Search results for: methane capture
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1507

Search results for: methane capture

667 MXene Quantum Dots Decorated Double-Shelled Ceo₂ Hollow Spheres for Efficient Electrocatalytic Nitrogen Oxidation

Authors: Quan Li, Dongcai Shen, Zhengting Xiao, Xin Liu Mingrui Wu, Licheng Liu, Qin Li, Xianguo Li, Wentai Wang

Abstract:

Direct electrocatalytic nitrogen oxidation (NOR) provides a promising alternative strategy for synthesizing high-value-added nitric acid from widespread N₂, which overcomes the disadvantages of the Haber-Bosch-Ostwald process. However, the NOR process suffers from the limitation of high N≡N bonding energy (941 kJ mol− ¹), sluggish kinetics, low efficiency and yield. It is a prerequisite to develop more efficient electrocatalysts for NOR. Herein, we synthesized double-shelled CeO₂ hollow spheres (D-CeO₂) and further modified with Ti₃C₂ MXene quantum dots (MQDs) for electrocatalytic N₂ oxidation, which exhibited a NO₃− yield of 71.25 μg h− ¹ mgcat− ¹ and FE of 31.80% at 1.7 V. The unique quantum size effect and abundant edge active sites lead to a more effective capture of nitrogen. Moreover, the double-shelled hollow structure is favorable for N₂ fixation and gathers intermediate products in the interlayer of the core-shell. The in-situ infrared Fourier transform spectroscopy confirmed the formation of *NO and NO₃− species during the NOR reaction, and the kinetics and possible pathways of NOR were calculated by density functional theory (DFT). In addition, a Zn-N₂ reaction device was assembled with D-CeO₂/MQDs as anode and Zn plate as cathode, obtaining an extremely high NO₃− yield of 104.57 μg h− ¹ mgcat− ¹ at 1 mA cm− ².

Keywords: electrocatalytic N₂ oxidation, nitrate production, CeO₂, MXene quantum dots, double-shelled hollow spheres

Procedia PDF Downloads 46
666 Signal Amplification Using Graphene Oxide in Label Free Biosensor for Pathogen Detection

Authors: Agampodi Promoda Perera, Yong Shin, Mi Kyoung Park

Abstract:

The successful detection of pathogenic bacteria in blood provides important information for early detection, diagnosis and the prevention and treatment of infectious diseases. Silicon microring resonators are refractive-index-based optical biosensors that provide highly sensitive, label-free, real-time multiplexed detection of biomolecules. We demonstrate the technique of using GO (graphene oxide) to enhance the signal output of the silicon microring optical sensor. The activated carboxylic groups in GO molecules bind directly to single stranded DNA with an amino modified 5’ end. This conjugation amplifies the shift in resonant wavelength in a real-time manner. We designed a capture probe for strain Staphylococcus aureus of 21 bp and a longer complementary target sequence of 70 bp. The mismatched target sequence we used was of Streptococcus agalactiae of 70 bp. GO is added after the complementary binding of the probe and target. GO conjugates to the unbound single stranded segment of the target and increase the wavelength shift on the silicon microring resonator. Furthermore, our results show that GO could successfully differentiate between the mismatched DNA sequences from the complementary DNA sequence. Therefore, the proposed concept could effectively enhance sensitivity of pathogen detection sensors.

Keywords: label free biosensor, pathogenic bacteria, graphene oxide, diagnosis

Procedia PDF Downloads 446
665 A Transformer-Based Approach for Multi-Human 3D Pose Estimation Using Color and Depth Images

Authors: Qiang Wang, Hongyang Yu

Abstract:

Multi-human 3D pose estimation is a challenging task in computer vision, which aims to recover the 3D joint locations of multiple people from multi-view images. In contrast to traditional methods, which typically only use color (RGB) images as input, our approach utilizes both color and depth (D) information contained in RGB-D images. We also employ a transformer-based model as the backbone of our approach, which is able to capture long-range dependencies and has been shown to perform well on various sequence modeling tasks. Our method is trained and tested on the Carnegie Mellon University (CMU) Panoptic dataset, which contains a diverse set of indoor and outdoor scenes with multiple people in varying poses and clothing. We evaluate the performance of our model on the standard 3D pose estimation metrics of mean per-joint position error (MPJPE). Our results show that the transformer-based approach outperforms traditional methods and achieves competitive results on the CMU Panoptic dataset. We also perform an ablation study to understand the impact of different design choices on the overall performance of the model. In summary, our work demonstrates the effectiveness of using a transformer-based approach with RGB-D images for multi-human 3D pose estimation and has potential applications in real-world scenarios such as human-computer interaction, robotics, and augmented reality.

