Search results for: artificial bee colony optimization

Authors: Tirtha Raj Adhikari, Lochan Prasad Devkota

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This research studied the hydrological regime of three glacierized river basins in Khumbu, Langtang and Annapurna regions of Nepal using the Hydraologiska Byrans Vattenbalansavde (HBV), HVB-light 3.0 model. Future scenario of discharge is also studied using downscaled climate data derived from statistical downscaling method. General Circulation Models (GCMs) successfully simulate future climate variability and climate change on a global scale; however, poor spatial resolution constrains their application for impact studies at a regional or a local level. The dynamically downscaled precipitation and temperature data from Coupled Global Circulation Model 3 (CGCM3) was used for the climate projection, under A2 and A1B SRES scenarios. In addition, the observed historical temperature, precipitation and discharge data were collected from 14 different hydro-metrological locations for the implementation of this study, which include watershed and hydro-meteorological characteristics, trends analysis and water balance computation. The simulated precipitation and temperature were corrected for bias before implementing in the HVB-light 3.0 conceptual rainfall-runoff model to predict the flow regime, in which Groups Algorithms Programming (GAP) optimization approach and then calibration were used to obtain several parameter sets which were finally reproduced as observed stream flow. Except in summer, the analysis showed that the increasing trends in annual as well as seasonal precipitations during the period 2001 - 2060 for both A2 and A1B scenarios over three basins under investigation. In these river basins, the model projected warmer days in every seasons of entire period from 2001 to 2060 for both A1B and A2 scenarios. These warming trends are higher in maximum than in minimum temperatures throughout the year, indicating increasing trend of daily temperature range due to recent global warming phenomenon. Furthermore, there are decreasing trends in summer discharge in Langtang Khola (Langtang region) which is increasing in Modi Khola (Annapurna region) as well as Dudh Koshi (Khumbu region) river basin. The flow regime is more pronounced during later parts of the future decades than during earlier parts in all basins. The annual water surplus of 1419 mm, 177 mm and 49 mm are observed in Annapurna, Langtang and Khumbu region, respectively.

Keywords: temperature, precipitation, water discharge, water balance, global warming

Procedia PDF Downloads 323

Authors: I. Valdez, J. Perdomo, M. Colindres, N. Castro

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Today, digital servo systems are extensively used in industrial manufacturing processes, robotic applications, vehicles and other areas. In such control systems, control action is provided by digital controllers with different compensation algorithms, which are designed to meet specific requirements for a given application. Due to the constant search for optimization in industrial processes, it is of interest to design digital controllers that offer ease of realization, improved computational efficiency, affordable return rates, and ease of tuning that ultimately improve the performance of the controlled actuators. There is a vast range of options of compensation algorithms that could be used, although in the industry, most controllers used are based on a PID structure. This research article compares different types of digital compensators implemented in a servo system for DC motor position control. PID compensation is evaluated on its two most common architectures: PID position form (1 DOF), and PID speed form (2 DOF). State feedback algorithms are also evaluated, testing two modern control theory techniques: discrete state observer for non-measurable variables tracking, and a linear quadratic method which allows a compromise between the theoretical optimal control and the realization that most closely matches it. The compared control systems’ performance is evaluated through simulations in the Simulink platform, in which it is attempted to model accurately each of the system’s hardware components. The criteria by which the control systems are compared are reference tracking and disturbance rejection. In this investigation, it is considered that the accurate tracking of the reference signal for a position control system is particularly important because of the frequency and the suddenness in which the control signal could change in position control applications, while disturbance rejection is considered essential because the torque applied to the motor shaft due to sudden load changes can be modeled as a disturbance that must be rejected, ensuring reference tracking. Results show that 2 DOF PID controllers exhibit high performance in terms of the benchmarks mentioned, as long as they are properly tuned. As for controllers based on state feedback, due to the nature and the advantage which state space provides for modelling MIMO, it is expected that such controllers evince ease of tuning for disturbance rejection, assuming that the designer of such controllers is experienced. An in-depth multi-dimensional analysis of preliminary research results indicate that state feedback control method is more satisfactory, but PID control method exhibits easier implementation in most control applications.

Keywords: control, DC motor, discrete PID, discrete state feedback

Procedia PDF Downloads 246

Authors: Kuniaki Kudo, Raviraj Solanki, Kaushal Patel, Yash Virani

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This research mainly concerns optimizing UI test automation for large-scale web applications. The test target application is the HHAexchange homecare management WEB application that seamlessly connects providers, state Medicaid programs, managed care organizations (MCOs), and caregivers through one platform with large-scale functionalities. This study focuses on user interface automation testing for the WEB application. The quality assurance team must execute many manual users interface test cases in the development process to confirm no regression bugs. The team automated 346 test cases; the UI automation test execution time was over 17 hours. The business requirement was reducing the execution time to release high-quality products quickly, and the quality assurance automation team modernized the test automation framework to optimize the execution time. The base of the WEB UI automation test environment is Selenium, and the test code is written in Python. Adopting a compilation language to write test code leads to an inefficient flow when introducing scalability into a traditional test automation environment. In order to efficiently introduce scalability into Test Automation, a scripting language was adopted. The scalability implementation is mainly implemented with AWS's serverless technology, an elastic container service. The definition of scalability here is the ability to automatically set up computers to test automation and increase or decrease the number of computers running those tests. This means the scalable mechanism can help test cases run parallelly. Then test execution time is dramatically decreased. Also, introducing scalable test automation is for more than just reducing test execution time. There is a possibility that some challenging bugs are detected by introducing scalable test automation, such as race conditions, Etc. since test cases can be executed at same timing. If API and Unit tests are implemented, the test strategies can be adopted more efficiently for this scalability testing. However, in WEB applications, as a practical matter, API and Unit testing cannot cover 100% functional testing since they do not reach front-end codes. This study applied a scalable UI automation testing strategy to the large-scale homecare management system. It confirmed the optimization of the test case execution time and the detection of a challenging bug. This study first describes the detailed architecture of the scalable test automation environment, then describes the actual performance reduction time and an example of challenging issue detection.

Keywords: aws, elastic container service, scalability, serverless, ui automation test

Procedia PDF Downloads 73

Authors: M. Beheshti, S. Saegrov, T. M. Muthanna

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Separated sewer systems are designed to transfer the wastewater from houses and industrial sections to wastewater treatment plants. Unwanted water in the sewer systems is a well-known problem, i.e. storm-water inflow is around 50% of the foul sewer, and groundwater infiltration to the sewer system can exceed 50% of total wastewater volume in deteriorated networks. Infiltration and inflow of non-sewer water (I/I) into sewer systems is unfavorable in separated sewer systems and can trigger overloading the system and reducing the efficiency of wastewater treatment plants. Moreover, I/I has negative economic, environmental, and social impacts on urban areas. Therefore, for having sustainable management of urban sewer systems, I/I of unwanted water into the urban sewer systems should be considered carefully and maintenance and rehabilitation plan should be implemented on these water infrastructural assets. This study presents a methodology to identify and quantify the level of I/I into the sewer system. Amount of I/I is evaluated by accurate flow measurement in separated sewer systems for specified isolated catchments in Trondheim city (Norway). Advanced information about the characteristics of I/I is gained by CCTV inspection of sewer pipelines with high I/I contribution. Achieving enhanced knowledge about the detection and localization of non-sewer water in foul sewer system during the wet and dry weather conditions will enable the possibility for finding the problem of sewer system and prioritizing them and taking decisions for rehabilitation and renewal planning in the long-term. Furthermore, preventive measures and optimization of sewer systems functionality and efficiency can be executed by maintenance of sewer system. In this way, the exploitation of sewer system can be improved by maintenance and rehabilitation of existing pipelines in a sustainable way by more practical cost-effective and environmental friendly way. This study is conducted on specified catchments with different properties in Trondheim city. Risvollan catchment is one of these catchments with a measuring station to investigate hydrological parameters through the year, which also has a good database. For assessing the infiltration in a separated sewer system, applying the flow rate measurement method can be utilized in obtaining a general view of the network condition from infiltration point of view. This study discusses commonly used and advanced methods of localizing and quantifying I/I in sewer systems. A combination of these methods give sewer operators the possibility to compare different techniques and obtain reliable and accurate I/I data which is vital for long-term rehabilitation plans.

