Search results for: particle swarm optimization algorithm (PSO)

Authors: G. Barański, P. Kacejko, M. Wendeker

Abstract:

The impact of the ignition advance control algorithms of the ASz-62IR-16X aircraft piston engine on a combustion process has been presented in this paper. This aircraft engine is a nine-cylinder 1000 hp engine with a special electronic control ignition system. This engine has two spark plugs per cylinder with an ignition advance angle dependent on load and the rotational speed of the crankshaft. Accordingly, in most cases, these angles are not optimal for power generated. The scope of this paper is focused on developing algorithms to control the ignition advance angle in an electronic ignition control system of an engine. For this type of engine, i.e. radial engine, an ignition advance angle should be controlled independently for each cylinder because of the design of such an engine and its crankshaft system. The ignition advance angle is controlled in an open-loop way, which means that the control signal (i.e. ignition advance angle) is determined according to the previously developed maps, i.e. recorded tables of the correlation between the ignition advance angle and engine speed and load. Load can be measured by engine crankshaft speed or intake manifold pressure. Due to a limited memory of a controller, the impact of other independent variables (such as cylinder head temperature or knock) on the ignition advance angle is given as a series of one-dimensional arrays known as corrective characteristics. The value of the ignition advance angle specified combines the value calculated from the primary characteristics and several correction factors calculated from correction characteristics. Individual cylinder control can proceed in line with certain indicators determined from pressure registered in a combustion chamber. Control is assumed to be based on the following indicators: maximum pressure, maximum pressure angle, indicated mean effective pressure. Additionally, a knocking combustion indicator was defined. Individual control can be applied to a single set of spark plugs only, which results from two fundamental ideas behind designing a control system. Independent operation of two ignition control systems – if two control systems operate simultaneously. It is assumed that the entire individual control should be performed for a front spark plug only and a rear spark plug shall be controlled with a fixed (or specific) offset relative to the front one or from a reference map. The developed algorithms will be verified by simulation and engine test sand experiments. This work has been financed by the Polish National Centre for Research and Development, INNOLOT, under Grant Agreement No. INNOLOT/I/1/NCBR/2013.

Keywords: algorithm, combustion process, radial engine, spark plug

Procedia PDF Downloads 266

Authors: M. Patterson, R. Bond, K. Cowan, M. Mulvenna, C. Reid, F. McMahon, P. McGowan, H. Cormican

Abstract:

This paper outlines the design of a simulator to allow for the optimisation of clinical workflows through a pathology laboratory and to improve the laboratory’s efficiency in the processing, testing, and analysis of specimens. Often pathologists have difficulty in pinpointing and anticipating issues in the clinical workflow until tests are running late or in error. It can be difficult to pinpoint the cause and even more difficult to predict any issues which may arise. For example, they often have no indication of how many samples are going to be delivered to the laboratory that day or at a given hour. If we could model scenarios using past information and known variables, it would be possible for pathology laboratories to initiate resource preparations, e.g. the printing of specimen labels or to activate a sufficient number of technicians. This would expedite the clinical workload, clinical processes and improve the overall efficiency of the laboratory. The simulator design visualises the workflow of the laboratory, i.e. the clinical tests being ordered, the specimens arriving, current tests being performed, results being validated and reports being issued. The simulator depicts the movement of specimens through this process, as well as the number of specimens at each stage. This movement is visualised using an animated flow diagram that is updated in real time. A traffic light colour-coding system will be used to indicate the level of flow through each stage (green for normal flow, orange for slow flow, and red for critical flow). This would allow pathologists to clearly see where there are issues and bottlenecks in the process. Graphs would also be used to indicate the status of specimens at each stage of the process. For example, a graph could show the percentage of specimen tests that are on time, potentially late, running late and in error. Clicking on potentially late samples will display more detailed information about those samples, the tests that still need to be performed on them and their urgency level. This would allow any issues to be resolved quickly. In the case of potentially late samples, this could help to ensure that critically needed results are delivered on time. The simulator will be created as a single-page web application. Various web technologies will be used to create the flow diagram showing the workflow of the laboratory. JavaScript will be used to program the logic, animate the movement of samples through each of the stages and to generate the status graphs in real time. This live information will be extracted from an Oracle database. As well as being used in a real laboratory situation, the simulator could also be used for training purposes. ‘Bots’ would be used to control the flow of specimens through each step of the process. Like existing software agents technology, these bots would be configurable in order to simulate different situations, which may arise in a laboratory such as an emerging epidemic. The bots could then be turned on and off to allow trainees to complete the tasks required at that step of the process, for example validating test results.

Keywords: laboratory-process, optimization, pathology, computer simulation, workflow

Procedia PDF Downloads 252

Authors: Barakat Adebisi Raji

Abstract:

In the workplace, Artificial Intelligence has, in recent years, emerged as a transformative technology that revolutionizes how organizations operate and perform tasks. It is a technology that has a significant impact on transportation, manufacturing, education, cyber security, robotics, agriculture, healthcare, and so many other organizations. By harnessing AI technology, workplaces can enhance productivity, streamline processes, and make more informed decisions. Given the potential of AI to change the way we work and its impact on the labor market in years to come, employers understand that it entails legal challenges and risks despite the advantages inherent in it. Therefore, as AI continues to integrate into various aspects of the workplace, understanding the legal and ethical implications becomes paramount. Also central to this study is the question of who is held liable where AI makes any defaults; the person (company) who created the AI, the person who programmed the AI algorithm or the person who uses the AI? Thus, the aim of this paper is to provide a detailed overview of how AI-related liabilities are addressed under each legal tradition and shed light on potential areas of accord and divergence between the two legal cultures. The objectives of this paper are to (i) examine the ability of Common law and Islamic law to accommodate the issues and damage caused by AI in the workplace and the legality of compensation for such injury sustained; (ii) to discuss the extent to which AI can be described as a legal personality to bear responsibility: (iii) examine the similarities and disparities between Common Law and Islamic Jurisprudence on the liability of AI in the workplace. The methodology adopted in this work was qualitative, and the method was purely a doctrinal research method where information is gathered from the primary and secondary sources of law, such as comprehensive materials found in journal articles, expert-authored books and online news sources. Comparative legal method was also used to juxtapose the approach of Islam and Common Law. The paper concludes that since AI, in its current legal state, is not recognized as a legal entity, operators or manufacturers of AI should be held liable for any damage that arises, and the determination of who bears the responsibility should be dependent on the circumstances surrounding each scenario. The study recommends the granting of legal personality to AI systems, the establishment of legal rights and liabilities for AI, the establishment of a holistic Islamic virtue-based AI ethics framework, and the consideration of Islamic ethics.

Keywords: AI, health- care, agriculture, cyber security, common law, Shari'ah

Procedia PDF Downloads 8

Authors: Jožef Ritonja, Bojan Grčar, Boštjan Polajžer

Abstract:

In recent years, electricity trade between neighboring countries has become increasingly intense. Increasing power transmission over long distances has resulted in an increase in the oscillations of the transmitted power. The damping of the oscillations can be carried out with the reconfiguration of the network or the replacement of generators, but such solution is not economically reasonable. The only cost-effective solution to improve the damping of power oscillations is to use power system stabilizers. Power system stabilizer represents a part of synchronous generator control system. It utilizes semiconductor’s excitation system connected to the rotor field excitation winding to increase the damping of the power system. The majority of the synchronous generators are equipped with the conventional power system stabilizers with fixed parameters. The control structure of the conventional power system stabilizers and the tuning procedure are based on the linear control theory. Conventional power system stabilizers are simple to realize, but they show non-sufficient damping improvement in the entire operating conditions. This is the reason that advanced control theories are used for development of better power system stabilizers. In this paper, the adaptive control theory for power system stabilizers design and synthesis is studied. The presented work is focused on the use of model reference adaptive control approach. Control signal, which assures that the controlled plant output will follow the reference model output, is generated by the adaptive algorithm. Adaptive gains are obtained as a combination of the "proportional" term and with the σ-term extended "integral" term. The σ-term is introduced to avoid divergence of the integral gains. The necessary condition for asymptotic tracking is derived by means of hyperstability theory. The benefits of the proposed model reference adaptive power system stabilizer were evaluated as objectively as possible by means of a theoretical analysis, numerical simulations and laboratory realizations. Damping of the synchronous generator oscillations in the entire operating range was investigated. Obtained results show the improved damping in the entire operating area and the increase of the power system stability. The results of the presented work will help by the development of the model reference power system stabilizer which should be able to replace the conventional stabilizers in power systems.

