Search results for: command line input

Authors: Madhavi Godavarthy

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The globalized world has been witnessing the ubiquity of the English language and has made it mandatory that students be equipped with the required Communication and soft skills. For students and especially for students studying in technical streams, gaining command over the English language is only a part of the bigger challenges they will face in the future. Linguistic capabilities if blended with the right attitude and a positive personality would deliver better results in the present environment of the digitalized world. An English classroom has that ‘space’; a space if utilized well by the teacher can pay rich dividends. The prescribed syllabus for English in the process of adapting itself to the challenges of a more and more technical world has meted out an indifferent treatment in including ‘literary’ material in their curriculum. A debate has always existed regarding the same and diversified opinions have been given. When the student is motivated to reach Literature through intrinsic motivation, it may contribute to his/her personality-development. In the present paper, the element of focus is on the scope and space to motivate students by creating a specific space for herself/himself amidst the schedules of the teaching-learning processes by taking into consideration a few literary excerpts for the purpose.

Keywords: English language, teaching and learning process, reader response theory, intrinsic motivation, literary texts

Procedia PDF Downloads 613

Authors: H. G. Noh, H. G. Park, B. S. Kang, J. Kim

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Electromagnetic forming process (EMF) is one of high-velocity forming processes using Lorentz force. Advantages of EMF are summarized as improvement of formability, reduction in wrinkling, non-contact forming. In this study, numerical simulations were conducted to determine the practical parameters for EMF process. A 2-D axis-symmetric electromagnetic model was considered based on the spiral type forming coil. In the numerical simulation, RLC circuit coupled with spiral coil was made to consider the design parameters such as system input current and electromagnetic force. In order to deform the sheet in the patter shape die, two types of spiral shape coil were considered to deform the pattern shape sheet. One is a spiral coil that has 6turns with dead zone at centre point. Another is a normal spiral coil without dead zone that has 8 turns. In the electric analysis, input current and magnetic force were compared and then plastic deformation was treated in the mechanical analysis for two coil cases. Deformation behaviour of dead zone coil case has good agreement with pattern shape die. As a result, deformation behaviour could be controlled by giving dead zone at centre of the coil in spiral shape coil case.

Keywords: electromagnetic forming, spiral coil, Lorentz force, manufacturing

Procedia PDF Downloads 304

Authors: Amin Nezarat, Naeime Seifadini

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Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic and air pollution. The majority of existing trip mode inference models operate based on human selected features and traditional machine learning algorithms. However, human selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and Bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

Keywords: predicting, deep learning, neural network, urban trip

Procedia PDF Downloads 137

Authors: Zahira Anane, Abdelhafid Bayadi

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Corona in the HV overhead transmission lines is an important source of attenuation and distortion of overvoltage surges. This phenomenon of distortion, which is superimposed on the distortion by skin effect, is due to the dissipation of energy by injection of space charges around the conductor, this process with place as soon as the instantaneous voltage exceeds the threshold voltage of the corona effect conductors. This paper presents a mathematical model to determine the corona inception voltage, the critical electric field and the corona radius, to predict the capacitive changes at conductor of transmission line due to corona. This model has been incorporated into the Alternative Transients Program version of the Electromagnetic Transients Program (ATP/EMTP) as a user defined component, using the MODELS interface with NORTON TYPE94 of this program and using the foreign subroutine. For obtained the displacement of corona charge hell, dichotomy mathematical method is used for this computation. The present corona model can be used for computing of distortion and attenuation of transient overvoltage waves being propagated in a transmission line of the very high voltage electric power.

Keywords: high voltage, corona, Type94 NORTON, dichotomy, ATP/EMTP, MODELS, distortion, foreign model

Procedia PDF Downloads 623

Authors: Armansyah, I. P. Almanar, M. Saiful Bahari Shaari, M. Shamil Jaffarullah, Nur’amirah Busu, M. Arif Fadzleen Zainal Abidin, M. Amlie A. Kasim

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Temperature distribution in Friction Stir Welding (FSW) of 6061-T6 Aluminum Alloy is modeled using the Finite Element Method (FEM). In order to obtain temperature distribution in the welded aluminum plates during welding operation, transient thermal finite element analyses are performed. Heat input from tool shoulder and tool pin are considered in the model. A moving heat source with a heat distribution simulating the heat generated by frictions between tool shoulder and workpiece is used in the analysis. Three-dimensional model for simulated process is carried out by using Altair HyperWork, a commercially available software. Transient thermal finite element analyses are performed in order to obtain the temperature distribution in the welded Aluminum plates during welding operation. The developed model was then used to show the effect of various input parameters such as total rate of welding speed and rotational speed on temperature distribution in the workpiece.

