Search results for: plant data

Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: Sinan Alsaadi, Mustafa Merdan

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Communication and access systems can be made with many methods in today’s world. These systems are such standards as Wifi, Wimax, Bluetooth, GPS and GPRS. Devices which use these standards also use system resources excessively in direct proportion to their transmission speed. However, large-scale data communication is not always needed. In such cases, a technology which will use system resources as little as possible and support smart network topologies has been needed in order to enable the transmissions of such small packet data and provide the control for this kind of devices. IEEE issued 802.15.4 standard upon this necessity and enabled the production of Zigbee protocol which takes these standards as its basis and devices which support this protocol. In our project, this communication protocol was preferred. The aim of this study is to provide the immediate data transmission of our robot from the field within the scope of the project. In addition, making the communication with the robot through Zigbee Protocol has also been aimed. While sitting on the computer, obtaining the desired data from the region where the robot is located has been taken as the basis. Arduino Uno R3 microcontroller which provides the control mechanism, 1298 shield as the motor driver.

Keywords: ZigBee, wireless network, remote monitoring, smart home, agricultural industry

Procedia PDF Downloads 273

Authors: Emielex M. Aguilar, Kristen Angela G. Cruz, Czarina Joie L. Rivera, Francis Dave C. Tan, Gavino Ivan N. Tanodra, Dianne Katrina G. Usana, Mary Grace T. Valentin, Nico Albert S. Vasquez, Edwin Monico C. Wee

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The risk of diabetes mellitus is increasing in the Philippines, with Metformin and Insulin as drugs commonly used for its management. The use of herbal medicines has grown increasingly, especially among the elderly population. Moringa oleifera or malunggay is one of the most common plants in the country, and several studies have shown the plant to exhibit a hypoglycemic property with its flavonoid content. This study aims to investigate the possible effects of concomitant use of Metformin and powdered M. oleifera leaves (PMOL) on blood glucose levels. Twenty male Sprague-Dawley rats were equally distributed into four groups. Fasting blood glucose levels of the rats were measured prior to experimentation. The following treatments were administered to the four groups, respectively: glucose only 2 g/kg; glucose 2 g/kg + Metformin 100 mg/kg; glucose 2 g/kg + PMOL 200 mg/kg; and glucose 2 g/kg + PMOL 200 mg/kg and Metformin 100 mg/kg. Blood glucose levels were determined on the 1st, 2nd, 3rd, and 4th hour post-treatment and compared between groups. Statistical analysis showed that the type of intervention did not show significance in the reduction of blood glucose levels when compared with the other groups (p=0.378), while the effect of time exhibited significance (p=0.000). The interaction between the type of intervention and time of blood glucose measurement was shown to be significant (p=0.024). Within each group, the control and PMOL-treated groups showed significant reduction in blood glucose levels over time with p-values of 0.000 and 0.000, respectively, while the Metformin-treated and the combination groups had p-values of 0.062 and 0.093, respectively, which are not significant. The descriptive data also showed that the mean total reduction of blood glucose levels of the Metformin and PMOL combination treatment group was lower than the PMOL-treated group alone, while the mean total reduction of blood glucose levels of the combination group was higher than the Metformin-treated group alone. Based on the results obtained, the combination of Metformin and PMOL did not significantly lower the blood glucose levels of the rats as compared to the other groups. However, the concomitant use of Metformin and PMOL may affect each other’s blood glucose lowering activity. Additionally, prolonged time of exposure and delay in the first blood glucose measurement after treatment could exhibit a significant effect in the blood glucose levels. Further studies are recommended regarding the effects of the concomitant use of the two agents on blood glucose levels.

