Search results for: input randomization

Authors: Jocelyn L. Alimondo, Juna O. Sabelo

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This study explored the MTB-MLE implementation practices of teachers in one monolingual elementary school and one multilingual elementary school in Benguet, Philippines. It used phenomenological approach employing participant-observation, focus group discussion and individual interview. Data were gathered using a video camera, an audio recorder, and an FGD guide and were treated through triangulation and coding. From the data collected, varied ways in implementing the MTB-MLE program were noted. These are: Teaching using a hybrid first language, teaching using a foreign LOI, using translation and multilingual instruction, and using L2/L3 to unlock L1. However, these practices come with challenges such as the a conflict between the mandated LOI and what pupils need, lack of proficiency of teachers in the mandated LOI, facing unreceptive parents, stagnation of knowledge resulting from over-familiarity of input, and zero learning resulting from an incomprehensible language input. From the practices and challenges experienced by the teachers, a model of MTB-MLE approach, the 3L-in-one approach, to teaching was created to illustrate the practice which teachers claimed to be the best way to address the challenges besetting them while at the same time satisfying the academic needs of their pupils. From the findings, this paper concludes that despite the challenges besetting the teachers, they still displayed creativity in coming up with relevant teaching practices, the unreceptiveness of some teachers and parents sprung from the fact that they do not understand the real concept of MTB-MLE, greater challenges are being faced by teachers in multilingual school due to the diverse linguistic background of their clients, and the most effective approach in implementing MTB-MLE is the multilingual approach, allowing the use of the pupils’ mother tongue, L2 (Filipino), L3 (English), and other languages familiar to the students.

Keywords: MTB-MLE Philippines, MTB-MLE model, first language, multilingual instruction

Procedia PDF Downloads 428

Authors: B. Białecka, Z. Adamczyk, M. Cempa

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The use of ultrasounds in zeolization of fly ash can increase the efficiency of this process. The molar ratios of the reagents, as well as the time and temperature of the synthesis, are the main parameters determining the type and properties of the zeolite formed. The aim of the work was to create hydrosodalite in a short time (8h), with low NaOH concentration (3 M) and in low temperature (80°C). A zeolite material contained in fly ash from hard coal combustion in one of Polish Power Plant was subjected to hydrothermal alkaline synthesis. The phase composition of the ash consisted mainly of glass, mullite, quartz, and hematite. The dominant chemical components of the ash were SiO₂ (over 50%mas.) and Al₂O₃ (more than 28%mas.), whereas the contents of the remaining components, except Fe₂O₃ (6.34%mas.), did not exceed 4% mas. The hydrothermal synthesis of the zeolite material was carried out in the following conditions: 3M-solution of NaOH, synthesis time – 8 hours, 40 kHz-frequency ultrasounds during the first two hours of synthesis. The mineral components of the input ash as well as product after synthesis were identified in microscopic observations, in transmitted light, using X-ray diffraction (XRD) and electron scanning microscopy (SEM/EDS). The chemical composition of the input ash was identified by the method of X-ray fluorescence (XRF). The obtained material apart from phases found in the initial fly ash sample, also contained new phases, i.e., hydrosodalite and NaP-type zeolite. The chemical composition in micro areas of grains indicated their diversity: i) SiO₂ content was in the range 30-59%mas., ii) Al₂O₃ content was in the range 24-35%mas., iii) Na₂O content was in the range 6-15%mas. This clearly indicates that hydrosodalite forms hypertrophies with NaP type zeolite as well as relict grains of fly ash. A small amount of potassium in the examined grains is noteworthy, which may indicate the substitution of sodium with potassium. This is confirmed by the high value of the correlation coefficient between these two components.

Keywords: fly ash, hydrosodalite, ultrasounds, zeolite

Procedia PDF Downloads 152

Authors: N.Revathy, C.Gomathi

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This work aims for a tunable capacitor as a sensor which can vary the control voltage of a voltage control oscillator in a ultra wide band (UWB) transmitter. In this paper power consumption is concentrated. The reason for choosing a capacitive sensing is it give slow temperature drift, high sensitivity and robustness. Previous works report a resistive sensing in a voltage control oscillator (VCO) not aiming at power consumption. But this work aims for power consumption of a capacitive sensing in ultra wide band transmitter. The ultra wide band transmitter to be used is a direct modulation of pulses. The VCO which is the heart of pulse generator of UWB transmitter works on the principle of voltage to frequency conversion. The VCO has and odd number of inverter stages which works on the control voltage input this input is now from a variable capacitor and the buffer stages is reduced from the previous work to maintain the oscillating frequency. The VCO is also aimed to consume low power. Then the concentration in choosing a variable capacitor is aimed. A compact model of a capacitor with the transient characteristics is to be designed with a movable dielectric and multi metal membranes. Previous modeling of the capacitor transient characteristics is with a movable membrane and a fixed membrane. This work aims at a membrane with a wide tuning suitable for ultra wide band transmitter.This is used in this work because a capacitive in a ultra wide transmitter need to be tuned in such a way that all satisfies FCC regulations.