Keywords: power system, stability, oscillations, power system stabilizer, model reference adaptive control

Procedia PDF Downloads 111

Authors: Huidan Yu, Anurag Deb, Rou Chen, I-Wen Wang

Abstract:

The use of left ventricle assist devices (LVADs) in patients with heart failure has been a proven and effective therapy for patients with severe end-stage heart failure. Due to the limited availability of suitable donor hearts, LVADs will probably become the alternative solution for patient with heart failure in the near future. While the LVAD is being continuously improved toward enhanced performance, increased device durability, reduced size, a better understanding of implantation management becomes critical in order to achieve better long-term blood supplies and less post-surgical complications such as thrombi generation. Important issues related to the LVAD implantation include the location of outflow grafting (OG), the angle of the OG, the combination between LVAD and native heart pumping, uniform or pulsatile flow at OG, etc. We have hypothesized that an optimal implantation of LVAD is patient specific. To test this hypothesis, we employ a novel in-house computational modeling technique, named InVascular, to conduct a systematic evaluation of cardiac output at aortic arch together with other pertinent hemodynamic quantities for each patient under various implantation scenarios aiming to get an optimal implantation strategy. InVacular is a powerful computational modeling technique that integrates unified mesoscale modeling for both image segmentation and fluid dynamics with the cutting-edge GPU parallel computing. It first segments the aortic artery from patient’s CT image, then seamlessly feeds extracted morphology, together with the velocity wave from Echo Ultrasound image of the same patient, to the computation model to quantify 4-D (time+space) velocity and pressure fields. Using one NVIDIA Tesla K40 GPU card, InVascular completes a computation from CT image to 4-D hemodynamics within 30 minutes. Thus it has the great potential to conduct massive numerical simulation and analysis. The systematic evaluation for one patient includes three OG anastomosis (ascending aorta, descending thoracic aorta, and subclavian artery), three combinations of LVAD and native heart pumping (1:1, 1:2, and 1:3), three angles of OG anastomosis (inclined upward, perpendicular, and inclined downward), and two LVAD inflow conditions (uniform and pulsatile). The optimal LVAD implantation is suggested through a comprehensive analysis of the cardiac output and related hemodynamics from the simulations over the fifty-four scenarios. To confirm the hypothesis, 5 random patient cases will be evaluated.

Keywords: graphic processing unit (GPU) parallel computing, left ventricle assist device (LVAD), lumped-parameter model, patient-specific computational hemodynamics

Procedia PDF Downloads 110

Authors: Agnieszka Krzemińska, Katarzyna Świderek, Vicente Molinier, Piotr Paneth

Abstract:

In order to understand in details the interactions between ligands and the enzyme isotope effects were studied between clinically used drugs that bind in the active site of Human Immunodeficiency Virus Reverse Transcriptase, HIV-1 RT, as well as triazole-based inhibitor that binds in the allosteric pocket of this enzyme. The magnitudes and origins of the resulting binding isotope effects were analyzed. Subsequently, binding isotope effect of the same triazole-based inhibitor bound in the active site were analyzed and compared. Together, these results show differences in binding origins in two sites of the enzyme and allow to analyze binding mode and place of newly synthesized inhibitors. Typical protocol is described below on the example of triazole ligand in the allosteric pocket. Triazole was docked into allosteric cavity of HIV-1 RT with Glide using extra-precision mode as implemented in Schroedinger software. The structure of HIV-1 RT was obtained from Protein Data Bank as structure of PDB ID 2RKI. The pKa for titratable amino acids was calculated using PROPKA software, and in order to neutralize the system 15 Cl- were added using tLEaP package implemented in AMBERTools ver.1.5. Also N-terminals and C-terminals were build using tLEaP. The system was placed in 144x160x144Å3 orthorhombic box of water molecules using NAMD program. Missing parameters for triazole were obtained at the AM1 level using Antechamber software implemented in AMBERTools. The energy minimizations were carried out by means of a conjugate gradient algorithm using NAMD. Then system was heated from 0 to 300 K with temperature increment 0.001 K. Subsequently 2 ns Langevin−Verlet (NVT) MM MD simulation with AMBER force field implemented in NAMD was carried out. Periodic Boundary Conditions and cut-offs for the nonbonding interactions, range radius from 14.5 to 16 Å, are used. After 2 ns relaxation 200 ps of QM/MM MD at 300 K were simulated. The triazole was treated quantum mechanically at the AM1 level, protein was described using AMBER and water molecules were described using TIP3P, as implemented in fDynamo library. Molecules 20 Å apart from the triazole were kept frozen, with cut-offs established on range radius from 14.5 to 16 Å. In order to describe interactions between triazole and RT free energy of binding using Free Energy Perturbation method was done. The change in frequencies from ligand in solution to ligand bounded in enzyme was used to calculate binding isotope effects.

Keywords: binding isotope effects, molecular dynamics, HIV, reverse transcriptase

Procedia PDF Downloads 405

Authors: Dipo Dunsin, Mohamed C. Ghanem, Karim Ouazzane

Abstract:

Digital investigators often have a hard time spotting evidence in digital information. It has become hard to determine which source of proof relates to a specific investigation. A growing concern is that the various processes, technology, and specific procedures used in the digital investigation are not keeping up with criminal developments. Therefore, criminals are taking advantage of these weaknesses to commit further crimes. In digital forensics investigations, artificial intelligence is invaluable in identifying crime. It has been observed that an algorithm based on artificial intelligence (AI) is highly effective in detecting risks, preventing criminal activity, and forecasting illegal activity. Providing objective data and conducting an assessment is the goal of digital forensics and digital investigation, which will assist in developing a plausible theory that can be presented as evidence in court. Researchers and other authorities have used the available data as evidence in court to convict a person. This research paper aims at developing a multiagent framework for digital investigations using specific intelligent software agents (ISA). The agents communicate to address particular tasks jointly and keep the same objectives in mind during each task. The rules and knowledge contained within each agent are dependent on the investigation type. A criminal investigation is classified quickly and efficiently using the case-based reasoning (CBR) technique. The MADIK is implemented using the Java Agent Development Framework and implemented using Eclipse, Postgres repository, and a rule engine for agent reasoning. The proposed framework was tested using the Lone Wolf image files and datasets. Experiments were conducted using various sets of ISA and VMs. There was a significant reduction in the time taken for the Hash Set Agent to execute. As a result of loading the agents, 5 percent of the time was lost, as the File Path Agent prescribed deleting 1,510, while the Timeline Agent found multiple executable files. In comparison, the integrity check carried out on the Lone Wolf image file using a digital forensic tool kit took approximately 48 minutes (2,880 ms), whereas the MADIK framework accomplished this in 16 minutes (960 ms). The framework is integrated with Python, allowing for further integration of other digital forensic tools, such as AccessData Forensic Toolkit (FTK), Wireshark, Volatility, and Scapy.