Keywords: multi-human 3D pose estimation, RGB-D images, transformer, 3D joint locations

Procedia PDF Downloads 62
664 Analysis of the Internal Mechanical Conditions in the Lower Limb Due to External Loads

Authors: Kent Salomonsson, Xuefang Zhao, Sara Kallin

Abstract:

Human soft tissue is loaded and deformed by any activity, an effect known as a stress-strain relationship, and is often described by a load and tissue elongation curve. Several advances have been made in the fields of biology and mechanics of soft human tissue. However, there is limited information available on in vivo tissue mechanical characteristics and behavior. Confident mechanical properties of human soft tissue cannot be extrapolated from e.g. animal testing. Thus, there is need for non invasive methods to analyze mechanical characteristics of soft human tissue. In the present study, the internal mechanical conditions of the lower limb, which is subject to an external load, is studied by use of the finite element method. A detailed finite element model of the lower limb is made possible by use of MRI scans. Skin, fat, bones, fascia and muscles are represented separately and the material properties for them are obtained from literature. Previous studies have been shown to address macroscopic deformation features, e.g. indentation depth, to a large extent. However, the detail in which the internal anatomical features have been modeled does not reveal the critical internal strains that may induce hypoxia and/or eventual tissue damage. The results of the present study reveals that lumped material models, i.e. averaging of the material properties for the different constituents, does not capture regions of critical strains in contrast to more detailed models.

Keywords: FEM, tissue, indentation, properties

Procedia PDF Downloads 344
663 Machine Learning Based Approach for Measuring Promotion Effectiveness in Multiple Parallel Promotions’ Scenarios

Authors: Revoti Prasad Bora, Nikita Katyal

Abstract:

Promotion is a key element in the retail business. Thus, analysis of promotions to quantify their effectiveness in terms of Revenue and/or Margin is an essential activity in the retail industry. However, measuring the sales/revenue uplift is based on estimations, as the actual sales/revenue without the promotion is not present. Further, the presence of Halo and Cannibalization in a multiple parallel promotions’ scenario complicates the problem. Calculating Baseline by considering inter-brand/competitor items or using Halo and Cannibalization's impact on Revenue calculations by considering Baseline as an interpretation of items’ unit sales in neighboring nonpromotional weeks individually may not capture the overall Revenue uplift in the case of multiple parallel promotions. Hence, this paper proposes a Machine Learning based method for calculating the Revenue uplift by considering the Halo and Cannibalization impact on the Baseline and the Revenue. In the first section of the proposed methodology, Baseline of an item is calculated by incorporating the impact of the promotions on its related items. In the later section, the Revenue of an item is calculated by considering both Halo and Cannibalization impacts. Hence, this methodology enables correct calculation of the overall Revenue uplift due a given promotion.

Keywords: Halo, Cannibalization, promotion, Baseline, temporary price reduction, retail, elasticity, cross price elasticity, machine learning, random forest, linear regression

Procedia PDF Downloads 157
662 Political Behavior and Democratic Values: Framing Analysis of Political Discussion Programs in Pakistan

Authors: Umair Nadeem, Sidra Umair

Abstract:

Political behavior of voters and democratic values have been observed an emerging phenomenon in recent years in Pakistan. Privatized TV news channels are taking one sided position on the political issues, corresponding with respective political parties. Since last decade, TV News Channels have undermined this monopoly. Elections 2013 were unique in Pakistan with reference to political behavior and democratic values. Partisan narratives and counter narratives have been witnessed on different TV channels, in last few years. These mediated events seem very important to study the political behavior and democratic values as the country is approaching towards elections 2018. This endeavor is an attempt to capture the framing of the parties, issues in the partisan media culture and framing effects on political behavior of voters. Data for this research come from two data set. Content analysis of selected representative talks shows broadcast on mainstream news channels provide an assessment of the framing while quantitative survey of the discussion program’s viewers from Lahore city provide an evidence of framing effects on political behavior on voters and on democratic values. Regression results help us to argue that the highly partisan shows are strong predictors of polarized views among the audience. Study also grasp the attention of scholars towards the implications of this phenomenon.