Keywords: capacitive sensing, ultra wide band transmitter, voltage control oscillator, FCC regulation

Procedia PDF Downloads 392

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

Procedia PDF Downloads 157

Authors: Khyamling Parane, B. M. Prabhu Prasad, Basavaraj Talawar

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Fast simulations are critical in reducing time to market in CMPs and SoCs. Several simulators have been used to evaluate the performance and power consumed by Network-on-Chips. Researchers and designers rely upon these simulators for design space exploration of NoC architectures. Our experiments show that simulating large NoC topologies take hours to several days for completion. To speed up the simulations, it is necessary to investigate and optimize the hotspots in simulator source code. Among several simulators available, we choose Booksim2.0, as it is being extensively used in the NoC community. In this paper, we analyze the cache and memory system behaviour of Booksim2.0 to accurately monitor input dependent performance bottlenecks. Our measurements show that cache and memory usage patterns vary widely based on the input parameters given to Booksim2.0. Based on these measurements, the cache configuration having least misses has been identified. To further reduce the cache misses, we use software optimization techniques such as removal of unused functions, loop interchanging and replacing post-increment operator with pre-increment operator for non-primitive data types. The cache misses were reduced by 18.52%, 5.34% and 3.91% by employing above technology respectively. We also employ thread parallelization and vectorization to improve the overall performance of Booksim2.0. The OpenMP programming model and SIMD are used for parallelizing and vectorizing the more time-consuming portions of Booksim2.0. Speedups of 2.93x and 3.97x were observed for the Mesh topology with 30 × 30 network size by employing thread parallelization and vectorization respectively.

Keywords: cache behaviour, network-on-chip, performance profiling, vectorization

Procedia PDF Downloads 200

Authors: F. A. Gdhaidh, K. Hussain, H. S. Qi

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A numerical study of natural convection heat transfer in water filled cavity has been examined in 3D for single phase liquid cooling system by using an array of parallel plate fins mounted to one wall of a cavity. The heat generated by a heat source represents a computer CPU with dimensions of 37.5×37.5 mm mounted on substrate. A cold plate is used as a heat sink installed on the opposite vertical end of the enclosure. The air flow inside the computer case is created by an exhaust fan. A turbulent air flow is assumed and k-ε model is applied. The fins are installed on the substrate to enhance the heat transfer. The applied power energy range used is between 15- 40W. In order to determine the thermal behaviour of the cooling system, the effect of the heat input and the number of the parallel plate fins are investigated. The results illustrate that as the fin number increases the maximum heat source temperature decreases. However, when the fin number increases to critical value the temperature start to increase due to the fins are too closely spaced and that cause the obstruction of water flow. The introduction of parallel plate fins reduces the maximum heat source temperature by 10% compared to the case without fins. The cooling system maintains the maximum chip temperature at 64.68℃ when the heat input was at 40 W which is much lower than the recommended computer chips limit temperature of no more than 85℃ and hence the performance of the CPU is enhanced.

Keywords: chips limit temperature, closed enclosure, natural convection, parallel plate, single phase liquid

Procedia PDF Downloads 266

Authors: Majid AghaAlikhani, Mostafa Hojati, Saeid Satari-Yuzbashkandi

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This paper studies energy use patterns and relationship between energy input and yield for soybean (Glycine max (L.) Merrill) in Mazandaran province of Iran. In this study, data were collected by administering a questionnaire in face-to-face interviews. Results revealed that the highest share of energy consumption belongs to chemical fertilizers (29.29%) followed by diesel (23.42%) and electricity (22.80%). Our investigations showed that a total energy input of 23404.1 MJ.ha-1 was consumed for soybean production. The energy productivity, specific energy, and net energy values were estimated as 0.12 kg MJ-1, 8.03 MJ kg-1, and 49412.71 MJ.ha-1, respectively. The ratio of energy outputs to energy inputs was 3.11. Obtained results indicated that direct, indirect, renewable and non-renewable energies were (56.83%), (43.17%), (15.78%) and (84.22%), respectively. Three econometric models were also developed to estimate the impact of energy inputs on yield. The results of econometric models revealed that impact of chemical, fertilizer, and water on yield were significant at 1% probability level. Also, direct and non-renewable energies were found to be rather high. Cost analysis revealed that total cost of soybean production per ha was around 518.43$. Accordingly, the benefit-cost ratio was estimated as 2.58. The energy use efficiency in soybean production was found as 3.11. This reveals that the inputs used in soybean production are used efficiently. However, due to higher rate of nitrogen fertilizer consumption, sustainable agriculture should be extended and extension staff could be proposed substitution of chemical fertilizer by biological fertilizer or green manure.