Keywords: artificial intelligence, computer science, criminal investigation, digital forensics

Procedia PDF Downloads 182

Authors: Eun-Taek Sin, Hyun-Ju Jang, Chang Geun Song, Yong-Sik Han

Abstract:

Due to the climate change, the coastal urban area repeatedly suffers from the loss of property and life by flooding. There are three main causes of inland submergence. First, when heavy rain with high intensity occurs, the water quantity in inland cannot be drained into rivers by increase in impervious surface of the land development and defect of the pump, storm sewer. Second, river inundation occurs then water surface level surpasses the top of levee. Finally, Coastal inundation occurs due to rising sea water. However, previous studies ignored the complex mechanism of flooding, and showed discrepancy and inadequacy due to linear summation of each analysis result. In this study, inland flooding and river inundation were analyzed together by HDM-2D model. Petrov-Galerkin stabilizing method and flux-blocking algorithm were applied to simulate the inland flooding. In addition, sink/source terms with exponentially growth rate attribute were added to the shallow water equations to include the inland flooding analysis module. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. The applications of developed model gave satisfactory results, and provided accurate prediction in comprehensive flooding analysis. To consider the coastal surge, another module was developed by adding seawater to the existing Inland Flooding-River Inundation binding module for comprehensive flooding analysis. Based on the combined modules, the Coastal Inundation – Inland & River Flow Interface was simulated by inputting the flow rate and depth data in artificial flume. Accordingly, it was able to analyze the flood patterns of coastal cities over time. This study is expected to help identify the complex causes of flooding in coastal areas where complex flooding occurs, and assist in analyzing damage to coastal cities. Acknowledgements—This research was supported by a grant ‘Development of the Evaluation Technology for Complex Causes of Inundation Vulnerability and the Response Plans in Coastal Urban Areas for Adaptation to Climate Change’ [MPSS-NH-2015-77] from the Natural Hazard Mitigation Research Group, Ministry of Public Safety and Security of Korea.

Keywords: flooding analysis, river inundation, inland flooding, 2D hydrodynamic model

Procedia PDF Downloads 335

Authors: Vinicius G. Goecks, Gregory M. Gremillion, William D. Nothwang

Abstract:

Modern approaches to train intelligent agents rely on prolonged training sessions, high amounts of input data, and multiple interactions with the environment. This restricts the application of these learning algorithms in robotics and real-world applications, in which there is low tolerance to inadequate actions, interactions are expensive, and real-time processing and action are required. This paper addresses this issue introducing CyberSteer, a novel approach to efficiently design intrinsic reward functions based on human intention to guide deep reinforcement learning agents with no environment-dependent rewards. CyberSteer uses non-expert human operators for initial demonstration of a given task or desired behavior. The trajectories collected are used to train a behavior cloning deep neural network that asynchronously runs in the background and suggests actions to the deep reinforcement learning module. An intrinsic reward is computed based on the similarity between actions suggested and taken by the deep reinforcement learning algorithm commanding the agent. This intrinsic reward can also be reshaped through additional human demonstration or critique. This approach removes the need for environment-dependent or hand-engineered rewards while still being able to safely shape the behavior of autonomous robotic agents, in this case, based on human intention. CyberSteer is tested in a high-fidelity unmanned aerial vehicle simulation environment, the Microsoft AirSim. The simulated aerial robot performs collision avoidance through a clustered forest environment using forward-looking depth sensing and roll, pitch, and yaw references angle commands to the flight controller. This approach shows that the behavior of robotic systems can be shaped in a reduced amount of time when guided by a non-expert human, who is only aware of the high-level goals of the task. Decreasing the amount of training time required and increasing safety during training maneuvers will allow for faster deployment of intelligent robotic agents in dynamic real-world applications.

Keywords: human-robot interaction, intelligent robots, robot learning, semisupervised learning, unmanned aerial vehicles

Procedia PDF Downloads 240

Authors: Kumar Shashvat, Amol P. Bhondekar

Abstract:

In the midst of the five senses, odor is the most reminiscent and least understood. Odor testing has been mysterious and odor data fabled to most practitioners. The delinquent of recognition and classification of odor is important to achieve. The facility to smell and predict whether the artifact is of further use or it has become undesirable for consumption; the imitation of this problem hooked on a model is of consideration. The general industrial standard for this classification is color based anyhow; odor can be improved classifier than color based classification and if incorporated in machine will be awfully constructive. For cataloging of odor for peas, trees and cashews various discriminative approaches have been used Discriminative approaches offer good prognostic performance and have been widely used in many applications but are incapable to make effectual use of the unlabeled information. In such scenarios, generative approaches have better applicability, as they are able to knob glitches, such as in set-ups where variability in the series of possible input vectors is enormous. Generative models are integrated in machine learning for either modeling data directly or as a transitional step to form an indeterminate probability density function. The algorithms or models Linear Discriminant Analysis and Naive Bayes Classifier have been used for classification of the odor of cashews. Linear Discriminant Analysis is a method used in data classification, pattern recognition, and machine learning to discover a linear combination of features that typifies or divides two or more classes of objects or procedures. The Naive Bayes algorithm is a classification approach base on Bayes rule and a set of qualified independence theory. Naive Bayes classifiers are highly scalable, requiring a number of restraints linear in the number of variables (features/predictors) in a learning predicament. The main recompenses of using the generative models are generally a Generative Models make stronger assumptions about the data, specifically, about the distribution of predictors given the response variables. The Electronic instrument which is used for artificial odor sensing and classification is an electronic nose. This device is designed to imitate the anthropological sense of odor by providing an analysis of individual chemicals or chemical mixtures. The experimental results have been evaluated in the form of the performance measures i.e. are accuracy, precision and recall. The investigational results have proven that the overall performance of the Linear Discriminant Analysis was better in assessment to the Naive Bayes Classifier on cashew dataset.

Keywords: odor classification, generative models, naive bayes, linear discriminant analysis

Procedia PDF Downloads 356

Authors: Renée M. Ripken, Johannes G. E. Gardeniers, Séverine Le Gac

Abstract:

Controlling the multiphase behavior of aqueous biomass mixtures is essential when working in the biomass conversion industry. Here, the vapor/liquid equilibria (VLE) of ethylene glycol, glycerol, and xylitol were studied for temperatures between 25 and 200 °C and pressures of 1 to 10 bar. These experiments were performed in a microfluidic platform, which exhibits excellent heat transfer properties so that equilibrium is reached fast. Firstly, the saturated vapor pressure as a function of the temperature and the substrate mole fraction of the substrate was calculated using AspenPlus with a Redlich-Kwong-Soave Boston-Mathias (RKS-BM) model. Secondly, we developed a high-pressure and high-temperature microfluidic set-up for experimental validation. Furthermore, we have studied the multiphase flow pattern that occurs after the saturation temperature was achieved. A glass-silicon microfluidic device containing a 0.4 or 0.2 m long meandering channel with a depth of 250 μm and a width of 250 or 500 μm was fabricated using standard microfabrication techniques. This device was placed in a dedicated chip-holder, which includes a ceramic heater on the silicon side. The temperature was controlled and monitored by three K-type thermocouples: two were located between the heater and the silicon substrate, one to set the temperature and one to measure it, and the third one was placed in a 300 μm wide and 450 μm deep groove on the glass side to determine the heat loss over the silicon. An adjustable back pressure regulator and a pressure meter were added to control and evaluate the pressure during the experiment. Aqueous biomass solutions (10 wt%) were pumped at a flow rate of 10 μL/min using a syringe pump, and the temperature was slowly increased until the theoretical saturation temperature for the pre-set pressure was reached. First and surprisingly, a significant difference was observed between our theoretical saturation temperature and the experimental results. The experimental values were 10’s of degrees higher than the calculated ones and, in some cases, saturation could not be achieved. This discrepancy can be explained in different ways. Firstly, the pressure in the microchannel is locally higher due to both the thermal expansion of the liquid and the Laplace pressure that has to be overcome before a gas bubble can be formed. Secondly, superheating effects are likely to be present. Next, once saturation was reached, the flow pattern of the gas/liquid multiphase system was recorded. In our device, the point of nucleation can be controlled by taking advantage of the pressure drop across the channel and the accurate control of the temperature. Specifically, a higher temperature resulted in nucleation further upstream in the channel. As the void fraction increases downstream, the flow regime changes along the channel from bubbly flow to Taylor flow and later to annular flow. All three flow regimes were observed simultaneously. The findings of this study are key for the development and optimization of a microreactor for hydrogen production from biomass.