Keywords: frictions stir welding, temperature distribution, finite element method, altair hyperwork

Procedia PDF Downloads 541

Authors: Evren Isen

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In grid-connected renewable energy systems, input power is controlled by AC/DC converter or/and DC/DC converter depending on output voltage of input source. The power is injected to DC-link, and DC-link voltage is regulated by inverter controlling the grid current. Inverter performance is considerable in grid-connected renewable energy systems to meet the utility standards. In this paper, modelling and simulation of hysteresis current controlled single-phase grid-connected inverter that is utilized in renewable energy systems, such as wind and solar systems, are presented. 2 kW single-phase grid-connected inverter is simulated in Simulink and modeled in Matlab-m-file. The grid current synchronization is obtained by phase locked loop (PLL) technique in dq synchronous rotating frame. Although dq-PLL can be easily implemented in three-phase systems, there is difficulty to generate β component of grid voltage in single-phase system because single-phase grid voltage exists. Inverse-Park PLL with low-pass filter is used to generate β component for grid angle determination. As grid current is controlled by constant bandwidth hysteresis current control (HCC) technique, average switching frequency and variation of switching frequency in a fundamental period are considered. 3.56% total harmonic distortion value of grid current is achieved with 0.5 A bandwidth. Average value of switching frequency and total harmonic distortion curves for different hysteresis bandwidth are obtained from model in m-file. Average switching frequency is 25.6 kHz while switching frequency varies between 14 kHz-38 kHz in a fundamental period. The average and maximum frequency difference should be considered for selection of solid state switching device, and designing driver circuit. Steady-state and dynamic response performances of the inverter depending on the input power are presented with waveforms. The control algorithm regulates the DC-link voltage by adjusting the output power.

Keywords: grid-connected inverter, hysteresis current control, inverter modelling, single-phase inverter

Procedia PDF Downloads 476

Authors: Galal H. Senussi, Muamar Benisa, Sanja Vasin

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In our business field today, one of the most important issues for any enterprises is cost minimization and profit maximization. Second issue is how to develop a strong and capable model that is able to give us desired forecasting of these two issues. Many researches deal with these issues using different methods. In this study, we developed a model for multi-criteria production program optimization, integrated with Artificial Neural Network. The prediction of the production cost and profit per unit of a product, dealing with two obverse functions at same time can be extremely difficult, especially if there is a great amount of conflict information about production parameters. Feed-Forward Neural Networks are suitable for generalization, which means that the network will generate a proper output as a result to input it has never seen. Therefore, with small set of examples the network will adjust its weight coefficients so the input will generate a proper output. This essential characteristic is of the most important abilities enabling this network to be used in variety of problems spreading from engineering to finance etc. From our results as we will see later, Feed-Forward Neural Networks has a strong ability and capability to map inputs into desired outputs.

Keywords: project profitability, multi-objective optimization, genetic algorithm, Pareto set, neural networks

Procedia PDF Downloads 445

Authors: Fidel Lòpez Saca, Carlos Avilés-Cruz, Miguel Magos-Rivera, José Antonio Lara-Chávez

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Automated object recognition and identification systems are widely used throughout the world, particularly in assembly lines, where they perform quality control and automatic part selection tasks. This article presents the design and implementation of an object recognition system in an assembly line. The proposed shapes-color recognition system is based on deep learning theory in a specially designed convolutional network architecture. The used methodology involve stages such as: image capturing, color filtering, location of object mass centers, horizontal and vertical object boundaries, and object clipping. Once the objects are cut out, they are sent to a convolutional neural network, which automatically identifies the type of figure. The identification system works in real-time. The implementation was done on a Raspberry Pi 3 system and on a Jetson-Nano device. The proposal is used in an assembly course of bachelor’s degree in industrial engineering. The results presented include studying the efficiency of the recognition and processing time.

Keywords: deep-learning, image classification, image identification, industrial engineering.

Procedia PDF Downloads 157

Authors: Sadık Ata, Kevser Dincer

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In this study, performance of proton exchange membrane (PEM) fuel cell was experimentally investigated and modelled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modelling technique. Coating on the anode side of the PEM fuel cell was accomplished with the spin method by using Yttria-stabilized zirconia (YSZ). Input parameters voltage density (V/cm2), and current density (A/cm2), temperature (°C), time (s); output parameter power density (W/cm2) were described by RBMTF if-then rules. Numerical parameters of input and output variables were fuzzificated as linguistic variables: Very Very Low (L1), Very Low (L2), Low (L3), Negative Medium (L4), Medium (L5), Positive Medium (L6), High (L7), Very High (L8) and Very Very High (L9) linguistic classes. The comparison between experimental data and RBMTF is done by using statistical methods like absolute fraction of variance (R2). The actual values and RBMTF results indicated that RBMTF can be successfully used for the analysis of performance of PEM fuel cell.