Keywords: Cobbe Douglas function, economical analysis, energy efficiency, energy use patterns, soybean

Procedia PDF Downloads 335

Authors: A. Ameur Ameur, H. Boukherrouba

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In red meat slaughterhouses a significant number of organs and carcasses were seized because of the presence of lesions of various origins. The objective of this study is to characterize and evaluate the frequency of these lesions in the slaughterhouse of the Wilaya of BOUIRA. On cattle slaughtered in 2646 and inspected 72% of these carcasses have been no seizures against 28% who have undergone at least one entry. 325 lung (44%), 164 livers (22%), 149 hearts (21%) are the main saisis.38 kidneys members (5%), 33 breasts (4%) and 16 whole carcasses (2%) are less seizures parties. The main reasons are the input hydatid cyst for most seized organs such as the lungs (64.5%), livers (51.8%), hearts (23.2%), hydronephrosis for the kidneys (39.4%), and chronic mastitis (54%) for the breasts. Then we recorded second-degree pneumonia (16%) to the lungs, chronic fascioliasis (25%) for livers. A significant difference was observed (p < 0.0001) by sex, race, origin and age of all cattle having been saisie.une a specific input patterns and So pathology was recorded based on race. The local breed presented (75.2%) of hydatid cyst, (95%) and chronic fascioliasis (60%) pyelonephritis, for against the improved breed presented the entire respiratory lesions include pneumonia (64%) the chronic tuberculosis (64%) and mastitis (76%). These results are an important step in the implementation of the concept of risk assessment as the scientific basis of food legislation, by the identification and characterization of macroscopic damage leading withdrawals in meat and to establish the level of inclusion of these injuries within the recommended risk assessment systems (HACCP).

Keywords: slaughterhouses, meat safety, seizure patterns, HACCP

Procedia PDF Downloads 466

Authors: Feiying Wu

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Chatbots are computer programs that attempt to engage a human in a dialogue, which originated in the 1960s with MIT's Eliza. However, they have become widespread more recently as advances in language technology have produced chatbots with increasing linguistic quality and sophistication, leading to their potential to serve as a tool for Computer-Assisted Language Learning(CALL). The aim of this article is to assess the feasibility of using two chatbots, Mitsuku and CleverBot, as pedagogical tools for learning English as a second language by stimulating L2 learners with distinct English proficiencies. Speaking of the input of stimulated learners, they are measured by AntWordProfiler to match the user's expected vocabulary proficiency. Totally, there are four chat sessions as each chatbot will converse with both beginners and advanced learners. For evaluation, it focuses on chatbots' responses from a linguistic standpoint, encompassing vocabulary and sentence levels. The vocabulary level is determined by the vocabulary range and the reaction to misspelled words. Grammatical accuracy and responsiveness to poorly formed sentences are assessed for the sentence level. In addition, the assessment of this essay sets 25% lexical and grammatical incorrect input to determine chatbots' corrective ability towards different linguistic forms. Based on statistical evidence and illustration of examples, despite the small sample size, neither Mitsuku nor CleverBot is ideal as educational tools based on their performance through word range, grammatical accuracy, topic range, and corrective feedback for incorrect words and sentences, but rather as a conversational tool for beginners of L2 English.

Keywords: chatbots, CALL, L2, corrective feedback

Procedia PDF Downloads 80

Authors: Nadeem Hassan, Farooq Ahmad

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The quality of education has a direct impact on individual, family, society, economy in general and the mankind as a whole. Because of that thousands of research papers and articles are written on the quality of education, billions of dollars are spent and continuously being spent on research and enhancing the quality of education. Academic programs accredited agencies define the various criterion of quality of education; academic institutions obtain accreditation from these agencies to ensure degree programs offered at their institution are of international standards. This R&D aims to build a web based analytical tool (OptiBaha) that finds the gaps in AlBaha University education system by taking input from stakeholders, including students, faculty, staff and management. The input/online-data collected by this tool will be analyzed on core areas of education as proposed by accredited agencies, CAC of ABET and NCAAA of KSA, including student background, language, culture, motivation, curriculum, teaching methodology, assessment and evaluation, performance and progress, facilities, availability of teaching materials, faculty qualification, monitoring, policies and procedures, and more. Based on different analytical reports, gaps will be highlighted, and remedial actions will be proposed. If the tool is implemented and made available through a continuous process the quality of education at AlBaha University can be enhanced, it will also help in fulfilling criterion of accreditation agencies. The tool will be generic in nature and ultimately can be used by any academic institution.

