Search results for: time truncated experiment

Authors: Atish Bagchi, Siva Chandrasekaran

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Background: According to World Bank national accounts data, the estimated global manufacturing value-added output in 2020 was 13.74 trillion USD. These manufacturing processes are monitored, modelled, and controlled by advanced, real-time, computer-based systems, e.g., Industrial IoT, PLC, SCADA, etc. These systems measure and manipulate a set of physical variables, e.g., temperature, pressure, etc. Despite the use of IoT, SCADA etc., in manufacturing, studies suggest that unplanned downtime leads to economic losses of approximately 864 billion USD each year. Therefore, real-time, accurate detection, classification and prediction of machine behaviour are needed to minimise financial losses. Although vast literature exists on time-series data processing using machine learning, the challenges faced by the industries that lead to unplanned downtimes are: The current algorithms do not efficiently handle the high-volume streaming data from industrial IoTsensors and were tested on static and simulated datasets. While the existing algorithms can detect significant 'point' outliers, most do not handle contextual outliers (e.g., values within normal range but happening at an unexpected time of day) or subtle changes in machine behaviour. Machines are revamped periodically as part of planned maintenance programmes, which change the assumptions on which original AI models were created and trained. Aim: This research study aims to deliver a Graph Neural Network(GNN)based hybrid forecasting model that interfaces with the real-time machine control systemand can detect, predict machine behaviour and behavioural changes (anomalies) in real-time. This research will help manufacturing industries and utilities, e.g., water, electricity etc., reduce unplanned downtimes and consequential financial losses. Method: The data stored within a process control system, e.g., Industrial-IoT, Data Historian, is generally sampled during data acquisition from the sensor (source) and whenpersistingin the Data Historian to optimise storage and query performance. The sampling may inadvertently discard values that might contain subtle aspects of behavioural changes in machines. This research proposed a hybrid forecasting and classification model which combines the expressive and extrapolation capability of GNN enhanced with the estimates of entropy and spectral changes in the sampled data and additional temporal contexts to reconstruct the likely temporal trajectory of machine behavioural changes. The proposed real-time model belongs to the Deep Learning category of machine learning and interfaces with the sensors directly or through 'Process Data Historian', SCADA etc., to perform forecasting and classification tasks. Results: The model was interfaced with a Data Historianholding time-series data from 4flow sensors within a water treatment plantfor45 days. The recorded sampling interval for a sensor varied from 10 sec to 30 min. Approximately 65% of the available data was used for training the model, 20% for validation, and the rest for testing. The model identified the anomalies within the water treatment plant and predicted the plant's performance. These results were compared with the data reported by the plant SCADA-Historian system and the official data reported by the plant authorities. The model's accuracy was much higher (20%) than that reported by the SCADA-Historian system and matched the validated results declared by the plant auditors. Conclusions: The research demonstrates that a hybrid GNN based approach enhanced with entropy calculation and spectral information can effectively detect and predict a machine's behavioural changes. The model can interface with a plant's 'process control system' in real-time to perform forecasting and classification tasks to aid the asset management engineers to operate their machines more efficiently and reduce unplanned downtimes. A series of trialsare planned for this model in the future in other manufacturing industries.

Keywords: GNN, Entropy, anomaly detection, industrial time-series, AI, IoT, Industry 4.0, Machine Learning

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Authors: Irsha Dhotre

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Cymbopogon citratus is generally known as Indian Lemongrass and is widely applicable in the cosmetic, pharmaceutical, dairy puddings, and food industries. To enhance the quality of extraction, microwave-oven-aided hydro distillation processes were implemented. The basic parameter which influences the rate of extraction is considered, such as the temperature of extraction, the time required for extraction, and microwave-oven power applied. Locally available CKP 25 Cymbopogon citratus was used for the extraction of essential oil. Optimization of Extractions Parameters and full factorial Box–Behnken design (BBD) evaluated by using Design expert 13 software. The regression model revealed that the optimum parameters required for extractions are a temperature of 35℃, a time of extraction of 130 minutes, and microwave-oven power of 700 W. The extraction efficiency of yield is 4.76%. Gas Chromatography-Mass Spectroscopy (GC-MS) analysis confirmed the significant components present in the extraction of lemongrass oil.