Keywords: biomass conversion, high pressure and high temperature microfluidics, multiphase, phase diagrams, superheating

Procedia PDF Downloads 193

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

Abstract:

Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: soccer, analytics, machine learning, database

Procedia PDF Downloads 213

Authors: Alvaro Figueira, Bruno Cabral

Abstract:

Nowadays, students are used to be assessed through an online platform. Educators have stepped up from a period in which they endured the transition from paper to digital. The use of a diversified set of question types that range from quizzes to open questions is currently common in most university courses. In many courses, today, the evaluation methodology also fosters the students’ online participation in forums, the download, and upload of modified files, or even the participation in group activities. At the same time, new pedagogy theories that promote the active participation of students in the learning process, and the systematic use of problem-based learning, are being adopted using an eLearning system for that purpose. However, although there can be a lot of feedback from these activities to student’s, usually it is restricted to the assessments of online well-defined tasks. In this article, we propose an automatic system that informs students of abnormal deviations of a 'correct' learning path in the course. Our approach is based on the fact that by obtaining this information earlier in the semester, may provide students and educators an opportunity to resolve an eventual problem regarding the student’s current online actions towards the course. Our goal is to prevent situations that have a significant probability to lead to a poor grade and, eventually, to failing. In the major learning management systems (LMS) currently available, the interaction between the students and the system itself is registered in log files in the form of registers that mark beginning of actions performed by the user. Our proposed system uses that logged information to derive new one: the time each student spends on each activity, the time and order of the resources used by the student and, finally, the online resource usage pattern. Then, using the grades assigned to the students in previous years, we built a learning dataset that is used to feed a machine learning meta classifier. The produced classification model is then used to predict the grades a learning path is heading to, in the current year. Not only this approach serves the teacher, but also the student to receive automatic feedback on her current situation, having past years as a perspective. Our system can be applied to online courses that integrate the use of an online platform that stores user actions in a log file, and that has access to other student’s evaluations. The system is based on a data mining process on the log files and on a self-feedback machine learning algorithm that works paired with the Moodle LMS.

Keywords: data mining, e-learning, grade prediction, machine learning, student learning path

Procedia PDF Downloads 98

Authors: Davide Cocchi, Andrea Zucchelli, Luca Raimondi, Maria Brugo Tommaso

Abstract:

The increasing attention is given to the issues of environmental pollution and climate change is exponentially stimulating the development of electrically propelled vehicles powered by renewable energy, in particular, the solar one. Given the small amount of solar energy that can be stored and subsequently transformed into propulsive energy, it is necessary to develop vehicles with high mechanical, electrical and aerodynamic efficiencies along with reduced masses. The reduction of the masses is of fundamental relevance especially for the unsprung masses, that is the assembly of those elements that do not undergo a variation of their distance from the ground (wheel, suspension system, hub, upright, braking system). Therefore, the reduction of unsprung masses is fundamental in decreasing the rolling inertia and improving the drivability, comfort, and performance of the vehicle. This principle applies even more in solar propelled vehicles, equipped with an electric motor that is connected directly to the wheel hub. In this solution, the electric motor is integrated inside the wheel. Since the electric motor is part of the unsprung masses, the development of compact and lightweight solutions is of fundamental importance. The purpose of this research is the design development and optimization of a CFRP 16 wheel hub motor for solar propulsion vehicles that can carry up to four people. In addition to trying to maximize aspects of primary importance such as mass, strength, and stiffness, other innovative constructive aspects were explored. One of the main objectives has been to achieve a high geometric packing in order to ensure a reduced lateral dimension, without reducing the power exerted by the electric motor. In the final solution, it was possible to realize a wheel hub motor assembly completely comprised inside the rim width, for a total lateral overall dimension of less than 100 mm. This result was achieved by developing an innovative connection system between the wheel and the rotor with a double purpose: centering and transmission of the driving torque. This solution with appropriate interlocking noses allows the transfer of high torques and at the same time guarantees both the centering and the necessary stiffness of the transmission system. Moreover, to avoid delamination in critical areas, evaluated by means of FEM analysis using 3D Hashin damage criteria, electrospun nanofibrous mats have been interleaved between CFRP critical layers. In order to reduce rolling resistance, the rim has been designed to withstand high inflation pressure. Laboratory tests have been performed on the rim using the Digital Image Correlation technique (DIC). The wheel has been tested for fatigue bending according to E/ECE/324 R124e.

Keywords: composite laminate, delamination, DIC, lightweight vehicle, motor hub wheel, nanofiber

Procedia PDF Downloads 187

Authors: Mariya V. Yanyukina, Michael A. Bolotov

Abstract:

Assembling accuracy is the degree of accordance between the actual values of the parameters obtained during assembly, and the values specified in the assembly drawings and technical specifications. However, the assembling accuracy depends not only on the quality of the production process but also on the correctness of the assembly process. Therefore, preliminary calculations of assembly stages are carried out to verify the correspondence of real geometric parameters to their acceptable values. In the aviation industry, most calculations involve interacting dimensional chains. This greatly complicates the task. Solving such problems requires a special approach. The purpose of this article is to carry out the problem of improving the technology of assembly of aviation units by use of computer calculations. One of the actual examples of the assembly unit, in which there is an interacting dimensional chain, is the turbine wheel of gas turbine engine. Dimensional chain of turbine wheel is formed by geometric parameters of disk and set of blades. The interaction of the dimensional chain consists in the formation of two chains. The first chain is formed by the dimensions that determine the location of the grooves for the installation of the blades, and the dimensions of the blade roots. The second dimensional chain is formed by the dimensions of the airfoil shroud platform. The interaction of the dimensional chain of the turbine wheel is the interdependence of the first and second chains by means of power circuits formed by a plurality of middle parts of the turbine blades. The timeliness of the calculation of the dimensional chain of the turbine wheel is the need to improve the technology of assembly of this unit. The task at hand contains geometric and mathematical components; therefore, its solution can be implemented following the algorithm: 1) research and analysis of production errors by geometric parameters; 2) development of a parametric model in the CAD system; 3) creation of set of CAD-models of details taking into account actual or generalized distributions of errors of geometrical parameters; 4) calculation model in the CAE-system, loading of various combinations of models of parts; 5) the accumulation of statistics and analysis. The main task is to pre-simulate the assembly process by calculating the interacting dimensional chains. The article describes the approach to the solution from the point of view of mathematical statistics, implemented in the software package Matlab. Within the framework of the study, there are data on the measurement of the components of the turbine wheel-blades and disks, as a result of which it is expected that the assembly process of the unit will be optimized by solving dimensional chains.