Keywords: academic quality, accreditation agencies, higher education, policies and procedures

Procedia PDF Downloads 303

Authors: Myungjin Lee, Daegun Han, Jongsung Kim, Soojun Kim, Hung Soo Kim

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Recently, the localized heavy rainfall and typhoons are frequently occurred due to the climate change and the damage is becoming bigger. Therefore, we may need a more accurate prediction of the rainfall and runoff. However, the gauge rainfall has the limited accuracy in space. Radar rainfall is better than gauge rainfall for the explanation of the spatial variability of rainfall but it is mostly underestimated with the uncertainty involved. Therefore, the ensemble of radar rainfall was simulated using error structure to overcome the uncertainty and gauge rainfall. The simulated ensemble was used as the input data of the rainfall-runoff models for obtaining the ensemble of runoff hydrographs. The previous studies discussed about the accuracy of the rainfall-runoff model. Even if the same input data such as rainfall is used for the runoff analysis using the models in the same basin, the models can have different results because of the uncertainty involved in the models. Therefore, we used two models of the SSARR model which is the lumped model, and the Vflo model which is a distributed model and tried to simulate the optimum runoff considering the uncertainty of each rainfall-runoff model. The study basin is located in Han river basin and we obtained one integrated runoff hydrograph which is an optimum runoff hydrograph using the blending methods such as Multi-Model Super Ensemble (MMSE), Simple Model Average (SMA), Mean Square Error (MSE). From this study, we could confirm the accuracy of rainfall and rainfall-runoff model using ensemble scenario and various rainfall-runoff model and we can use this result to study flood control measure due to climate change. Acknowledgements: This work is supported by the Korea Agency for Infrastructure Technology Advancement(KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 18AWMP-B083066-05).

Keywords: radar rainfall ensemble, rainfall-runoff models, blending method, optimum runoff hydrograph

Procedia PDF Downloads 280

Authors: C. Nathanailides, S. Anastasiou, A. Dimitroglou, P. Logothetis, G. Kanlis

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In the present work we reviewed historical data of the Greek Marine aquaculture industry including adoption of new methods and technological innovation. The results indicate that the industry exhibited a rapid rise in production efficiency, employment and adoption of new technologies which reduced outbreaks of diseases, reduced production risk and the price of the farmed fish. The improvements of total quality practices and technological input on the Greek Aquaculture industry include improved survival, growth and body shape of farmed fish, which resulted from development of new aquaculture feeds and the genetic selection of the bloodstock. Also improvements in the quality of the final product were achieved via technological input in the methods and technology applied during harvesting, packaging, and transportation-preservation of farmed fish ensuring high quality of the product from the fish farm to the plate of the consumers. These parameters (health management, nutrition, genetics, harvesting and post-harvesting methods and technology) changed significantly over the last twenty years and the results of these improvements are reflected in the production efficiency of the Aquaculture industry and the quality of the final product. It is concluded that the Greek aquaculture industry exhibited a rapid growth, adoption of technologies and supply was stabilized after the global financial crisis, nevertheless, the development of the Greek aquaculture industry is currently limited by international trade sanctions, credit crunch, and increased taxation and not by limited technology or resources.

Keywords: innovation, aquaculture, total quality, management

Procedia PDF Downloads 373

Authors: Catalina Albornoz, Giacomo Barbieri

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Crop monitoring has shown to reduce vulnerability to spreading plagues and pathologies in crops. Remote sensing with Unmanned Aerial Vehicles (UAVs) has made crop monitoring more precise, cost-efficient and accessible. Nowadays, remote monitoring involves calculating maps of vegetation indices by using different software that takes either Truecolor (RGB) or multispectral images as an input. These maps are then used to segment the crop into management zones. Finally, knowing the spectral signature of a crop (the reflected radiation as a function of wavelength) can be used as an input for decision-making and crop characterization. The calculation of vegetation indices using software such as Pix4D has high precision for monoculture plantations. However, this paper shows that using this software on mixed crops may lead to errors resulting in an incorrect segmentation of the field. Within this work, authors propose to filter all the elements different from the main crop before the calculation of vegetation indices and the spectral signature. A filter based on the Sobel method for border detection is used for filtering a coffee crop. Results show that segmentation into management zones changes with respect to the traditional situation in which a filter is not applied. In particular, it is shown how the values of the spectral signature change in up to 17% per spectral band. Future work will quantify the benefits of filtering through the comparison between in situ measurements and the calculated vegetation indices obtained through remote sensing.