Keywords: Box–Behnken design, Cymbopogon citratus, hydro distillation, microwave-oven, response surface methodology

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Authors: Sagir M. Yusuf, Chris Baber

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In this paper, we describe how Bayesian inferential reasoning will contributes in obtaining a well-satisfied prediction for Distributed Constraint Optimization Problems (DCOPs) with uncertainties. We also demonstrate how DCOPs could be merged to multi-agent knowledge understand and prediction (i.e. Situation Awareness). The DCOPs functions were merged with Bayesian Belief Network (BBN) in the form of situation, awareness, and utility nodes. We describe how the uncertainties can be represented to the BBN and make an effective prediction using the expectation-maximization algorithm or conjugate gradient descent algorithm. The idea of variable prediction using Bayesian inference may reduce the number of variables in agents’ sampling domain and also allow missing variables estimations. Experiment results proved that the BBN perform compelling predictions with samples containing uncertainties than the perfect samples. That is, Bayesian inference can help in handling uncertainties and dynamism of DCOPs, which is the current issue in the DCOPs community. We show how Bayesian inference could be formalized with Distributed Situation Awareness (DSA) using uncertain and missing agents’ data. The whole framework was tested on multi-UAV mission for forest fire searching. Future work focuses on augmenting existing architecture to deal with dynamic DCOPs algorithms and multi-agent information merging.

Keywords: DCOP, multi-agent reasoning, Bayesian reasoning, swarm intelligence

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Authors: Ali Saghaei, Negar Ghotbeddin, Ebrahim Rajabzadeh Ghatrami, Milad Maniat

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The use of herbs as natural additives in fish diets are used to enhance the efficiency and safety systems. The use of herbs, garlic, due to the structure and composition of it has beneficial role in human nutrition and animal nutrition. This study was conducted evaluate the effect different levels of garlic (Allium sativum) powder on the some serum biochemical parameters of Oscar fish (Astronotus ocellatus). Fish were divided into four groups fed on diets containing garlic in different levels; 5 g kg˗1, 10 g kg-1, 20 g kg-1, 30 g kg-1 diet and the control group diet was without garlic. A total number of 300 fish was used and Triplicate groups of Oscar fish with initial weight of 12.43±0.24 g were hand-fed to visual satiation at three meals per day. The experiment extended for two months. Total Protein (TP), Albumin (ALB), Globulin (GLB) and Albumin/Globulin (A/G) ratio, were determined. Based on the results, no significant differences were seen among treatments and control groups during the experimental period for TP, ALB, GLB, and A/G ratio (p > 0.05). Although, the highest amount of serum total protein and globulin levels were observed in diet containing 10 g kg-1 of garlic. Also, the highest value of albumin and A/G were observed in diet containing 20 g kg-1 of garlic, but there were no significant difference with other treatments. The results of this study show that addition of garlic Allium sativum to fish diet can improve fish health.

Keywords: garlic (Allium sativum), serum, Oscar fish (Astronotus ocellatus), iran

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Authors: Ramesh Rajagopalan, Adam Dahlstrom

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Noise removal techniques play a vital role in the performance of electrocardiographic (ECG) signal processing systems. ECG signals can be corrupted by various kinds of noise such as baseline wander noise, electromyographic interference, and power-line interference. One of the significant challenges in ECG signal processing is the degradation caused by additive 50 or 60 Hz power-line interference. This work investigates the removal of power line interference and suppression of transient response for filtering noise corrupted ECG signals. We demonstrate the effectiveness of Infinite Impulse Response (IIR) notch filter with time varying pole radius for improving the transient behavior. The temporary change in the pole radius of the filter diminishes the transient behavior. Simulation results show that the proposed IIR filter with time varying pole radius outperforms traditional IIR notch filters in terms of mean square error and transient suppression.

