Search results for: real ranks

Authors: Danlei Yang, Luofeng Huang

Abstract:

The integration of digital twin technology with renewable energy systems offers an innovative approach to predicting and optimising performance throughout the entire lifecycle. A digital twin is a continuously updated virtual replica of a real-world entity, synchronised with data from its physical counterpart and environment. Many digital twin companies today claim to have mature digital twin products, but their focus is primarily on equipment visualisation. However, the core of a digital twin should be its model, which can mirror, shadow, and thread with the real-world entity, which is still underdeveloped. For a floating solar energy system, a digital twin model can be defined in three aspects: (a) the physical floating solar energy system along with environmental factors such as solar irradiance and wave dynamics, (b) a digital model powered by artificial intelligence (AI) algorithms, and (c) the integration of real system data with the AI-driven model and a user interface. The experimental setup for the floating solar energy system, is designed to replicate real-ocean conditions of floating solar installations within a controlled laboratory environment. The system consists of a water tank that simulates an aquatic surface, where a floating catamaran structure supports a solar panel. The solar simulator is set up in three positions: one directly above and two inclined at a 45° angle in front and behind the solar panel. This arrangement allows the simulation of different sun angles, such as sunrise, midday, and sunset. The solar simulator is positioned 400 mm away from the solar panel to maintain consistent solar irradiance on its surface. Stability for the floating structure is achieved through ropes attached to anchors at the bottom of the tank, which simulates the mooring systems used in real-world floating solar applications. The floating solar energy system's sensor setup includes various devices to monitor environmental and operational parameters. An irradiance sensor measures solar irradiance on the photovoltaic (PV) panel. Temperature sensors monitor ambient air and water temperatures, as well as the PV panel temperature. Wave gauges measure wave height, while load cells capture mooring force. Inclinometers and ultrasonic sensors record heave and pitch amplitudes of the floating system’s motions. An electric load measures the voltage and current output from the solar panel. All sensors collect data simultaneously. Artificial neural network (ANN) algorithms are central to developing the digital model, which processes historical and real-time data, identifies patterns, and predicts the system’s performance in real time. The data collected from various sensors are partly used to train the digital model, with the remaining data reserved for validation and testing. The digital twin model combines the experimental setup with the ANN model, enabling monitoring, analysis, and prediction of the floating solar energy system's operation. The digital model mirrors the functionality of the physical setup, running in sync with the experiment to provide real-time insights and predictions. It provides useful industrial benefits, such as informing maintenance plans as well as design and control strategies for optimal energy efficiency. In long term, this digital twin will help improve overall solar energy yield whilst minimising the operational costs and risks.

Keywords: digital twin, floating solar energy system, experiment setup, artificial intelligence

Procedia PDF Downloads 4

Authors: Vito M. Manghisi, Michele Gattullo, Alessandro Evangelista, Enricoandrea Laviola

Abstract:

In this work, we compared two solutions for displaying a demo version of an Augmented Reality (AR) manual when the real product is not available, opting to replace it with its computer-aided design (CAD) model. AR has been proved to be effective in maintenance and assembly operations by many studies in the literature. However, most of them present solutions for existing products, usually converting old, printed manuals into AR manuals. In this case, authoring consists of defining how to convey existing instructions through AR. It is not a simple choice, and demo versions are created to test the design goodness. However, this becomes impossible when the product is not physically available, as for new products. A solution could be creating an entirely virtual environment with the product and the instructions. However, in this way, user interaction is completely different from that in the real application, then it would be hard testing the usability of the AR manual. This work aims to propose and compare two different solutions for the displaying of a demo version of an AR manual to support authoring in case of a product that is not physically available. We used as a case study that of an innovative semi-hermetic compressor that has not yet been produced. The applications were developed for a handheld device, using Unity 3D. The main issue was how to show the compressor and attach instructions on it. In one approach, we used Vuforia natural feature tracking to attach a CAD model of the compressor to a 2D image that is a drawing in scale 1:1 of the top-view of the CAD model. In this way, during the AR manual demonstration, the 3D model of the compressor is displayed on the user's device in place of the real compressor, and all the virtual instructions are attached to it. In the other approach, we first created a support application that shows the CAD model of the compressor on a marker. Then, we registered a video of this application, moving around the marker, obtaining a video that shows the CAD model from every point of view. For the AR manual, we used the Vuforia model target (360° option) to track the CAD model of the compressor, as it was the real compressor. Then, during the demonstration, the video is shown on a fixed large screen, and instructions are displayed attached to it in the AR manual. The first solution presents the main drawback to keeping the printed image with everyone working on the authoring of the AR manual, but allows to show the product in a real scale and interaction during the demonstration is very simple. The second one does not need a printed marker during the demonstration but a screen. Still, the compressor model is resized, and interaction is awkward since the user has to play the video on the screen to rotate the compressor. The two solutions were evaluated together with the company, and the preferred was the first one due to a more natural interaction.