Keywords: accuracy, assembly, interacting dimension chains, turbine

Procedia PDF Downloads 349

Authors: Sam Derakhshan Deilami, Iain N. Kings, Lynne E. Macaskie, Brajendra K. Sharma, Anthony V. Bridgwater, Joseph Wood

Abstract:

This paper reports the application of a bacteria-supported palladium catalyst to the hydrodeoxygenation (HDO) of pyrolysis bio-oil, towards producing an upgraded transport fuel. Biofuels are key to the timely replacement of fossil fuels in order to mitigate the emissions of greenhouse gases and depletion of non-renewable resources. The process is an essential step in the upgrading of bio-oils derived from industrial by-products such as agricultural and forestry wastes, the crude oil from pyrolysis containing a large amount of oxygen that requires to be removed in order to create a fuel resembling fossil-derived hydrocarbons. The bacteria supported catalyst manufacture is a means of utilizing recycled metals and second life bacteria, and the metal can also be easily recovered from the spent catalysts after use. Comparisons are made between bio-Pd, and a conventional activated carbon supported Pd/C catalyst. Bio-oil was produced by fast pyrolysis of beechwood at 500 C at a residence time below 2 seconds, provided by Aston University. 5 wt % BioPd/C was prepared under reducing conditions, exposing cells of E. coli MC4100 to a solution of sodium tetrachloropalladate (Na2PdCl4), followed by rinsing, drying and grinding to form a powder. Pd/C was procured from Sigma-Aldrich. The HDO experiments were carried out in a 100 mL Parr batch autoclave using ~20g bio-crude oil and 0.6 g bio-Pd/C catalyst. Experimental variables investigated for optimization included temperature (160-350C) and reaction times (up to 5 h) at a hydrogen pressure of 100 bar. Most of the experiments resulted in an aqueous phase (~40%) and an organic phase (~50-60%) as well as gas phase (<5%) and coke (<2%). Study of the temperature and time upon the process showed that the degree of deoxygenation increased (from ~20 % up to 60 %) at higher temperatures in the region of 350 C and longer residence times up to 5 h. However minimum viscosity (~0.035 Pa.s) occurred at 250 C and 3 h residence time, indicating that some polymerization of the oil product occurs at the higher temperatures. Bio-Pd showed a similar degree of deoxygenation (~20 %) to Pd/C at lower temperatures of 160 C, but did not rise as steeply with temperature. More coke was formed over bio-Pd/C than Pd/C at temperatures above 250 C, suggesting that bio-Pd/C may be more susceptible to coke formation than Pd/C. Reactions occurring during bio-oil upgrading include catalytic cracking, decarbonylation, decarboxylation, hydrocracking, hydrodeoxygenation and hydrogenation. In conclusion, it was shown that bio-Pd/C displays an acceptable rate of HDO, which increases with residence time and temperature. However some undesirable reactions also occur, leading to a deleterious increase in viscosity at higher temperatures. Comparisons are also drawn with earlier work on the HDO of Chlorella derived bio-oil manufactured from micro-algae via hydrothermal liquefaction. Future work will analyze the kinetics of the reaction and investigate the effect of bi-metallic catalysts.

Keywords: bio-oil, catalyst, palladium, upgrading

Procedia PDF Downloads 153

Authors: Keda Li, Hong Hu

Abstract:

Auxetic materials, as an emerging artificial designed metamaterial has attracted growing attention due to their promising negative Poisson’s ratio behaviors and tunable properties. The conventional auxetic lattice structures for which the deformation process is governed by a bending-dominated mechanism have faced the limitation of poor mechanical performance for many potential engineering applications. Recently, both load-bearing and energy absorption capabilities have become a crucial consideration in auxetic structure design. This study reports the finite element analysis of a class of hexagonal double-arrowhead auxetic structures with enhanced stiffness and energy absorption performance. The structure design was developed by extending the traditional double-arrowhead honeycomb to a hexagon frame, the stretching-dominated deformation mechanism was determined according to Maxwell’s stability criterion. The finite element (FE) models of 2D lattice structures established with stainless steel material were analyzed in ABAQUS/Standard for predicting in-plane structural deformation mechanism, failure process, and compressive elastic properties. Based on the computational simulation, the parametric analysis was studied to investigate the effect of the structural parameters on Poisson’s ratio and mechanical properties. The geometrical optimization was then implemented to achieve the optimal Poisson’s ratio for the maximum specific energy absorption. In addition, the optimized 2D lattice structure was correspondingly converted into a 3D geometry configuration by using the orthogonally splicing method. The numerical results of 2D and 3D structures under compressive quasi-static loading conditions were compared separately with the traditional double-arrowhead re-entrant honeycomb in terms of specific Young's moduli, Poisson's ratios, and specified energy absorption. As a result, the energy absorption capability and stiffness are significantly reinforced with a wide range of Poisson’s ratio compared to traditional double-arrowhead re-entrant honeycomb. The auxetic behaviors, energy absorption capability, and yield strength of the proposed structure are adjustable with different combinations of joint angle, struts thickness, and the length-width ratio of the representative unit cell. The numerical prediction in this study suggests the proposed concept of hexagonal double-arrowhead structure could be a suitable candidate for the energy absorption applications with a constant request of load-bearing capacity. For future research, experimental analysis is required for the validation of the numerical simulation.

Keywords: auxetic, energy absorption capacity, finite element analysis, negative Poisson's ratio, re-entrant hexagonal honeycomb

Procedia PDF Downloads 64

Authors: Sergey Nikitenko, Vladimir Klishin, Yury Malakhov, Elena Goosen

Abstract:

Transition to renewable energy sources (solar, wind, bioenergy, etc.) and launching of alternative energy generation has weakened the role of coal as a source of energy. The Paris Agreement and assumption of obligations by many nations to orderly reduce CO₂ emissions by means of technological modernization and climate change adaptation has abridged coal demand yet more. This paper aims to assess current resilience of the coal industry to stress and to define prospects for coal production optimization using high technologies pursuant to global challenges and requirements of energy transition. Our research is based on the resilience concept adapted to the coal industry. It is proposed to divide the coal sector into segments depending on the prevailing value chains (VC). Four representative models of VC are identified in the coal sector. The most promising lines of upgrading VC in the coal industry include: •Elongation of VC owing to introduction of clean technologies of coal conversion and utilization; •Creation of parallel VC by means of waste management; •Branching of VC (conversion of a company’s VC into a production network). The upgrade effectiveness is governed in many ways by applicability of advanced coal processing technologies, usability of waste, expandability of production, entrance to non-rival markets and localization of new segments of VC in receiving regions. It is also important that upgrade of VC by means of formation of agile high-tech inter-industry production networks within the framework of operating surface and underground mines can reduce social, economic and ecological risks associated with closure of coal mines. Such promising route of VC upgrade is application of methanotrophic bacteria to produce protein to be used as feed-stuff in fish, poultry and cattle breeding, or in production of ferments, lipoids, sterols, antioxidants, pigments and polysaccharides. Closed mines can use recovered methane as a clean energy source. There exist methods of methane utilization from uncontrollable sources, including preliminary treatment and recovery of methane from air-and-methane mixture, or decomposition of methane to hydrogen and acetylene. Separated hydrogen is used in hydrogen fuel cells to generate power to feed the process of methane utilization and to supply external consumers. Despite the recent paradigm of carbon-free energy generation, it is possible to preserve the coal mining industry using the differentiated approach to upgrade of value chains based on flexible technologies with regard to specificity of mining companies.

Keywords: resilience, resilience concept, resilience indicator, resilience in the Russian coal industry, value chains

Procedia PDF Downloads 78

Authors: David A. Ullisch, Yantree D. Sankar-Thomas, Stefan Wilke, Thomas Selge, Matthias Pump, Thomas Leibold, Kai Schütte, Gilbert Gorr

Abstract:

Plants have been considered as a source of natural substances for ages. Secondary metabolites from plants are utilized especially in medical applications but are more and more interesting as cosmetical ingredients and in the field of nutraceuticals. However, supply of compounds from natural harvest can be limited by numerous factors i.e. endangered species, low product content, climate impacts and cost intensive extraction. Especially in the pharmaceutical industry the ability to provide sufficient amounts of product and high quality are additional requirements which in some cases are difficult to fulfill by plant harvest. Whereas in many cases the complexity of secondary metabolites precludes chemical synthesis on a reasonable commercial basis, plant cells contain the biosynthetic pathway – a natural chemical factory – for a given compound. A promising approach for the sustainable production of natural products can be plant cell fermentation (PCF®). A thoroughly accomplished development process comprises the identification of a high producing cell line, optimization of growth and production conditions, the development of a robust and reliable production process and its scale-up. In order to address persistent, long lasting production, development of cryopreservation protocols and generation of working cell banks is another important requirement to be considered. So far the most prominent example using a PCF® process is the production of the anticancer compound paclitaxel. To demonstrate the power of plant suspension cultures here we present three case studies: 1) For more than 17 years Phyton produces paclitaxel at industrial scale i.e. up to 75,000 L in scale. With 60 g/kg dw this fully controlled process which is applied according to GMP results in outstanding high yields. 2) Thapsigargin is another anticancer compound which is currently isolated from seeds of Thapsia garganica. Thapsigargin is a powerful cytotoxin – a SERCA inhibitor – and the precursor for the derivative ADT, the key ingredient of the investigational prodrug Mipsagargin (G-202) which is in several clinical trials. Phyton successfully generated plant cell lines capable to express this compound. Here we present data about the screening for high producing cell lines. 3) The third case study covers ingenol-3-mebutate. This compound is found in the milky sap of the intact plants of the Euphorbiacae family at very low concentrations. Ingenol-3-mebutate is used in Picato® which is approved against actinic keratosis. Generation of cell lines expressing significant amounts of ingenol-3-mebutate is another example underlining the strength of plant cell culture. The authors gratefully acknowledge Inspyr Therapeutics for funding.