Keywords: proton exchange membrane (PEM), fuel cell, rule-based Mamdani-type fuzzy (RMBTF) modeling, yttria-stabilized zirconia (YSZ)

Procedia PDF Downloads 361

Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 315

Authors: Rui Zhao, Dongxu Zhang, Min Wan

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Composite materials are widely used in the fields of aviation, aerospace, and transportation due to their lightweight and high strength. Functionalization of composite structures is a hot topic in the future development of composite materials. This article proposed a polyimide-resin based composite material with a sensing function. This material can serve as a sensor to achieve deformation monitoring of metal sheets in room temperature environments. In the deformation process of metal sheets, the slope of the linear fitting line for the corresponding material resistance change rate is different in the elastic stage and the plastic strengthening stage. Therefore, the slope of the material resistance change rate can be used to characterize the deformation stage of the metal sheet. In addition, the resistance change rate of the material exhibited a good negative linear relationship with temperature in a high-temperature environment, and the determination coefficient of the linear fitting line for the change rate of material resistance in the range of 520-650℃ was 0.99. These results indicate that the material has the potential to be applied in the monitoring of mechanical properties of structural materials and temperature monitoring of high-temperature environments.

Keywords: polyimide, composite, sensing, resistance change rate

Procedia PDF Downloads 79

Authors: Huyen T. Pham, Nhue P. Nguyen, Tien Q. Phi, Phuong T. Dang, Hy G. Le

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Since the marine environmental conditions are extremely different from the other ones, so that marine actinomycetes might produce novel bioactive compounds. Therefore, actinomycete strains were screened from marine water and sediment samples collected from the coastal areas of Northern Vietnam. Ninety-nine actinomycete strains were obtained on starch-casein agar media by dilution technique, only seven strains, named HP112, HP12, HP411, HPN11, HP 11, HPT13 and HPX12, showed significant antibacterial activity against both gram-positive and gram-negative bacteria (Bacillus subtilis ATCC 6633, Staphylococcus epidemidis ATCC 12228, Escherichia coli ATCC 11105). Further studies were carried out with the most active HP411strain against Candida albicans ATCC 10231. This strain could grow rapidly on starch casein agar and other media with high salt containing 7-10% NaCl at 28-30oC. Spore-chain of HP411 showed an elongated and circular shape with 10 to 30 spores/chain. Identification of the strain was carried out by employing the taxonomical studies including the 16S rRNA sequence. Based on phylogenetic and phenotypic evidence it is proposed that HP411 to be belongs to species Streptomyces variabilis. The potent of the crude extract of fermentation broth of HP411that are effective against wide range of pathogens: both gram-positive, gram-negative and fungi. Further studies revealed that the crude extract HP411 could obtain the anticancer activity for cancer cell lines: Hep-G2 (liver cancer cell line); RD (cardiac and skeletal muscle letters cell line); FL (membrane of the uterus cancer cell line). However, the actinomycetes from marine ecosystem will be useful for the discovery of new drugs in the furture.

Keywords: marine actinomycetes, antibacterial, anticancer, Streptomyces variabilis

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Authors: Jingwei Wang, Anthony G. Fane, Jia Wei Chew

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The use of fluidized Granular Activated Carbon (GAC) as a means of mitigation membrane fouling in membrane bioreactors (MBRs) has received much attention in recent years, especially in anaerobic fluidized bed membrane bioreactors (AFMBRs). It has been affirmed that the unsteady-state tangential shear conferred by GAC fluidization on membrane surface suppressed the extent of membrane fouling with energy consumption much lower than that of bubbling (i.e., air sparging). In a previous work, the hydrodynamics of the fluidized GAC particles were correlated with membrane fouling mitigation effectiveness. Results verified that the momentum transfer from particle to membrane held a key in fouling mitigation. The goal of the current work is to understand the effect of fluidized GAC on membrane critical flux. Membrane critical flux values were measured by a vertical Direct Observation Through the Membrane (DOTM) setup. The polystyrene particles (known as latex particles) with the particle size of 5 µm were used as model foulant thus to give the number of the foulant on the membrane surface. Our results shed light on the positive effect of fluidized GAC enhancing the critical membrane flux by an order-of-magnitude as compared to that of liquid shear alone. Membrane fouling mitigation was benefitted by the increasing of power input.

Keywords: membrane fouling mitigation, liquid-solid fluidization, critical flux, energy input

Procedia PDF Downloads 404

Authors: Remi Safi, Aline Hamade, Najat Bteich, Jamal El Saghir, Mona Diab Assaf, Marwan El-Sabban, Fadia Najjar

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Estrogen is considered a risk factor for breast cancer since it promotes breast-cell proliferation. The jaesckeanadiol-3-p-hydroxyphenylpropanoate, a hemi-synthetic analogue of the natural phytoestrogen ferutinin (jaesckeanadiol-p-hydroxybenzoate), is designed to be devoid of estrogenic activity. This analogue induces a cytotoxic effect 30 times higher than that of ferutinin towards MCF-7 breast cancer cell line. We compared these two compounds with respect to their effect on proliferation, cell cycle distribution and cancer stem-like cells in the MCF-7 cell line. Treatment with ferutinin (30 μM) and its analogue (1 μM) produced a significant accumulation of cells at the pre G0/G1 cell cycle phase and triggered apoptosis. Importantly, this compound retains its anti-proliferative activity against breast cancer stem/progenitor cells that are naturally insensitive to ferutinin at the same dose. These results position ferutinin analogue as an effective compound inhibiting the proliferation of estrogen-dependent breast cancer cells and consistently targeting their stem-like cells.