Keywords: blood glucose levels, concomitant use, metformin, Moringa oleifera

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Authors: Hojat Heidari-Bafroui, Amer Charbaji, Constantine Anagnostopoulos, Mohammad Faghri

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Phosphorus is an essential nutrient for plant life which is most frequently found as phosphate in water. Once phosphate is found in abundance in surface water, a series of adverse effects on an ecosystem can be initiated. Therefore, a portable and reliable method is needed to monitor the phosphate concentrations in the field. In this paper, an inexpensive dip strip device with the ascorbic acid/antimony reagent dried on blotting paper along with wet chemistry is developed for the detection of low concentrations of phosphate in water. Ammonium molybdate and sulfuric acid are separately stored in liquid form so as to improve significantly the lifetime of the device and enhance the reproducibility of the device’s performance. The limit of detection and quantification for the optimized device are 0.134 ppm and 0.472 ppm for phosphate in water, respectively. The device’s shelf life, storage conditions, and limit of detection are superior to what has been previously reported for the paper-based phosphate detection devices.

Keywords: phosphate detection, paper-based device, molybdenum blue method, colorimetric assay

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Authors: Massimiliano Condotta, Paolo Ruggeri, Chiara Scanagatta, Giovanni Borga

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Architects and urban planners, when designing and renewing cities, have to face a complex set of problems, including the issues of noise and air pollution which are considered as hot topics (i.e., the Clean Air Act of London and the Soundscape definition). It is usually taken for granted that these problems go by together because the noise pollution present in cities is often linked to traffic and industries, and these produce air pollutants as well. Traffic congestion can create both noise pollution and air pollution, because NO₂ is mostly created from the oxidation of NO, and these two are notoriously produced by processes of combustion at high temperatures (i.e., car engines or thermal power stations). We can see the same process for industrial plants as well. What have to be investigated – and is the topic of this paper – is whether or not there really is a correlation between noise pollution and air pollution (taking into account NO₂) in urban areas. To evaluate if there is a correlation, some low-cost methodologies will be used. For noise measurements, the OpeNoise App will be installed on an Android phone. The smartphone will be positioned inside a waterproof box, to stay outdoor, with an external battery to allow it to collect data continuously. The box will have a small hole to install an external microphone, connected to the smartphone, which will be calibrated to collect the most accurate data. For air, pollution measurements will be used the AirMonitor device, an Arduino board to which the sensors, and all the other components, are plugged. After assembling the sensors, they will be coupled (one noise and one air sensor) and placed in different critical locations in the area of Mestre (Venice) to map the existing situation. The sensors will collect data for a fixed period of time to have an input for both week and weekend days, in this way it will be possible to see the changes of the situation during the week. The novelty is that data will be compared to check if there is a correlation between the two pollutants using graphs that should show the percentage of pollution instead of the values obtained with the sensors. To do so, the data will be converted to fit on a scale that goes up to 100% and will be shown thru a mapping of the measurement using GIS methods. Another relevant aspect is that this comparison can help to choose which are the right mitigation solutions to be applied in the area of the analysis because it will make it possible to solve both the noise and the air pollution problem making only one intervention. The mitigation solutions must consider not only the health aspect but also how to create a more livable space for citizens. The paper will describe in detail the methodology and the technical solution adopted for the realization of the sensors, the data collection, noise and pollution mapping and analysis.

Keywords: air quality, data analysis, data collection, NO₂, noise mapping, noise pollution, particulate matter

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Authors: Shyh Ming Chern

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Cubic equations of state (EoS), popular due to their simple mathematical form, ease of use, semi-theoretical nature and, reasonable accuracy are normally fitted to vapor-liquid equilibrium P-v-T data. As a result, They often show poor accuracy in the region near and above the critical point. In this study, the performance of the renowned Peng-Robinson (PR) and Patel-Teja (PT) EoS’s around the critical area has been examined against the P-v-T data of water. Both of them display large deviations at critical point. For instance, PR-EoS exhibits discrepancies as high as 47% for the specific volume, 28% for the enthalpy departure and 43% for the entropy departure at critical point. It is shown that incorporating P-v-T data of the supercritical region into the retuning of a cubic EoS can improve its performance above the critical point dramatically. Adopting a retuned acentric factor of 0.5491 instead of its genuine value of 0.344 for water in PR-EoS and a new F of 0.8854 instead of its original value of 0.6898 for water in PT-EoS reduces the discrepancies to about one third or less.