Keywords: democratic values, partisan media, polarized views, political behavior

Procedia PDF Downloads 160
661 Interaction between Breathiness and Nasality: An Acoustic Analysis

Authors: Pamir Gogoi, Ratree Wayland

Abstract:

This study investigates the acoustic measures of breathiness when coarticulated with nasality. The acoustic correlates of breathiness and nasality that has already been well established after years of empirical research. Some of these acoustic parameters - like low frequency peaks and wider bandwidths- are common for both nasal and breathy voice. Therefore, it is likely that these parameters interact when a sound is coarticulated with breathiness and nasality. This leads to the hypothesis that the acoustic parameters, which usually act as robust cues in differentiating between breathy and modal voice, might not be reliable cues for differentiating between breathy and modal voice when breathiness is coarticulated with nasality. The effect of nasality on the perception of breathiness has been explored in earlier studies using synthesized speech. The results showed that perceptually, nasality and breathiness do interact. The current study investigates if a similar pattern is observed in natural speech. The study is conducted on Marathi, an Indo-Aryan language which has a three-way contrast between nasality and breathiness. That is, there is a phonemic distinction between nasals, breathy voice and breathy-nasals. Voice quality parameters like – H1-H2 (Difference between the amplitude of first and second harmonic), H1-A3 (Difference between the amplitude of first harmonic and third formant, CPP (Cepstral Peak Prominence), HNR (Harmonics to Noise ratio) and B1 (Bandwidth of first formant) were extracted. Statistical models like linear mixed effects regression and Random Forest classifiers show that measures that capture the noise component in the signal- like CPP and HNR- can classify breathy voice from modal voice better than spectral measures when breathy voice is coarticulated with nasality.

Keywords: breathiness, marathi, nasality, voice quality

Procedia PDF Downloads 74
660 Empirical Roughness Progression Models of Heavy Duty Rural Pavements

Authors: Nahla H. Alaswadko, Rayya A. Hassan, Bayar N. Mohammed

Abstract:

Empirical deterministic models have been developed to predict roughness progression of heavy duty spray sealed pavements for a dataset representing rural arterial roads. The dataset provides a good representation of the relevant network and covers a wide range of operating and environmental conditions. A sample with a large size of historical time series data for many pavement sections has been collected and prepared for use in multilevel regression analysis. The modelling parameters include road roughness as performance parameter and traffic loading, time, initial pavement strength, reactivity level of subgrade soil, climate condition, and condition of drainage system as predictor parameters. The purpose of this paper is to report the approaches adopted for models development and validation. The study presents multilevel models that can account for the correlation among time series data of the same section and to capture the effect of unobserved variables. Study results show that the models fit the data very well. The contribution and significance of relevant influencing factors in predicting roughness progression are presented and explained. The paper concludes that the analysis approach used for developing the models confirmed their accuracy and reliability by well-fitting to the validation data.

Keywords: roughness progression, empirical model, pavement performance, heavy duty pavement

Procedia PDF Downloads 158
659 Vision-Based Daily Routine Recognition for Healthcare with Transfer Learning

Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

Abstract:

We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

Procedia PDF Downloads 119
658 Modern Scotland Yard: Improving Surveillance Policies Using Adversarial Agent-Based Modelling and Reinforcement Learning

Authors: Olaf Visker, Arnout De Vries, Lambert Schomaker

Abstract:

Predictive policing refers to the usage of analytical techniques to identify potential criminal activity. It has been widely implemented by various police departments. Being a relatively new area of research, there are, to the author’s knowledge, no absolute tried, and true methods and they still exhibit a variety of potential problems. One of those problems is closely related to the lack of understanding of how acting on these prediction influence crime itself. The goal of law enforcement is ultimately crime reduction. As such, a policy needs to be established that best facilitates this goal. This research aims to find such a policy by using adversarial agent-based modeling in combination with modern reinforcement learning techniques. It is presented here that a baseline model for both law enforcement and criminal agents and compare their performance to their respective reinforcement models. The experiments show that our smart law enforcement model is capable of reducing crime by making more deliberate choices regarding the locations of potential criminal activity. Furthermore, it is shown that the smart criminal model presents behavior consistent with popular crime theories and outperforms the baseline model in terms of crimes committed and time to capture. It does, however, still suffer from the difficulties of capturing long term rewards and learning how to handle multiple opposing goals.

Keywords: adversarial, agent based modelling, predictive policing, reinforcement learning

Procedia PDF Downloads 136
657 Design of a Computer Vision Based Exercise Video Game for Senior Citizens

Authors: June Tay, Ivy Chia

Abstract:

There are numerous changes, both mental and physical, taking place when people age. We need to understand the different aspects required for healthy living, including meeting nutritional needs, regular physical activities to keep agility, sufficient rest and sleep to have physical and mental well-being, social engagement to avoid the risk of social isolation and depression, and access to healthcare to detect and manage chronic conditions. Promoting physical activities for an ageing population is necessary as many may have enjoyed sedentary lifestyles for some time. In our study, we evaluate the considerations when designing a computer vision video game for the elderly. We need to design some low-impact activities, such as stretching and gentle movements, because some elderly individuals may have joint pains or mobility issues. The exercise game should consist of simple movements that are easy to follow and remember. It should be fun and enjoyable so that they can be motivated to do some exercise. Social engagement can keep the elderly motivated and competitive, and they are more willing to engage in game exercises. Elderly citizens can compare their game scores and try to improve them. We propose a computer vision-based video game for the elderly that will capture and track the movement of the elderly hand pushing a ball on the screen into a circle. It can be easily set up using a PC laptop with a webcam. Our video game adhered to the design framework we employed, and it encompassed ease of use, a simple graphical interface, easy-to-play game exercise, and fun gameplay.