Keywords: ferutinin, hemi-synthetic analogue, breast cancer, estrogen, stem/progenitor cells

Procedia PDF Downloads 187

Authors: Shinji Ukita, Naohiro Nakamura, Yuji Miyazu

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In considering the dynamic characteristics of structure, natural frequency and damping ratio are useful indicator. When performing dynamic design, it's necessary to select an appropriate initial damping model and hysteretic model. In the linear region, the setting of initial damping model influences the response, and in the nonlinear region, the combination of initial damping model and hysteretic model influences the response. However, the dynamic characteristics of structure in the nonlinear region remain unclear. In this paper, we studied the effect of setting of initial damping model and hysteretic model on the dynamic characteristics of structure. On initial damping model setting, Initial stiffness proportional, Tangent stiffness proportional, and Rayleigh-type were used. On hysteretic model setting, TAKEDA model and Normal-trilinear model were used. As a study method, dynamic analysis was performed using a lumped mass model of base-fixed. During analysis, the maximum acceleration of input earthquake motion was gradually increased from 1 to 600 gal. The dynamic characteristics were calculated using the ARX model. Then, the characteristics of 1st and 2nd natural frequency and 1st damping ratio were evaluated. Input earthquake motion was simulated wave that the Building Center of Japan has published. On the building model, an RC building with 30×30m planes on each floor was assumed. The story height was 3m and the maximum height was 18m. Unit weight for each floor was 1.0t/m2. The building natural period was set to 0.36sec, and the initial stiffness of each floor was calculated by assuming the 1st mode to be an inverted triangle. First, we investigated the difference of the dynamic characteristics depending on the difference of initial damping model setting. With the increase in the maximum acceleration of the input earthquake motions, the 1st and 2nd natural frequency decreased, and the 1st damping ratio increased. Then, in the natural frequency, the difference due to initial damping model setting was small, but in the damping ratio, a significant difference was observed (Initial stiffness proportional≒Rayleigh type>Tangent stiffness proportional). The acceleration and the displacement of the earthquake response were largest in the tangent stiffness proportional. In the range where the acceleration response increased, the damping ratio was constant. In the range where the acceleration response was constant, the damping ratio increased. Next, we investigated the difference of the dynamic characteristics depending on the difference of hysteretic model setting. With the increase in the maximum acceleration of the input earthquake motions, the natural frequency decreased in TAKEDA model, but in Normal-trilinear model, the natural frequency didn’t change. The damping ratio in TAKEDA model was higher than that in Normal-trilinear model, although, both in TAKEDA model and Normal-trilinear model, the damping ratio increased. In conclusion, in initial damping model setting, the tangent stiffness proportional was evaluated the most. In the hysteretic model setting, TAKEDA model was more appreciated than the Normal-trilinear model in the nonlinear region. Our results would provide useful indicator on dynamic design.

Keywords: initial damping model, damping ratio, dynamic analysis, hysteretic model, natural frequency

Procedia PDF Downloads 173

Authors: Xiang Yu, Chuntao Feng, Lu Yang, Meiyang Song, Wenhao Zhou

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The existing two-dimensional micro-Doppler features extraction ignores the correlation information between the spatial and temporal dimension features. For the range-Doppler map, the time dimension is introduced, and a frequency modulation continuous wave (FMCW) radar human fall detection algorithm based on time-varying range-Doppler features is proposed. Firstly, the range-Doppler sequence maps are generated from the echo signals of the continuous motion of the human body collected by the radar. Then the three-dimensional data cube composed of multiple frames of range-Doppler maps is input into the three-dimensional Convolutional Neural Network (3D CNN). The spatial and temporal features of time-varying range-Doppler are extracted by the convolution layer and pool layer at the same time. Finally, the extracted spatial and temporal features are input into the fully connected layer for classification. The experimental results show that the proposed fall detection algorithm has a detection accuracy of 95.66%.

Keywords: FMCW radar, fall detection, 3D CNN, time-varying range-doppler features

Procedia PDF Downloads 119

Authors: David F. Nettleton, Christian Wasiak, Jonas Dorissen, David Gillen, Alexandr Tretyak, Elodie Bugnicourt, Alejandro Rosales

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In this work, preliminary results are given for the modeling and calibration of two inline processes, pultrusion, and laser ablation, using machine learning techniques. The end product of the processes is the core of a medical guidewire, manufactured to comply with a user specification of diameter and flexibility. An ensemble approach is followed which requires training several models. Two state of the art machine learning algorithms are benchmarked: Kernel Recursive Least Squares (KRLS) and Support Vector Regression (SVR). The final objective is to build a precise digital model of the pultrusion and laser ablation process in order to calibrate the resulting diameter and flexibility of a medical guidewire, which is the end product while taking into account the friction on the forming die. The result is an ensemble of models, whose output is within a strict required tolerance and which covers the required range of diameter and flexibility of the guidewire end product. The modeling and automatic calibration of complex in-line industrial processes is a key aspect of the Industry 4.0 movement for cyber-physical systems.