Keywords: augmented reality, human computer interaction, operating instructions, maintenance, assembly

Procedia PDF Downloads 126

Authors: Merve Kadioğlu, Nevin H. Şahin

Abstract:

Purpose: This research is organized to determine the influence of the web-based learning program. The program has been developed to gain information about normal delivery skill that is one of the topics of nursing students who take the woman health and illness. Material and Methods: The methodology of this study was applied as pre-test post-test single-group quasi-experimental. The pilot study consisted of 28 nursing student study groups who agreed to participate in the study. The findings were gathered via web-based technologies: student information form, information evaluation tests, Web Based Training Material Evaluation Scale and web-based learning environment feedback form. In the analysis of the data, the percentage, frequency and Wilcoxon Signed Ranks Test were used. The Web Based Instruction Program was developed in the light of full learning model, Mayer's research-based multimedia development principles and Gagne's Instructional Activities Model. Findings: The average scores of it was determined in accordance with the web-based educational material evaluation scale: ‘Instructional Suitability’ 4.45, ‘Suitability to Educational Program’ 4.48, ‘Visual Adequacy’ 4.53, ‘Programming Eligibility / Technical Adequacy’ 4.00. Also, the participants mentioned that the program is successful and useful. A significant difference was found between the pre-test and post-test results of the seven modules (p < 0.05). Results: According to pilot study data, the program was rated ‘very good’ by the study group. It was also found to be effective in increasing knowledge about normal labor.

Keywords: normal delivery, web-based learning, nursing students, e-learning

Procedia PDF Downloads 176

Authors: Risikat Oladoyin S. Dauda, Onyebuchi Iwegbu

Abstract:

This study investigates the response of human development outcomes to selected macroeconomic indicators in Nigeria. Human development outcomes is measured by human development index while the selected macroeconomic variables are inflation rate, real interest rate, government capital expenditure, real exchange rate, current account balance, and savings. Structural Vector Autoregression (SVAR) technique is employed in examining the response of human development index to the macroeconomic shocks. The result from the forecast error variance decomposition and Impulse-Response analysis reveals that fiscal policy (government capital expenditure) shock is the greatest determinant of human development outcomes. This result reiterates the role which the government plays in improving the welfare of the citizenry. The fiscal policy tool is pivotal in human development which comes in the form of investment in education, health, housing, and infrastructure. Further conclusion drawn from this study is that human development outcome positively and significantly responds to shocks from real interest rate, a monetary policy transmission variable and is felt greatly in the short run period. The policy implication of this study is that if capital budget implementation falls below expectations, human development will be engendered. Hence, efforts should be made to ensure that full implementation and appraisal of government capital expenditure is taken sacrosanct as any shock from such plan, engenders human development outcome.

Keywords: human development outcome, macroeconomic outcomes, structural vector autoregression, SVAR

Procedia PDF Downloads 150

Authors: Ionel Zagan, Vasile Gheorghita Gaitan, Adrian Brezulianu

Abstract:

This paper presents the contributions in designing a mobile system named Tele-ECG implemented for remote monitoring of cardiac patients. For a better flexibility of this application, the authors chose to implement a local memory and multiple communication interfaces. The project described in this presentation is based on the ARM Cortex M0+ microcontroller and the ADAS1000 dedicated chip necessary for the collection and transmission of Electrocardiogram signals (ECG) from the patient to the microcontroller, without altering the performances and the stability of the system. The novelty brought by this paper is the implementation of a remote monitoring system for cardiac patients, having a real-time behavior and multiple interfaces. The microcontroller is responsible for processing digital signals corresponding to ECG and also for the implementation of communication interface with the main server, using GSM/Bluetooth SIMCOM SIM800C module. This paper translates all the characteristics of the Tele-ECG project representing a feasible implementation in the biomedical field. Acknowledgment: This paper was supported by the project 'Development and integration of a mobile tele-electrocardiograph in the GreenCARDIO© system for patients monitoring and diagnosis - m-GreenCARDIO', Contract no. BG58/30.09.2016, PNCDI III, Bridge Grant 2016, using the infrastructure from the project 'Integrated Center for research, development and innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for fabrication and control', Contract No. 671/09.04.2015, Sectoral Operational Program for Increase of the Economic Competitiveness co-funded from the European Regional Development Fund.

Keywords: Tele-ECG, real-time cardiac monitoring, electrocardiogram, microcontroller

Procedia PDF Downloads 269

Authors: Gilberto Goracci, Fabio Curti

Abstract:

This work presents a robust, light, and inexpensive algorithm to perform autonomous orbit determination using onboard magnetometer data in real-time. Magnetometers are low-cost and reliable sensors typically available on a spacecraft for attitude determination purposes, thus representing an interesting choice to perform real-time orbit determination without the need to add additional sensors to the spacecraft itself. Magnetic field measurements can be exploited by Extended/Unscented Kalman Filters (EKF/UKF) for orbit determination purposes to make up for GPS outages, yielding errors of a few kilometers and tens of meters per second in the position and velocity of a spacecraft, respectively. While this level of accuracy shows that Kalman filtering represents a solid baseline for autonomous orbit determination, it is not enough to provide a reliable state estimation in the absence of GPS signals. This work combines the solidity and reliability of the EKF with the versatility of a Recurrent Neural Network (RNN) architecture to further increase the precision of the state estimation. Deep learning models, in fact, can grasp nonlinear relations between the inputs, in this case, the magnetometer data and the EKF state estimations, and the targets, namely the true position, and velocity of the spacecraft. The model has been pre-trained on Sun-Synchronous orbits (SSO) up to 2126 kilometers of altitude with different initial conditions and levels of noise to cover a wide range of possible real-case scenarios. The orbits have been propagated considering J2-level dynamics, and the geomagnetic field has been modeled using the International Geomagnetic Reference Field (IGRF) coefficients up to the 13th order. The training of the module can be completed offline using the expected orbit of the spacecraft to heavily reduce the onboard computational burden. Once the spacecraft is launched, the model can use the GPS signal, if available, to fine-tune the parameters on the actual orbit onboard in real-time and work autonomously during GPS outages. In this way, the provided module shows versatility, as it can be applied to any mission operating in SSO, but at the same time, the training is completed and eventually fine-tuned, on the specific orbit, increasing performances and reliability. The results provided by this study show an increase of one order of magnitude in the precision of state estimate with respect to the use of the EKF alone. Tests on simulated and real data will be shown.