Keywords: notch filter, ECG, transient, pole radius

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Authors: Maryam I. Abdullahi, Suleiman Aliyu, Armaya'u Hamisu Bichi

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The study was conducted at the University Fish Farm, Federal University Dutsinma. The aim of the study was to determine the effects of dietary supplementation of Allium sativum and Zingiber officinale mixture on growth performance, feed utilization and survival of C. gariepinus fingerling reared in tank system. The experimental setup comprised of four treatment (4) groups labeled as T1, T2, T3 and T4, each treatment replicated 3 times with ten (10) fingerlings in each replicate respectively. Treatment 1 contained 0.5% of Zingiber officinale and 0.5% of Allium sativum (ZO-AS: 1.0%), Treatment 2 contained 0.75% Zingiber officinale, and 0.75% garlic (ZO-AS: 1.5%) while T3 contained 1% ginger and 1% Allium sativum (ZO-AS: 2.0%) respectively. The experiment lasted for twelve (12) weeks (84 days). The survival rate ranges from 90% - 100%. With a higher Final Mean Weight (893.10) and Percentage Mean Weight (942.65) as compared to the control group and others. There was no significant difference (p > 0.05) in the FMW (893.10) of the fish fed 1.5g/kg of Garlic and Ginger diets than the control (687.00). The SGR (1.20) of fish-fed Zingiber officinale and Allium sativum fortified diets shows that there is no significant difference between treatments fed 1.5g/kg Zingiber officinale and Allium sativum and the control group. Generally, there was an increased survival rate in the experimental fish-fed Zingiber officinale and Allium sativum-supplemented diets as compared to the control.

Keywords: clarias gariepinus, zingiber officinale, allium sativum, fingerlings

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: A. Senthil Kumar, V. Murali Bhaskaran

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In the information technology ground, people are using various tools and software for their official use and personal reasons. Nowadays, people are worrying to choose data accessing and extraction tools at the time of buying and selling their products. In addition, worry about various quality factors such as price, durability, color, size, and availability of the product. The main purpose of the research study is to find solutions to these unsolved existing problems. The proposed algorithm is a Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective strategic decision at all the levels of data extraction, uses a real time textile dataset and analyzes the results. Finally, the results are obtained and compared with the existing measurement methods such as PCC, SLCF, and VSS. The result accuracy is higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson’s Correlation Coefficient (PCC), VSS (Vector Space Similarity)

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Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: David Gamboa, Bernardo Herrera, Karen Cacua

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Nanomaterials have been explored as alternatives to reduce particulate matter from diesel engines, which is one of the most common pollutants of the air in urban centers. However, the use of nanomaterials as additives for diesel has to overcome the instability of the dispersions to be considered viable for commercial use. In this work, functionalization of carbon nanotubes with amide groups was performed to improve the stability of these nanomaterials in a mix of 90% petroleum diesel and 10% palm oil biodiesel (B10) in concentrations of 50 and 100 ppm. The resulting nano fuel was used as the fuel for a stationary internal combustion engine, where the particulate matter, NOx, and CO were measured. The results showed that the use of amide groups significantly enhances the time for the carbon nanotubes to remain suspended in the fuel, and at the same time, these nanomaterials helped to reduce the particulate matter and NOx emissions. However, the CO emissions with nano fuel were higher than those ones with the combustion of B10. These results suggest that carbon nanotubes have thermal and catalytic effects on the combustion of B10.

Keywords: carbon nanotubes, diesel, internal combustion engine, particulate matter

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Authors: Haggay Etkes

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This study provides evidence for the impact of monthly interviews conducted for the Israeli Labor Force Surveys (LFSs) on estimated flows between labor force (LF) statuses and on coefficients in fixed-effects estimations. The study uses the natural experiment of parallel interviews for the quarterly and the monthly LFSs in Israel in 2011 for demonstrating that the Labor Force Participation (LFP) rate of Jewish persons who participated in the monthly LFS increased between interviews, while in the quarterly LFS it decreased. Interestingly, the estimated impact on the LFP rate of self-reporting individuals is 2.6–3.5 percentage points while the impact on the LFP rate of individuals whose data was reported by another member of their household (a proxy), is lower and statistically insignificant. The relative increase of the LFP rate in the monthly survey is a result of a lower rate of exit from the LF and a somewhat higher rate of entry into the LF relative to these flows in the quarterly survey. These differing flows have a bearing on labor search models as the monthly survey portrays a labor market with less friction and a “steady state” LFP rate that is 5.9 percentage points higher than the quarterly survey. The study also demonstrates that monthly interviews affect a specific group (45–64 year-olds); thus the sign of coefficient of age as an explanatory variable in fixed-effects regressions on LFP is negative in the monthly survey and positive in the quarterly survey.

