotation of the traveler at higher speed includes strong frictional forces, which in turn generates heat. Different ring/traveler systems concerning with its geometries, material combinations and coatings have already been implemented to solve the frictional problem. However, such developments can neither completely solve the frictional problem nor increase the productivity. The friction free superconducting magnetic bearing (SMB) system can be a right alternative replacing the existing ring/traveler system. The unique concept of SMB bearings is that they possess a self-stabilizing behavior, i.e. they remain fully passive without any necessity for expensive position sensing and control. Within the framework of a research project funded by German research foundation (DFG), suitable concepts of the SMB-system have been designed, developed, and integrated as a twisting device of ring spinning replacing the existing ring/traveler system. With the help of the developed mathematical model and experimental investigation, the physical limitations of this innovative twisting device in the spinning process have been determined. The interaction among the parameters of the spinning process and the superconducting twisting element has been further evaluated, which derives the concrete information regarding the new spinning process. Moreover, the influence of the implemented SMB twisting system on the yarn quality has been analyzed with respect to different process parameters. The presented work reveals the enormous potential of the innovative twisting mechanism, so that the productivity of the ring spinning process especially in case of thermoplastic materials can be at least doubled for the first time in a hundred years. The SMB ring spinning tester has also been presented in the international fair “International Textile Machinery Association (ITMA) 2015”.

Keywords: ring spinning, superconducting magnetic bearing, yarn properties, productivity

Procedia PDF Downloads 228

Authors: Binder Hans

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Cancer is no longer seen as solely a genetic disease where genetic defects such as mutations and copy number variations affect gene regulation and eventually lead to aberrant cell functioning which can be monitored by transcriptome analysis. It has become obvious that epigenetic alterations represent a further important layer of (de-)regulation of gene activity. For example, aberrant DNA methylation is a hallmark of many cancer types, and methylation patterns were successfully used to subtype cancer heterogeneity. Hence, unraveling the interplay between different omics levels such as genome, transcriptome and epigenome is inevitable for a mechanistic understanding of molecular deregulation causing complex diseases such as cancer. This objective requires powerful downstream integrative bioinformatics methods as an essential prerequisite to discover the whole genome mutational, transcriptome and epigenome landscapes of cancer specimen and to discover cancer genesis, progression and heterogeneity. Basic challenges and tasks arise ‘beyond sequencing’ because of the big size of the data, their complexity, the need to search for hidden structures in the data, for knowledge mining to discover biological function and also systems biology conceptual models to deduce developmental interrelations between different cancer states. These tasks are tightly related to cancer biology as an (epi-)genetic disease giving rise to aberrant genomic regulation under micro-environmental control and clonal evolution which leads to heterogeneous cellular states. Machine learning algorithms such as self organizing maps (SOM) represent one interesting option to tackle these bioinformatics tasks. The SOMmethod enables recognizing complex patterns in large-scale data generated by highthroughput omics technologies. It portrays molecular phenotypes by generating individualized, easy to interpret images of the data landscape in combination with comprehensive analysis options. Our image-based, reductionist machine learning methods provide one interesting perspective how to deal with massive data in the discovery of complex diseases, gliomas, melanomas and colon cancer on molecular level. As an important new challenge, we address the combined portrayal of different omics data such as genome-wide genomic, transcriptomic and methylomic ones. The integrative-omics portrayal approach is based on the joint training of the data and it provides separate personalized data portraits for each patient and data type which can be analyzed by visual inspection as one option. The new method enables an integrative genome-wide view on the omics data types and the underlying regulatory modes. It is applied to high and low-grade gliomas and to melanomas where it disentangles transversal and longitudinal molecular heterogeneity in terms of distinct molecular subtypes and progression paths with prognostic impact.

Keywords: integrative bioinformatics, machine learning, molecular mechanisms of cancer, gliomas and melanomas

Procedia PDF Downloads 139

Authors: Vincenzo Piemonte

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The liver is a complex, large-scale biochemical reactor which plays a unique role in the human physiology. When liver ceases to perform its physiological activity, a functional replacement is required. Actually, liver transplantation is the only clinically effective method of treating severe liver disease. Anyway, the aforementioned therapeutic approach is hampered by the disparity between organ availability and the number of patients on the waiting list. In order to overcome this critical issue, research activities focused on liver support device systems (LSDs) designed to bridging patients to transplantation or to keep them alive until the recovery of native liver function. In recirculating albumin dialysis devices, such as MARS (Molecular Adsorbed Recirculating System), adsorption is one of the fundamental steps in albumin-dialysate regeneration. Among the albumin-bound toxins that must be removed from blood during liver-failure therapy, bilirubin and tryptophan can be considered as representative of two different toxin classes. The first one, not water soluble at physiological blood pH and strongly bounded to albumin, the second one, loosely albumin bound and partially water soluble at pH 7.4. Fixed bed units are normally used for this task, and the design of such units requires information both on toxin adsorption equilibrium and kinetics. The most common adsorptive media used in LSDs are activated carbon, non-ionic polymeric resins and anionic resins. In this paper, bilirubin adsorption isotherms on different adsorptive media, such as polymeric resin, albumin-coated resin, anionic resin, activated carbon and alginate beads with entrapped albumin are presented. By comparing all the results, it can be stated that the adsorption capacity for bilirubin of the five different media increases in the following order: Alginate beads < Polymeric resin < Albumin-coated resin < Activated carbon < Anionic resin. The main focus of this paper is to provide useful guidelines for the optimization of liver support devices which implement adsorption columns to remove albumin-bound toxins from albumin dialysate solutions.