Keywords: Ingenol-3-mebutate, plant cell culture, sustainability, thapsigargin

Procedia PDF Downloads 217

Authors: Sunghun Jung, Wonkook Kim

Abstract:

Due to the difficulty of the utilization of satellite and an aircraft, conventional ocean color remote sensing has a disadvantage in that it is difficult to obtain images of desired places at desired times. These disadvantages make it difficult to capture the anomalies such as the occurrence of the red tide which requires immediate observation. It is also difficult to understand the phenomena such as the resuspension-precipitation process of suspended solids and the spread of low-salinity water originating in the coastal areas. For the remote sensing reflectance of seawater, above-water radiometers (AWR) have been used either by carrying portable AWRs on a ship or installing those at fixed observation points on the Ieodo ocean research station, Socheongcho base, and etc. In particular, however, it requires the high cost to measure the remote reflectance in various seawater environments at various times and it is even not possible to measure it at the desired frequency in the desired sea area at the desired time. Also, in case of the stationary observation, it is advantageous that observation data is continuously obtained, but there is the disadvantage that data of various sea areas cannot be obtained. It is possible to instantly capture various marine phenomena occurring on the coast using the unmanned aerial system (UAS) including vertical takeoff and landing (VTOL) type unmanned aerial vehicles (UAV) since it could move and hover at the one location and acquire data of the desired form at a high resolution. To remotely estimate seawater constituents, it is necessary to install an ultra-spectral sensor. Also, to calculate reflected light from the surface of the sea in consideration of the sun’s incident light, a total of three sensors need to be installed on the UAV. The remote sensing reflectance of seawater is the most basic optical property for remotely estimating color components in seawater and we could remotely estimate the chlorophyll concentration, the suspended solids concentration, and the dissolved organic amount. Estimating seawater physics from the remote sensing reflectance requires the algorithm development using the accumulation data of seawater reflectivity under various seawater and atmospheric conditions. The UAS with three AWRs is developed for the remote reflection sensing on the surface of the sea. Throughout the paper, we explain the details of each UAS component, system operation scenarios, and simulation and experiment results. The UAS consists of a UAV, a solar tracker, a transmitter, a ground control station (GCS), three AWRs, and two gimbals.

Keywords: above-water radiometers (AWR), ground control station (GCS), unmanned aerial system (UAS), unmanned aerial vehicle (UAV)

Procedia PDF Downloads 141

Authors: Gautier Viaud, Paul-Henry Cournède

Abstract:

Plant growth models have been used extensively for the prediction of the phenotypic performance of plants. However, they remain most often calibrated for a given genotype and therefore do not take into account genotype by environment interactions. One way of achieving such an objective is to consider Bayesian hierarchical models. Three levels can be identified in such models: The first level describes how a given growth model describes the phenotype of the plant as a function of individual parameters, the second level describes how these individual parameters are distributed within a plant population, the third level corresponds to the attribution of priors on population parameters. Thanks to the Bayesian framework, choosing appropriate priors for the population parameters permits to derive analytical expressions for the full conditional distributions of these population parameters. As plant growth models are of a nonlinear nature, individual parameters cannot be sampled explicitly, and a Metropolis step must be performed. This allows for the use of a hybrid Gibbs--Metropolis sampler. A generic approach was devised for the implementation of both general state space models and estimation algorithms within a programming platform. It was designed using the Julia language, which combines an elegant syntax, metaprogramming capabilities and exhibits high efficiency. Results were obtained for Arabidopsis thaliana on both simulated and real data. An organ-scale Greenlab model for the latter is thus presented, where the surface areas of each individual leaf can be simulated. It is assumed that the error made on the measurement of leaf areas is proportional to the leaf area itself; multiplicative normal noises for the observations are therefore used. Real data were obtained via image analysis of zenithal images of Arabidopsis thaliana over a period of 21 days using a two-step segmentation and tracking algorithm which notably takes advantage of the Arabidopsis thaliana phyllotaxy. Since the model formulation is rather flexible, there is no need that the data for a single individual be available at all times, nor that the times at which data is available be the same for all the different individuals. This allows to discard data from image analysis when it is not considered reliable enough, thereby providing low-biased data in large quantity for leaf areas. The proposed model precisely reproduces the dynamics of Arabidopsis thaliana’s growth while accounting for the variability between genotypes. In addition to the estimation of the population parameters, the level of variability is an interesting indicator of the genotypic stability of model parameters. A promising perspective is to test whether some of the latter should be considered as fixed effects.

Keywords: bayesian, genotypic differentiation, hierarchical models, plant growth models

Procedia PDF Downloads 280

Authors: Garry Gorman, Nigel McKelvey, James Connolly

Abstract:

This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

Procedia PDF Downloads 62

Authors: Dian Mayasari, Yosi Bayu Murti, Sylvia Utami Tunjung Pratiwi, Sudarsono

Abstract:

Melastoma malabathricum L. is an Indo-Pacific herb that has been traditionally used to treat several ailments such as wounds, dysentery, diarrhea, toothache, and diabetes. This plant is common across tropical Indo-Pacific archipelagos and is tolerant of a range of soils, from low-lying areas subject to saltwater inundation to the salt-free conditions of mountain slopes. How the soil and environmental variation influences secondary metabolite production in the herb, and an understanding of the plant’s utility as traditional medicine, remain largely unknown and unexplored. The objective of this study is to evaluate the variability of the metabolic profiles of M. malabathricum L. across its geographic distribution. By employing high-performance liquid chromatography-diode array detector (HPLC-DAD), a highly established, simple, sensitive, and reliable method was employed for establishing the chemical fingerprints of 72 samples of M. malabathricum L. leaves from various geographical locations in Indonesia. Specimens collected from six terrestrial and archipelago regions of Indonesia were analyzed by HPLC to generate chromatogram peak profiles that could be compared across each region. Data corresponding to the common peak areas of HPLC chromatographic fingerprint were analyzed by hierarchical component analysis (HCA) and principal component analysis (PCA) to extract information on the most significant variables contributing to characterization and classification of analyzed samples data. Principal component values were identified as PC1 and PC2 with 41.14% and 19.32%, respectively. Based on variety and origin, the high-performance liquid chromatography method validated the chemical fingerprint results used to screen the in vitro antioxidant activity of M. malabathricum L. The result shows that the developed method has potential values for the quality of similar M. malabathrium L. samples. These findings provide a pathway for the development and utilization of references for the identification of M. malabathricum L. Our results indicate the importance of considering geographic distribution during field-collection efforts as they demonstrate regional metabolic variation in secondary metabolites of M. malabathricum L., as illustrated by HPLC chromatogram peaks and their antioxidant activities. The results also confirm the utility of this simple approach to a rapid evaluation of metabolic variation between plants and their potential ethnobotanical properties, potentially due to the environments from whence they were collected. This information will facilitate the optimization of growth conditions to suit particular medicinal qualities.