Keywords: measurement error, surveys, search, LFSs

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Authors: Jirod Chaisan, Somchai Maneewan, Chantana Punlek, Ninnart Rachapradit, Surapong Chirarattananon, Pattana Rakkwamsuk

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In this paper, simulation modeling of heat transfer, air flow and distribution emitted from CO2 was performed in a regenerated air. The study room was divided in 3 types: common room, small lecture room and large lecture room under evaluated condition in two case: released and unreleased CO2 including of used hybrid air ventilation system for regenerated air under Thailand climate conditions. The carbon dioxide was located on the center of the room and released rate approximately 900-1200 ppm corresponded with indoor air quality standard (IAQs). The indoor air in the thermal comfort zone was calculated and simulated with the numerical method that using real data from the handbook guideline. The results of the study showed that in the case of hybrid air ventilation system explained thermal and CO2 distribution due to the system was adapted significantly in the comfort zone. The results showed that when CO2 released on the center of the other room, the CO2 high concentration in comfort zone so used hybrid air ventilation that decreased CO2 with regeneration air including of reduced temperature indoor. However, the study is simulation modeling and guideline only so the future should be the experiment of hybrid air ventilation system for evaluated comparison of the systems.

Keywords: air ventilation, indoor air quality, thermal comfort, thermoelectric, photovoltaic, dehumidify

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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Authors: Chen-Yin Kuo, Yung-Hsin Lee

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Previous studies applying residual income valuation (RIV) model generally use panel data and single-equation model to forecast stock prices. Unlike these, this paper uses Taiwan longitudinal data to estimate multi-equation time-series models such as Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and conduct out-of-sample forecasting. Further, this work assesses their forecasting performance by two instruments. In favor of extant research, the major finding shows that VECM outperforms other three models in forecasting for three stock sectors over entire horizons. It implies that an error correction term containing long-run information contributes to improve forecasting accuracy. Moreover, the pattern of composite shows that at longer horizon, VECM produces the greater reduction in errors, and performs substantially better than VAR.

Keywords: residual income valuation model, vector error correction model, out of sample forecasting, forecasting accuracy

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: Jin Ho Kim, Yujeong Shin, Seong-Jin Cho, Dong-Jin Kim, U-Syn Ha

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Due to the development of the information industry and technologies, cellular phones have must not only function to communicate, but also have functions such as the Internet, e-banking, entertainment, etc. These phones are called smartphones. The performance of smartphones has improved, because of the various functions of smartphones, and the capacity of the battery has been increased gradually. Recently, linear generators have been embedded in smartphones in order to recharge the smartphone's battery. In this study, optimization is performed and an array change of permanent magnets is examined in order to increase efficiency. We propose an optimal design using design of experiments (DOE) to maximize the generated induced voltage. The thickness of the poleshoe and permanent magnet (PM), the height of the poleshoe and PM, and the thickness of the coil are determined to be design variables. We made 25 sampling points using an orthogonal array according to four design variables. We performed electromagnetic finite element analysis to predict the generated induced voltage using the commercial electromagnetic analysis software ANSYS Maxwell. Then, we made an approximate model using the Kriging algorithm, and derived optimal values of the design variables using an evolutionary algorithm. The commercial optimization software PIAnO (Process Integration, Automation, and Optimization) was used with these algorithms. The result of the optimization shows that the generated induced voltage is improved.

Keywords: smartphone, linear generator, design of experiment, approximate model, optimal design

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Authors: A. Zarei, M. Porkhalili, B. Gholamhosseini

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In this experiment, sixty Hy-Line (W-36) laying hens were selected in 40weeks of age. Experimental diets were consumed for 12 weeks duration by them. The experimental design was completely randomized block included four treatments and each of them with five replications and three sample in each replicate. Treatments were as follow: Basal diet+probiotic, basal diet + prebiotic and basal diet+probiotic+ prebiotic. Performance traits were measured such as: hen production, egg weight, feed intake, feed conversion ratio ,shell thickness, shell strength, shell weight, hough unit, yolk color, and yolk cholesterol. Blood parameters like; Ca, cholesterol, triglyceride, VLDL and antibody titer and so morphological of intestine were determined. At the end of experimental period, after sampling from end of cecum, bacterial colony count was measured. Results showed; shell weight was significantly greater than other treatments in probiotic treatment.Yolk weight in prebiotic treatment was significantly greater than other treatments. The ratio of height of villi to dept of crypt cells in duodenum, jejunum, ileum and secum in prebiotic treatment were significantly greater. Results from the other traits were not significant between treatments, however there were totally good results in other traits with simultaneous usage of probiotic and prebiotic.