Keywords: calibration, data modeling, industrial processes, machine learning

Procedia PDF Downloads 295

Authors: Ali Ates, Sadık Ata, Kevser Dincer

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In this study, performance of the driver seat‘s localized cooling and heating in a standard car air conditioning system was experimentally investigated and modeled with Rule-Based Mamdani-Type Fuzzy (RBMTF) modeling technique. Climate function at automobile is an important variable for thermal comfort. In the experimental study localized heating and cooling performances have been examined with the aid of a mechanism established to a vehicle. The equipment’s used in the experimental setup/mechanism have been provided and assembled. During the measurement, the status of the performance level has been determined. Input parameters revolutions per minute and time; output parameters car seat cooling temperature, car back cooling temperature, car seat heating temperature, car back heating temperature were described by RBMTF if-the rules. Numerical parameters of input and output variables were fuzzificated as linguistic variables: Very Very Low (L1), Very Low (L2), Low (L3), Negative Medium (L4), Medium (L5), High (L7), Very High (L8) and Very Very High (L9) linguistic classes. The comparison between experimental data and RBMTF is done by using statistical methods like absolute fraction of variance (R2). The actual values and RBMTF results indicated that RBMTF could be successfully used in standard car air conditioning system.

Keywords: air conditioning system, cooling-heating, RMBTF modelling, car seat

Procedia PDF Downloads 350

Authors: Liubov Magerramova, Mikhail Petrov, Vladimir Isakov, Liana Shcherbinina, Suren Gukasyan, Daniil Povalyukhin, Olga Klimova-Korsmik, Darya Volosevich

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Aircraft engines are developing along the path of increasing resource, strength, reliability, and safety. The building of gas turbine engine body parts is a complex design and technological task. Particularly complex in the design and manufacturing are the casings of the input stages of helicopter gearboxes and central drives of aircraft engines. Traditional technologies, such as precision casting or isothermal forging, are characterized by significant limitations in parts production. For parts like housing, additive technologies guarantee spatial freedom and limitless or flexible design. This article presents the results of computational and experimental studies. These investigations justify the applicability of additive technologies (AT) to reduce the weight of aircraft housing gearbox parts by up to 32%. This is possible due to geometrical optimization compared to the classical, less flexible manufacturing methods and as-casted aircraft parts with over-insured values of safety factors. Using an example of the body of the input stage of an aircraft gearbox, visualization of the layer-by-layer manufacturing of a part based on thermal deformation was demonstrated.

Keywords: additive technologies, gas turbine engines, topological optimization, synthesis process

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Authors: Raghavi Janaswamy, Saraswathi K. Vasudev

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Music is one of the supreme arts of expressions, next to the speech itself. Its evolution over centuries has paved the way with a variety of training protocols and performing methods. Indian classical music is one of the most elaborate and refined systems with immense emphasis on the voice culture related to range, breath control, quality of the tone, flexibility and diction. Several exercises namely saraliswaram, jantaswaram, dhatuswaram, upper stayi swaram, alamkaras and varnams lay the required foundation to gain the voice culture and deeper understanding on the voice development and further on to the intricacies of the raga system. This article narrates a few of the Carnatic music training methods with an emphasis on the advanced practice methods for articulating the vocal skills, continuity in the voice, ability to produce gamakams, command in the multiple speeds of rendering with reasonable volume. The creativity on these exercises and their impact on the voice production are discussed. The articulation of the outlined conscious practice methods and vocal exercises bestow the optimum use of the natural human vocal system to not only enhance the signing quality but also to gain health benefits.

Keywords: Carnatic music, Saraliswaram, Varnam, vocal training

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Authors: Manita Ale, Ganesh P. Shivakoti, Ram C. Bastakoti

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Irrigation system, a system for enhancing agricultural productivity, requires regular maintenance in order to avoid irregular allocation of water. For maintenance of the system in long run, farmers’ participation plays a key role increasing the performance of system. The performance of any irrigation system mainly relies on various factors which affect collective action plus decision making, as well as their shared impacts. The paper consists of system level information that were collected from 12 Irrigation Systems (IS) from three-sampled districts of Nepal and the household information that were collected from 160 irrigation water users. The results reveal that, out of 12 sampled irrigation systems, only 4 systems shows high performance levels. The high performance level of those systems was characterized on the basis of adequate availability of water, good maintenance of system infrastructure, and conformance to existing rules followed. In addition, the paper compares different irrigation systems based on trust, reciprocity, cropping intensity, command area and yield as tools to indicate the importance of collective action in performance of irrigation system.