Keywords: artificial intelligence, extended Kalman filter, orbit determination, magnetic field

Procedia PDF Downloads 103

Authors: B. S. Yadav, A. Mani, S. Srivastava

Abstract:

Chickpea (Cicer arietinum L.) is an annual, self-pollinating, diploid (2n = 2x = 16) pulse crop that ranks second in world legume production after common bean (Phaseolus vulgaris). ICC 4958 flowers approximately 39 days after sowing under peninsular Indian conditions and the crop matures in less than 90 days in rained environments. The estimated collective yield losses due to abiotic stresses (6.4 million t) have been significantly higher than for biotic stresses (4.8 million t). Most legumes are known to be salt sensitive, and therefore, it is becoming increasingly important to produce cultivars tolerant to high-salinity in addition to other abiotic and biotic stresses for sustainable chickpea production. Our aim was to identify the genes that are involved in the defence mechanism against heavy metal toxicity in chickpea and establish the biological network of heavy metal toxicity in chickpea. ICC4958 variety of chick pea was taken and grown in normal condition and 150µM concentration of different heavy metal salt like CdCl₂, K₂Cr2O₇, NaAsO₂. At 15th day leave samples were collected and stored in RNA Later solution microarray was performed for checking out differential gene expression pattern. Our studies revealed that 111 common genes that involved in defense mechanism were up regulated and 41 genes were commonly down regulated during treatment of 150µM concentration of CdCl₂, K₂Cr₂O₇, and NaAsO₂. Biological network study shows that the genes which are differentially expressed are highly connected and having high betweenness and centrality.

Keywords: abiotic stress, biological network, chickpea, microarray

Procedia PDF Downloads 194

Authors: Khalid Bashir, Hammad Zahid

Abstract:

Most of the investors consider that economic and financial information is the most important at the time of making investment decisions. But it is not true, as in the past two decades, the Behavioral aspects and the behavioral biases have gained an important place in the decision-making process of an investor. This study is basically conducted on this fact. The purpose of this study is to examine the impact of behavioral factors on the decision-making of the individual real estate investor in Pakistan. Some important behavioral factors like overconfidence, anchoring, gambler’s fallacy, home bias, loss aversion, regret aversion, mental accounting, herding and representativeness are used in this study to find their impact on the psychology of individual investors. The targeted population is the real estate investor of Pakistan, and a sample of 650 investors is selected on the basis of convenience sampling technique. The data is collected through the questionnaire with a response rate of 46.15 %. Descriptive statistical techniques and SEM are used to analyze the data by using statistical software. The results revealed the fact that some behavioral factors have a significant impact on the decision-making of investors. Among all the behavioral biases, overconfidence, anchoring, gambler’s fallacy, loss aversion and representativeness have a significant positive impact on the decision-making of the individual investor, while the rest of biases like home bias, regret aversion, mental accounting, herding have less impact on the decision-making process of an individual.

Keywords: behavioral finance, anchoring, gambler’s fallacy, loss aversion

Procedia PDF Downloads 69

Authors: Fulya Gokalp Yavuz, Mark Daniel Ward

Abstract:

This study focuses on a contemporary and inevitable topic of Data Science and its exemplary application for early career building: Big Data and Leaving Learning Community (LLC). ‘Academia’ and ‘Industry’ have a common sense on the importance of Big Data. However, both of them are in a threat of missing the training on this interdisciplinary area. Some traditional teaching doctrines are far away being effective on Data Science. Practitioners needs some intuition and real-life examples how to apply new methods to data in size of terabytes. We simply explain the scope of Data Science training and exemplified its early stage application with LLC, which is a National Science Foundation (NSF) founded project under the supervision of Prof. Ward since 2014. Essentially, we aim to give some intuition for professors, researchers and practitioners to combine data science tools for comprehensive real-life examples with the guides of mentees’ feedback. As a result of discussing mentoring methods and computational challenges of Big Data, we intend to underline its potential with some more realization.