Keywords: flow rate measurement, infiltration and inflow (I/I), non-sewer water, separated sewer systems, sustainable management

Procedia PDF Downloads 302

Authors: Ella Tyuryumina, Alexey Neznanov

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This study is an attempt to obtain reliable data on the natural history of breast cancer growth. We analyze the opportunities for using classical mathematical models (exponential and logistic tumor growth models, Gompertz and von Bertalanffy tumor growth models) to try to describe growth of the primary tumor and the secondary distant metastases of human breast cancer. The research aim is to improve predicting accuracy of breast cancer progression using an original mathematical model referred to CoMPaS and corresponding software. We are interested in: 1) modelling the whole natural history of the primary tumor and the secondary distant metastases; 2) developing adequate and precise CoMPaS which reflects relations between the primary tumor and the secondary distant metastases; 3) analyzing the CoMPaS scope of application; 4) implementing the model as a software tool. The foundation of the CoMPaS is the exponential tumor growth model, which is described by determinate nonlinear and linear equations. The CoMPaS corresponds to TNM classification. It allows to calculate different growth periods of the primary tumor and the secondary distant metastases: 1) ‘non-visible period’ for the primary tumor; 2) ‘non-visible period’ for the secondary distant metastases; 3) ‘visible period’ for the secondary distant metastases. The CoMPaS is validated on clinical data of 10-years and 15-years survival depending on the tumor stage and diameter of the primary tumor. The new predictive tool: 1) is a solid foundation to develop future studies of breast cancer growth models; 2) does not require any expensive diagnostic tests; 3) is the first predictor which makes forecast using only current patient data, the others are based on the additional statistical data. The CoMPaS model and predictive software: a) fit to clinical trials data; b) detect different growth periods of the primary tumor and the secondary distant metastases; c) make forecast of the period of the secondary distant metastases appearance; d) have higher average prediction accuracy than the other tools; e) can improve forecasts on survival of breast cancer and facilitate optimization of diagnostic tests. The following are calculated by CoMPaS: the number of doublings for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases; tumor volume doubling time (days) for ‘non-visible’ and ‘visible’ growth period of the secondary distant metastases. The CoMPaS enables, for the first time, to predict ‘whole natural history’ of the primary tumor and the secondary distant metastases growth on each stage (pT1, pT2, pT3, pT4) relying only on the primary tumor sizes. Summarizing: a) CoMPaS describes correctly the primary tumor growth of IA, IIA, IIB, IIIB (T1-4N0M0) stages without metastases in lymph nodes (N0); b) facilitates the understanding of the appearance period and inception of the secondary distant metastases.

Keywords: breast cancer, exponential growth model, mathematical model, metastases in lymph nodes, primary tumor, survival

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Authors: Khodzhaberdi Allaberdiev

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Aromaticity, one of the most important concepts in organic chemistry, has attracted considerable interest from both experimentalists and theoreticians. The geometry optimization of p-substituted o-(N-dialkyl)aminomethylphenols, o-DEAMPH XC₆ H₅CH ₂Y (X=p-OCH₃, CH₃, H, F, Cl, Br, COCH₃, COOCH₃, CHO, CN and NO₂, Y=o-N (C₂H₅)₂, o-DEAMPHs have been performed in the gas phase using the B3LYP/6-311+G(d,p) level. Aromaticities of the considered molecules were investigated using different indices included geometrical (HOMA and Bird), electronic (FLU, PDI and SA) magnetic (NICS(0), NICS(1) and NICS(1)zz indices. The linear dependencies were obtained between some aromaticity indices. The best correlation is observed between the Bird and PDI indices (R² =0.9240). However, not all types of indices or even different indices within the same type correlate well among each other. Surprisingly, for studied molecules in which geometrical and electronic cannot correctly give the aromaticity of ring, the magnetism based index successfully predicts the aromaticity of systems. 1H NMR spectra of compounds were obtained at B3LYP/6–311+G(d,p) level using the GIAO method. Excellent linear correlation (R²= 0.9996) between values the chemical shift of hydrogen atom obtained experimentally of 1H NMR and calculated using B3LYP/6–311+G(d,p) demonstrates a good assignment of the experimental values chemical shift to the calculated structures of o-DEAMPH. It is found that the best linear correlation with the Hammett substituent constants is observed for the NICS(1)zz index in comparison with the other indices: NICS(1)zz =-21.5552+1,1070 σp- (R²=0.9394). The presence intramolecular hydrogen bond in the studied molecules also revealed changes the aromatic character of substituted o-DEAMPHs. The HOMA index predicted for R=NO2 the reduction in the π-electron delocalization of 3.4% was about double that observed for p-nitrophenol. The influence intramolecular H-bonding on aromaticity of benzene ring in the ground state (S0) are described by equations between NICS(1)zz and H-bond energies: experimental, Eₑₓₚ, predicted IR spectroscopical, Eν and topological, EQTAIM with correlation coefficients R² =0.9666, R² =0.9028 and R² =0.8864, respectively. The NICS(1)zz index also correlates with usual descriptors of the hydrogen bond, while the other indices do not give any meaningful results. The influence of the intramolecular H-bonding formation on the aromaticity of some substituted o-DEAMPHs is criteria to consider the multidimensional character of aromaticity. The linear relationships as well as revealed between NICS(1)zz and both pyramidality nitrogen atom, ΣN(C₂H₅)₂ and dihedral angle, φ CAr – CAr -CCH₂ –N, to characterizing out-of-plane properties.These results demonstrated the nonplanar structure of o-DEAMPHs. Finally, when considering dependencies of NICS(1)zz, were excluded data for R=H, because the NICS(1) and NICS(1)zz values are the most negative for unsubstituted DEAMPH, indicating its highest aromaticity; that was not the case for NICS(0) index.

Keywords: aminomethylphenols, DFT, aromaticity, correlations

Procedia PDF Downloads 171

Authors: Christine F. Boos, Fernando M. Azevedo

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Electroencephalogram (EEG) is a record of the electrical activity of the brain that has many applications, such as monitoring alertness, coma and brain death; locating damaged areas of the brain after head injury, stroke and tumor; monitoring anesthesia depth; researching physiology and sleep disorders; researching epilepsy and localizing the seizure focus. Epilepsy is a chronic condition, or a group of diseases of high prevalence, still poorly explained by science and whose diagnosis is still predominantly clinical. The EEG recording is considered an important test for epilepsy investigation and its visual analysis is very often applied for clinical confirmation of epilepsy diagnosis. Moreover, this EEG analysis can also be used to help define the types of epileptic syndrome, determine epileptiform zone, assist in the planning of drug treatment and provide additional information about the feasibility of surgical intervention. In the context of diagnosis confirmation the analysis is made using long term EEG recordings with at least 24 hours long and acquired by a minimum of 24 electrodes in which the neurophysiologists perform a thorough visual evaluation of EEG screens in search of specific electrographic patterns called epileptiform discharges. Considering that the EEG screens usually display 10 seconds of the recording, the neurophysiologist has to evaluate 360 screens per hour of EEG or a minimum of 8,640 screens per long term EEG recording. Analyzing thousands of EEG screens in search patterns that have a maximum duration of 200 ms is a very time consuming, complex and exhaustive task. Because of this, over the years several studies have proposed automated methodologies that could facilitate the neurophysiologists’ task of identifying epileptiform discharges and a large number of methodologies used neural networks for the pattern classification. One of the differences between all of these methodologies is the type of input stimuli presented to the networks, i.e., how the EEG signal is introduced in the network. Five types of input stimuli have been commonly found in literature: raw EEG signal, morphological descriptors (i.e. parameters related to the signal’s morphology), Fast Fourier Transform (FFT) spectrum, Short-Time Fourier Transform (STFT) spectrograms and Wavelet Transform features. This study evaluates the application of these five types of input stimuli and compares the classification results of neural networks that were implemented using each of these inputs. The performance of using raw signal varied between 43 and 84% efficiency. The results of FFT spectrum and STFT spectrograms were quite similar with average efficiency being 73 and 77%, respectively. The efficiency of Wavelet Transform features varied between 57 and 81% while the descriptors presented efficiency values between 62 and 93%. After simulations we could observe that the best results were achieved when either morphological descriptors or Wavelet features were used as input stimuli.