Keywords: equation of state, EoS, supercritical water, SCW

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Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

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Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

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Authors: Miraz Hafiz Rossy

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As part of planning to address shortfalls in fresh water supply for the world, Sea water can be a huge source of fresh water. But Desalinating sea water to get fresh water could require a lots of fossil fuels. To save the fossil fuel in terms of save the green world but meet the up growing need for fresh water, a very useful but energy efficient method needs to be introduced. Vacuum desalination of sea water using only the Renewable energy can be an effective solution to this issue. Taking advantage of sensitivity of water's boiling point to air pressure a vacuum desalination water treatment plant can be designed which would only use sea water as feed water and solar energy as fuel to produce fresh drinking water. The study indicates that reducing the air pressure to a certain value water can be boiled at very low temperature. Using solar energy to provide the condensation and the vacuum creation would be very useful and efficient. Compared to existing resources, desalination is considered to be expensive, but using only renewable energy the cost can be reduced significantly. Despite its very few drawbacks, it can be considered a possible solution to the world's fresh water shortages.

Keywords: desalination, scarcity of fresh water, water purification, water treatment

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Authors: Jack R. McKenzie, Peter A. Appleby, Thomas House, Neil Walton

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Cold-start is a notoriously difficult problem which can occur in recommendation systems, and arises when there is insufficient information to draw inferences for users or items. To address this challenge, a contextual bandit algorithm – the Fast Approximate Bayesian Contextual Cold Start Learning algorithm (FAB-COST) – is proposed, which is designed to provide improved accuracy compared to the traditionally used Laplace approximation in the logistic contextual bandit, while controlling both algorithmic complexity and computational cost. To this end, FAB-COST uses a combination of two moment projection variational methods: Expectation Propagation (EP), which performs well at the cold start, but becomes slow as the amount of data increases; and Assumed Density Filtering (ADF), which has slower growth of computational cost with data size but requires more data to obtain an acceptable level of accuracy. By switching from EP to ADF when the dataset becomes large, it is able to exploit their complementary strengths. The empirical justification for FAB-COST is presented, and systematically compared to other approaches on simulated data. In a benchmark against the Laplace approximation on real data consisting of over 670, 000 impressions from autotrader.co.uk, FAB-COST demonstrates at one point increase of over 16% in user clicks. On the basis of these results, it is argued that FAB-COST is likely to be an attractive approach to cold-start recommendation systems in a variety of contexts.

Keywords: cold-start learning, expectation propagation, multi-armed bandits, Thompson Sampling, variational inference

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Authors: Reginawanti Hindersah, Widiya Septiani Perdanawati, Dewi Azizah Sulaksana, Hidiyah Ayu Ma’rufah

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Maize in some region in East Nusa Tenggara Indonesia bordering Republic Democratic of Timor Leste is important local food crop and commonly cultivated using conventional method without appropriate plant nutrition system so that productivity is still low. A way to enhance local corn yield is adding biofertilizer containing nitrogen (N2) fixing bacteria such as Azotobacter. The purpose of this research was to determine N2 fixation, cytokinin as well as exopolysachharide production capacity of six indigenous Azotobacter strains in pure culture. The N2 fixation capacities of native 3 day old Azotobacter strains added to Ashby Media varied from 0.01 to 0.39 µM/g/hour. Cytokinin production of these strain in liquid culture of N-free Media was 0.11 to 40.04 ppm while exopolysachharide content in liquid culture of Vermani Media varied from 0.4 to 27.3 g/L. This results demonstrate that some local Azotobacter strains might be used as biofertilizer.