Keywords: about computer vision, video games, gerontology technology, caregiving

Procedia PDF Downloads 55
656 Green Synthesis of Magnetic, Silica Nanocomposite and Its Adsorptive Performance against Organochlorine Pesticides

Authors: Waleed A. El-Said, Dina M. Fouad, Mohamed H. Aly, Mohamed A. El-Gahami

Abstract:

Green synthesis of nanomaterials has received increasing attention as an eco-friendly technology in materials science. Here, we have used two types of extractions from green tea leaf (i.e. total extraction and tannin extraction) as reducing agents for a rapid, simple and one step synthesis method of mesoporous silica nanoparticles (MSNPs)/iron oxide (Fe3O4) nanocomposite based on deposition of Fe3O4 onto MSNPs. MSNPs/Fe3O4 nanocomposite were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray, vibrating sample magnetometer, N2 adsorption, and high-resolution transmission electron microscopy. The average mesoporous silica particle diameter was found to be around 30 nm with high surface area (818 m2/gm). MSNPs/Fe3O4 nanocomposite was used for removing lindane pesticide (an environmental hazard material) from aqueous solutions. Fourier transform infrared, UV-vis, High-performance liquid chromatography and gas chromatography techniques were used to confirm the high ability of MSNPs/Fe3O4 nanocomposite for sensing and capture of lindane molecules with high sorption capacity (more than 89%) that could develop a new eco-friendly strategy for detection and removing of pesticide and as a promising material for water treatment application.

Keywords: green synthesis, mesoporous silica, magnetic iron oxide NPs, adsorption Lindane

Procedia PDF Downloads 418
655 Evaluation of Sloshing in Process Equipment for Floating Cryogenic Application

Authors: Bo Jin

Abstract:

A variety of process equipment having flow in and out is widely used in industrial land-based cryogenic facilities. In some of this equipment, such as vapor-liquid separator, a liquid level is established during the steady operation. As the implementation of such industrial processes extends to off-shore floating facilities, it is important to investigate the effect of sea motion on the process equipment partially filled with liquid. One important aspect to consider is the occurrence of sloshing therein. The flow characteristics are different from the classical study of sloshing, where the fluid is enclosed inside a vessel (e.g., storage tank) with no flow in or out. Liquid inside process equipment continuously flows in and out of the system. To understand this key difference, a Computational Fluid Dynamics (CFD) model is developed to simulate the liquid motion inside a partially filled cylinder with and without continuous flow in and out. For a partially filled vertical cylinder without any continuous flow in and out, the CFD model is found to be able to capture the well-known sloshing behavior documented in the literature. For the cylinder with a continuous steady flow in and out, the CFD simulation results demonstrate that the continuous flow suppresses sloshing. Given typical cryogenic fluid has very low viscosity, an analysis based on potential flow theory is developed to explain why flow into and out of the cylinder changes the natural frequency of the system and thereby suppresses sloshing. This analysis further validates the CFD results.

Keywords: computational fluid dynamics, CFD, cryogenic process equipment, off-shore floating processes, sloshing

Procedia PDF Downloads 116
654 Gis Database Creation for Impacts of Domestic Wastewater Disposal on BIDA Town, Niger State Nigeria

Authors: Ejiobih Hyginus Chidozie

Abstract:

Geographic Information System (GIS) is a configuration of computer hardware and software specifically designed to effectively capture, store, update, manipulate, analyse and display and display all forms of spatially referenced information. GIS database is referred to as the heart of GIS. It has location data, attribute data and spatial relationship between the objects and their attributes. Sewage and wastewater management have assumed increased importance lately as a result of general concern expressed worldwide about the problems of pollution of the environment contamination of the atmosphere, rivers, lakes, oceans and ground water. In this research GIS database was created to study the impacts of domestic wastewater disposal methods on Bida town, Niger State as a model for investigating similar impacts on other cities in Nigeria. Results from GIS database are very useful to decision makers and researchers. Bida Town was subdivided into four regions, eight zones, and 24 sectors based on the prevailing natural morphology of the town. GIS receiver and structured questionnaire were used to collect information and attribute data from 240 households of the study area. Domestic wastewater samples were collected from twenty four sectors of the study area for laboratory analysis. ArcView 3.2a GIS software, was used to create the GIS databases for ecological, health and socioeconomic impacts of domestic wastewater disposal methods in Bida town.