Keywords: coffee, filtering, mixed crop, precision agriculture, remote sensing, spectral signature

Procedia PDF Downloads 389

Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

Procedia PDF Downloads 161

Authors: Songul Cinaroglu

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Regional planning in health has gained speed for developing countries in recent years. In Turkey, 89 different Public Hospital Unions (PHUs) were conducted based on provincial levels. In this study technical efficiencies of 89 PHUs were examined by using Data Envelopment Analysis (DEA) and machine learning techniques by dividing them into two clusters in terms of similarities of input and output indicators. Number of beds, physicians and nurses determined as input variables and number of outpatients, inpatients and surgical operations determined as output indicators. Before performing DEA, PHUs were grouped into two clusters. It is seen that the first cluster represents PHUs which have higher population, demand and service density than the others. The difference between clusters was statistically significant in terms of all study variables (p ˂ 0.001). After clustering, DEA was performed for general and for two clusters separately. It was found that 11% of PHUs were efficient in general, additionally 21% and 17% of them were efficient for the first and second clusters respectively. It is seen that PHUs, which are representing urban parts of the country and have higher population and service density, are more efficient than others. Random forest decision tree graph shows that number of inpatients is a determinative factor of efficiency of PHUs, which is a measure of service density. It is advisable for public health policy makers to use statistical learning methods in resource planning decisions to improve efficiency in health care.

Keywords: public hospital unions, efficiency, data envelopment analysis, random forest

Procedia PDF Downloads 127

Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

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Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

Procedia PDF Downloads 216

Authors: M. Tarik Boyraz, M. Bilge Imer

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Conventionally cast nickel-based super alloys, such as commercial alloy IN 738 LC, are widely used in manufacturing of industrial gas turbine blades. With carefully designed microstructure and the existence of alloying elements, the blades show improved mechanical properties at high operating temperatures and corrosive environment. The aim of this work is to model and estimate these mechanical properties of IN 738 LC alloy solely based on simulations for projected heat treatment conditions or service conditions. The microstructure (size, fraction and frequency of gamma prime- γ′ and carbide phases in gamma- γ matrix, and grain size) of IN 738 LC needs to be optimized to improve the high temperature mechanical properties by heat treatment process. This process can be performed at different soaking temperature, time and cooling rates. In this work, micro-structural evolution studies were performed experimentally at various heat treatment process conditions, and these findings were used as input for further simulation studies. The operation time, soaking temperature and cooling rate provided by experimental heat treatment procedures were used as micro-structural simulation input. The results of this simulation were compared with the size, fraction and frequency of γ′ and carbide phases, and grain size provided by SEM (EDS module and mapping), EPMA (WDS module) and optical microscope for before and after heat treatment. After iterative comparison of experimental findings and simulations, an offset was determined to fit the real time and theoretical findings. Thereby, it was possible to estimate the final micro-structure without any necessity to carry out the heat treatment experiment. The output of this microstructure simulation based on heat treatment was used as input to estimate yield stress and creep properties. Yield stress was calculated mainly as a function of precipitation, solid solution and grain boundary strengthening contributors in microstructure. Creep rate was calculated as a function of stress, temperature and microstructural factors such as dislocation density, precipitate size, inter-particle spacing of precipitates. The estimated yield stress values were compared with the corresponding experimental hardness and tensile test values. The ability to determine best heat treatment conditions that achieve the desired microstructural and mechanical properties were developed for IN 738 LC based completely on simulations.

Keywords: heat treatment, IN738LC, simulations, super-alloys

Procedia PDF Downloads 248

Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 133

Authors: Kamyar Kildashti, Rasoul Mirghaderi

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This paper describes the three-dimensional seismic performance assessment of a high-rise steel moment-frame setback tower, designed and detailed per the 2010 ASCE7, under multiple support excitations. The vulnerability analyses are conducted based on nonlinear history analyses under a set of multi-directional strong ground motion records which are scaled to design-based site-specific spectrum in accordance with ASCE41-13. Spatial variation of input motions between far distant supports of each part of the tower is considered by defining time lag. Plastic hinge monotonic and cyclic behavior for prequalified steel connections, panel zones, as well as steel columns is obtained from predefined values presented in TBI Guidelines, PEER/ATC72 and FEMA P440A to include stiffness and strength degradation. Inter-story drift ratios, residual drift ratios, as well as plastic hinge rotation demands under multiple support excitations, are compared to those obtained from uniform support excitations. Performance objectives based on acceptance criteria declared by TBI Guidelines are compared between uniform and multiple support excitations. The results demonstrate that input motion discrepancy results in detrimental effects on the local and global response of the tower.