Keywords: Big Data, computation, mentoring, training

Procedia PDF Downloads 361

Authors: Vishwambhar Prasad Sati

Abstract:

India’s economy is agriculture dominated. About 70% of the total population depends on practicing agriculture. Out of an estimated 140.3 million ha net cultivated area, 79.44 million ha (57%) is rain-fed, contributing 44% of the total food grain production. Meanwhile, India ranks second and shares 11.3% of the arable land of the world. It means that India has a high potential to harness agricultural resources for present and future food security. However, about 21.9% of people are living below the poverty line, and similarly, a large number of people are deprived or insecure about food. This situation is most critical in rural areas, where about 70% population lives. The study examines the present status, future trends, and challenges of food and agriculture in India. Time series data of the last three decades was gathered from secondary sources on area, production, and yield of crops; irrigated area; production of major crops; area, production, and yield of crops in the major food-producing states of India; food storage and poverty. The data were analyzed using descriptive statistics, correlation methods, and a regression model. State-level data on area, production, and yield of crops and irrigation facilities were indexed into levels, and the potentials of food production in the major food-producing states were observed. It was noted that the progressive growth rate of food production is higher than the population, which means that food is enough to feed the population; however, it is not accessible to all optimally because of wastage, leakage, lack of food storage, and proper distribution of food. If food is stored and distributed properly, there would not be any food shortage in India, the study revealed.

Keywords: agriculture, food production, population growth, poverty, future trends

Procedia PDF Downloads 99

Authors: R. Santos Alimi

Abstract:

Using time-series techniques, this study empirically tested the validity of existing theory which stipulates there is a nonlinear relationship between government size and economic growth; such that government spending is growth-enhancing at low levels but growth-retarding at high levels, with the optimal size occurring somewhere in between. This study employed three estimation equations. First, for the size of government, two measures are considered as follows: (i) share of total expenditures to gross domestic product, (ii) share of recurrent expenditures to gross domestic product. Second, the study adopted real GDP (without government expenditure component), as a variant measure of economic growth other than the real total GDP, in estimating the optimal level of government expenditure. The study is based on annual Nigeria country-level data for the period 1970 to 2012. Estimation results show that the inverted U-shaped curve exists for the two measures of government size and the estimated optimum shares are 19.81% and 10.98%, respectively. Finally, with the adoption of real GDP (without government expenditure component), the optimum government size was found to be 12.58% of GDP. Our analysis shows that the actual share of government spending on average (2000 - 2012) is about 13.4%.This study adds to the literature confirming that the optimal government size exists not only for developed economies but also for developing economy like Nigeria. Thus, a public intervention threshold level that fosters economic growth is a reality; beyond this point economic growth should be left in the hands of the private sector. This finding has a significant implication for the appraisal of government spending and budgetary policy design.

Keywords: public expenditure, economic growth, optimum level, fully modified OLS

Procedia PDF Downloads 420

Authors: Niloofar Zebarjad

Abstract:

This research aims at finding a tool to provide basketball players with real-time audio feedback on their shooting form in free throw shots. Free throws played a pivotal role in taking the lead in fierce competitions. The major problem in performing an accurate free throw seems to be improper training. Since the arm movement during the free throw shot is complex, the coach or the athlete might miss the movement details during practice. Hence, there is a necessity to create a system that measures arm movements' critical characteristics and control for improper kinematics. The proposed setup in this study quantifies arm kinematics and provides real-time feedback as an audio signal consisting of a gyroscope sensor. Spatial shoulder angle data are transmitted in a mobile application in real-time and can be saved and processed for statistical and analysis purposes. The proposed system is easy to use, inexpensive, portable, and real-time applicable. Objectives: This research aims to modify and control the free throw using audio feedback and determine if and to what extent the new setup reduces errors in arm formations during throws and finally assesses the successful throw rate. Methods: One group of elite basketball athletes and two novice athletes (control and study group) participated in this study. Each group contains 5 participants being studied in three separate sessions over a week. Results: Empirical results showed enhancements in the free throw shooting style, shot pocket (SP), and locked position (LP). The mean values of shoulder angle were controlled on 25° and 45° for SP and LP, respectively, recommended by valid FIBA references. Conclusion: Throughout the experiments, the system helped correct and control the shoulder angles toward the targeted pattern of shot pocket (SP) and locked position (LP). According to the desired results for arm motion, adding another sensor to measure and control the elbow angle is recommended.

Keywords: audio-feedback, basketball, free-throw, locked-position, motor-control, shot-pocket

Procedia PDF Downloads 293

Authors: Chiau Yuan Lim, Hayyiratul Fatimah Mohd Zaid, Fai Kait Chong

Abstract:

Deep Eutectic Solvent (DES) is used in various applications due to its simplicity in synthesis procedure, biodegradable, inexpensive and easily available chemical ingredients. Graphene Oxide is a popular catalyst that being used in various processes due to its stacking carbon sheets in layer which theoretically rapid up the catalytic processes. In this study, choline chloride based DESs were synthesized and ChCl-PEG(1:4) was found to be the most effective DES in performing desulfurization, which it is able to remove up to 47.4% of the sulfur content in the model oil in just 10 minutes, and up to 95% of sulfur content after repeat the process for six times. ChCl-PEG(1:4) able to perform up to 32.7% desulfurization on real diesel after 6 multiple stages. Thus, future research works should focus on removing the impurities on real diesel before utilising DESs in petroleum field.