Keywords: Artificial neural network, electroencephalogram signal, pattern recognition, signal processing

Procedia PDF Downloads 507

Authors: A. T. Al-Isawi, P. E. F. Collins

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The behaviour of structures exposed to fire after an earthquake is not a new area of engineering research, but there remain a number of areas where further work is required. Such areas relate to the way in which seismic excitation is applied to a structure, taking into account the effect of soil-structure interaction (SSI) and the method of analysis, in addition to identifying the excitation load properties. The selection of earthquake data input for use in nonlinear analysis and the method of analysis are still challenging issues. Thus, realistic artificial ground motion input data must be developed to certify that site properties parameters adequately describe the effects of the nonlinear inelastic behaviour of the system and that the characteristics of these parameters are coherent with the characteristics of the target parameters. Conversely, ignoring the significance of some attributes, such as frequency content, soil site properties and earthquake parameters may lead to misleading results, due to the misinterpretation of required input data and the incorrect synthesise of analysis hypothesis. This paper presents a study of the post-earthquake fire (PEF) performance of a multi-storey steel-framed building resting on soft clay, taking into account the effects of the nonlinear inelastic behaviour of the structure and soil, and the soil-structure interaction (SSI). Structures subjected to an earthquake may experience various levels of damage; the geometrical damage, which indicates the change in the initial structure’s geometry due to the residual deformation as a result of plastic behaviour, and the mechanical damage which identifies the degradation of the mechanical properties of the structural elements involved in the plastic range of deformation. Consequently, the structure presumably experiences partial structural damage but is then exposed to fire under its new residual material properties, which may result in building failure caused by a decrease in fire resistance. This scenario would be more complicated if SSI was also considered. Indeed, most earthquake design codes ignore the probability of PEF as well as the effect that SSI has on the behaviour of structures, in order to simplify the analysis procedure. Therefore, the design of structures based on existing codes which neglect the importance of PEF and SSI can create a significant risk of structural failure. In order to examine the criteria for the behaviour of a structure under PEF conditions, a two-dimensional nonlinear elasto-plastic model is developed using ABAQUS software; the effects of SSI are included. Both geometrical and mechanical damages have been taken into account after the earthquake analysis step. For comparison, an identical model is also created, which does not include the effects of soil-structure interaction. It is shown that damage to structural elements is underestimated if SSI is not included in the analysis, and the maximum percentage reduction in fire resistance is detected in the case when SSI is included in the scenario. The results are validated using the literature.

Keywords: Abaqus Software, Finite Element Analysis, post-earthquake fire, seismic analysis, soil-structure interaction

Procedia PDF Downloads 106

Authors: Luz Stella Cadavid-Rodriguez, Viviana E. Castro-Lopez

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Fish waste (FW) has a high content of potentially biodegradable components, so it is amenable to be digested anaerobically. In this line, anaerobic digestion (AD) of FW has been studied for biogas production. Nevertheless, intermediate products such as volatile fatty acids (VFA), generated during the acidogenic stage, have been scarce investigated, even though they have a high potential as a renewable source of carbon. In the literature, there are few studies about the Inoculum-Substrate (I/S) ratio on acidogenesis. On the other hand, it is well known that pH is a critical factor in the production of VFA. The optimum pH for the production of VFA seems to change depending on the substrate and can vary in a range between 5.25 and 11. Nonetheless, the literature about VFA production from protein-rich waste, such as FW, is scarce. In this context, it is necessary to deepen on the determination of the optimal operating conditions of acidogenic fermentation for VFA production from protein-rich waste. Therefore, the aim of this research was to optimize the volatile fatty acid production from artisanal fishing waste, studying the effect of pH and the I/S ratio on the acidogenic process. For this research, the inoculum used was a methanogenic sludge (MS) obtained from a UASB reactor treating wastewater of a slaughterhouse plant, and the FW was collected in the port of Tumaco (Colombia) from the local artisanal fishers. The acidogenic fermentation experiments were conducted in batch mode, in 500 mL glass bottles as anaerobic reactors, equipped with rubber stoppers provided with a valve to release biogas. The effective volume used was 300 mL. The experiments were carried out for 15 days at a mesophilic temperature of 37± 2 °C and constant agitation of 200 rpm. The effect of 3 pH levels: 5, 7, 9, coupled with five I/S ratios, corresponding to 0.20, 0.15, 0.10, 0.05, 0.00 was evaluated taking as a response variable the production of VFA. A complete randomized block design was selected for the experiments in a 5x3 factorial arrangement, with two repetitions per treatment. At the beginning and during the process, pH in the experimental reactors was adjusted to the corresponding values of 5, 7, and 9 using 1M NaOH or 1M H2SO4, as was appropriated. In addition, once the optimum I/S ratio was determined, the process was evaluated at this condition without pH control. The results indicated that pH is the main factor in the production of VFA, obtaining the highest concentration with neutral pH. By reducing the I/S ratio, as low as 0.05, it was possible to maximize VFA production. Thus, the optimum conditions found were natural pH (6.6-7.7) and I/S ratio of 0.05, with which it was possible to reach a maximum total VFA concentration of 70.3 g Ac/L, whose major components were acetic acid (35%) and butyric acid (32%). The findings showed that the acidogenic fermentation of FW is an efficient way of producing VFA and that the operating conditions can be simple and economical.

Keywords: acidogenesis, artisanal fishing waste, inoculum to substrate ratio, volatile fatty acids

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Authors: Mohammad Karimi Moridani, Mohammad Abdi Zadeh, Zahra Shahiazar Mazraeh

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In recent years, the use of intelligent systems in biomedical engineering has increased dramatically, especially in the diagnosis of various diseases. Also, due to the relatively simple recording of the electrocardiogram signal (ECG), this signal is a good tool to show the function of the heart and diseases associated with it. The aim of this paper is to design an intelligent system for automatically detecting a normal electrocardiogram signal from abnormal one. Using this diagnostic system, it is possible to identify a person's heart condition in a very short time and with high accuracy. The data used in this article are from the Physionet database, available in 2016 for use by researchers to provide the best method for detecting normal signals from abnormalities. Data is of both genders and the data recording time varies between several seconds to several minutes. All data is also labeled normal or abnormal. Due to the low positional accuracy and ECG signal time limit and the similarity of the signal in some diseases with the normal signal, the heart rate variability (HRV) signal was used. Measuring and analyzing the heart rate variability with time to evaluate the activity of the heart and differentiating different types of heart failure from one another is of interest to the experts. In the preprocessing stage, after noise cancelation by the adaptive Kalman filter and extracting the R wave by the Pan and Tampkinz algorithm, R-R intervals were extracted and the HRV signal was generated. In the process of processing this paper, a new idea was presented that, in addition to using the statistical characteristics of the signal to create a return map and extraction of nonlinear characteristics of the HRV signal due to the nonlinear nature of the signal. Finally, the artificial neural networks widely used in the field of ECG signal processing as well as distinctive features were used to classify the normal signals from abnormal ones. To evaluate the efficiency of proposed classifiers in this paper, the area under curve ROC was used. The results of the simulation in the MATLAB environment showed that the AUC of the MLP and SVM neural network was 0.893 and 0.947, respectively. As well as, the results of the proposed algorithm in this paper indicated that the more use of nonlinear characteristics in normal signal classification of the patient showed better performance. Today, research is aimed at quantitatively analyzing the linear and non-linear or descriptive and random nature of the heart rate variability signal, because it has been shown that the amount of these properties can be used to indicate the health status of the individual's heart. The study of nonlinear behavior and dynamics of the heart's neural control system in the short and long-term provides new information on how the cardiovascular system functions, and has led to the development of research in this field. Given that the ECG signal contains important information and is one of the common tools used by physicians to diagnose heart disease, but due to the limited accuracy of time and the fact that some information about this signal is hidden from the viewpoint of physicians, the design of the intelligent system proposed in this paper can help physicians with greater speed and accuracy in the diagnosis of normal and patient individuals and can be used as a complementary system in the treatment centers.