Keywords: high-rise building, nonlinear time history analysis, multiple support excitation, performance-based design

Procedia PDF Downloads 285

Authors: Pierluigi Gallo, Francesco Randazzo, Ignazio Gallo

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In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allows studying effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic.

Keywords: auction, aggregation, fair, group buying, social buying

Procedia PDF Downloads 294

Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Naohiro Nakamura, Takuya Kinoshita, Hiroshi Fukuyama

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The 2011 off The Pacific Coast of Tohoku Earthquake caused considerable damage to wide areas of eastern Japan. A large number of earthquake observation records were obtained at various places. To design more earthquake-resistant buildings and improve earthquake disaster prevention, it is necessary to utilize these data to analyze and evaluate the behavior of a building during an earthquake. This paper presents an earthquake response simulation analysis (hereafter a seismic response analysis) that was conducted using data recorded during the main earthquake (hereafter the main shock) as well as the earthquakes before and after it. The data were obtained at a high-rise steel-reinforced concrete (SRC) building in the bay area of Tokyo. We first give an overview of the building, along with the characteristics of the earthquake motion and the building during the main shock. The data indicate that there was a change in the natural period before and after the earthquake. Next, we present the results of our seismic response analysis. First, the analysis model and conditions are shown, and then, the analysis result is compared with the observational records. Using the analysis result, we then study the effect of soil-structure interaction on the response of the building. By identifying the characteristics of the building during the earthquake (i.e., the 1st natural period and the 1st damping ratio) by the Auto-Regressive eXogenous (ARX) model, we compare the analysis result with the observational records so as to evaluate the accuracy of the response analysis. In this study, a lumped-mass system SR model was used to conduct a seismic response analysis using observational data as input waves. The main results of this study are as follows: 1) The observational records of the 3/11 main shock put it between a level 1 and level 2 earthquake. The result of the ground response analysis showed that the maximum shear strain in the ground was about 0.1% and that the possibility of liquefaction occurring was low. 2) During the 3/11 main shock, the observed wave showed that the eigenperiod of the building became longer; this behavior could be generally reproduced in the response analysis. This prolonged eigenperiod was due to the nonlinearity of the superstructure, and the effect of the nonlinearity of the ground seems to have been small. 3) As for the 4/11 aftershock, a continuous analysis in which the subject seismic wave was input after the 3/11 main shock was input was conducted. The analyzed values generally corresponded well with the observed values. This means that the effect of the nonlinearity of the main shock was retained by the building. It is important to consider this when conducting the response evaluation. 4) The first period and the damping ratio during a vibration were evaluated by an ARX model. Our results show that the response analysis model in this study is generally good at estimating a change in the response of the building during a vibration.

Keywords: ARX model, response analysis, SRC building, the 2011 off the Pacific Coast of Tohoku Earthquake

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Authors: Anwar Hussain, Muhammad Iqbal

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The changes in the prices of the agriculture commodities combined with changes in population and agriculture productivity affect farmers’ profitability and standard of living. This study intends to estimate various domestic terms of trade for agriculture sector and also to assess the volatility in the standard of living and profitability of farmers. The terms of trade has been estimated for Pakistan and its provinces using producer prices indices, consumer price indices, input prices indices and quantity indices using the data for the period 1990-91 to 2008-09. The domestic terms of trade of agriculture sector has been improved in terms of both approaches i.e. the ratio of producer prices indices to consumer prices indices and the real per capita income approach. However, the cross province estimates indicated that the terms of trade also improved for Khyber Pakhtunkhwa, Sindh and Punjab while Balochistan’s domestic terms of trade deteriorated drastically. In other words the standard of living of the farmers in Pakistan and its provinces except Balochistan improved. Using the input prices, the domestic terms of trade deteriorated for Pakistan as a whole and its provinces as well. This also explores that as a whole the profitability of the farmers reduced during the study period. The farmers pay more prices for inputs as compared to they receive for their produce. This further indicates that the poverty at the gross root level has been increased. Further, summing, the standard of living of the farmers improved but their profitability reduced, which indicates that the farmers do not completely rely on the farm income but also utilize some other sources of income for their livelihood. The study supports to give subsidies on farm inputs so as to improve the profitability of the farmers.