Keywords: collective action, decision-making, farmers’ participation, performance

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Authors: Babak Bahri, Fatemeh Yassaee Meybodi, Changiz Eslahchi

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In the pursuit of effective cancer therapies, the exploration of combinatorial drug regimens is crucial to leverage synergistic interactions between drugs, thereby improving treatment efficacy and overcoming drug resistance. However, identifying synergistic drug pairs poses challenges due to the vast combinatorial space and limitations of experimental approaches. This study introduces ClusterSyn, a machine learning (ML)-powered framework for classifying anti-cancer drug synergy scores. ClusterSyn employs a two-step approach involving drug clustering and synergy score prediction using a fully connected deep neural network. For each cell line in the training dataset, a drug graph is constructed, with nodes representing drugs and edge weights denoting synergy scores between drug pairs. Drugs are clustered using the Markov clustering (MCL) algorithm, and vectors representing the similarity of drug pairs to each cluster are input into the deep neural network for synergy score prediction (synergy or antagonism). Clustering results demonstrate effective grouping of drugs based on synergy scores, aligning similar synergy profiles. Subsequently, neural network predictions and synergy scores of the two drugs on others within their clusters are used to predict the synergy score of the considered drug pair. This approach facilitates comparative analysis with clustering and regression-based methods, revealing the superior performance of ClusterSyn over state-of-the-art methods like DeepSynergy and DeepDDS on diverse datasets such as Oniel and Almanac. The results highlight the remarkable potential of ClusterSyn as a versatile tool for predicting anti-cancer drug synergy scores.

Keywords: drug synergy, clustering, prediction, machine learning., deep learning

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Authors: Vano Benidze, Ioseb Berikashvili

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Since 2003, Georgia has implemented many successful reforms, however, economic growth, poverty alleviation and unemployment reduction are still major challenges facing country’s economy. This is due to the fact that most reforms during the past 2 decades were mainly geared toward improving the institutional environment, while economy’s sectoral composition and industrial policy were largely ignored. Each individual sector plays its own specific role in the functioning of the whole economy that cannot be accomplished by any other sector. However, given the unavoidable reality that one sector uses intermediate inputs from other industries to produce its output and sells part of its output to other sectors, the importance of sectors should consider these sectoral interdependencies as well. Simply put, not all industries are equally useful for economic growth and development. In this context, the aim of this paper is to identify the key economic sectors of the Georgian economy. Leontief input-output analysis has been used in deriving backward and forwards linkages for all sectors in the Georgian economy for 2020 and 2021. Sectors with both high backward and forward linkages have been identified as key sectors of the economy. The results obtained are beneficial for the success of the economic and industrial policy of Georgia. If targeted properly by thoughtful policy intervention, key sectors identified in this paper will have a high potential of spreading growth impulses throughout the economy and will possibly generate higher economic growth.

Keywords: structural change, key sectors, development strategies, input-output analysis

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Authors: Vipul M. Patel, H. B. Mehta

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Closed Loop Pulsating Heat Pipe (CLPHP) is a passive two-phase heat transfer device having potential to achieve high heat transfer rates over conventional cooling techniques. It is found in electronics cooling due to its outstanding characteristics such as excellent heat transfer performance, simple, reliable, cost effective, compact structure and no external mechanical power requirement etc. Comprehensive understanding of the thermo-hydrodynamic mechanism of CLPHP is still lacking due to its contradictory results available in the literature. The present paper discusses the experimental study on 9 turn CLPHP. Inner and outer diameters of the copper tube are 2 mm and 4 mm respectively. The lengths of the evaporator, adiabatic and condenser sections are 40 mm, 100 mm and 50 mm respectively. Water is used as working fluid. The Filling Ratio (FR) is kept as 50% throughout the investigations. The gravitational effect is studied by placing the evaporator heater at different orientations such as horizontal (90 degree), vertical top (180 degree) and bottom (0 degree) as well as inclined top (135 degree) and bottom (45 degree). Heat input is supplied in the range of 10-50 Watt. Heat transfer mechanism is natural convection in the condenser section. Vacuum pump is used to evacuate the system up to 10^-5 bar. The results demonstrate the influence of input heat flux and gravity on the thermal performance of the CLPHP.

Keywords: CLPHP, gravity effect, start up, two-phase flow

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Authors: Teh Kean Kheng, Low Soon Yee, Burra Venkata Durga Kumar

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In 2022, as the future world becomes increasingly computer-related, more individuals are attempting to study coding for themselves or in school. This is because they have discovered the value of learning code and the benefits it will provide them. But learning coding is difficult for most people. Even senior programmers that have experience for a decade year still need help from the online source while coding. The reason causing this is that coding is not like talking to other people; it has the specific syntax to make the computer understand what we want it to do, so coding will be hard for normal people if they don’t have contact in this field before. Coding is hard. If a user wants to learn bash code with bash prompt, it will be harder because if we look at the bash prompt, we will find that it is just an empty box and waiting for a user to tell the computer what we want to do, if we don’t refer to the internet, we will not know what we can do with the prompt. From here, we can conclude that the bash prompt is not user-friendly for new users who are learning bash code. Our goal in writing this paper is to give an idea to implement a user-friendly Bash prompt in Ubuntu OS using Artificial Intelligent (AI) to lower the threshold of learning in Bash code, to make the user use their own words and concept to write and learn Bash code.