Keywords: neart rate variability, signal processing, linear and non-linear features, classification methods, ROC Curve

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Authors: Ahmed O. Apampa, Yinusa A. Jimoh

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The potential economic viability and environmental benefits of using a biomass waste, such as corn cob ash (CCA) as pozzolan in stabilizing soils for road pavement construction in a sub-tropical region was investigated. Corn cob was obtained from Maya in South West Nigeria and processed to ash of characteristics similar to Class C Fly Ash pozzolan as specified in ASTM C618-12. This was then blended with ordinary Portland cement in the CCA:OPC ratios of 1:1, 1:2 and 2:1. Each of these blends was then mixed with lateritic soil of ASHTO classification A-2-6(3) in varying percentages from 0 – 7.5% at 1.5% intervals. The soil-CCA-Cement mixtures were thereafter tested for geotechnical index properties including the BS Proctor Compaction, California Bearing Ratio (CBR) and the Unconfined Compression Strength Test. The tests were repeated for soil-cement mix without any CCA blending. The cost of the binder inputs and optimal blends of CCA:OPC in the stabilized soil were thereafter analyzed by developing algorithms that relate the experimental data on strength parameters (Unconfined Compression Strength, UCS and California Bearing Ratio, CBR) with the bivariate independent variables CCA and OPC content, using Matlab R2011b. An optimization problem was then set up minimizing the cost of chemical stabilization of laterite with CCA and OPC, subject to the constraints of minimum strength specifications. The Evolutionary Engine as well as the Generalized Reduced Gradient option of the Solver of MS Excel 2010 were used separately on the cells to obtain the optimal blend of CCA:OPC. The optimal blend attaining the required strength of 1800 kN/m2 was determined for the 1:2 CCA:OPC as 5.4% mix (OPC content 3.6%) compared with 4.2% for the OPC only option; and as 6.2% mix for the 1:1 blend (OPC content 3%). The 2:1 blend did not attain the required strength, though over a 100% gain in UCS value was obtained over the control sample with 0% binder. Upon the fact that 0.97 tonne of CO2 is released for every tonne of cement used (OEE, 2001), the reduced OPC requirement to attain the same result indicates the possibility of reducing the net CO2 contribution of the construction industry to the environment ranging from 14 – 28.5% if CCA:OPC blends are widely used in soil stabilization, going by the results of this study. The paper concludes by recommending that Nigeria and other developing countries in the sub-tropics with abundant stock of biomass waste should look in the direction of intensifying the use of biomass waste as fuel and the derived ash for the production of pozzolans for road-works, thereby reducing overall green house gas emissions and in compliance with the objectives of the United Nations Framework on Climate Change.

Keywords: corn cob ash, biomass waste, lateritic soil, unconfined compression strength, CO2 emission

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Gabriel Wosiak, Felipe Staciaki, Eryka Nobrega, Ernesto Pereira

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Hydrogen is an energy carrier with potential applications in various industries. Alkaline electrolysis is a commonly used method for hydrogen production; however, its energy cost remains relatively high compared to other methods. This is due in part to interfacial pH changes that occur during the electrolysis process. Interfacial pH changes refer to the changes in pH that occur at the interface between the cathode electrode and the electrolyte solution. These changes are caused by the electrochemical reactions at both electrodes, which consume or produces hydroxide ions (OH-) from the electrolyte solution. This results in an important change in the local pH at the electrode surface, which can have several impacts on the energy consumption and durability of electrolysers. One impact of interfacial pH changes is an increase in the overpotential required for hydrogen production. Overpotential is the difference between the theoretical potential required for a reaction to occur and the actual potential that is applied to the electrodes. In the case of water electrolysis, the overpotential is caused by a number of factors, including the mass transport of reactants and products to and from the electrodes, the kinetics of the electrochemical reactions, and the interfacial pH. An increase in the interfacial pH at the anode surface in alkaline conditions can lead to an increase in the overpotential for hydrogen production. This is because the lower local pH makes it more difficult for the hydroxide ions to be oxidized. As a result, there is an increase in the required energy to the process occur. In addition to increasing the overpotential, interfacial pH changes can also lead to the degradation of the electrodes. This is because the lower pH can make the electrode more susceptible to corrosion. As a result, the electrodes may need to be replaced more frequently, which can increase the overall cost of water electrolysis. The method presented in the paper addresses the issue of interfacial pH changes by using a cell design with a different cell design, introducing the electrode asymmetry. This design helps to mitigate the pH gradient at the anode/electrolyte interface, which reduces the overpotential and improves the energy efficiency of the electrolyser. The method was tested using a multivariate approach in both laboratory and industrial current density conditions and validated the results with numerical simulations. The results demonstrated a clear improvement (11.6%) in energy efficiency, providing an important contribution to the field of sustainable energy production. The findings of the paper have important implications for the development of cost-effective and sustainable hydrogen production methods. By mitigating interfacial pH changes, it is possible to improve the energy efficiency of alkaline electrolysis and make it a more competitive option for hydrogen production.

Keywords: electrolyser, interfacial pH, numerical simulation, optimization, asymmetric cell

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Authors: Vinay Kumar Vanjakula, Frank Adam, Nils Goseberg, Christian Windt

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When a structure is installed on a seabed, the presence of the structure will influence the flow field around it. The changes in the flow field include, formation of vortices, turbulence generation, waves or currents flow breaking and pressure differentials around the seabed sediment. These changes allow the local seabed sediment to be carried off and results in Scour (erosion). These are a threat to the structure's stability. In recent decades, rapid developments of research work and the knowledge of scour On fixed structures (bridges and Monopiles) in rivers and oceans has been carried out, and very limited research work on scour and liquefaction for gravity anchors, particularly for floating Tension Leg Platform (TLP) substructures. Due to its importance and need for enhancement of knowledge in scour and liquefaction around marine structures, the MarTERA funded a three-year (2020-2023) research program called NuLIMAS (Numerical Modeling of Liquefaction Around Marine Structures). It’s a group consists of European institutions (Universities, laboratories, and consulting companies). The objective of this study is to build a numerical model that replicates the reality, which indeed helps to simulate (predict) underwater flow conditions and to study different marine scour and Liquefication situations. It helps to design a heavyweight anchor for the TLP substructure and to minimize the time and expenditure on experiments. And also, the achieved results and the numerical model will be a basis for the development of other design and concepts For marine structures. The Computational Fluid Dynamics (CFD) numerical model will build in OpenFOAM. A conceptual design of heavyweight anchor for TLP substructure is designed through taking considerations of available state-of-the-art knowledge on scour and Liquefication concepts and references to Previous existing designs. These conceptual designs are validated with the available similar experimental benchmark data and also with the CFD numerical benchmark standards (CFD quality assurance study). CFD optimization model/tool is designed as to minimize the effect of fluid flow, scour, and Liquefication. A parameterized model is also developed to automate the calculation process to reduce user interactions. The parameters such as anchor Lowering Process, flow optimized outer contours, seabed interaction study, and FSSI (Fluid-Structure-Seabed Interactions) are investigated and used to carve the model as to build an optimized anchor.

Keywords: gravity anchor, liquefaction, scour, computational fluid dynamics

Procedia PDF Downloads 127

Authors: Mamunur Rashid, Jialin Yan

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The current years have witnessed a rapid evolution and integration of artificial intelligence (AI) in various fields, prominently influencing the education industry. Acknowledging this transformative wave, AI tools like ChatGPT and Grammarly have undeniably introduced perspectives and skills, enriching the educational experiences of higher education students. The prevalence of AI utilization in higher education also drives an increasing number of researchers' attention in various dimensions. Departments, offices, and professors in universities also designed and released a set of policies and guidelines on using AI effectively. In regard to this, the study targets exploring and analyzing graduate students' perspectives regarding AI guidelines set by teachers. A mixed-methods study will be mainly conducted in this study, employing in-depth interviews and focus groups to investigate and collect students' perspectives. Relevant materials, such as syllabi and course instructions, will also be analyzed through the documentary analysis to facilitate understanding of the study. Surveys will also be used for data collection and students' background statistics. The integration of both interviews and surveys will provide a comprehensive array of student perspectives across various academic disciplines. The study is anchored in the theoretical framework of self-determination theory (SDT), which emphasizes and explains the students' perspective under the AI guidelines through three core needs: autonomy, competence, and relatedness. This framework is instrumental in understanding how AI guidelines influence students' intrinsic motivation and sense of empowerment in their learning environments. Through qualitative analysis, the study reveals a sense of confusion and uncertainty among students regarding the appropriate application and ethical considerations of AI tools, indicating potential challenges in meeting their needs for competence and autonomy. The quantitative data further elucidates these findings, highlighting a significant communication gap between students and educators in the formulation and implementation of AI guidelines. The critical findings of this study mainly come from two aspects: First, the majority of graduate students are uncertain and confused about relevant AI guidelines given by teachers. Second, this study also demonstrates that the design and effectiveness of course materials, such as the syllabi and instructions, also need to adapt in regard to AI policies. It indicates that certain of the existing guidelines provided by teachers lack consideration of students' perspectives, leading to a misalignment with students' needs for autonomy, competence, and relatedness. More emphasize and efforts need to be dedicated to both teacher and student training on AI policies and ethical considerations. To conclude, in this study, graduate students' perspectives on teacher-provided AI guidelines are explored and reflected upon, calling for additional training and strategies to improve how these guidelines can be better disseminated for their effective integration and adoption. Although AI guidelines provided by teachers may be helpful and provide new insights for students, educational institutions should take a more anchoring role to foster a motivating, empowering, and student-centered learning environment. The study also provides some relevant recommendations, including guidance for students on the ethical use of AI and AI policy training for teachers in higher education.