Keywords: environment, GIS, pollution, software, wastewater

Procedia PDF Downloads 405
653 A Coupled Extended-Finite-Discrete Element Method: On the Different Contact Schemes between Continua and Discontinua

Authors: Shervin Khazaeli, Shahab Haj-zamani

Abstract:

Recently, advanced geotechnical engineering problems related to soil movement, particle loss, and modeling of local failure (i.e. discontinua) as well as modeling the in-contact structures (i.e. continua) are of the great interest among researchers. The aim of this research is to meet the requirements with respect to the modeling of the above-mentioned two different domains simultaneously. To this end, a coupled numerical method is introduced based on Discrete Element Method (DEM) and eXtended-Finite Element Method (X-FEM). In the coupled procedure, DEM is employed to capture the interactions and relative movements of soil particles as discontinua, while X-FEM is utilized to model in-contact structures as continua, which may consist of different types of discontinuities. For verification purposes, the new coupled approach is utilized to examine benchmark problems including different contacts between/within continua and discontinua. Results are validated by comparison with those of existing analytical and numerical solutions. This study proves that extended-finite-discrete element method can be used to robustly analyze not only contact problems, but also other types of discontinuities in continua such as (i) crack formations and propagations, (ii) voids and bimaterial interfaces, and (iii) combination of previous cases. In essence, the proposed method can be used vastly in advanced soil-structure interaction problems to investigate the micro and macro behaviour of the surrounding soil and the response of the embedded structure that contains discontinuities.

Keywords: contact problems, discrete element method, extended-finite element method, soil-structure interaction

Procedia PDF Downloads 481
652 Ice Load Measurements on Known Structures Using Image Processing Methods

Authors: Azam Fazelpour, Saeed R. Dehghani, Vlastimil Masek, Yuri S. Muzychka

Abstract:

This study employs a method based on image analyses and structure information to detect accumulated ice on known structures. The icing of marine vessels and offshore structures causes significant reductions in their efficiency and creates unsafe working conditions. Image processing methods are used to measure ice loads automatically. Most image processing methods are developed based on captured image analyses. In this method, ice loads on structures are calculated by defining structure coordinates and processing captured images. A pyramidal structure is designed with nine cylindrical bars as the known structure of experimental setup. Unsymmetrical ice accumulated on the structure in a cold room represents the actual case of experiments. Camera intrinsic and extrinsic parameters are used to define structure coordinates in the image coordinate system according to the camera location and angle. The thresholding method is applied to capture images and detect iced structures in a binary image. The ice thickness of each element is calculated by combining the information from the binary image and the structure coordinate. Averaging ice diameters from different camera views obtains ice thicknesses of structure elements. Comparison between ice load measurements using this method and the actual ice loads shows positive correlations with an acceptable range of error. The method can be applied to complex structures defining structure and camera coordinates.

Keywords: camera calibration, ice detection, ice load measurements, image processing

Procedia PDF Downloads 352
651 Road Condition Monitoring Using Built-in Vehicle Technology Data, Drones, and Deep Learning

Authors: Judith Mwakalonge, Geophrey Mbatta, Saidi Siuhi, Gurcan Comert, Cuthbert Ruseruka

Abstract:

Transportation agencies worldwide continuously monitor their roads' conditions to minimize road maintenance costs and maintain public safety and rideability quality. Existing methods for carrying out road condition surveys involve manual observations of roads using standard survey forms done by qualified road condition surveyors or engineers either on foot or by vehicle. Automated road condition survey vehicles exist; however, they are very expensive since they require special vehicles equipped with sensors for data collection together with data processing and computing devices. The manual methods are expensive, time-consuming, infrequent, and can hardly provide real-time information for road conditions. This study contributes to this arena by utilizing built-in vehicle technologies, drones, and deep learning to automate road condition surveys while using low-cost technology. A single model is trained to capture flexible pavement distresses (Potholes, Rutting, Cracking, and raveling), thereby providing a more cost-effective and efficient road condition monitoring approach that can also provide real-time road conditions. Additionally, data fusion is employed to enhance the road condition assessment with data from vehicles and drones.