Keywords: agricultural terms of trade, farmers’ profitability, farmers’ standard of living, consumer and producer price indices, quantity indices

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Authors: K. Hariohm, V. Prakash, J. Saravana Kumar

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Introduction and Rationale: Increased scope of Physiotherapy (PT) practice also has contributed to research in the field of PT. It is essential to determine the production and quality of the clinical trials from India since, it may reflect the scientific growth of the profession. These trends can be taken as a baseline to measure our performance and also can be used as a guideline for the future trials. Objective: To quantify and analyze qualitatively the RCT’s from India from the period 2000-2013’ May, and classify data for the information process. Methods: Studies were searched in the Medline database using the key terms “India”, “Indian”, “Physiotherapy”. Clinical trials only with PT authors were included. Trials out of scope of PT practice and on animals were excluded. Retrieved valid articles were analyzed for published year, type of participants, area of study, PEDro score, outcome measure domains of impairment, activity, participation; ‘a priori’ sample size calculation, region, and explanation of the intervention. Result: 45 valid articles were retrieved from the year 2000-2013’ May. The majority of articles were done on symptomatic participants (81%). The frequencies of conditions repeated more were low back pain (n-7) and diabetes (n-4). PEDro score with mode 5 and upper limit of 8 and lower limit 4 was found. 97.2% of studies measure the outcome at the impairment level, 34% in activity level, and 27.8% in participation level. 29.7% of studies did ‘a priori’ sample size calculation. Correlation of year trend and PEDro score found to be not significant (p>.05). Individual PEDro item analysis showed, randomization (100%), concealment (33%) baseline (76%), blinding-subject, therapist, assessor (9.1%, 0%, 10%), follow-up (89%) ITT (15%), statistics between groups (100%), measures of variance (88 %). Conclusion: The trend shows an upward slope in terms of RCTs published from India which is a good indicator. The qualitative analysis showed some gaps in the clinical trial design, which can be expected to be, fulfilled by the future researchers.

Keywords: RCT, PEDro, physical therapy, rehabilitation

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Authors: Rihab Hamdi, Amel Hadri Hamida, Fatiha Khelili, Sakina Zerouali, Ouafae Bennis

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This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is effectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their effectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept.

Keywords: DC-DC converter, hysteresis modulation, parallel multi-cells converter, pulse-width modulation, robustness, sliding mode control

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Authors: Ekpeti Bukola Grace, Mahmoudi Sabar Esmail, Chaer Issa

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Global energy consumption has been increasing steadily over the last half - century, and this trend is projected to continue. As energy demand rises in many countries throughout the world due to population growth, natural ventilation in buildings has been identified as a viable option for lowering these demands, saving costs, and also lowering CO2 emissions. However, natural ventilation is driven by forces that are generally unpredictable in nature thus, it is important to manage the resulting airflow in order to maintain pleasant indoor conditions, making it a complex system that necessitates specific control approaches. The effective application of fuzzy logic technique amidst other intelligent systems is one of the best ways to bridge this gap, as its control dynamics relates more to human reasoning and linguistic descriptions. This article reviewed existing literature and presented practical solutions by applying fuzzy logic control with optimized techniques, selected input parameters, and expert rules to design a more effective control system. The control monitors used indoor temperature, outdoor temperature, carbon-dioxide levels, wind velocity, and rain as input variables to the system, while the output variable remains the control of window opening. This is achieved through the use of fuzzy logic control tool box in MATLAB and running simulations on SIMULINK to validate the effectiveness of the proposed system. Comparison analysis model via simulation is carried out, and with the data obtained, an improvement in control actions and energy savings was recorded.

Keywords: fuzzy logic, intelligent control systems, natural ventilation, optimization

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Authors: Humphrey Agevi, Harrison Tsingalia, Richard Onwonga, Shem Kuyah