Keywords: higher education policy, graduate students’ perspectives, higher education teacher, AI guidelines, AI in education

Procedia PDF Downloads 47

Authors: dr. Bendes Ákos

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Digital skepticism, a critical stance towards digital technology and its pervasive influence on society, presents significant challenges when analyzed from a legal philosophical perspective. This abstract aims to explore the intersection of digital skepticism and legal philosophy, emphasizing the implications for justice, rights, and the rule of law in the digital age. Digital skepticism arises from concerns about privacy, security, and the ethical implications of digital technology. It questions the extent to which digital advancements enhance or undermine fundamental human values. Legal philosophy, which interrogates the foundations and purposes of law, provides a framework for examining these concerns critically. One key area where digital skepticism and legal philosophy intersect is in the realm of privacy. Digital technologies, particularly data collection and surveillance mechanisms, pose substantial threats to individual privacy. Legal philosophers must grapple with questions about the limits of state power and the protection of personal autonomy. They must consider how traditional legal principles, such as the right to privacy, can be adapted or reinterpreted in light of new technological realities. Security is another critical concern. Digital skepticism highlights vulnerabilities in cybersecurity and the potential for malicious activities, such as hacking and cybercrime, to disrupt legal systems and societal order. Legal philosophy must address how laws can evolve to protect against these new forms of threats while balancing security with civil liberties. Ethics plays a central role in this discourse. Digital technologies raise ethical dilemmas, such as the development and use of artificial intelligence and machine learning algorithms that may perpetuate biases or make decisions without human oversight. Legal philosophers must evaluate the moral responsibilities of those who design and implement these technologies and consider the implications for justice and fairness. Furthermore, digital skepticism prompts a reevaluation of the concept of the rule of law. In an increasingly digital world, maintaining transparency, accountability, and fairness becomes more complex. Legal philosophers must explore how legal frameworks can ensure that digital technologies serve the public good and do not entrench power imbalances or erode democratic principles. Finally, the intersection of digital skepticism and legal philosophy has practical implications for policy-making. Legal scholars and practitioners must work collaboratively to develop regulations and guidelines that address the challenges posed by digital technology. This includes crafting laws that protect individual rights, ensure security, and promote ethical standards in technology development and deployment. In conclusion, digital skepticism provides a crucial lens for examining the impact of digital technology on law and society. A legal philosophical approach offers valuable insights into how legal systems can adapt to protect fundamental values in the digital age. By addressing privacy, security, ethics, and the rule of law, legal philosophers can help shape a future where digital advancements enhance, rather than undermine, justice and human dignity.

Keywords: legal philosophy, privacy, security, ethics, digital skepticism

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Authors: Gunnar Oskarsson, Irena Georgsdottir

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Tourism has been growing incredibly fast during the past years, including the cruise industry, which is gaining increasing popularity among various groups of travelers. It is a challenging task for companies serving cruise ship passengers with local products and services at the point of destination to reach them in due time with information about their offerings, as well learning how to adapt their offerings and messages to the type of customers arriving on each particular occasion. Although some research has been conducted in this sphere, there is still limited knowledge about many specifics within this sector of the tourist industry. The objective of this research is to examine one of these, with the main goal of studying the segmentation of cruise passengers and to learn about marketing practices directed towards them. A qualitative research method, based on in-depth interviews, was used, as this provides an opportunity to gain insight into the participants’ perspectives. Interviews were conducted with 10 respondents from different companies in the tourist industry in Iceland, who interact with cruise passengers on a regular basis in their work environment. The main objective was to gain an understanding of what distinguishes different customer groups, or segments, in this industry, and of the marketing approaches directed towards them. The main findings reveal that participants note the strongest difference between cruise passengers of different nationalities, passengers coming on different ships (size and type), and passengers arriving at different times of the year. A drastic difference was noticed between nationalities in four main segments, American, British, Other European, and Asian customers, although some of these segments could be divided into even further sub-segments. Other important differencing factors were size and type of ships, quality or number of stars on the ship, and travelling time of the year. Companies serving cruise ship passengers, as well as the customers themselves, could benefit if the offerings of services were designed specifically for particular segments within the industry. Concerning marketing towards cruise passengers, the results indicate that it is carried out almost exclusively through the Internet using; a reliable website and, search engine optimization. Marketing is also by word-of-mouth. This research can assist practitioners by offering a deeper understanding of the approaches that may be effective in marketing local products and services to cruise ship passengers, based on their segmentation and by identifying effective ways to reach them. The research, furthermore, provides a valuable contribution to marketing knowledge for the benefit of an increasingly important market segment in a fast growing tourist industry.

Keywords: capabilities, global integration, internationalisation, SMEs

Procedia PDF Downloads 390

Authors: Kamile Nazan Turhan

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When work on edible packaging began, the hope was to provide a sustainable alternative to reduce plastic consumption. Edible packaging can take the form of a coating or film that provides better food protection. They have the potential to be a feasible alternative to traditional plastic food packaging as they can increase the shelf life of foods by reducing their respiration rate and water loss and protecting them from physical damage and microbial spoilage, preventing post-harvest loss. Edible films and coatings can extend the shelf life of the food product and improve food quality by regulating the transfer of moisture, oxygen, carbon dioxide, lipids, aroma, and taste compounds in food systems. The main advantage of using edible packaging is that it reduces the amount of plastic waste produced. Another interesting advantage is that some edible food packaging elements may have added vitamins, probiotics, and other nutrients, bioactive compounds that support technological and biological properties. Despite the use of many different biomaterials in the production of edible packaging, most of the problems experienced are similar. Among these problems are hydrophilicity, low water stability, high humidity sensitivity, poor moisture resistance, high water vapor permeability, poor mechanical properties and machinability, fragility, insolubility, low melting point, sensitivity to pH, temperature, ionic, electro reactions, brittle, and hard structure, low thermal stability, hardness, and hazy film are available. Edible films have the disadvantage of being difficult to apply to the food surface due to their weak barrier and mechanical properties. One of the most important problems is that the developed films have a brittle structure since biomaterials are generally hydrophilic. A plasticizer is added to increase the flexibility of the film, and as the plasticizer content increases, the film permeability also increases. The correct choice of plasticizer for a particular biopolymer allows optimization of the mechanical properties of the film with minimal increase in film permeability. The effect of the amount and type of plasticizer in achieving the desired mechanical properties with optimum permeability is constantly being investigated by researchers. However, it should be noted that in the studies conducted so far, plasticized biofilms cannot provide high mechanical strength or good flexibility compared to synthetic polymer materials. In this review, according to the results of the research in the literature, the disadvantages of implementing edible packaging will be revealed and possible solutions will be presented.

Keywords: edible packaging, biyomaterial, disadvantages, production

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Authors: Christian Doppler, Gernot Hirschl, Gerhard Zsiga

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Starting from 2020, an EU-wide CO2-limitation of 95g/km is scheduled for the average of an OEMs passenger car fleet. Considering that, further measures of optimization on the diesel cycle will be necessary in order to reduce fuel consumption and emissions while keeping performance values adequate at the least. The present article deals with charge air cooling (CAC) on the basis of a diesel passenger car model in a 0D/1D-working process calculation environment. The considered engine is a 2.4 litre EURO VI diesel engine with variable geometry turbocharger (VGT) and low-pressure exhaust gas recirculation (LP EGR). The object of study was the impact of charge air cooling on the engine working process at constant boundary conditions which could have been conducted with an available and validated engine model in AVL BOOST. Part load was realized with constant power and NOx-emissions, whereas full load was accomplished with a lambda control in order to obtain maximum engine performance. The informative results were used to implement a simulation model in Matlab/Simulink which is further integrated into a full vehicle simulation environment via coupling with ICOS (Independent Co-Simulation Platform). Next, the dynamic engine behavior was validated and modified with load steps taken from the engine test bed. Due to the modular setup in the Co-Simulation, different CAC-models have been simulated quickly with their different influences on the working process. In doing so, a new cooler variation isn’t needed to be reproduced and implemented into the primary simulation model environment, but is implemented quickly and easily as an independent component into the simulation entity. By means of the association of the engine model, longitudinal dynamics vehicle model and different CAC models (air/air & water/air variants) in both steady state and transient operational modes, statements are gained regarding fuel consumption, NOx-emissions and power behavior. The fact that there is no more need of a complex engine model is very advantageous for the overall simulation volume. Beside of the simulation with the mentioned demonstrator engine, there have also been conducted several experimental investigations on the engine test bench. Here the comparison of a standard CAC with an intake-manifold-integrated CAC was executed in particular. Simulative as well as experimental tests showed benefits for the water/air CAC variant (on test bed especially the intake manifold integrated variant). The benefits are illustrated by a reduced pressure loss and a gain in air efficiency and CAC efficiency, those who all lead to minimized emission and fuel consumption for stationary and transient operation.