Keywords: user-friendly, bash code, artificial intelligence, threshold, semantic similarity, lexical similarity

Procedia PDF Downloads 141

Authors: Rachel Goh, Alexander McNab, Brent Alsop, David O'Hare

Abstract:

In a busy airport, the decision whether to take passengers aside and search their luggage for dangerous items can have important consequences. If an officer fails to search and stop a bag containing a dangerous object, a life-threatening incident might occur. But stopping a bag unnecessarily means that the officer might lose time searching the bag and face an angry passenger. Passengers’ bags, however, are often cluttered with personal belongings of varying shapes and sizes. It can be difficult to determine what is dangerous or not, especially if the decisions must be made quickly in cases of busy flight schedules. Additionally, the decision to search bags is often made with input from the surrounding officers on duty. This scenario raises several questions: 1) Past findings suggest that humans are more reliant on an automated aid when under time pressure in a visual search task, but does this translate to human-human reliance? 2) Are humans more likely to agree with another person if the person is assumed to be an expert or a novice in these ambiguous situations? In the present study, forty-one participants performed a simulated luggage-screening task. They were partnered with an advisor of two different statuses (expert vs. novice), but of equal accuracy (90% correct). Participants made two choices each trial: their first choice with no advisor input, and their second choice after advisor input. The second choice was made within either 2 seconds or 8 seconds; failure to do so resulted in a long time-out period. Under the 2-second time pressure, participants were more likely to disagree with their own first choice and agree with the expert advisor, regardless of whether the expert was right or wrong, but especially when the expert suggested that the bag was safe. The findings indicate a tendency for people to assume less responsibility for their decisions and defer to their partner, especially when a quick decision is required. This over-reliance on others’ opinions might have negative consequences in real life, particularly when relying on fallible human judgments. More awareness is needed regarding how a stressful environment may influence reliance on other’s opinions, and how better techniques are needed to make the best decisions under high stress and time pressure.

Keywords: advisors, decision-making, time pressure, trust

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Authors: Jia-Shiun Chen, Hsiu-Ying Hwang

Abstract:

Hybrid electric vehicles can reduce pollution and improve fuel economy. Power-split hybrid electric vehicles (HEVs) provide two power paths between the internal combustion engine (ICE) and energy storage system (ESS) through the gears of an electrically variable transmission (EVT). EVT allows ICE to operate independently from vehicle speed all the time. Therefore, the ICE can operate in the efficient region of its characteristic brake specific fuel consumption (BSFC) map. The two-mode powertrain can operate in input-split or compound-split EVT modes and in four different fixed gear configurations. Power-split architecture is advantageous because it combines conventional series and parallel power paths. This research focuses on input-split and compound-split modes in the two-mode power-split powertrain. Fuzzy Logic Control (FLC) for an internal combustion engine (ICE) and PI control for electric machines (EMs) are derived for the urban driving cycle simulation. These control algorithms reduce vehicle fuel consumption and improve ICE efficiency while maintaining the state of charge (SOC) of the energy storage system in an efficient range.

Keywords: hybrid electric vehicle, fuel economy, two-mode hybrid, fuzzy control

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Authors: Maikasuwa Mohammed Abubakar, Okebiorun Ola, M. H. Sidi, Ala Ahmed Ladan, Ango Aabdullahi Kamba

Abstract:

The study examines the economic analysis of cassava value chain by farmers in Ilesa West Local Government Area of Osun State. Simple random sampling technique was used to collect data from 200 respondents from purposively selected wards in the L.G.A. The data collected were analyzed using budgetary analysis and value addition model. The result shows that an average total cost incurred by the input dealers was ₦9,062,127.74 while the average net profit realized was ₦1,038,102.40. Other actors such as producers, processors and marketers incurred an average total cost of ₦23,324.00, ₦130,177.00 and ₦523,755.00 per production season, respectively and the average net profit realized was ₦102,614.00 for cassava producers, ₦51,131.00 for cassava processors and ₦79,045.00 for cassava marketers during cassava production season. Further analysis shows the rate of investment for cassava input dealers was ₦0.1, for cassava producers was ₦4.4, for cassava processors were ₦0.40 and for cassava marketers was ₦0.20. This indicated that rate of return on cassava was higher in cassava production than in others corridors along the value chain of cassava. However, value added the cassava producers (₦102,536.16/season) was the highest when compared with value added by cassava processors (₦51,853.82/season) and cassava marketers (₦100,885.56/season).