Keywords: azotobacter, local isolate, N fixation, phythohormone, exopolysaccaride

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Authors: Nuo Duan, Yi Pik Cheng

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This paper presents the data of a series of two-dimensional Discrete Element Method (DEM) simulations of a large-diameter rigid monopile subjected to cyclic loading under a high gravitational force. At present, monopile foundations are widely used to support the tall and heavy wind turbines, which are also subjected to significant from wind and wave actions. A safe design must address issues such as rotations and changes in soil stiffness subject to these loadings conditions. Design guidance on the issue is limited, so are the availability of laboratory and field test data. The interpretation of these results in sand, such as the relation between loading and displacement, relies mainly on empirical correlations to pile properties. Regarding numerical models, most data from Finite Element Method (FEM) can be found. They are not comprehensive, and most of the FEM results are sensitive to input parameters. The micro scale behaviour could change the mechanism of the soil-structure interaction. A DEM model was used in this paper to study the cyclic lateral loads behaviour. A non-dimensional framework is presented and applied to interpret the simulation results. The DEM data compares well with various set of published experimental centrifuge model test data in terms of lateral deflection. The accumulated permanent pile lateral displacements induced by the cyclic lateral loads were found to be dependent on the characteristics of the applied cyclic load, such as the extent of the loading magnitudes and directions.

Keywords: cyclic loading, DEM, numerical modelling, sands

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Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: particle swarm optimization, GIS, traffic data, outliers

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Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

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Authors: K. Y. Chen, H. Chua, C. W. Kan

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Environmental investment is an important issue in many countries. In this study, we will first review the environmental issues related to India and their effect on the economical development. Secondly, economic data would be collected from government yearly statistics. The statistics would also include the environmental investment information of India. Finally, we would co-relate the data in order to find out the relationship between environmental investment and sustainable development in India. Therefore, in the paper, we aim to analyse the effect of an environmental investment on the sustainable development in India. Based on the economic data collected, India is in development status with fast population and GDP growth speed. India is facing the environment problems due to its high-speed development. However, the environment investment could give a positive impact on the sustainable development in India. The environmental investment is keeping in the same growth rate with GDP. Acknowledgment: Authors would like to thank the financial support from the Hong Kong Polytechnic University for this work.

Keywords: India, environmental investment, sustainable development, analysis

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Authors: Asmiaty Sahur, Ambo Ala, Baharuddin Patanjengi, Elkawakib Syam'un

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This study aimed to isolate and characterize the indigenous Rhizosphere bacteria producing Gibberellin Acid as plant growth isolated from local soybean of three different areas in South Sulawesi, Indonesia. Several soil samples of soybean plants were collected from the Rhizosphere of local soybeans in three different areas of South Sulawesi such as Soppeng, Bone and Takalar. There were 56 isolates of bacteria were isolated and grouped into gram-positive bacteria and gram negative bacteria .There are 35 isolates produce a thick slime or slimy when cultured on media Natrium Broth and the remaining of those produced spores. The results showed that of potential bacterial isolated produced Gibberellin Acid in high concentration. The best isolate of Rhizosphere bacteria for the production of Gibberellin Acid is with concentration 2%. There are 4 isolates that had higher concentration are AKB 19 (4.67 mg/ml) followed by RKS 17 (3.80 mg/ml), RKS 25 (3.70 mg / ml) and RKS 24 (3.29 mg/ml) respectively.

Keywords: rhizosphere, bacteria, gibberellin acid, soybeans

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Authors: R. Periyasamy, Deepak Joshi, Sneh Anand

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Foot posture assessment is important to evaluate foot type, causing gait and postural defects in all age groups. Although different methods are used for classification of foot arch type in clinical/research examination, there is no clear approach for selecting the most appropriate measurement system. Therefore, the aim of this study was to develop a system for evaluation of foot type as clinical decision-making aids for diagnosis of flat and normal arch based on the Arch Index (AI) and foot pressure distribution parameter - Power Ratio (PR) data. The accuracy of the system was evaluated for 27 subjects with age ranging from 24 to 65 years. Foot area measurements (hind foot, mid foot, and forefoot) were acquired simultaneously from foot pressure intensity image using portable PedoPowerGraph system and analysis of the image in frequency domain to obtain foot pressure distribution parameter - PR data. From our results, we obtain 100% classification accuracy of normal and flat foot by using the linear discriminant analysis method. We observe there is no misclassification of foot types because of incorporating foot pressure distribution data instead of only arch index (AI). We found that the mid-foot pressure distribution ratio data and arch index (AI) value are well correlated to foot arch type based on visual analysis. Therefore, this paper suggests that the proposed system is accurate and easy to determine foot arch type from arch index (AI), as well as incorporating mid-foot pressure distribution ratio data instead of physical area of contact. Hence, such computational tool based system can help the clinicians for assessment of foot structure and cross-check their diagnosis of flat foot from mid-foot pressure distribution.