Keywords: air/water-charge air cooler, co-simulation, diesel working process, EURO VI fuel consumption

Procedia PDF Downloads 249

Authors: Francesca Marsili

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The reliability assessment of existing structures with probabilistic methods is becoming an increasingly important and frequent engineering task. However probabilistic reliability methods are based on an exhaustive knowledge of the stochastic modeling of the variables involved in the assessment; at the moment standards for the modeling of variables are absent, representing an obstacle to the dissemination of probabilistic methods. The framework according to probability distribution functions (PDFs) are established is represented by the Bayesian statistics, which uses Bayes Theorem: a prior PDF for the considered parameter is established based on information derived from the design stage and qualitative judgments based on the engineer past experience; then, the prior model is updated with the results of investigation carried out on the considered structure, such as material testing, determination of action and structural properties. The application of Bayesian statistics arises two different kind of problems: 1. The results of the updating depend on the engineer previous experience; 2. The updating of the prior PDF can be performed only if the structure has been tested, and quantitative data that can be statistically manipulated have been collected; performing tests is always an expensive and time consuming operation; furthermore, if the considered structure is an ancient building, destructive tests could compromise its cultural value and therefore should be avoided. In order to solve those problems, an interesting research path is represented by investigating Artificial Intelligence (AI) techniques that can be useful for the automation of the modeling of variables and for the updating of material parameters without performing destructive tests. Among the others, one that raises particular attention in relation to the object of this study is constituted by Case-Based Reasoning (CBR). In this application, cases will be represented by existing buildings where material tests have already been carried out and an updated PDFs for the material mechanical parameters has been computed through a Bayesian analysis. Then each case will be composed by a qualitative description of the material under assessment and the posterior PDFs that describe its material properties. The problem that will be solved is the definition of PDFs for material parameters involved in the reliability assessment of the considered structure. A CBR system represent a good candi¬date in automating the modelling of variables because: 1. Engineers already draw an estimation of the material properties based on the experience collected during the assessment of similar structures, or based on similar cases collected in literature or in data-bases; 2. Material tests carried out on structure can be easily collected from laboratory database or from literature; 3. The system will provide the user of a reliable probabilistic description of the variables involved in the assessment that will also serve as a tool in support of the engineer’s qualitative judgments. Automated modeling of variables can help in spreading probabilistic reliability assessment of existing buildings in the common engineering practice, and target at the best intervention and further tests on the structure; CBR represents a technique which may help to achieve this.

Keywords: reliability assessment of existing buildings, Bayesian analysis, case-based reasoning, historical structures

Procedia PDF Downloads 320

Authors: Sanjana S. Mallya, Roshan Arvind Sivakumar

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Orthotics is the branch of medicine that deals with the provision and use of artificial casts or braces to alter the biomechanical structure of the limb and provide support for the limb. Custom-made orthoses provide more comfort and can correct issues better than those available over-the-counter. However, they are expensive and require intricate modelling of the limb. Traditional methods of modelling involve creating a plaster of Paris mould of the limb. Lately, CAD/CAM and 3D printing processes have improved the accuracy and reduced the production time. Ordinarily, digital cameras are used to capture the features of the limb from different views to create a 3D model. We propose a system to model the limb using Microsoft Kinect2 sensor. The Kinect can capture RGB and depth frames simultaneously up to 30 fps with sufficient accuracy. The region of interest is captured from three views, each shifted by 90 degrees. The RGB and depth data are fused into a single RGB-D frame. The resolution of the RGB frame is 1920px x 1080px while the resolution of the Depth frame is 512px x 424px. As the resolution of the frames is not equal, RGB pixels are mapped onto the Depth pixels to make sure data is not lost even if the resolution is lower. The resulting RGB-D frames are collected and using the depth coordinates, a three dimensional point cloud is generated for each view of the Kinect sensor. A common reference system was developed to merge the individual point clouds from the Kinect sensors. The reference system consisted of 8 coloured cubes, connected by rods to form a skeleton-cube with the coloured cubes at the corners. For each Kinect, the region of interest is the square formed by the centres of the four cubes facing the Kinect. The point clouds are merged by considering one of the cubes as the origin of a reference system. Depending on the relative distance from each cube, the three dimensional coordinate points from each point cloud is aligned to the reference frame to give a complete point cloud. The RGB data is used to correct for any errors in depth data for the point cloud. A triangular mesh is generated from the point cloud by applying Delaunay triangulation which generates the rough surface of the limb. This technique forms an approximation of the surface of the limb. The mesh is smoothened to obtain a smooth outer layer to give an accurate model of the limb. The model of the limb is used as a base for designing the custom orthotic brace or cast. It is transferred to a CAD/CAM design file to design of the brace above the surface of the limb. The proposed system would be more cost effective than current systems that use MRI or CT scans for generating 3D models and would be quicker than using traditional plaster of Paris cast modelling and the overall setup time is also low. Preliminary results indicate that the accuracy of the Kinect2 is satisfactory to perform modelling.

Keywords: 3d scanning, mesh generation, Microsoft kinect, orthotics, registration

Procedia PDF Downloads 167

Authors: Ichiro Takahashi

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One conundrum that macroeconomic theory faces is to explain how an economy can revive from depression, in which the aggregate demand has fallen substantially below its productive capacity. This paper examines an autonomous stabilizing mechanism using an agent-based Wicksell-Keynes macroeconomic model. This paper focuses on the effects of the number of firms and the length of the gestation period for investment that are often assumed to be one in a mainstream macroeconomic model. The simulations found the virtual economy was highly unstable, or more precisely, collapsing when these parameters are fixed at one. This finding may even suggest us to question the legitimacy of these common assumptions. A perpetual decline in capital stock will eventually encourage investment if the capital stock is short-lived because an inactive investment will result in insufficient productive capacity. However, for an economy characterized by a roundabout production method, a gradual decline in productive capacity may not be able to fall below the aggregate demand that is also shrinking. Naturally, one would then ask if our economy cannot rely on an external stimulus such as population growth and technological progress to revive investment, what factors would provide such a buoyancy for stimulating investments? The current paper attempts to answer this question by employing the artificial macroeconomic model mentioned above. The baseline model has the following three features: (1) the multi-period gestation for investment, (2) a large number of heterogeneous firms, (3) demand-constrained firms. The instability is a consequence of the following dynamic interactions. (a) A multiple-period gestation period means that once a firm starts a new investment, it continues to invest over some subsequent periods. During these gestation periods, the excess demand created by the investing firm will spill over to ignite new investment of other firms that are supplying investment goods: the presence of multi-period gestation for investment provides a field for investment interactions. Conversely, the excess demand for investment goods tends to fade away before it develops into a full-fledged boom if the gestation period of investment is short. (b) A strong demand in the goods market tends to raise the price level, thereby lowering real wages. This reduction of real wages creates two opposing effects on the aggregate demand through the following two channels: (1) a reduction in the real labor income, and (2) an increase in the labor demand due to the principle of equality between the marginal labor productivity and real wage (referred as the Walrasian labor demand). If there is only a single firm, a lower real wage will increase its Walrasian labor demand, thereby an actual labor demand tends to be determined by the derived labor demand. Thus, the second positive effect would not work effectively. In contrast, for an economy with a large number of firms, Walrasian firms will increase employment. This interaction among heterogeneous firms is a key for stability. A single firm cannot expect the benefit of such an increased aggregate demand from other firms.