Keywords: choline chloride, deep eutectic solvent, fuel desulfurization, graphene oxide

Procedia PDF Downloads 149

Authors: Danilo López, Edwin Rivas, Leyla López

Abstract:

This paper presents the development of an algorithm that predicts the arrival of a secondary user (SU) to a base station (BS) in a cognitive network based on infrastructure, requesting a Best Effort (BE) or Real Time (RT) type of service with a determined bandwidth (BW) implementing neural networks. The algorithm dynamically uses a neural network construction technique using the geometric pyramid topology and trains a Multilayer Perceptron Neural Networks (MLPNN) based on the historical arrival of an SU to estimate future applications. This will allow efficiently managing the information in the BS, since it precedes the arrival of the SUs in the stage of selection of the best channel in CRN. As a result, the software application determines the probability of arrival at a future time point and calculates the performance metrics to measure the effectiveness of the predictions made.

Keywords: cognitive radio, base station, best effort, MLPNN, prediction, real time

Procedia PDF Downloads 330

Authors: Bairam Khan, Premlata Vaishnava

Abstract:

The ability to conceptualize one entity in terms of another allows us to communicate through metaphors. This central feature of human cognition has evolved with the development of language, and the processing of metaphors is without any conscious appraisal and is quite effortless. South Asians, like other speech communities, have been using the kitchenary [culinary] metaphor in a very simple yet interesting way and are known for bringing into new and unique constellations wherever they are. This composite feature of our language is used to communicate in a precise and compact manner and maneuvers the expression. The present study explores the role of kitchenary metaphors in the making and shaping of idioms by applying Cognitive Metaphor Theories. Drawing on examples from a corpus of adverts, print, and electronic media, the study looks at the metaphorical language used by real people in real situations. The overarching theme throughout the course is that kitchenary metaphors are powerful tools of expression in Hindi-Urdu.

Keywords: cognitive metaphor theories, kitchenary metaphors, hindi-urdu print, and electronic media, grammatical structure of kitchenary metaphors of hindi-urdu

Procedia PDF Downloads 92

Authors: Tooba Aslam, Efthalia Chatzisymeon

Abstract:

Untreated Municipal Wastewater (MWW) is renowned as the utmost harmful pollution caused to environmental water due to the high presence of nutrients and organic contaminants. Removal of Chemical Oxygen Demand (COD) from synthetic as well as municipal wastewater is investigated by using acid mine drainage as a source of iron to initiate the solar photo-Fenton treatment of municipal wastewater. In this study, Acid Mine Drainage (AMD) and different minerals enriched in iron, such as goethite, hematite, magnetite, and magnesite, have been used as the source of iron to initiate the photo-Fenton process. Co-treatment of real municipal wastewater and acid mine drainage /minerals is widely examined. The effects of different parameters such as minerals recovery from AMD, AMD as a source of iron, H₂O₂ concentration, and COD concentrations on the COD percentage removal of the process are studied. The results show that, out of all the four minerals, only hematite (1g/L) could remove 30% of the pollutants at about 100 minutes and 1000 ppm of H₂O₂. The addition of AMD as a source of iron is performed and compared with both synthetic as well as real wastewater from South Africa under the same conditions, i.e., 1000 ppm of H₂O₂, ambient temperature, 2.8 pH, and solar simulator. In the case of synthetic wastewater, the maximum removal (56%) is achieved with 50 ppm of iron (AMD source) at 160 minutes. On the other hand, in real wastewater, the removal efficiency is 99% with 30 ppm of iron at 90 minutes and 96% with 50 ppm of iron at 120 minutes. In conclusion, overall, the co-treatment of AMD and MWW by solar photo-Fenton treatment appears to be an effective and promising method to remove organic materials from Municipal wastewater.

Keywords: municipal wastewater treatment, acid mine drainage, co-treatment, COD removal, solar photo-Fenton, circular economy

Procedia PDF Downloads 86

Authors: Ji-yang Yu, Dan Huang, Guo-ping Feng, Xin Li, Lu-yuan Wang

Abstract:

The navigation satellite network, especially the Beidou MEO Constellation, can relay data effectively with wide coverage and is applied in navigation, detection, and position widely. But the constellation has not been completed, and the amount of satellites on-board is not enough to cover the earth, which makes the data-relay disrupted or delayed in the transition process. The data-relay function needs to tolerant the delay or disruption in some extension, which make the Beidou MEO Constellation a delay/disruption-tolerant network (DTN). The traditional DTN designs mainly employ the relay table as the basic of data path schedule computing. But in practical application, especially in critical condition, such as the war-time or the infliction heavy losses on the constellation, parts of the nodes may become invalid, then the traditional DTN design could be useless. Furthermore, when transmitting the critical message in the navigation system, the maximum priority strategy is used, but the nodes still inquiry the relay table to design the path, which makes the delay more than minutes. Under this circumstances, it needs a function which could compute the optimum data path on-board in real-time according to the constellation states. The on-board critical message transmission design for navigation satellite delay/disruption-tolerant network (DTN) is proposed, according to the characteristics of navigation satellite network. With the real-time computation of parameters in the network link, the least-delay transition path is deduced to retransmit the critical message in urgent conditions. First, the DTN model for constellation is established based on the time-varying matrix (TVM) instead of the time-varying graph (TVG); then, the least transition delay data path is deduced with the parameters of the current node; at last, the critical message transits to the next best node. For the on-board real-time computing, the time delay and misjudges of constellation states in ground stations are eliminated, and the residual information channel for each node can be used flexibly. Compare with the minute’s delay of traditional DTN; the proposed transmits the critical message in seconds, which improves the re-transition efficiency. The hardware is implemented in FPGA based on the proposed model, and the tests prove the validity.