Keywords: fingerprint, high performance liquid chromatography, Melastoma malabathricum l., metabolic profiles, principal component analysis

Procedia PDF Downloads 130

Authors: Adriana Postal, Silvio C. Sampaio, Marcio A. Villas Boas, Josué P. Castro, Ralpho R. Reis

Abstract:

This study deals with the estimation of reference evapotranspiration (ET₀) in an agricultural context, focusing on efficient irrigation management to meet the growing interest in the sustainable management of water resources. Given the importance of water in agriculture and its scarcity in many regions, efficient use of this resource is essential to ensure food security and environmental sustainability. The methodology used involved the application of artificial intelligence techniques, specifically Multilayer Perceptron (MLP) Artificial Neural Networks (ANNs), to predict ET₀ in the state of Paraná, Brazil. The models were trained and validated with meteorological data from the Brazilian National Institute of Meteorology (INMET), together with data obtained from a producer's weather station in the western region of Paraná. Two optimizers (SGD and Adam) and different meteorological variables, such as temperature, humidity, solar radiation, and wind speed, were explored as inputs to the models. Nineteen configurations with different input variables were tested; amidst them, configuration 9, with 8 input variables, was identified as the most efficient of all. Configuration 10, with 4 input variables, was considered the most effective, considering the smallest number of variables. The main conclusions of this study show that MLP ANNs are capable of accurately estimating ET₀, providing a valuable tool for irrigation management in agriculture. Both configurations (9 and 10) showed promising performance in predicting ET₀. The validation of the models with cultivator data underlined the practical relevance of these tools and confirmed their generalization ability for different field conditions. The results of the statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2), showed excellent agreement between the model predictions and the observed data, with MAE as low as 0.01 mm/day and 0.03 mm/day, respectively. In addition, the models achieved an R2 between 0.99 and 1, indicating a satisfactory fit to the real data. This agreement was also confirmed by the Kolmogorov-Smirnov test, which evaluates the agreement of the predictions with the statistical behavior of the real data and yields values between 0.02 and 0.04 for the producer data. In addition, the results of this study suggest that the developed technique can be applied to other locations by using specific data from these sites to further improve ET₀ predictions and thus contribute to sustainable irrigation management in different agricultural regions. To summarize, this study has helped to advance research in the field of irrigation management in agriculture. It provides an accessible and effective approach to ET₀ estimation that has the potential to significantly improve water use efficiency and promote agricultural sustainability in different contexts.

Keywords: agricultural technology, neural networks in agriculture, water efficiency, water use optimization

Procedia PDF Downloads 17

Authors: E. Nakamachi, S. Tanaka, K. Yamamoto, Y. Morita

Abstract:

In this study, we developed a three-dimensional (3D) direct current electric field (DCEF) stimulation bio-reactor for axonal outgrowth enhancement to generate the neural network of the central nervous system (CNS). By using our newly developed 3D DCEF stimulation bio-reactor, we cultured the rat pheochromocytoma cells (PC12) and investigated the effects on the axonal extension enhancement and network generation. Firstly, we designed and fabricated a 3D bio-reactor, which can load DCEF stimulation on PC12 cells embedded in the collagen gel as extracellular environment. The connection between the electrolyte and the medium using salt bridges for DCEF stimulation was introduced to avoid the cell death by the toxicity of metal ion. The distance between the salt bridges was adopted as the design variable to optimize a structure for uniform DCEF stimulation, where the finite element (FE) analyses results were used. Uniform DCEF strength and electric flux vector direction in the PC12 cells embedded in collagen gel were examined through measurements of the fabricated 3D bio-reactor chamber. Measurement results of DCEF strength in the bio-reactor showed a good agreement with FE results. In addition, the perfusion system was attached to maintain pH 7.2 ~ 7.6 of the medium because pH change was caused by DCEF stimulation loading. Secondly, we disseminated PC12 cells in collagen gel and carried out 3D culture. Finally, we measured the morphology of PC12 cell bodies and neurites by the multiphoton excitation fluorescence microscope (MPM). The effectiveness of DCEF stimulation to enhance the axonal outgrowth and the neural network generation was investigated. We confirmed that both an increase of mean axonal length and axogenesis rate of PC12, which have been exposed 5 mV/mm for 6 hours a day for 4 days in the bioreactor. We found following conclusions in our study. 1) Design and fabrication of DCEF stimulation bio-reactor capable of 3D culture nerve cell were completed. A uniform electric field strength of average value of 17 mV/mm within the 1.2% error range was confirmed by using FE analyses, after the structure determination through the optimization process. In addition, we attached a perfusion system capable of suppressing the pH change of the culture solution due to DCEF stimulation loading. 2) Evaluation of DCEF stimulation effects on PC12 cell activity was executed. The 3D culture of PC 12 was carried out adopting the embedding culture method using collagen gel as a scaffold for four days under the condition of 5.0 mV/mm and 10mV/mm. There was a significant effect on the enhancement of axonal extension, as 11.3% increase in an average length, and the increase of axogenesis rate. On the other hand, no effects on the orientation of axon against the DCEF flux direction was observed. Further, the network generation was enhanced to connect longer distance between the target neighbor cells by DCEF stimulation.

Keywords: PC12, DCEF stimulation, 3D bio-reactor, axonal extension, neural network generation

Procedia PDF Downloads 159

Authors: Imed Feki, Faouzi Msahli

Abstract:

Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

Procedia PDF Downloads 573

Authors: Jeanne Leblond, Warren Viricel, Amira Mbarek

Abstract:

Although several products have reached the market, gene therapeutics are still in their first stages and require optimization. It is possible to improve their lacking efficiency by the use of carefully engineered vectors, able to carry the genetic material through each of the biological barriers they need to cross. In particular, getting inside the cell is a major challenge, because these hydrophilic nucleic acids have to cross the lipid-rich plasmatic and/or endosomal membrane, before being degraded into lysosomes. It takes less than one hour for newly endocytosed liposomes to reach highly acidic lysosomes, meaning that the degradation of the carried gene occurs rapidly, thus limiting the transfection efficiency. We propose to use a new pH-sensitive lipid able to change its conformation upon protonation at endosomal pH values, leading to the disruption of the lipidic bilayer and thus to the fast release of the nucleic acids into the cytosol. It is expected that this new pH-sensitive mechanism promote endosomal escape of the gene, thereby its transfection efficiency. The main challenge of this work was to design a preparation presenting fast-responding lipidic bilayer destabilization properties at endosomal pH 5 while remaining stable at blood pH value and during storage. A series of pH-sensitive lipids able to perform a conformational switch upon acidification were designed and synthesized. Liposomes containing these switchable lipids, as well as co-lipids were prepared and characterized. The liposomes were stable at 4°C and pH 7.4 for several months. Incubation with siRNA led to the full entrapment of nucleic acids as soon as the positive/negative charge ratio was superior to 2. The best liposomal formulation demonstrated a silencing efficiency up to 10% on HeLa cells, very similar to a commercial agent, with a lowest toxicity than the commercial agent. Using flow cytometry and microscopy assays, we demonstrated that drop of pH was required for the transfection efficiency, since bafilomycin blocked the transfection efficiency. Additional evidence was brought by the synthesis of a negative control lipid, which was unable to switch its conformation, and consequently exhibited no transfection ability. Mechanistic studies revealed that the uptake was mediated through endocytosis, by clathrin and caveolae pathways, as reported for previous lipid nanoparticle systems. This potent system was used for the treatment of hypercholesterolemia. The switchable lipids were able to knockdown PCSK9 expression on human hepatocytes (Huh-7). Its efficiency is currently evaluated on in vivo mice model of PCSK9 KO mice. In summary, we designed and optimized a new cationic pH-sensitive lipid for gene delivery. Its transfection efficiency is similar to the best available commercial agent, without the usually associated toxicity. The promising results lead to its use for the treatment of hypercholesterolemia on a mice model. Anticancer applications and pulmonary chronic disease are also currently investigated.