Keywords: arch index, computational tool, static foot pressure intensity image, foot pressure distribution, linear discriminant analysis

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Authors: Bahloul Amel

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The present study debates students’ perceptions of the use of technology in learning English as a Foreign Language. Its aim is to explore and understand students’ preparation and presentation of Posters, PowerPoint and Animated Videos by drawing attention to visual and oral elements. The data is collected through observations and semi-structured interviews and analyzed through phenomenological data analysis steps. The themes emerged from the data, visual learning satisfaction in using information and communication technology, providing structure to oral presentation, learning from peers’ presentations, draw attention to using Posters, PowerPoint and Animated Videos as each supports visual learning and organization of thoughts in oral presentations.

Keywords: EFL, posters, PowerPoint presentations, Animated Videos, visual learning

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Authors: S. K. Ashiquer Rahman

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the paper concentrations on the importance of hourly compensation, emphasizing the significance of the unemployment rate. There are the two most important factors of a nation these are its unemployment rate and hourly compensation. These are not merely statistics but they have profound effects on individual, families, and the economy. They are inversely related to one another. When we consider the unemployment rate that will probably decline as hourly compensations in manufacturing rise. But when we reduced the unemployment rates and increased job prospects could result from higher compensation. That’s why, the increased hourly compensation in the manufacturing sector that could have a favorable effect on job changing issues. Moreover, the relationship between hourly compensation and unemployment is complex and influenced by broader economic factors. In this paper, we use panel data regression models to evaluate the expected link between hourly compensation and unemployment rate in order to determine the effect of hourly compensation on unemployment rate. We estimate the fixed effects model, evaluate the error components, and determine which model (the FEM or ECM) is better by pooling all 60 observations. We then analysis and review the data by comparing 3 several countries (United States, Canada and the United Kingdom) using panel data regression models. Finally, we provide result, analysis and a summary of the extensive research on how the hourly compensation effects on the unemployment rate. Additionally, this paper offers relevant and useful informational to help the government and academic community use an econometrics and social approach to lessen on the effect of the hourly compensation on Unemployment rate to eliminate the problem.

Keywords: hourly compensation, Unemployment rate, panel data regression models, dummy variables, random effects model, fixed effects model, the linear regression model

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Authors: Aabiah Nayeem, Fariha Shafiq, Mustabshra Aftab, Rabia Saman Pirzada, Samia Ghazala

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In this modern era of technology, the concept of Internet of Things is very popular in every domain. It is a widely distributed system of things in which the data collected from sensory devices is transmitted, analyzed locally/collectively then broadcasted to network where action can be taken remotely via mobile/web apps. Today’s mobile computing is also gaining importance as the services are provided during mobility. Through mobile computing, data are transmitted via computer without physically connected to a fixed point. The challenge is to provide services with high speed and security. Also, the data gathered from the mobiles must be processed in a secured way. Mobile computing is strongly influenced by internet of things. In this paper, we have discussed security issues and challenges of internet of things and mobile computing and we have compared both of them on the basis of similarities and dissimilarities.

Keywords: embedded computing, internet of things, mobile computing, wireless technologies

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Authors: Fatemeh Havas Beigi

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The purpose of this study is to identify the objectives of a multicultural curriculum for the primary education period from the perspective of ethnic teachers and education experts and cultural professionals. The research paradigm is interpretive, the research approach is qualitative, the research strategy is content analysis, the sampling method is purposeful and it is a snowball, and the sample of informants in the research for Iranian ethnic teachers and experts until the theoretical saturation was estimated to be 67 people. The data collection tools used were based on semi-structured interviews and individual interviews and focal interviews were used to collect information. The data format was also in audio format and the first period coding and the second coding were used to analyze the data. Based on data analysis 11 Objective: Paying attention to ethnic equality, expanding educational opportunities and justice, peaceful coexistence, anti-ethnic and racial discrimination education, paying attention to human value and dignity, accepting religious diversity, getting to know ethnicities and cultures, promoting teaching-learning, fostering self-confidence, building national unity, and developing cultural commonalities for a multicultural curriculum were identified.