Keywords: critical message, DTN, navigation satellite, on-board, real-time

Procedia PDF Downloads 340

Authors: Bairam Khan, Premlata Vaishnava

Abstract:

The ability to conceptualize one entity in terms of another allows us to communicate through metaphors. This central feature of human cognition has evolved with the development of language, and the processing of metaphors is without any conscious appraisal and is quite effortless. South Asians, like other speech communities, have been using the kitchenary [culinary] metaphor in a very simple yet interesting way and are known for bringing into new and unique constellations wherever they are. This composite feature of our language is used to communicate in a precise and compact manner and maneuvers the expression. The present study explores the role of kitchenary metaphors in the making and shaping of idioms by applying Cognitive Metaphor Theories. Drawing on examples from a corpus of adverts, print, and electronic media, the study looks at the metaphorical language used by real people in real situations. The overarching theme throughout the course is that kitchenary metaphors are powerful tools of expression in Hindi-Urdu.

Keywords: cognitive metaphor theory, source domain, target domain, signifier- signified, kitchenary, ethnocultural elements of south asia and hindi- urdu language

Procedia PDF Downloads 76

Authors: Hossam M. Moftah, Aboul Ella Hassanien

Abstract:

In the recent years the importance of automatic traffic control has increased due to the traffic jams problem especially in big cities for signal control and efficient traffic management. Traffic counting as a kind of traffic control is important to know the road traffic density in real time. This paper presents a fast and robust traffic counting system using different image processing techniques. The proposed system is composed of the following four fundamental building phases: image acquisition, pre-processing, object detection, and finally counting the connected objects. The object detection phase is comprised of the following five steps: subtracting the background, converting the image to binary, closing gaps and connecting nearby blobs, image smoothing to remove noises and very small objects, and detecting the connected objects. Experimental results show the great success of the proposed approach.

Keywords: traffic counting, traffic management, image processing, object detection, computer vision

Procedia PDF Downloads 290

Authors: Sandy Van Vuuren, Lerato Kose, Annah Moteetee

Abstract:

Lesotho, a country surrounded by South Africa has one of the highest rates of sexually transmitted infections (STI’s) in the world. In fact, the country ranks third highest with respect to infections related to the human immunodeficiency virus (HIV). Despite the high prevalence of STI’s, treatment has been a challenge due to limited accessibility to health facilities. An estimated 77% of the population lives in rural areas and more than 60% of the country is mountainous. Therefore, many villages remain accessible only by foot or horse-back. Thus, the Basotho (indigenous people from Lesotho) have a rich cultural heritage of plant use. The aim of this study was to determine what plant species are used for the treatment of STI’s and which of these have in vitro efficacy against pathogens such as Candida albicans, Gardnerella vaginalis, Oligella ureolytica, and Neisseria gonorrhoeae. A total of 34 medicinal plants were reported by traditional practitioners for the treatment of STI’s. Sixty extracts, both aqueous and organic (mixture of methanol and dichloromethane), from 24 of the recorded plant species were assessed for antimicrobial activity using the minimum inhibition concentration (MIC) micro-titre plate dilution assay. Neisseria gonorrhoeae (ATCC 19424) was found to be the most susceptible among the test pathogens, with the majority of the extracts (21) displaying noteworthy activity (MIC values ≤ 1 mg/ml). Helichrysum caespititium was found to be the most antimicrobially active species (MIC value of 0.01 mg/ml). The results of this study support, to some extent, the traditional medicinal uses of the evaluated plants for the treatment of STI’s, particularly infections related to gonorrhoea.

Keywords: Africa, Candida albicans, Gardnerella vaginalis, Neisseria gonorrhoeae, Oligella urealytica

Procedia PDF Downloads 282

Authors: Matthieu-P. Schapranow

Abstract:

Recruitment of participants for clinical trials requires the screening of a big number of potential candidates, i.e. the testing for trial-specific inclusion and exclusion criteria, which is a time-consuming and complex task. Today, a significant amount of time is spent on identification of adequate trial participants as their selection may affect the overall study results. We introduce a unique patient eligibility metric, which allows systematic ranking and classification of candidates based on trial-specific filter criteria. Our web application enables real-time analysis of patient data and assessment of candidates using freely definable inclusion and exclusion criteria. As a result, the overall time required for identifying eligible candidates is tremendously reduced whilst additional degrees of freedom for evaluating the relevance of individual candidates are introduced by our contribution.

Keywords: in-memory technology, clinical trials, screening, eligibility metric, data analysis, clustering

Procedia PDF Downloads 492

Authors: Sinchan Ghosh, Fahad Al Basir, Santanu Ray, Sabyasachi Bhattacharya

Abstract:

The cooperatively breeding birds have two major ranks for the sexually mature birds. The breeders mate and produce offspring while the non-breeding helpers increase the chick production rate through help in mate-finding and allo-parenting. However, the chicks also cooperate to raise their younger siblings through warming, defending and food sharing. Although, the existing literatures describes the evolution of allo-parenting in birds but do not differentiate the significance of allo-parenting in sexually immature and mature helpers separately. This study addresses the significance of both immature and mature helpers’ contribution to the total sustainable bird population in a breeding site using Blue-tailed bee-eater as a test-bed species. To serve this purpose, a mathematical model has been built considering each social status and chicks as separate but interactive compartments. Also, to observe the dynamics of each social status with changing prey abundance, a prey population has been introduced as an additional compartment. The model was analyzed for stability condition and was validated using field-data. A simulation experiment was then performed to observe the change in equilibria with a varying helping rate from both the helpers. The result from the simulation experiment suggest that the cooperative breeding population changes its population sizes significantly with a change in helping rate from the sexually immature helpers. On the other hand, the mature helpers do not contribute to the stability of the population equilibrium as much as the immature helpers.