Keywords: probiotic, prebiotic, laying hens, performance, blood parameters, intestine morphology

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Authors: A. Naamane, M. Hasnaoui

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Numerical modeling of fluid flows, whether compressible or incompressible, laminar or turbulent presents a considerable contribution in the scientific and industrial fields. However, the development of an approximate model of a supersonic flow requires the introduction of specific and more precise techniques and methods. For this purpose, the object of this paper is modeling a supersonic flow of inviscid fluid around a dihedral airfoil. Based on the thin airfoils theory and the non-dimensional stationary Steichen equation of a two-dimensional supersonic flow in isentropic evolution, we obtained a solution for the downstream velocity potential of the oblique shock at the second order of relative thickness that characterizes a perturbation parameter. This result has been dealt with by the asymptotic analysis and characteristics method. In order to validate our model, the results are discussed in comparison with theoretical and experimental results. Indeed, firstly, the comparison of the results of our model has shown that they are quantitatively acceptable compared to the existing theoretical results. Finally, an experimental study was conducted using the AF300 supersonic wind tunnel. In this experiment, we have considered the incident upstream Mach number over a symmetrical dihedral airfoil wing. The comparison of the different Mach number downstream results of our model with those of the existing theoretical data (relative margin between 0.07% and 4%) and with experimental results (concordance for a deflection angle between 1° and 11°) support the validation of our model with accuracy.

Keywords: asymptotic modelling, dihedral airfoil, supersonic flow, supersonic wind tunnel

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Authors: Malek Bendjaballah, Hacina Saidi, Sarra Hamidoud

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The aim of this study is to model and optimize the performance of a new electrocoagulation (E.C) process for the treatment of wastewater as well as the energy consumption in order to extrapolate it to the industrial scale. Through judicious application of an experimental design (DOE), it has been possible to evaluate the individual effects and interactions that have a significant influence on both objective functions (maximizing efficiency and minimizing energy consumption) by using aluminum electrodes as sacrificial anode. Preliminary experiments have shown that the pH of the medium, the applied potential and the treatment time with E.C are the main parameters. A factorial design 33 has been adopted to model performance and energy consumption. Under optimal conditions, the pollution reduction efficiency is 93%, combined with a minimum energy consumption of 2.60.10-3 kWh / mg-COD. The potential or current applied and the processing time and their interaction were the most influential parameters in the mathematical models obtained. The results of the modeling were also correlated with the experimental ones. The results offer promising opportunities to develop a clean process and inexpensive technology to eliminate or reduce wastewater,

Keywords: electrocoagulation, green process, experimental design, optimization

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Authors: A. Novikova, L. Rocchi, G. Startiene

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Market and non-market outputs are produced jointly in agriculture. Their supply depends on the intensity and type of production. The role of agriculture as an economic activity and its effects are important for the Lithuanian case study, as agricultural land covers more than a half of country. Positive and negative externalities, created in agriculture are not considered in the market. Therefore, specific techniques such as stated preferences methods, in particular choice experiments (CE) are used for evaluation of non-market outputs in agriculture. The main aim of this paper is to present construction of the research path for evaluation of non-market farming system outputs in Lithuania. The conventional and organic farming, covering crops (including both cereal and industrial crops) and livestock (including dairy and cattle) production has been selected. The CE method and nested logit (NL) model were selected as appropriate for evaluation of non-market outputs of different farming systems in Lithuania. A pilot survey was implemented between October–November 2018, in order to test and improve the CE questionnaire. The results of the survey showed that the questionnaire is accepted and well understood by the respondents. The econometric modelling showed that the selected NL model could be used for the main survey. The understanding of the differences between organic and conventional farming by residents was identified. It was revealed that they are more willing to choose organic farming in comparison to conventional farming.