Keywords: road conditions, built-in vehicle technology, deep learning, drones

Procedia PDF Downloads 96
650 Leadership Development of Professional Ethiopian Women in Science, Technology, Engineering, and Mathematics: Insights Gained through an Onsite Culturally Embedded Workshop

Authors: Araceli Martinez Ortiz, Gillian Bayne, Solomon Abraham

Abstract:

This paper describes research led by faculty from three American universities and four Ethiopian universities on the delivery of professional leadership development for early-career female Ethiopian university instructors in the Science, Technology, Engineering, and Mathematics (STEM) fields. The objective was to carry out a case study focused on the impact of an innovative intervention program designed to assist in the empowerment and leadership development related to teaching effectiveness, scholarly activity participation, and professional service participation by female instructors. This research was conducted utilizing a case study methodology for the weeklong intervention and a survey to capture the voices of the leadership program participants. The data regarding insights into the challenges and opportunities for women in these fields is presented. The research effort project expands upon existing linkages between universities to support professional development and research effort in this region of the world. Findings indicate the positive reception of this kind of professional development by the participating women. Survey data also reflects the particular cultural challenges professional women in STEM education face in Ethiopia as well as the global challenges of balancing family expectations with career development.

Keywords: Ethiopian women, STEM leadership, professional development, gender equity

Procedia PDF Downloads 90
649 A Uniformly Convergent Numerical Scheme for a Singularly Perturbed Volterra Integrodifferential Equation

Authors: Nana Adjoah Mbroh, Suares Clovis Oukouomi Noutchie

Abstract:

Singularly perturbed problems are parameter dependent problems, and they play major roles in the modelling of real-life situational problems in applied sciences. Thus, designing efficient numerical schemes to solve these problems is of much interest since the exact solutions of such problems may not even exist. Generally, singularly perturbed problems are identified by a small parameter multiplying at least the highest derivative in the equation. The presence of this parameter causes the solution of these problems to be characterized by rapid oscillations. This unique feature renders classical numerical schemes inefficient since they are unable to capture the behaviour of the exact solution in the part of the domain where the rapid oscillations are present. In this paper, a numerical scheme is proposed to solve a singularly perturbed Volterra Integro-differential equation. The scheme is based on the midpoint rule and employs the non-standard finite difference scheme to solve the differential part whilst the composite trapezoidal rule is used for the integral part. A fully fledged error estimate is performed, and Richardson extrapolation is applied to accelerate the convergence of the scheme. Numerical simulations are conducted to confirm the theoretical findings before and after extrapolation.

Keywords: midpoint rule, non-standard finite difference schemes, Richardson extrapolation, singularly perturbed problems, trapezoidal rule, uniform convergence

Procedia PDF Downloads 114
648 The Impact of Sustainable Farm Management on Paddy Farmers’ Livelihood: The Case of Malaysia

Authors: Roslina Kamaruddin

Abstract:

The paddy farmer’s performance and ability to improve productivity for increased incomes is driven by their level of farm management practices. Knowledge on the nature and level of sustainable farm management (SFM) practice provides opportunities for supporting the competitive advantages of paddy farmers to sustainably break away from the poverty cycle. Little attention has been given to measuring the performance and impact of SFM for the improvement of paddy farmer's livelihood in Malaysia. Without understanding SFM, it is difficult to make policies and provide targeted, impactful support to paddy farmers. The objective of this study is to assess the level of SFM among paddy farmers by calculating the Sustainable Farm Management Index (SFMI) using the Rice Check (RC) guideline established by the Department of Agriculture. The structured questionnaire was designed to capture the nine elements of farming practices based on the RC and was then distributed to 788 paddy farmers in Malaysia's main granary areas, namely MADA, KADA, and BLS. Each practice was given a score to determine whether the guidelines were followed. The index ranges from 0 to 100, with 0 being unsustainable and 100 being highly sustainable. A multiple regression analysis was employed as well to estimate the effects of SFM adoption on farmer livelihoods. The findings show that adopting SFM has a positive and significant effect on farmers' livelihoods. The paper, therefore, recommends that farmers should be educated on the importance of sustainable farming practices as this is essential for the sustainable livelihood development of poor farmers who rely on government subsidies.