Keywords: objective, multicultural curriculum, connect, elementary education period

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Authors: Kaarthik K., Harshini S., Karthika M., Kripanandhini T.

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Water is an essential part of living organisms. Major water pollution is caused due to contamination of industrial wastewater in the river. The most important step in bringing wastewater contaminants down to levels that are safe for nature is wastewater treatment. The contamination of river water harms both humans who consume it and the aquatic life that lives there. We introduce a new cloud-based industrial IoT paradigm in this work for real-time control and monitoring of wastewater. The proposed system prevents prohibited entry of industrial wastewater into the plant by monitoring temperature, hydrogen power (pH), CO₂ and turbidity factors from the wastewater input that the wastewater treatment facility will process. Real-time sensor values are collected and uploaded to the cloud by the system using an IoT Wi-Fi Module. By doing so, we can prevent the contamination of industrial wastewater entering the river earlier, and the necessary actions will be taken by the users. The proposed system's results are 90% efficient, preventing water pollution due to industry and protecting human lives.

Keywords: sensors, pH, CO₂, temperature, turbidity

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Prajwal Eachempati, Laurent Muzellec, Ashish Kumar Jha

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We use Facebook as a case study to investigate the complex relationship between the firm’s public discourse (and actions) surrounding data privacy and the performance of a business model based on monetizing user’s data. We do so by looking at the evolution of public discourse over time (2004–2021) and relate topics to revenue and stock market evolution Drawing from archival sources like Zuckerberg We use LDA topic modelling algorithm to reveal 19 topics regrouped in 6 major themes. We first show how, by using persuasive and convincing language that promises better protection of consumer data usage, but also emphasizes greater user control over their own data, the privacy issue is being reframed as one of greater user control and responsibility. Second, we aim to understand and put a value on the extent to which privacy disclosures have a potential impact on the financial performance of social media firms. There we found significant relationship between the topics pertaining to privacy and social media/technology, sentiment score and stock market prices. Revenue is found to be impacted by topics pertaining to politics and new product and service innovations while number of active users is not impacted by the topics unless moderated by external control variables like Return on Assets and Brand Equity.

Keywords: public discourses, data protection, social media, privacy, topic modeling, business models, financial performance

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Authors: Shashank Tiwari, S. P. Mahapatra

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The aim of this technique is to reduce the steps of process scales up, save time & cost of the industries. This technique will minimise the steps of process scale up. The new steps are, Novel Lab Scale, Novel Lab Scale Trials, Novel Trial Batches, Novel Exhibit Batches, Novel Validation Batches. In these steps, it is not divided to validation batches in three parts but the data of trials batches, Exhibit Batches and Validation batches are use and compile for production and used for validation. It also increases the batch size of the trial, exhibit batches. The new size of trials batches is not less than fifty Thousand, the exhibit batches increase up to two lack and the validation batches up to five lack. After preparing the batches all their data & drugs use for stability & maintain the validation record and compile data for the technology transfer in production department for preparing the marketed size batches.

Keywords: batches, technique, preparation, scale up, validation

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Authors: Adrian J. Forca, Simeon J. Cainday III

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The Jordan Water District Interactive Billing and Accounting Information Systems is designed for Jordan Water District to uplift the efficiency and effectiveness of its services to its customers. It is designed to process computations of water bills in accurate and fast way through automating the manual process and ensures that correct rates and fees are applied. In addition to billing process, a mobile app will be integrated into it to support rapid and accurate water bill generation. An interactive feature will be incorporated to support electronic billing to customers who wish to receive water bills through the use of electronic mail. The system will also improve, organize and avoid data inaccuracy in accounting processes because data will be stored in a database which is designed logically correct through normalization. Furthermore, strict programming constraints will be plunged to validate account access privilege based on job function and data being stored and retrieved to ensure data security, reliability, and accuracy. The system will be able to cater the billing and accounting services of Jordan Water District resulting in setting forth the manual process and adapt to the modern technological innovations.