Keywords: liposomes, siRNA, pH-sensitive, molecular switch

Procedia PDF Downloads 182

Authors: Ghada Abo-Zaid

Abstract:

The main objective of this study was to examine the association between the elevated level of CRP and incidence of hypertension before and after adjusting by age, BMI, gender, SES, smoking, diabetes, cholesterol LDL and cholesterol HDL and to determine whether the association were differ by race. Method: Cross sectional data for participations from age 17 to age 74 years who included in The National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 were analysed. CRP level was classified into three categories ( > 3mg/L, between 1mg/LL and 3mg/L, and < 3 mg/L). Blood pressure categorization was done using JNC 7 algorithm Hypertension defined as either systolic blood pressure (SBP) of 140 mmHg or more and disystolic blood pressure (DBP) of 90mmHg or greater, otherwise a self-reported prior diagnosis by a physician. Pre-hypertension was defined as (139 > SBP > 120 or 89 > DPB > 80). Multinominal regression model was undertaken to measure the association between CRP level and hypertension. Results: In univariable models, CRP concentrations > 3 mg/L were associated with a 73% greater risk of incident hypertension compared with CRP concentrations < 1 mg/L (Hypertension: odds ratio [OR] = 1.73; 95% confidence interval [CI], 1.50-1.99). Ethnic comparisons showed that American Mexican had the highest risk of incident hypertension (odds ratio [OR] = 2.39; 95% confidence interval [CI], 2.21-2.58).This risk was statistically insignificant, however, either after controlling by other variables (Hypertension: OR = 0.75; 95% CI, 0.52-1.08,), or categorized by race [American Mexican: odds ratio [OR] = 1.58; 95% confidence interval [CI], 0,58-4.26, Other Hispanic: odds ratio [OR] = 0.87; 95% confidence interval [CI], 0.19-4.42, Non-Hispanic white: odds ratio [OR] = 0.90; 95% confidence interval [CI], 0.50-1.59, Non-Hispanic Black: odds ratio [OR] = 0.44; 95% confidence interval [CI], 0.22-0,87]. The same results were found for pre-hypertension, and the Non-Hispanic black showed the highest significant risk for Pre-Hypertension (odds ratio [OR] = 1.60; 95% confidence interval [CI], 1.26-2.03). When CRP concentrations were between 1.0-3.0 mg/L, in an unadjusted models prehypertension was associated with higher likelihood of elevated CRP (OR = 1.37; 95% CI, 1.15-1.62). The same relationship was maintained in Non-Hispanic white, Non-Hispanic black, and other race (Non-Hispanic white: OR = 1.24; 95% CI, 1.03-1.48, Non-Hispanic black: OR = 1.60; 95% CI, 1.27-2.03, other race: OR = 2.50; 95% CI, 1.32-4.74) while the association was insignificant with American Mexican and other Hispanic. In the adjusted model, the relationship between CRP and prehypertension were no longer available. In contrary, Hypertension was not independently associated with elevated CRP, and the results were the same after grouped by race or adjusted by the confounder variables. The same results were obtained when SBP or DBP were on a continuous measure. Conclusions: This study confirmed the existence of an association between hypertension, prehypertension and elevated level of CRP, however this association was no longer available after adjusting by other variables. Ethic group differences were statistically significant at the univariable models, while it disappeared after controlling by other variables.

Keywords: CRP, hypertension, ethnicity, NHANES, blood pressure

Procedia PDF Downloads 389

Authors: Damtew Samson Zerihun

Abstract:

This paper primarily describes the concept of a hierarchical safety control (HSC) in discrete manufacturing to up-hold productivity with human intervention and machine failures using a systematic approach, through increasing the system availability and using additional knowledge on machines so as to improve the human machine collaboration (HMC). It also highlights the implemented PLC safety algorithm, in applying this generic concept to a concrete pro-duction line using a lab demonstrator called FATIE (Factory Automation Test and Integration Environment). Furthermore, the paper describes a model and provide a systematic representation of human-machine collabora-tion in discrete manufacturing and to this end, the Hierarchical Safety Control concept is proposed. This offers a ge-neric description of human-machine collaboration based on Finite State Machines (FSM) that can be applied to vari-ous discrete manufacturing lines instead of using ad-hoc solutions for each line. With its reusability, flexibility, and extendibility, the Hierarchical Safety Control scheme allows upholding productivity while maintaining safety with reduced engineering effort compared to existing solutions. The approach to the solution begins with a successful partitioning of different zones around the Integrated Manufacturing System (IMS), which are defined by operator tasks and the risk assessment, used to describe the location of the human operator and thus to identify the related po-tential hazards and trigger the corresponding safety functions to mitigate it. This includes selective reduced speed zones and stop zones, and in addition with the hierarchical safety control scheme and advanced safety functions such as safe standstill and safe reduced speed are used to achieve the main goals in improving the safe Human Ma-chine Collaboration and increasing the productivity. In a sample scenarios, It is shown that an increase of productivity in the order of 2.5% is already possible with a hi-erarchical safety control, which consequently under a given assumptions, a total sum of 213 € could be saved for each intervention, compared to a protective stop reaction. Thereby the loss is reduced by 22.8%, if occasional haz-ard can be refined in a hierarchical way. Furthermore, production downtime due to temporary unavailability of safety devices can be avoided with safety failover that can save millions per year. Moreover, the paper highlights the proof of the development, implementation and application of the concept on the lab demonstrator (FATIE), where it is realized on the new safety PLCs, Drive Units, HMI as well as Safety devices in addition to the main components of the IMS.

Keywords: discrete automation, hierarchical safety controller, human machine collaboration, programmable logical controller

Procedia PDF Downloads 346

Authors: Sakshi Gupta, Anupam Agrawal, Ekta Singla

Abstract:

An exoskeleton design for rehabilitation purpose encounters many challenges, including ergonomically acceptable wearing technology, architectural design human-motion compatibility, actuation type, human-robot interaction, etc. In this paper, a passive architecture for upper limb exoskeleton is proposed for assisting in rehabilitation tasks. Kinematic modelling is detailed for task-based kinematic synthesis of the wearable exoskeleton for self-feeding tasks. The exoskeleton architecture possesses expansion and torsional springs which are able to store and redistribute energy over the human arm joints. The elastic characteristics of the springs have been optimized to minimize the mechanical work of the human arm joints. The concept of hybrid combination of a 4-bar parallelogram linkage and a serial linkage were chosen, where the 4-bar parallelogram linkage with expansion spring acts as a rigid structure which is used to provide the rotational degree-of-freedom (DOF) required for lowering and raising of the arm. The single linkage with torsional spring allows for the rotational DOF required for elbow movement. The focus of the paper is kinematic modelling, analysis and task-based synthesis framework for the proposed architecture, keeping in considerations the essential tasks of self-feeding and self-exercising during rehabilitation of partially healthy person. Rehabilitation of primary functional movements (activities of daily life, i.e., ADL) is routine activities that people tend to every day such as cleaning, dressing, feeding. We are focusing on the feeding process to make people independent in respect of the feeding tasks. The tasks are focused to post-surgery patients under rehabilitation with less than 40% weakness. The challenges addressed in work are ensuring to emulate the natural movement of the human arm. Human motion data is extracted through motion-sensors for targeted tasks of feeding and specific exercises. Task-based synthesis procedure framework will be discussed for the proposed architecture. The results include the simulation of the architectural concept for tracking the human-arm movements while displaying the kinematic and static study parameters for standard human weight. D-H parameters are used for kinematic modelling of the hybrid-mechanism, and the model is used while performing task-based optimal synthesis utilizing evolutionary algorithm.

Keywords: passive mechanism, task-based synthesis, emulating human-motion, exoskeleton

Procedia PDF Downloads 114