Keywords: sustainable farm management, paddy farming, rice check, granary areas, farmers livelihood

Procedia PDF Downloads 81
647 Sediment Trapping by Seagrass Blades under Oscillatory Flow

Authors: Aina Barcelona, Carolyn Oldham, Jordi Colomer, Jordi Garcia-Orellana, Teresa Serra

Abstract:

Seagrass meadows increase the sedimentation within the canopy. However, there is still a lack of knowledge about how seagrasses impact the vertical distribution of sediment coming from external sources and reaches the meadow. This study aims to determine the number of particles retained by a seagrass meadow. Based on the hydrodynamics in the vertical direction, a meadow can be separated into different compartments: the blades, the seabed, within the canopy layer, and the above canopy layer. A set of laboratory experiments were conducted under different hydrodynamic conditions and canopy densities with the purpose to mimic the real field conditions. This study demonstrates and quantifies that seagrass meadows decrease the volume of the suspended sediment by two mechanisms: capturing the suspended sediment by the seagrass blades and promoting the particle sedimentation to the seabed. This study also demonstrates that the number of sediment particles trapped by single seagrass blades decreases with canopy density. However, when considering the trapping by the total number of blades, the sediment captured by all the blades of the meadow increases with canopy density. Furthermore, comparing with the bare seabed, this study demonstrated that there is a reduction in the suspended particles within the canopy, which implies an improvement in the water clarity. In addition, the particle sedimentation on the seabed increases with the canopy density compared with the bare seabed, making evident the contribution of the vegetation in enhancing sedimentation.

Keywords: seagrass, sediment capture, turbulent kinetic energy, oscillatory flow

Procedia PDF Downloads 219
646 Statistical Time-Series and Neural Architecture of Malaria Patients Records in Lagos, Nigeria

Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

Abstract:

Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

Procedia PDF Downloads 59
645 Impact of Integrated Signals for Doing Human Activity Recognition Using Deep Learning Models

Authors: Milagros Jaén-Vargas, Javier García Martínez, Karla Miriam Reyes Leiva, María Fernanda Trujillo-Guerrero, Francisco Fernandes, Sérgio Barroso Gonçalves, Miguel Tavares Silva, Daniel Simões Lopes, José Javier Serrano Olmedo

Abstract:

Human Activity Recognition (HAR) is having a growing impact in creating new applications and is responsible for emerging new technologies. Also, the use of wearable sensors is an important key to exploring the human body's behavior when performing activities. Hence, the use of these dispositive is less invasive and the person is more comfortable. In this study, a database that includes three activities is used. The activities were acquired from inertial measurement unit sensors (IMU) and motion capture systems (MOCAP). The main objective is differentiating the performance from four Deep Learning (DL) models: Deep Neural Network (DNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and hybrid model Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), when considering acceleration, velocity and position and evaluate if integrating the IMU acceleration to obtain velocity and position represent an increment in performance when it works as input to the DL models. Moreover, compared with the same type of data provided by the MOCAP system. Despite the acceleration data is cleaned when integrating, results show a minimal increase in accuracy for the integrated signals.

Keywords: HAR, IMU, MOCAP, acceleration, velocity, position, feature maps

Procedia PDF Downloads 80
644 Let’s Make Waves – Changing the Landscape for the Solent’s Film Industry

Authors: Roy Hanney

Abstract:

This research study aims to develop an evidential basis to inform strategic development of the film industry in the Solent (south central) region of the UK. The density of the creative industries around the region is driving the growth of jobs. Yet, film production in particular, appears to struggle with field configuration, lacks ecological cohesion, and suffers from underdeveloped ecosystems when compared to other areas bordering the region. Though thriving, a lack of coordinated leadership results in the continued reproduction of an ill-configured, constricted and socio-economically filtered workforce. One that struggles to seize strategic opportunities arising as a consequence of the ongoing investment in UK film production around the west of London. Taking a participatory approach, the study seeks to avoid the universalism of place marketing and focus on the situatedness of the region and its specific cultural, social, and economic contexts. The staging of a series of high profile networking events provided a much needed field configuring activity and enabled the capture of voices of those currently working in the sector. It will also provided the opportunity for an exploratory network mapping of the regional creative industries as a value exchange ecosystem. It is understood that a focus on production is not in itself a solution to the challenges faced in the region. There is a need to address issues of access as a counterbalance to skewed representation among the creative workforces thus the study also aims to report on opportunities for embedding diversity and inclusion in any strategic solutions.