Keywords: agent-based macroeconomic model, business cycle, demand constraint, gestation period, representative agent model, stability

Procedia PDF Downloads 146

Authors: Cedillo Ortiz Cesar Isaac, Marañón-Ruiz Virginia-Francisca, Lozano-Alvarez Juan Antonio, Jáuregui-Rincón Juan, Roger Chiu Zarate

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Introduction: The contamination of water with the azo-dye is a problem worldwide as although wastewater contaminate is treated in a municipal sewage system, still contain a considerable amount of dyes. In the present, there are different processes denominated tertiary method in which it is possible to lower the concentration of the dye. One of these methods is by adsorption onto various materials which can be organic or inorganic materials. The xanthan is a biomaterial as removal agents to decrease the dye content in aqueous solution. The Zimm-Bragg model described the experimental isotherms obtained when this biopolymer was used in the removal of textile dyes. Nevertheless, it was not established if a possible correlation between dye structure and removal efficiency exists. In this sense, the principal objective of this report is to propose a qualitative relationship between the structure of three azo-dyes (Congo Red (CR), Methyl Red (MR) and Methyl Orange (MO)) and their removal efficiency from aqueous environment when xanthan are used as dye sequestering agents. Methods: The dyes were subjected to different pH and ionic strength values to obtain the conditions of maximum dye removal. Afterward, these conditions were used to perform the adsorption isotherm as was reported in the previous study in our group. The Zimm-Bragg model was used to describe the experimental data and the parameters of nucleation (Ku) and cooperativity (U) were obtained by optimization using the R statistical software. The spectra from UV-Visible (aqueous solution), Infrared absorption and Raman spectroscopies (dry samples) were obtained from the biopolymer-dye complex. Results: The removal percent with xanthan in each dye are as follows: with CR had 99.98 % when the pH is 12 and ionic strength is 10.12, with MR had 84.79 % when the pH is 9.5 and ionic strength is 43 and finally the MO had 30 % in pH 4 and 72. It can be seen that when xanthan is used to remove the dyes, exists a lower dependence between structure and removal efficiency. This may be due to the different tendency to form aggregates of each dye. This aggregation capacity and the charge of each dye resulting from the pH and ionic strength values of aqueous solutions are key factors in the dye removal. The experimental isotherm of MR was only that adequately described by Zimm-Bragg model. Because with the CR had the 100 % of remove thus is very difficult obtain de experimental isotherm and finally MO had results fluctuating and therefore was impossible get the accurate data. Conclusions: The study of the removal of three dyes with xanthan as dye sequestering agents suggests that aggregation capacity of dyes and the charge resulting from structural characteristics such as molecular weight and functional groups have a relationship with the removal efficiency. Acknowledgements: We are gratefully acknowledged support for this project by Consejo Nacional de Ciencia y Tecnología, México (CONACyT, Grant No. 632694.)

Keywords: adsorption, azo dyes, xanthan gum, Zimm Bragg theory

Procedia PDF Downloads 267

Authors: E. G. Karvelas, C. Liosis, A. Theodorakakos, T. E. Karakasidis

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Magnetic navigation of the drug inside the human vessels is a very important concept since the drug is delivered to the desired area. Consequently, the quantity of the drug required to reach therapeutic levels is being reduced while the drug concentration at targeted sites is increased. Magnetic navigation of drug agents can be achieved with the use of magnetic nanoparticles where anti-tumor agents are loaded on the surface of the nanoparticles. The magnetic field that is required to navigate the particles inside the human arteries is produced by a magnetic resonance imaging (MRI) device. The main factors which influence the efficiency of the usage of magnetic nanoparticles for biomedical applications in magnetic driving are the size and the magnetization of the biocompatible nanoparticles. In this study, a computational platform for the simulation of the optimal gradient magnetic fields for the navigation of magnetic nanoparticles inside a carotid artery is presented. For the propulsion model of the particles, seven major forces are considered, i.e., the magnetic force from MRIs main magnet static field as well as the magnetic field gradient force from the special propulsion gradient coils. The static field is responsible for the aggregation of nanoparticles, while the magnetic gradient contributes to the navigation of the agglomerates that are formed. Moreover, the contact forces among the aggregated nanoparticles and the wall and the Stokes drag force for each particle are considered, while only spherical particles are used in this study. In addition, gravitational forces due to gravity and the force due to buoyancy are included. Finally, Van der Walls force and Brownian motion are taken into account in the simulation. The OpenFoam platform is used for the calculation of the flow field and the uncoupled equations of particles' motion. To verify the optimal gradient magnetic fields, a covariance matrix adaptation evolution strategy (CMAES) is used in order to navigate the particles into the desired area. A desired trajectory is inserted into the computational geometry, which the particles are going to be navigated in. Initially, the CMAES optimization strategy provides the OpenFOAM program with random values of the gradient magnetic field. At the end of each simulation, the computational platform evaluates the distance between the particles and the desired trajectory. The present model can simulate the motion of particles when they are navigated by the magnetic field that is produced by the MRI device. Under the influence of fluid flow, the model investigates the effect of different gradient magnetic fields in order to minimize the distance of particles from the desired trajectory. In addition, the platform can navigate the particles into the desired trajectory with an efficiency between 80-90%. On the other hand, a small number of particles are stuck to the walls and remains there for the rest of the simulation.

Keywords: artery, drug, nanoparticles, navigation

Procedia PDF Downloads 95

Authors: Yazan S. Khaled, Shazana Shamsuddin, Jim Tiernan, Mike McPherson, Thomas Hughes, Paul Millner, David G. Jayne

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Introduction: There is a need for real-time imaging of colorectal cancer (CRC) to allow tailored surgery to the disease stage. Fluorescence guided laparoscopic imaging of primary colorectal cancer and the draining lymphatics would potentially bring stratified surgery into clinical practice and realign future CRC management to the needs of patients. Fluorescent nanoparticles can offer many advantages in terms of intra-operative imaging and therapy (theranostic) in comparison with traditional soluble reagents. Nanoparticles can be functionalised with diverse reagents and then targeted to the correct tissue using an antibody or Affimer (artificial binding protein). We aimed to develop and test fluorescent silica nanoparticles and targeted against CRC using an anti-carcinoembryonic antigen (CEA) Affimer (Aff). Methods: Anti-CEA and control Myoglobin Affimer binders were subcloned into the expressing vector pET11 followed by transformation into BL21 Star™ (DE3) E.coli. The expression of Affimer binders was induced using 0.1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG). Cells were harvested, lysed and purified using nickle chelating affinity chromatography. The photosensitiser Foslip (soluble analogue of 5,10,15,20-Tetra(m-hydroxyphenyl) chlorin) was incorporated into the core of silica nanoparticles using water-in-oil microemulsion technique. Anti-CEA or control Affs were conjugated to silica nanoparticles surface using sulfosuccinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (sulfo SMCC) chemical linker. Binding of CEA-Aff or control nanoparticles to colorectal cancer cells (LoVo, LS174T and HC116) was quantified in vitro using confocal microscopy. Results: The molecular weights of the obtained band of Affimers were ~12.5KDa while the diameter of functionalised silica nanoparticles was ~80nm. CEA-Affimer targeted nanoparticles demonstrated 9.4, 5.8 and 2.5 fold greater fluorescence than control in, LoVo, LS174T and HCT116 cells respectively (p < 0.002) for the single slice analysis. A similar pattern of successful CEA-targeted fluorescence was observed in the maximum image projection analysis, with CEA-targeted nanoparticles demonstrating 4.1, 2.9 and 2.4 fold greater fluorescence than control particles in LoVo, LS174T, and HCT116 cells respectively (p < 0.0002). There was no significant difference in fluorescence for CEA-Affimer vs. CEA-Antibody targeted nanoparticles. Conclusion: We are the first to demonstrate that Foslip-doped silica nanoparticles conjugated to anti-CEA Affimers via SMCC allowed tumour cell-specific fluorescent targeting in vitro, and had shown sufficient promise to justify testing in an animal model of colorectal cancer. CEA-Affimer appears to be a suitable targeting molecule to replace CEA-Antibody. Targeted silica nanoparticles loaded with Foslip photosensitiser is now being optimised to drive photodynamic killing, via reactive oxygen generation.

Keywords: colorectal cancer, silica nanoparticles, Affimers, antibodies, imaging

Procedia PDF Downloads 220

Authors: Ionut Vintu, Stefan Laible, Ruth Schulz

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Agricultural robotics has been developing steadily over recent years, with the goal of reducing and even eliminating pesticides used in crops and to increase productivity by taking over human labor. The majority of crops are arranged in rows. The first step towards autonomous robots, capable of driving in fields and performing crop-handling tasks, is for robots to robustly detect the rows of plants. Recent work done towards autonomous driving between plant rows offers big robotic platforms equipped with various expensive sensors as a solution to this problem. These platforms need to be driven over the rows of plants. This approach lacks flexibility and scalability when it comes to the height of plants or distance between rows. This paper proposes instead an algorithm that makes use of cheaper sensors and has a higher variability. The main application is in tree nurseries. Here, plant height can range from a few centimeters to a few meters. Moreover, trees are often removed, leading to gaps within the plant rows. The core idea is to combine row detection algorithms with graph-based localization methods as they are used in SLAM. Nodes in the graph represent the estimated pose of the robot, and the edges embed constraints between these poses or between the robot and certain landmarks. This setup aims to improve individual plant detection and deal with exception handling, like row gaps, which are falsely detected as an end of rows. Four methods were developed for detecting row structures in the fields, all using a point cloud acquired with a 3D LiDAR as an input. Comparing the field coverage and number of damaged plants, the method that uses a local map around the robot proved to perform the best, with 68% covered rows and 25% damaged plants. This method is further used and combined with a graph-based localization algorithm, which uses the local map features to estimate the robot’s position inside the greater field. Testing the upgraded algorithm in a variety of simulated fields shows that the additional information obtained from localization provides a boost in performance over methods that rely purely on perception to navigate. The final algorithm achieved a row coverage of 80% and an accuracy of 27% damaged plants. Future work would focus on achieving a perfect score of 100% covered rows and 0% damaged plants. The main challenges that the algorithm needs to overcome are fields where the height of the plants is too small for the plants to be detected and fields where it is hard to distinguish between individual plants when they are overlapping. The method was also tested on a real robot in a small field with artificial plants. The tests were performed using a small robot platform equipped with wheel encoders, an IMU and an FX10 3D LiDAR. Over ten runs, the system achieved 100% coverage and 0% damaged plants. The framework built within the scope of this work can be further used to integrate data from additional sensors, with the goal of achieving even better results.