Keywords: adsorptive media, adsorption equilibrium, artificial liver devices, bilirubin, mathematical modelling

Procedia PDF Downloads 250

Authors: Mohamed Korany, Azza Gazy, Essam Khamis, Marwa Adel, Miranda Fawzy

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Background: Two new, simple and specific methods; First, a Zero-crossing first-derivative technique and second, a chemometric-assisted spectrophotometric artificial neural network (ANN) were developed and validated in accordance with ICH guidelines. Both methods were used for the simultaneous estimation of the two closely related antioxidant nutraceuticals ; Coenzyme Q10 (Q) ; also known as Ubidecarenone or Ubiquinone-10, and Vitamin E (E); alpha-tocopherol acetate, in their pharmaceutical binary mixture. Results: For first method: By applying the first derivative, both Q and E were alternatively determined; each at the zero-crossing of the other. The D1 amplitudes of Q and E, at 285 nm and 235 nm respectively, were recorded and correlated to their concentrations. The calibration curve is linear over the concentration range of 10-60 and 5.6-70 μg mL-1 for Q and E, respectively. For second method: ANN (as a multivariate calibration method) was developed and applied for the simultaneous determination of both analytes. A training set (or a concentration set) of 90 different synthetic mixtures containing Q and E, in wide concentration ranges between 0-100 µg/mL and 0-556 µg/mL respectively, were prepared in ethanol. The absorption spectra of the training sets were recorded in the spectral region of 230–300 nm. A Gradient Descend Back Propagation ANN chemometric calibration was computed by relating the concentration sets (x-block) to their corresponding absorption data (y-block). Another set of 45 synthetic mixtures of the two drugs, in defined range, was used to validate the proposed network. Neither chemical separation, preparation stage nor mathematical graphical treatment were required. Conclusions: The proposed methods were successfully applied for the assay of Q and E in laboratory prepared mixtures and combined pharmaceutical tablet with excellent recoveries. The ANN method was superior over the derivative technique as the former determined both drugs in the non-linear experimental conditions. It also offers rapidity, high accuracy, effort and money saving. Moreover, no need for an analyst for its application. Although the ANN technique needed a large training set, it is the method of choice in the routine analysis of Q and E tablet. No interference was observed from common pharmaceutical additives. The results of the two methods were compared together

Keywords: coenzyme Q10, vitamin E, chemometry, quantitative analysis, first derivative spectrophotometry, artificial neural network

Procedia PDF Downloads 436

Authors: Ehsan Javanmardemamgheisi, Javad Mehr, Jason Ali-Lavroff, Damien Holloway, Michael Davis

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During the last decades, a growing interest in faster and more efficient waterborne transportation has led to the development of high-speed vessels for both commercial and military applications. To satisfy this global demand, a wide variety of arrangements of high-speed crafts have been proposed by designers. Among them, high-speed catamarans have proven themselves to be a suitable Roll-on/Roll-off configuration for carrying passengers and cargo due to widely spaced demi hulls, a wide deck zone, and a high ratio of deadweight to displacement. To improve passenger comfort and crew workability and enhance the operability and performance of high-speed catamarans, mitigating the severity of motions and structural loads using Ride Control Systems (RCS) is essential.In this paper, a set of towing tank tests was conducted on a 2.5 m scaled model of a 112 m Incat Tasmania high-speed catamaran in irregular head seas to investigate the effect of different ride control algorithms including linear and nonlinear versions of the heave control, pitch control, and local control on motion responses and passenger comfort of the full-scale ship. The RCS included a centre bow-fitted T-Foil and two transom-mounted stern tabs. All the experiments were conducted at the Australian Maritime College (AMC) towing tank at a model speed of 2.89 m/s (37 knots full scale), a modal period of 1.5 sec (10 sec full scale) and two significant wave heights of 60 mm and 90 mm, representing full-scale wave heights of 2.7 m and 4 m, respectively. Spectral analyses were performed using Welch’s power spectral density method on the vertical motion time records of the catamaran model to calculate heave and pitch Response Amplitude Operators (RAOs). Then, noting that passenger discomfort arises from vertical accelerations and that the vertical accelerations vary at different longitudinal locations within the passenger cabin due to the variations in amplitude and relative phase of the pitch and heave motions, the vertical accelerations were calculated at three longitudinal locations (LCG, T-Foil, and stern tabs). Finally, frequency-weighted Root Mean Square (RMS) vertical accelerations were calculated to estimate Motion Sickness Dose Value (MSDV) of the ship based on ISO 2631-recommendations. It was demonstrated that in small seas, implementing a nonlinear pitch control algorithm reduces the peak pitch motions by 41%, the vertical accelerations at the forward location by 46%, and motion sickness at the forward position by around 20% which provides great potential for further improvement in passenger comfort, crew workability, and operability of high-speed catamarans.