Keywords: Cassava, value chain, Ilesa West, Nigeria

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Authors: Renga Rao Krishnamoorthy, Raihan Tahir

Abstract:

The quality of classroom learning and teaching delivery has been and will continue to be debated at various levels worldwide. The regional cooperation programme to improve the quality and labour market orientation of the Technical and Vocational Education and Training (RECOTVET), ‘Deutsche Gesellschaft für Internationale Zusammenarbeit’ (GIZ), in line with the sustainable development goals (SDG), has taken the initiative in the development of quality TVET in the ASEAN region by developing the Quality Toolbox for Better TVET Delivery (Quality Toolbox). This initiative aims to provide quick and practical materials to trainers, instructors, and personnel involved in education and training at an institute to shape the quality of classroom learning and teaching. The Quality Toolbox for Better TVET Delivery was developed in three stages: literature review and development, validation, and finalization. Thematic areas in the Quality Toolbox were derived from collective input of concerns and challenges raised from experts’ workshops through moderated sessions involving representatives of TVET institutes from 9 ASEAN Member States (AMS). The sessions were facilitated by professional moderators and international experts. TVET practitioners representing AMS further analysed and discussed the structure of the Quality Toolbox and content of thematic areas and outlined a set of specific requirements and recommendations. The application exercise of the Quality Toolbox was carried out by TVET institutes among ASM. Experience sharing sessions from participating ASEAN countries were conducted virtually. The findings revealed that TVET institutes use two types of approaches in shaping the quality of learning and teaching, which is ascribed to inductive or deductive, shaping of quality in learning and teaching is a non-linear process and finally, Q-tools can be adopted and adapted to shape the quality of learning and teaching at TVET institutes in the following: improvement of the institutional quality, improvement of teaching quality and improvement on the organisation of learning and teaching for students and trainers. The Quality Toolbox has good potential to be used at education and training institutes to shape quality in learning and teaching.

Keywords: AMS, GIZ, RECOTVET, quality tools

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Authors: John Lachapelle

Abstract:

Introduction: Neural stimulation is increasingly targeted toward treatment of back pain, PTSD, Parkinson’s disease, and for sensory perception. Sensory recording during stimulation is important in order to examine neural response to stimulation. Most neural amplifiers (headstages) focus on noise efficiency factor (NEF). Conversely, neural headstages need to handle artifacts from several sources including power lines, movement (EMG), and neural stimulation itself. In this work a layered approach to artifact rejection is used to reduce corruption of the neural ENG signal by 60dBv, resulting in recovery of sensory signals in rats and primates that would previously not be possible. Methods: The approach combines analog techniques to reduce and handle unwanted signal amplitudes. The methods include optimized (1) sensory electrode placement, (2) amplifier configuration, and (3) artifact blanking when necessary. The techniques together are like concentric moats protecting a castle; only the wanted neural signal can penetrate. There are two conditions in which the headstage operates: unwanted artifact < 50mV, linear operation, and artifact > 50mV, fast-settle gain reduction signal limiting (covered in more detail in a separate paper). Unwanted Signals at the headstage input: Consider: (a) EMG signals are by nature < 10mV. (b) 60 Hz power line signals may be > 50mV with poor electrode cable conditions; with careful routing much of the signal is common to both reference and active electrode and rejected in the differential amplifier with <50mV remaining. (c) An unwanted (to the neural recorder) stimulation signal is attenuated from stimulation to sensory electrode. The voltage seen at the sensory electrode can be modeled Φ_m=I_o/4πσr. For a 1 mA stimulation signal, with 1 cm spacing between electrodes, the signal is <20mV at the headstage. Headstage ASIC design: The front end ASIC design is designed to produce < 1% THD at 50mV input; 50 times higher than typical headstage ASICs, with no increase in noise floor. This requires careful balance of amplifier stages in the headstage ASIC, as well as consideration of the electrodes effect on noise. The ASIC is designed to allow extremely small signal extraction on low impedance (< 10kohm) electrodes with configuration of the headstage ASIC noise floor to < 700nV/rt-Hz. Smaller high impedance electrodes (> 100kohm) are typically located closer to neural sources and transduce higher amplitude signals (> 10uV); the ASIC low-power mode conserves power with 2uV/rt-Hz noise. Findings: The enhanced neural processing ASIC has been compared with a commercial neural recording amplifier IC. Chronically implanted primates at MGH demonstrated the presence of commercial neural amplifier saturation as a result of large environmental artifacts. The enhanced artifact suppression headstage ASIC, in the same setup, was able to recover and process the wanted neural signal separately from the suppressed unwanted artifacts. Separately, the enhanced artifact suppression headstage ASIC was able to separate sensory neural signals from unwanted artifacts in mouse-implanted peripheral intrafascicular electrodes. Conclusion: Optimizing headstage ASICs allow observation of neural signals in the presence of large artifacts that will be present in real-life implanted applications, and are targeted toward human implantation in the DARPA HAPTIX program.

Keywords: ASIC, biosensors, biomedical signal processing, biomedical sensors

Procedia PDF Downloads 327