Keywords: Blue-tailed bee eater, Altruism, Mathematical Ethology, Behavioural modelling

Procedia PDF Downloads 160

Authors: Kien Vu, Jien Min Soh, Mohamed Jahangir Abubacker, Piyawut Pattamanon, Soojin Lee, Suvro Banerjee

Abstract:

Recent advances in Generative Artificial Intelligence (GenAI), in particular Large Language Models (LLMs) have shown promise to disrupt multiple industries at scale. However, LLMs also present unique challenges, notably, these so-called "hallucination" which is the generation of outputs that are not grounded in the input data that hinders its adoption into production. Common practice to mitigate hallucination problem is utilizing Retrieval Agmented Generation (RAG) system to ground LLMs'response to ground truth. RAG converts the grounding documents into embeddings, retrieve the relevant parts with vector similarity between user's query and documents, then generates a response that is not only based on its pre-trained knowledge but also on the specific information from the retrieved documents. However, the RAG system is not suitable for tabular data and subsequent data analysis tasks due to multiple reasons such as information loss, data format, and retrieval mechanism. In this study, we have explored a novel methodology that combines planning-and-execution and code generation agents to enhance LLMs' data analysis capabilities. The approach enables LLMs to autonomously dissect a complex analytical task into simpler sub-tasks and requirements, then convert them into executable segments of code. In the final step, it generates the complete response from output of the executed code. When deployed beta version on DataSense, the property insight tool of PropertyGuru, the approach yielded promising results, as it was able to provide market insights and data visualization needs with high accuracy and extensive coverage by abstracting the complexities for real-estate agents and developers from non-programming background. In essence, the methodology not only refines the analytical process but also serves as a strategic tool for real estate professionals, aiding in market understanding and enhancement without the need for programming skills. The implication extends beyond immediate analytics, paving the way for a new era in the real estate industry characterized by efficiency and advanced data utilization.

Keywords: large language model, reasoning, planning and execution, code generation, natural language processing, prompt engineering, data analysis, real estate, data sense, PropertyGuru

Procedia PDF Downloads 86

Authors: Morteza Ahmadpur, Ilgin Gokasar, Saman Ghaffarian

Abstract:

That Transportation system plays an important role in management of societies is an undeniable fact and it is one of the most challenging issues in human beings routine life. But by increasing the population in urban areas, the demand for transportation modes also increase. Accordingly, it is obvious that more flexible and dynamic transportation system is required to satisfy peoples’ requirements. Nowadays, there is significant increase in number of environmental issues all over the world which is because of human activities. New technological achievements bring new horizons for humans and so they changed the life style of humans in every aspect of their life and transportation is not an exception. By using new technology, societies can modernize their transportation system and increase the feasibility of their system. Real–time Taxi sharing systems is one of the novel and most modern systems all over the world. For establishing this kind of system in an urban area it is required to use the most advanced technologies in a transportation system. GPS navigation devices, computers and social networks are just some parts of this kind of system. Like carpooling, real-time taxi sharing is one of the best ways to better utilize the empty seats in most cars and taxis, thus decreasing energy consumption and transport costs. It can serve areas not covered by a public transit system and act as a transit feeder service. Taxi sharing is also capable of serving one-time trips, not only recurrent commute trips or scheduled trips. In this study, we describe the requirements and parameters that we need to establish a useful real-time ride sharing system for an urban area. The parameters and requirements of this study can be used in any urban area.

Keywords: transportation, intelligent transportation systems, ride-sharing, taxi sharing

Procedia PDF Downloads 427

Authors: Cristian Pauna

Abstract:

Trading the financial markets is a widespread activity today. A large number of investors, companies, public of private funds are buying and selling every day in order to make profit. Algorithmic trading is the prevalent method to make the trade decisions after the electronic trading release. The orders are sent almost instantly by computers using mathematical models. This paper will present a price prediction methodology based on a multi-dimensional neural network. Using the Fisher transform, the neural network will be instructed for a low-latency auto-adaptive process in order to predict the price evolution for the next period of time. The model is designed especially for algorithmic trading and uses the real-time price series. It was found that the characteristics of the Fisher function applied at the nodes scale level can generate reliable trading signals using the neural network methodology. After real time tests it was found that this method can be applied in any timeframe to trade the financial markets. The paper will also include the steps to implement the presented methodology into an automated trading system. Real trading results will be displayed and analyzed in order to qualify the model. As conclusion, the compared results will reveal that the neural network methodology applied together with the Fisher transform at the nodes level can generate a good price prediction and can build reliable trading signals for algorithmic trading.