Keywords: accounting, bill, information system, interactive

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Authors: Ghazal Faraj, Andras Micsik

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The CIDOC Conceptual Reference Model (CRM) is an extensible ontology that provides integrated access to heterogeneous and digital datasets. The CIDOC-CRM offers a “semantic glue” intended to promote accessibility to several diverse and dispersed sources of cultural heritage data. That is achieved by providing a formal structure for the implicit and explicit concepts and their relationships in the cultural heritage field. The COURAGE (“Cultural Opposition – Understanding the CultuRal HeritAGE of Dissent in the Former Socialist Countries”) project aimed to explore methods about socialist-era cultural resistance during 1950-1990 and planned to serve as a basis for further narratives and digital humanities (DH) research. This project highlights the diversity of flourished alternative cultural scenes in Eastern Europe before 1989. Moreover, the dataset of COURAGE is an online RDF-based registry that consists of historical people, organizations, collections, and featured items. For increasing the inter-links between different datasets and retrieving more relevant data from various data silos, a shared federated ontology for reconciled data is needed. As a first step towards these goals, a full understanding of the CIDOC CRM ontology (target ontology), as well as the COURAGE dataset, was required to start the work. Subsequently, the queries toward the ontology were determined, and a table of equivalent properties from COURAGE and CIDOC CRM was created. The structural diagrams that clarify the mapping process and construct queries are on progress to map person, organization, and collection entities to the ontology. Through mapping the COURAGE dataset to CIDOC-CRM ontology, the dataset will have a common ontological foundation with several other datasets. Therefore, the expected results are: 1) retrieving more detailed data about existing entities, 2) retrieving new entities’ data, 3) aligning COURAGE dataset to a standard vocabulary, 4) running distributed SPARQL queries over several CIDOC-CRM datasets and testing the potentials of distributed query answering using SPARQL. The next plan is to map CIDOC-CRM to other upper-level ontologies or large datasets (e.g., DBpedia, Wikidata), and address similar questions on a wide variety of knowledge bases.

Keywords: CIDOC CRM, cultural heritage data, COURAGE dataset, ontology alignment

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Authors: Matodzi Rapalalani

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In context with Spencer's (2018) classic labelling theory, labels influence the perceptions of both the individual and other members of society. That is, once labelled, the individual act in ways that confirm the stereotypes attached to the label. This study, therefore, investigates the understanding of gendered labelling and its effects on Vhavenda women. Gender socialization and patriarchy have been viewed as the core causes of the problem. The literature presented the development of gendered labelling, forms of it, and other aspects. A qualitative method of data collection was used in this study, and semi-structural interviews were conducted. A total of 6 participants were used as it is easy to deal with a small sample. Thematic analysis was used as the data was interpreted and analyzed. Ethical issues such as confidentiality, informed consent, and voluntary participation were considered. Through the analysis and data interpretation, causes such as lack of Christian values, insecurities, and lust were mentioned as well as some of the effects such as frustrations, increased divorce, and low self-esteem.

Keywords: gender, naming, Venda, women, African culture

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Authors: Kathleen Cravero, Amanda Nace, Samantha Ski