Keywords: 3D LiDAR, agricultural robots, graph-based localization, row detection

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Authors: Eric Pla Erra, Mariana Jimenez Martinez

Abstract:

While aggregated loads at a community level tend to be easier to predict, individual household load forecasting present more challenges with higher volatility and uncertainty. Furthermore, the drastic changes that our behavior patterns have suffered due to the COVID-19 pandemic have modified our daily electrical consumption curves and, therefore, further complicated the forecasting methods used to predict short-term electric load. Load forecasting is vital for the smooth and optimized planning and operation of our electric grids, but it also plays a crucial role for individual domestic consumers that rely on a HEMS (Home Energy Management Systems) to optimize their energy usage through self-generation, storage, or smart appliances management. An accurate forecasting leads to higher energy savings and overall energy efficiency of the household when paired with a proper HEMS. In order to study how COVID-19 has affected the accuracy of forecasting methods, an evaluation of the performance of a state-of-the-art LGBM (Light Gradient Boosting Model) will be conducted during the transition between pre-pandemic and lockdowns periods, considering day-ahead electric load forecasting. LGBM improves the capabilities of standard Decision Tree models in both speed and reduction of memory consumption, but it still offers a high accuracy. Even though LGBM has complex non-linear modelling capabilities, it has proven to be a competitive method under challenging forecasting scenarios such as short series, heterogeneous series, or data patterns with minimal prior knowledge. An adaptation of the LGBM model – called “resilient LGBM” – will be also tested, incorporating a concept drift detection technique for time series analysis, with the purpose to evaluate its capabilities to improve the model’s accuracy during extreme events such as COVID-19 lockdowns. The results for the LGBM and resilient LGBM will be compared using standard RMSE (Root Mean Squared Error) as the main performance metric. The models’ performance will be evaluated over a set of real households’ hourly electricity consumption data measured before and during the COVID-19 pandemic. All households are located in the city of Barcelona, Spain, and present different consumption profiles. This study is carried out under the ComMit-20 project, financed by AGAUR (Agència de Gestiód’AjutsUniversitaris), which aims to determine the short and long-term impacts of the COVID-19 pandemic on building energy consumption, incrementing the resilience of electrical systems through the use of tools such as HEMS and artificial intelligence.

Keywords: concept drift, forecasting, home energy management system (HEMS), light gradient boosting model (LGBM)

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Authors: Farina Riaz, Shahab Abdulla, Srinjoy Ganguly, Hajime Suzuki, Ravinesh C. Deo, Susan Hopkins

Abstract:

Recently, quantum machine learning has received significant attention. For various types of data, including text and images, numerous quantum machine learning (QML) models have been created and are being tested. Images are exceedingly complex data components that demand more processing power. Despite being mature, classical machine learning still has difficulties with big data applications. Furthermore, quantum technology has revolutionized how machine learning is thought of, by employing quantum features to address optimization issues. Since quantum hardware is currently extremely noisy, it is not practicable to run machine learning algorithms on it without risking the production of inaccurate results. To discover the advantages of quantum versus classical approaches, this research has concentrated on colored image data. Deep learning classification models are currently being created on Quantum platforms, but they are still in a very early stage. Black and white benchmark image datasets like MNIST and Fashion MINIST have been used in recent research. MNIST and CIFAR-10 were compared for binary classification, but the comparison showed that MNIST performed more accurately than colored CIFAR-10. This research will evaluate the performance of the QML algorithm on the colored benchmark dataset CIFAR-10 to advance QML's real-time applicability. However, deep learning classification models have not been developed to compare colored images like Quantum Convolutional Neural Network (QCNN) to determine how much it is better to classical. Only a few models, such as quantum variational circuits, take colored images. The methodology adopted in this research is a hybrid approach by using penny lane as a simulator. To process the 10 classes of CIFAR-10, the image data has been translated into grey scale and the 28 × 28-pixel image containing 10,000 test and 50,000 training images were used. The objective of this work is to determine how much the quantum approach can outperform a classical approach for a comprehensive dataset of color images. After pre-processing 50,000 images from a classical computer, the QCNN model adopted a hybrid method and encoded the images into a quantum simulator for feature extraction using quantum gate rotations. The measurements were carried out on the classical computer after the rotations were applied. According to the results, we note that the QCNN approach is ~12% more effective than the traditional classical CNN approaches and it is possible that applying data augmentation may increase the accuracy. This study has demonstrated that quantum machine and deep learning models can be relatively superior to the classical machine learning approaches in terms of their processing speed and accuracy when used to perform classification on colored classes.

Keywords: CIFAR-10, quantum convolutional neural networks, quantum deep learning, quantum machine learning

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Authors: Onada Olawale Ahmed

Abstract:

As the intensification of aquaculture production increases on global scale, the growing concern of fish farmers around the world is related to cost of fish production, where cost of feeding takes substantial percentage. Fishmeal (FM) is one of the most expensive ingredients, and its high dependence in aqua-feed production translates to high cost of feeding of stocked fish. However, to reach a sustainable aquaculture, new alternative protein sources including cheaper plant or animal origin proteins are needed to be introduced for stable aqua-feed production. Spirulina is a cyanobacterium that has good nutrient profile that could be useful in aquaculture. This review therefore emphasizes on the nutritional value of Spirulina as a potential replacement of FM in aqua-feed. Spirulina is a planktonic photosynthetic filamentous cyanobacterium that forms massive populations in tropical and subtropical bodies of water with high levels of carbonate and bicarbonate. Spirulina grows naturally in nutrient rich alkaline lake with water salinity ( > 30 g/l) and high pH (8.5–11.0). Its artificial production requires luminosity (photo-period 12/12, 4 luxes), temperature (30 °C), inoculum, water stirring device, dissolved solids (10–60 g/litre), pH (8.5– 10.5), good water quality, and macro and micronutrient presence (C, N, P, K, S, Mg, Na, Cl, Ca and Fe, Zn, Cu, Ni, Co, Se). Spirulina has also been reported to grow on agro-industrial waste such as sugar mill waste effluent, poultry industry waste, fertilizer factory waste, and urban waste and organic matter. Chemical composition of Spirulina indicates that it has high nutritional value due to its content of 55-70% protein, 14-19% soluble carbohydrate, high amount of polyunsaturated fatty acids (PUFAs), 1.5–2.0 percent of 5–6 percent total lipid, all the essential minerals are available in spirulina which contributes about 7 percent (average range 2.76–3.00 percent of total weight) under laboratory conditions, β-carotene, B-group vitamin, vitamin E, iron, potassium and chlorophyll are also available in spirulina. Spirulina protein has a balanced composition of amino acids with concentration of methionine, tryptophan and other amino acids almost similar to those of casein, although, this depends upon the culture media used. Positive effects of spirulina on growth, feed utilization and stress and disease resistance of cultured fish have been reported in earlier studies. Spirulina was reported to replace up to 40% of fishmeal protein in tilapia (Oreochromis mossambicus) diet and even higher replacement of fishmeal was possible in common carp (Cyprinus carpio), partial replacement of fish meal with spirulina in diets for parrot fish (Oplegnathus fasciatus) and Tilapia (Orechromis niloticus) has also been conducted. Spirulina have considerable potential for development, especially as a small-scale crop for nutritional enhancement and health improvement of fish. It is important therefore that more research needs to be conducted on its production, inclusion level in aqua-feed and its possible potential use of aquaculture.

Keywords: aquaculture, spirulina, fish nutrition, fish feed

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

Abstract:

The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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