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Most of the world ecosystems have been affected by the effects of climate change. Efforts have been made to mitigate against climate change effects. While most studies have been done in forest ecosystems and pure plant plantations, trees on farms including agroforestry have only received attention recently. Agroforestry systems and tree cover on agricultural lands make an important contribution to climate change mitigation but are not systematically accounted for in the global carbon budgets. This study sought to: (i) determine tree diversity in different agroforestry practices; (ii) determine tree biomass in different agroforestry practices. Study area was determined according to the Land degradation surveillance framework (LSDF). Two study sites were established. At each of the site, a 5km x 10km block was established on a map using Google maps and satellite images. Way points were then uploaded in a GPS helped locate the blocks on the ground. In each of the blocks, Nine (8) sentinel clusters measuring 1km x 1km were randomized. Randomization was done in a common spreadsheet program and later be downloaded to a Global Positioning System (GPS) so that during surveys the researchers were able to navigate to the sampling points. In each of the sentinel cluster, two farm boundaries were randomly identified for convenience and to avoid bias. This led to 16 farms in Kakamega South and 16 farms in Kakamega North totalling to 32 farms in Kakamega Site. Species diversity was determined using Shannon wiener index. Tree biomass was determined using allometric equation. Two agroforestry practices were found; homegarden and hedgerow. Species diversity ranged from 0.25-2.7 with a mean of 1.8 ± 0.10. Species diversity in homegarden ranged from 1-2.7 with a mean of 1.98± 0.14. Hedgerow species diversity ranged from 0.25-2.52 with a mean of 1.74± 0.11. Total Aboveground Biomass (AGB) determined was 13.96±0.37 Mgha-1. Homegarden with the highest abundance of trees had higher above ground biomass (AGB) compared to hedgerow agroforestry. This study is timely as carbon budgets in the agroforestry can be incorporated in the global carbon budgets and improve the accuracy of national reporting of greenhouse gases.

Keywords: agroforestry, allometric equations, biomass, climate change

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: John Lloyd Angolluan, Jennile Caday, Crystal Mae Estrella, Reike Alliyah Taladua, Zion Michael Ysulat

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One of the requirements of having the ability to communicate in English is by having adequate vocabulary. Nowadays, people are more engaged in watching movie streams on which they can watch movies in a very portable way, such as Netflix. Wherein Netflix became global demand for online media has taken off in recent years. This research aims to know whether the use of bimodal subtitles on Netflix English movies can enhance vocabulary. This study is quantitative and utilizes a descriptive method, and this study aims to explore the use of bimodal subtitles on Netflix English movies to enhance the vocabulary of students. The respondents of the study were the selected Second-year English majors of Rizal Technological University Pasig and Boni Campus using the purposive sampling technique. The researcher conducted a survey questionnaire through the use of Google Forms. In this study, the weighted mean was used to evaluate the student's responses to the statement of the problems of the study of the use of bimodal subtitles on Netflix English movies. The findings of this study revealed that the bimodal subtitle on Netflix English movies enhanced students’ vocabulary learning acquisition by providing learners with access to large amounts of real and comprehensible language input, whether accidentally or intentionally, and it turns out that bimodal subtitles on Netflix English movies help students recognize vocabulary, which has a positive impact on their vocabulary building. Therefore, the researchers advocate that watching English Netflix movies enhances students' vocabulary by using bimodal subtitled movie material during their language learning process, which may increase their motivation and the usage of bimodal subtitles in learning new vocabulary. Bimodal subtitles need to be incorporated into educational film activities to provide students with a vast amount of input to expand their vocabulary.

Keywords: bimodal subtitles, Netflix, English movies, vocabulary, subtitle, language, media

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Authors: Erna Judith Roach, Nalini Bhaskaranand

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Background & Significance: Asthma is the most common chronic disease among children. Education is the cornerstone of management of asthma to help the affected children. In India there are about 1.5- 3.0 million asthmatic children in the age group of 5-11 years. Many parents face management dilemmas in administration of medications to their children. Mothers being primary caregivers of children are often responsible for administering medications to them. The purpose of the study was to develop an educational package on techniques of drug administration for mothers of asthmatic children and determine its effectiveness in terms of improvement in skill in drug administration. Methodology: A quasi- experimental time series pre-test post -test control group design was used. Mothers of asthmatic children attending paediatric outpatient departments of selected hospitals along with their children between 5 and 12 years were included. Sample size consisted of 40 mothers in the experimental and 40 mothers in the control groups. Block randomization was used to assign samples to both the groups. The data collection instruments used were Baseline Proforma, Clinical Proforma, Daily asthma drug intake and symptoms diary and Observation Rating Scales on technique of using a metered dose inhaler with spacer; metered dose inhaler with facemask; metered dose inhaler alone and dry powder inhaler. The educational package consisted of a video and booklet on techniques of drug administration. Data were collected at baseline, 1, 3 and 6 months. Findings: The mean post-test scores in techniques of drug administration were higher than the mean pre-test scores in the experimental group in all techniques. The Friedman test (p < 0.01), Wilcoxon Signed Rank test (p < 0.008) and Mann Whitney U (p < 0.01) showed statistically significant difference in the experimental group than the control group. There was significant decrease in the average number of symptom days (11 Vs. 4 days/ month) and hospital visits (5 to 1 per month) in the experimental group when compared to the control group. Conclusion: The educational package was found to be effective in improving the skill of mothers in drug administration in all the techniques, especially with using the metered dose inhaler with spacer.

Keywords: childhood asthma, drug administration, mothers of children, inhaler

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