Keywords: choice experiments, farming system, Lithuania market outputs, non-market outputs

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Authors: Sihem Oujdi, Hafida Belbachir

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Existing methods of data mining cannot be applied on spatial data because they require spatial specificity consideration, as spatial relationships. This paper focuses on the classification with decision trees, which are one of the data mining techniques. We propose an extension of the C4.5 algorithm for spatial data, based on two different approaches Join materialization and Querying on the fly the different tables. Similar works have been done on these two main approaches, the first - Join materialization - favors the processing time in spite of memory space, whereas the second - Querying on the fly different tables- promotes memory space despite of the processing time. The modified C4.5 algorithm requires three entries tables: a target table, a neighbor table, and a spatial index join that contains the possible spatial relationship among the objects in the target table and those in the neighbor table. Thus, the proposed algorithms are applied to a spatial data pattern in the accidentology domain. A comparative study of our approach with other works of classification by spatial decision trees will be detailed.

Keywords: C4.5 algorithm, decision trees, S-CART, spatial data mining

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Authors: Saurabh Basu, Sudin Ganguly

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Conductance in graphene nanoribbons (GNR) in presence of magnetic (for example, Iron) and non-magnetic (for example, Gold) adatoms are explored theoretically within a Kane-Mele model for their possible spintronic applications and topologically non-trivial properties. In our work, we have considered the magnetic adatoms to induce a Rashba spin-orbit coupling (RSOC) and an exchange bias field, while the non-magnetic ones induce an RSOC and an intrinsic spin-orbit (SO) coupling. Even though RSOC is present in both, they, however, represent very different physical situations, where the magnetic adatoms do not preserve the time reversal symmetry, while the non-magnetic case does. This has important implications on the topological properties. For example, the non-magnetic adatoms, for moderately strong values of SO, the GNR denotes a quantum spin Hall insulator as evident from a 2e²/h plateau in the longitudinal conductance and presence of distinct conducting edge states with an insulating bulk. Since the edge states are protected by time reversal symmetry, the magnetic adatoms in GNR yield trivial insulators and do not possess any non-trivial topological property. However, they have greater utility than the non-magnetic adatoms from the point of view of spintronic applications. Owing to the broken spatial symmetry induced by the presence of adatoms of either type, all the x, y and z components of the spin-polarized conductance become non-zero (only the y-component survives in pristine Graphene owing to a mirror symmetry present there) and hence become suitable for spintronic applications. However, the values of the spin polarized conductances are at least two orders of magnitude larger in the case of magnetic adatoms than their non-magnetic counterpart, thereby ensuring more efficient spintronic applications. Further the applications are tunable by altering the adatom densities.

Keywords: magnetic and non-magnetic adatoms, quantum spin hall phase, spintronic applications, spin polarized conductance, time reversal symmetry

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Authors: Haziyah Hussin

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Hazi Attire software is purposely designed to be used for pattern drafting of the Malay Traditional Women Apparel. It is software created using LISP Program that works under AutoCAD engine and able to draft various patterns for Malay women apparels from fitted, semi-fitted and loose silhouettes. It is fully automatic and the user can select styles from the menu on the screen and enter the measurements. Within five seconds patterns are ready to be printed and sewn. Hazi Attire is different from other programmes available in the market since it is fully automatic, user-friendly and able to print selected pattern chosen quickly and accurately. With this software (Hazi Attire), the selected styles can be generated the pattern according to made-to-measure or standard sizes. It would benefit the apparel industries by reducing manufacturing lead time and cycle time.

Keywords: basic pattern, pattern drafting, toile, Malay traditional women apparel, the measurement parameters, fitted, semi-fitted and loose silhouette

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Authors: Chebli Derradji, Abdallah Bouguettoucha, Zoubir Manaa, S. Nacef, A. Amrane