Keywords: high-speed catamarans, ride control system, response amplitude operators, vertical accelerations, motion sickness, irregular waves, towing tank tests.

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Authors: Ahmed Abdulrahman, Jalal Foroozesh

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CO₂ based enhanced oil recovery (EOR) techniques have gained massive attention from major oil firms since they resolve the industry's two main concerns of CO₂ contribution to the greenhouse effect and the declined oil production. Carbonated water injection (CWI) is a promising EOR technique that promotes safe and economic CO₂ storage; moreover, it mitigates the pitfalls of CO₂ injection, which include low sweep efficiency, early CO₂ breakthrough, and the risk of CO₂ leakage in fractured formations. One of the main challenges that hinder the wide adoption of this EOR technique is the complexity of accurate modeling of the kinetics of CO₂ mass transfer. The mechanisms of CO₂ mass transfer during CWI include the slow and gradual cross-phase CO₂ diffusion from carbonated water (CW) to the oil phase and the CO₂ dispersion (within phase diffusion and mechanical mixing), which affects the oil physical properties and the spatial spreading of CO₂ inside the reservoir. A 2D non-equilibrium compositional simulator has been developed using a fully implicit finite difference approximation. The material balance term (k) was added to the governing equation to account for the slow cross-phase diffusion of CO₂ from CW to the oil within the gird cell. Also, longitudinal and transverse dispersion coefficients have been added to account for CO₂ spatial distribution inside the oil phase. The CO₂-oil diffusion coefficient was calculated using the Sigmund correlation, while a scale-dependent dispersivity was used to calculate CO₂ mechanical mixing. It was found that the CO₂-oil diffusion mechanism has a minor impact on oil recovery, but it tends to increase the amount of CO₂ stored inside the formation and slightly alters the residual oil properties. On the other hand, the mechanical mixing mechanism has a huge impact on CO₂ spatial spreading (accurate prediction of CO₂ production) and the noticeable change in oil physical properties tends to increase the recovery factor. A sensitivity analysis has been done to investigate the effect of formation heterogeneity (porosity, permeability) and injection rate, it was found that the formation heterogeneity tends to increase CO₂ dispersion coefficients, and a low injection rate should be implemented during CWI.

Keywords: CO₂ mass transfer, carbonated water injection, CO₂ dispersion, CO₂ diffusion, cross phase CO₂ diffusion, within phase CO2 diffusion, CO₂ mechanical mixing, non-equilibrium simulation

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Authors: Ruth Hegarty, Noel Connaughton

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Sustainability reporting has become a priority for many global multinational companies. This is associated with ever-increasing expectations from key stakeholders for companies to be transparent about their strategies, activities and management with regard to sustainability issues. The Global Reporting Initiative (GRI) encourages reporters to only provide information on the issues that are really critical in order to achieve the organisation’s goals for sustainability and manage its impact on environment and society. A key challenge for most reporting organisations is how to identify relevant issues for sustainability reporting and prioritise those material issues in accordance with company and stakeholder needs. A recent study indicates that most of the largest companies listed on the world’s stock exchanges are failing to provide data on key sustainability indicators such as employee turnover, energy, greenhouse gas emissions (GHGs), injury rate, pay equity, waste and water. This paper takes an indepth look at the approaches used by a select number of international sustainability leader corporates to identify key sustainability issues. The research methodology involves performing a detailed analysis of the sustainability report content of up to 50 companies listed on the 2014 Dow Jones Sustainability Indices (DJSI). The most recent sustainability report content found on the GRI Sustainability Disclosure Database is then compared with 91 GRI Specific Standard Disclosures and a small number of GRI Standard Disclosures. Preliminary research indicates significant gaps in the information disclosed in corporate sustainability reports versus the indicator content specified in the GRI Content Index. The following outlines some of the key findings to date: Most companies made a partial disclosure with regard to the Economic indicators of climate change risks and infrastructure investments, but did not focus on the associated negative impacts. The top Environmental indicators disclosed were energy consumption and reductions, GHG emissions, water withdrawals, waste and compliance. The lowest rates of indicator disclosure included biodiversity, water discharge, mitigation of environmental impacts of products and services, transport, environmental investments, screening of new suppliers and supply chain impacts. The top Social indicators disclosed were new employee hires, rates of injury, freedom of association in operations, child labour and forced labour. Lesser disclosure rates were reported for employee training, composition of governance bodies and employees, political contributions, corruption and fines for non-compliance. The reporting on most other Social indicators was found to be poor. In addition, most companies give only a brief explanation on how material issues are defined, identified and ranked. Data on the identification of key stakeholders and the degree and nature of engagement for determining issues and their weightings is also lacking. Generally, little to no data is provided on the algorithms used to score an issue. Research indicates that most companies lack a rigorous and thorough methodology to systematically determine the material issues of sustainability reporting in accordance with company and stakeholder needs.

Keywords: identification of key stakeholders, material issues, sustainability reporting, transparency

Procedia PDF Downloads 299