Keywords: algorithmic trading, automated trading systems, financial markets, high-frequency trading, neural network

Procedia PDF Downloads 160

Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

Abstract:

Oil spills from tankers can cause significant harm to the environment and local communities, as well as have economic consequences. Early predictions of oil spills can help to minimize these impacts. Our proposed system uses machine learning and neural networks to predict potential oil spills by monitoring data from ship Automatic Identification Systems (AIS). The model analyzes ship movements, speeds, and changes in direction to identify patterns that deviate from the norm and could indicate a potential spill. Our approach not only identifies anomalies but also predicts spills before they occur, providing early detection and mitigation measures. This can prevent or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model's performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real-world oil spills, highlighting areas for improvement and further research.

Keywords: Anomaly Detection, Oil Spill Prediction, Machine Learning, Image Processing, Graph Neural Network (GNN)

Procedia PDF Downloads 72

Authors: Suvarna Kumar Gogula, Sandhya Devi Gogula, P. Chanakya

Abstract:

Augmented reality is defined as the collection of the digital (or) computer generated information like images, audio, video, 3d models, etc. and overlay them over the real time environment. Augmented reality can be thought as a blend between completely synthetic and completely real. Augmented reality provides scope in a wide range of industries like manufacturing, retail, gaming, advertisement, tourism, etc. and brings out new dimensions in the modern digital world. As it overlays the content, it makes the users enhance the knowledge by providing the content blended with real world. In this application, we integrated augmented reality with data analytics and integrated with cloud so the virtual content will be generated on the basis of the data present in the database and we used marker based augmented reality where every marker will be stored in the database with corresponding unique ID. This application can be used in wide range of industries for different business processes, but in this paper, we mainly focus on the marketing industry which helps the customer in gaining the knowledge about the products in the market which mainly focus on their prices, customer feedback, quality, and other benefits. This application also focuses on providing better market strategy information for marketing managers who obtain the data about the stocks, sales, customer response about the product, etc. In this paper, we also included the reports from the feedback got from different people after the demonstration, and finally, we presented the future scope of Augmented Reality in different business processes by integrating with new technologies like cloud, big data, artificial intelligence, etc.

Keywords: augmented reality, data analytics, catch room, marketing and sales

Procedia PDF Downloads 236

Authors: Timothy L. Porter, T. Randy Dillingham

Abstract:

Vegetation growth and decomposition, along with soil microbial activity play a complex role in the production of greenhouse gases originating in forest soils. The absorption or emission (respiration) of these gases is a function of many factors relating to the soils themselves, the plants, and the environment in which the plants are growing. For this study, we have constructed a battery-powered, portable field mass spectrometer for use in analyzing gases in the soils surrounding trees, plants, and other areas. We have used the instrument to sample in real-time the greenhouse gases carbon dioxide and methane in soils where plant life may be contributing to the production of gases such as methane. Gases such as isoprene, which may help correlate gas respiration to microbial activity have also been measured. The instrument is composed of a quadrupole mass spectrometer with part per billion or better sensitivity, coupled to battery-powered turbo and diaphragm pumps. A unique ambient air pressure differentially pumped intake apparatus allows for the real-time sampling of gases in the soils from the surface to several inches below the surface. Results show that this instrument is capable of instant, part-per-billion sensitivity measurement of carbon dioxide and methane in the near surface region of various forest soils. We have measured differences in soil respiration resulting from forest thinning, forest burning, and forest logging as compared to pristine, untouched forests. Further studies will include measurements of greenhouse gas respiration as a function of temperature, microbial activity as measured by isoprene production, and forest restoration after fire.

Keywords: forest, soil, greenhouse, quadrupole

Procedia PDF Downloads 114

Authors: Christian Böning, Henrik Prinzhorn, Eric C. Hund, Malte Stonis

Abstract:

In this article, the flexible job-shop scheduling problem is extended by consideration of energy costs which arise owing to the power peak, and further decision variables such as work in process and throughput time are incorporated into the objective function. This enables a production plan to be simultaneously optimized in respect of the real arising energy and logistics costs. The energy-costs-aware flexible job-shop scheduling problem (EFJSP) which arises is described mathematically, and a memetic algorithm (MA) is presented as a solution. In the MA, the evolutionary process is supplemented with a local search. Furthermore, repair procedures are used in order to rectify any infeasible solutions that have arisen in the evolutionary process. The potential for lowering the real arising costs of a production plan through consideration of energy consumption levels is highlighted.

Keywords: energy costs, flexible job-shop scheduling, memetic algorithm, power peak

Procedia PDF Downloads 343

Authors: Veronica Diaz Quezada

Abstract:

The society of the 21st century requires training of engineers capable of solving routine and non-routine problems in applications of the limit of real functions, as part of the course Calculus I. For this purpose, research was conducted with a methodological design that combines quantitative and qualitative procedures and that aims, to identify and to characterize the types of problems according to their nature and context, through the application of a mathematics test; to know— through a questionnaire— the opinion of difficulties in their solution, previous and missing knowledge of some students of three engineering careers of a state university in Chile. This research is completed with three case studies. The results favor the performance of students in solving problems of a fantasist and realistic context, but these do not guarantee mathematical skills which are necessary to solve non-routine problems of limit applications. In conclusion, through this research, it became clear that the students of the three engineerings do not have all the necessary skills to solve problems of application of the limit of a function of the real variable.

Keywords: case studies, engineering program, limits, problem solving

Procedia PDF Downloads 127