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The Violence Against Children Surveys (VACS) is nationally representative surveys of male and female youth ages 13-24, designed to measure the burden of sexual, physical, and emotional violence experienced in childhood and adolescence. As of 2019, 24 countries implemented or are in the process of implementing a VACS, covering over ten percent of the world’s child population. Since the first article using VACS data from Swaziland was published in 2009, several peer-reviewed articles have been published on the VACS. However, no publications to date have analyzed the breadth of the work and analyzed how the data are represented in the peer-reviewed literature. In this study, we conducted a literature review of all peer-reviewed research that used VACS data or discussed the implementation and methodology of the VACS. The literature review revealed several important findings. Between 2009 and July 2019, thirty-five peer-reviewed articles using VACS data from 12 countries have been published. Twenty of the studies focus on one country, while 15 of the studies focus on two or more countries. Some countries are featured in the literature more than others, for example Kenya (N=14), Malawi (N=12), and Tanzania (N=12). A review of the research by gender demonstrates that research on violence against boys is under-represented. Only two studies specifically focused on boys/young men, while 11 studies focused only on violence against girls. This is despite research which suggests boys and girls experience similar rates of violence. A review of the publications by type of violence revealed significant differences in the types of violence being featured in the literature. Thirteen publications specifically focused on sexual violence, while three studies focused on physical violence, and only one study focused on emotional violence. Almost 70% of the peer-reviewed articles (24 of the 35) were first-authored by someone at the U.S. Centers for Disease Control and Prevention. There were very few first authors from VACS countries, which raises questions about who is leveraging the data and the extent to which capacities for data liberation are being developed within VACS countries. The VACS provide an unprecedented amount of information on the prevalence and past-year incidence of violence against children. Through a review of the peer-reviewed literature on the VACS we can begin to identify trends and gaps in how the data is being used as well as identify areas for further research.

Keywords: data to action, global health, implementation science, violence against children surveys

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Authors: Jana Abu Ahmada, Zaineb Mohamed, Ilyasse Aksikas

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The linear quadratic control system of hyperbolic first order partial differential equations (PDEs) are presented. The aim of this research is to control chemical reactions. This is achieved by converting the PDEs system to ordinary differential equations (ODEs) using the method of characteristics to reduce the system to control it by using the integral reinforcement learning. The designed controller is applied to a catalytic cracking reactor. Background—Transport-Reaction systems cover a large chemical and bio-chemical processes. They are best described by nonlinear PDEs derived from mass and energy balances. As a main application to be considered in this work is the catalytic cracking reactor. Indeed, the cracking reactor is widely used to convert high-boiling, high-molecular weight hydrocarbon fractions of petroleum crude oils into more valuable gasoline, olefinic gases, and others. On the other hand, control of PDEs systems is an important and rich area of research. One of the main control techniques is feedback control. This type of control utilizes information coming from the system to correct its trajectories and drive it to a desired state. Moreover, feedback control rejects disturbances and reduces the variation effects on the plant parameters. Linear-quadratic control is a feedback control since the developed optimal input is expressed as feedback on the system state to exponentially stabilize and drive a linear plant to the steady-state while minimizing a cost criterion. The integral reinforcement learning policy iteration technique is a strong method that solves the linear quadratic regulator problem for continuous-time systems online in real time, using only partial information about the system dynamics (i.e. the drift dynamics A of the system need not be known), and without requiring measurements of the state derivative. This is, in effect, a direct (i.e. no system identification procedure is employed) adaptive control scheme for partially unknown linear systems that converges to the optimal control solution. Contribution—The goal of this research is to Develop a characteristics-based optimal controller for a class of hyperbolic PDEs and apply the developed controller to a catalytic cracking reactor model. In the first part, developing an algorithm to control a class of hyperbolic PDEs system will be investigated. The method of characteristics will be employed to convert the PDEs system into a system of ODEs. Then, the control problem will be solved along the characteristic curves. The reinforcement technique is implemented to find the state-feedback matrix. In the other half, applying the developed algorithm to the important application of a catalytic cracking reactor. The main objective is to use the inlet fraction of gas oil as a manipulated variable to drive the process state towards desired trajectories. The outcome of this challenging research would yield the potential to provide a significant technological innovation for the gas industries since the catalytic cracking reactor is one of the most important conversion processes in petroleum refineries.

Keywords: PDEs, reinforcement iteration, method of characteristics, riccati equation, cracking reactor

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Authors: Stylianos Kampakis

Abstract:

This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

Procedia PDF Downloads 449