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Antibiotics are major pollutants of wastewater not only due to their stability in biological systems, but also due to their impact on public health. Their degradation by means of hydroxyl radicals generated through the application of microwave in the presence of hydrogen peroxide and two solid catalysts, iron-based synthetic clay (LDHs) and goethite (FeOOH) have been examined. A drastic reduction of the degradation yield was observed above pH 4, and hence the optimal conditions were found to be a pH of 3, 0.1 g/L of clay, a somewhat low amount of H2O2 (1.74 mmol/L) and a microwave intensity of 850 W. It should be observed that to maintain an almost constant temperature, a cooling with cold water was always applied between two microwaves running; and hence the ratio between microwave heating time and cooling time was 1. The obtained SMX degradation was 98.8 ± 0.2% after 30 minutes of microwave treatment. It should be observed that in the absence of the solid catalyst, LDHs, no SMX degradation was observed. From this, the use of microwave in the presence of a solid source of iron (LDHs) appears to be an efficient solution for the treatment of wastewater containing SMX.

Keywords: microwave, fenton, heterogenous fenton, degradation, oxidation, antibiotics

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Authors: Christopher Benedikt Jakob

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In our current society, there is no need to take a look at the wristwatch to know the exact time. Smartphones, the watch in the car or the computer watch, inform us about the time too. Over hundreds of years, luxury watches have held a fascination for human beings. Consumers buy watches that cost thousands of euros, although they could buy much cheaper watches which also fulfill the function to indicate the correct time. This shows that the functional value has got a minor meaning with reference to the buying-reasons as regards luxury watches. For a few years, people have an increased demand to track data like their walking distance per day or to track their sleep for example. Smart watches enable consumers to get information about these data. There exists a trend that people intend to optimise parts of their social life, and thus they get the impression that they are able to optimise themselves as human beings. With the help of smart watches, they are able to optimise parts of their productivity and to realise their targets at the same time. These smart watches are also offered as luxury models, and the question is: how will customers of traditional luxury watches react? Therefore this study has the intention to give answers to the question why people are willing to spend an enormous amount of money on the consumption of luxury watches. The self-expression model, the relationship basis model, the functional benefit representation model and the means-end-theory are chosen as an appropriate methodology to find reasons why human beings purchase specific luxury watches and luxury smart watches. This evaluative approach further discusses these strategies concerning for example if consumers buy luxury watches/smart watches to express the current self or the ideal self and if human beings make decisions on expected results. The research critically evaluates that relationships are compared on the basis of their advantages. Luxury brands offer socio-emotional advantages like social functions of identification and that the strong brand personality of luxury watches and luxury smart watches helps customers to structure and retrieve brand awareness which simplifies the process of decision-making. One of the goals is to identify if customers know why they like specific luxury watches and dislike others although they are produced in the same country and cost comparable prices. It is very obvious that the market for luxury watches especially for luxury smart watches is changing way faster than it has been in the past. Therefore the research examines the market changing parameters in detail.

Keywords: buying-behaviour, brand management, consumer, luxury watch, smart watch

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Authors: Pia Kastl

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By combining face-to-face sessions with digital selflearning units, the learning process can be enhanced and learning success improved. Potentials of blended learning are the flexibility and possibility to get in touch with lecturers and fellow students face-toface. It also offers the opportunity to individualize and self-regulate the learning process. Aim of this article is to analyse how different learning environments affect students’ learning behavior and how digital tools can be used effectively. The analysis also considers the extent to which the field of study affects the students’ preferences. Semi-structured interviews were conducted with students from different disciplines at two German universities (N= 60). The questions addressed satisfaction and perception of online, faceto-face and blended learning courses. In addition, suggestions for improving learning experience and the use of digital tools in the different learning environments were surveyed. The results show that being present on campus has a positive impact on learning success and online teaching facilitates flexible learning. Blended learning can combine the respective benefits, although one challenge is to keep the time investment within reasonable limits. The use of digital tools differs depending on the subject. Medical students are willing to use digital tools to improve their learning success and voluntarily invest more time. Students of the humanities and social sciences, on the other hand, are reluctant to invest additional time. They do not see extra study material as an additional benefit their learning success. This study illustrates how these heterogenous demands on learning environments can be met. In addition, potential for improvement will be identified in order to foster both learning process and learning success. Learning environments can be meaningfully enriched with digital elements to address student diversity in higher education.

Keywords: blended learning, higher education, diversity, learning styles

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Authors: Laboratory of Semiconducting, Metallic Materials (LMSM) Biskra Algeria

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InZnSnO2 (IZTO)/β-Ga2O3 Schottky solar barrier photodetector (PhD) exposed to 255 nm was simulated and compared to the measurement. Numerical simulations successfully reproduced the photocurrent at reverse bias and response by taking into account several factors, such as conduction mechanisms and material parameters. By adopting reducing the density of the trap as an improvement. The effect of reducing the bulk trap densities on the photocurrent, response, and time-dependent (continuous conductivity) was studied. As the trap density decreased, the photocurrent increased. The response was 0.04 A/W for the low Ga2O3 trap density. The estimated decay time for the lowest intensity ET (0.74, 1.04 eV) is 0.05 s and is shorter at ∼0.015 s for ET (0.55 eV). This indicates that the shallow traps had the dominant effect (ET = 0.55 eV) on the continuous photoconductivity phenomenon. Furthermore, with decreasing trap densities, this PhD can be considered as a self-powered solar-blind photodiode (SBPhD).

Keywords: IZTO/β-Ga2O3, self-powered solar-blind photodetector, numerical simulation, bulk traps

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Authors: Daniel Assefa, Engeda Dessalegn, Chetan Chauhan

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Moringa stenopetala is a socioeconomic valued tree that is widely available and cultivated in the Southern part of Ethiopia. The leaves have been traditionally used as a food source with high nutritional and medicinal values. The present work was carried out to evaluate the effect of thermal treatment on the total phenolic content, antioxidant and alpha-amylase inhibition activities of aqueous leaf extracts during maceration and different decoction time interval (5, 10 and 15 min). The total phenolic content was determined by the Folin-ciocalteu methods whereas antioxidant activities were determined by 2,2-diphenyl-1-picryl-hydrazyl(DPPH) radical scavenging, reducing power and ferrous ion chelating assays and alpha-amylase inhibition activity was determined using 3,5-dinitrosalicylic acid method. Total phenolic content ranged from 34.35 to 39.47 mgGAE/g. Decoction for 10 min extract showed ferrous ion chelating (92.52), DPPH radical scavenging (91.52%), alpha-amylase inhibition (69.06%) and ferric reducing power (0.765), respectively. DPPH, reducing power and alpha-amylase inhibition activities showed positive linear correlation (R2=0.853, R2= 0.857 and R2=0.930), respectively with total phenolic content but ferrous ion chelating activity was found to be weakly correlated (R2=0.481). Based on the present investigation, it could be concluded that major loss of total phenolic content, antioxidant and alpha-amylase inhibition activities of the crude leaf extracts of Moringa stenopetala leaves were observed at decoction time for 15 min. Therefore, to maintain the total phenolic content, antioxidant, and alpha-amylase inhibition activities of leaves, cooking practice should be at the optimum decoction time (5-10 min).

Keywords: alpha-amylase inhibition, antioxidant, Moringa stenopetala, total phenolic content

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: general appliance model, non intrusive load monitoring, events detection, unsupervised techniques;

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Authors: Hankyu Lee, Seoung Bum Kim

Abstract:

The issue with imbalance and overlapping in the class distribution becomes important in various applications of data mining. The imbalanced dataset is a special case in classification problems in which the number of observations of one class (i.e., major class) heavily exceeds the number of observations of the other class (i.e., minor class). Overlapped dataset is the case where many observations are shared together between the two classes. Imbalanced and overlapped data can be frequently found in many real examples including fraud and abuse patients in healthcare, quality prediction in manufacturing, text classification, oil spill detection, remote sensing, and so on. The class imbalance and overlap problem is the challenging issue because this situation degrades the performance of most of the standard classification algorithms. In this study, we propose a classification procedure that can effectively handle imbalanced and overlapped datasets by splitting data space into three parts: nonoverlapping, light overlapping, and severe overlapping and applying the classification algorithm in each part. These three parts were determined based on the Hausdorff distance and the margin of the modified support vector machine. An experiments study was conducted to examine the properties of the proposed method and compared it with other classification algorithms. The results showed that the proposed method outperformed the competitors under various imbalanced and overlapped situations. Moreover, the applicability of the proposed method was demonstrated through the experiment with real data.

Keywords: classification, imbalanced data with class overlap, split data space, support vector machine

Procedia PDF Downloads 295