Keywords: creative, industries, ecosystem, ecology

Procedia PDF Downloads 84
643 Classification of Manufacturing Data for Efficient Processing on an Edge-Cloud Network

Authors: Onyedikachi Ulelu, Andrew P. Longstaff, Simon Fletcher, Simon Parkinson

Abstract:

The widespread interest in 'Industry 4.0' or 'digital manufacturing' has led to significant research requiring the acquisition of data from sensors, instruments, and machine signals. In-depth research then identifies methods of analysis of the massive amounts of data generated before and during manufacture to solve a particular problem. The ultimate goal is for industrial Internet of Things (IIoT) data to be processed automatically to assist with either visualisation or autonomous system decision-making. However, the collection and processing of data in an industrial environment come with a cost. Little research has been undertaken on how to specify optimally what data to capture, transmit, process, and store at various levels of an edge-cloud network. The first step in this specification is to categorise IIoT data for efficient and effective use. This paper proposes the required attributes and classification to take manufacturing digital data from various sources to determine the most suitable location for data processing on the edge-cloud network. The proposed classification framework will minimise overhead in terms of network bandwidth/cost and processing time of machine tool data via efficient decision making on which dataset should be processed at the ‘edge’ and what to send to a remote server (cloud). A fast-and-frugal heuristic method is implemented for this decision-making. The framework is tested using case studies from industrial machine tools for machine productivity and maintenance.

Keywords: data classification, decision making, edge computing, industrial IoT, industry 4.0

Procedia PDF Downloads 160
642 Adaptive Strategies of Maize in Leaf Traits to N Deficiency

Authors: Panpan Fan, Bo Ming, Niels Anten, Jochem Evers, Yaoyao Li, Shaokun Li, Ruizhi xie

Abstract:

Nitrogen (N) utilization for crop production under N deficiency conditions is subject to a trade-off between maintaining specific leaf N content (SLN), important for radiation-use efficiency (RUE), versus maintaining leaf area (LA) development, important for light capture. This paper aims to explore how maize deals with this trade-off through responses in SLN, LA and their underlying traits during the vegetative and reproductive growth stages. In a ten-year N fertilization trial in Jilin province, Northeast China, three N fertilizer levels have been maintained: N-deficiency (N0), low N supply (N1), and high N supply (N2). We analyzed data from years 8 and 10 of this experiment for two common hybrids. Under N deficiency, maize plants maintained LA and decreased SLN during vegetative stages, while both LA and SLN decreased comparably during reproductive stages. Canopy-average specific leaf area (SLA) decreased sharply during vegetative stages and slightly during reproductive stages, mainly because senesced leaves in the lower canopy had a higher SLA. In the vegetative stage, maize maintained leaf area at low N by maintaining leaf biomass (albeit hence having N content/mass) and slightly increasing SLA. These responses to N deficiency were stronger in maize hybrid XY335 than in ZD958. We conclude the main strategy of maize to cope with low N is to maintain plant growth, mainly by increasing SLA throughout the plant during early growth. N was too limiting for either strategy to be followed during later growth stages.

Keywords: leaf N content per unit leaf area, N deficiency, specific leaf area, maize strateg

Procedia PDF Downloads 72
641 Optimal 3D Deployment and Path Planning of Multiple Uavs for Maximum Coverage and Autonomy

Authors: Indu Chandran, Shubham Sharma, Rohan Mehta, Vipin Kizheppatt

Abstract:

Unmanned aerial vehicles are increasingly being explored as the most promising solution to disaster monitoring, assessment, and recovery. Current relief operations heavily rely on intelligent robot swarms to capture the damage caused, provide timely rescue, and create road maps for the victims. To perform these time-critical missions, efficient path planning that ensures quick coverage of the area is vital. This study aims to develop a technically balanced approach to provide maximum coverage of the affected area in a minimum time using the optimal number of UAVs. A coverage trajectory is designed through area decomposition and task assignment. To perform efficient and autonomous coverage mission, solution to a TSP-based optimization problem using meta-heuristic approaches is designed to allocate waypoints to the UAVs of different flight capacities. The study exploits multi-agent simulations like PX4-SITL and QGroundcontrol through the ROS framework and visualizes the dynamics of UAV deployment to different search paths in a 3D Gazebo environment. Through detailed theoretical analysis and simulation tests, we illustrate the optimality and efficiency of the proposed methodologies.

Keywords: area coverage, coverage path planning, heuristic algorithm, mission monitoring, optimization, task assignment, unmanned aerial vehicles

Procedia PDF Downloads 190
640 The Discursive Construction of Emotions in the Headlines of French Newspapers on Seismic Disasters

Authors: Mirela-Gabriela Bratu

Abstract:

The main objective of this study is to highlight the way in which emotions are constructed discursively in the French written press, more particularly in the titles of informative articles. To achieve this objective, we will begin the study with the theoretical part, which aims to capture the characteristics of journalistic discourse, to which we will add clues of emotions that we will identify in the titles of the articles. The approach is based on the empirical results from the analysis of the articles published on the earthquake that took place on August 24, 2016, in Italy, as described by two French national daily newspapers: Le Monde and Le Point. The corpus submitted to the analysis contains thirty-seven titles, published between August 24, 2016, and August 24, 2017. If the textual content of the speech offers information respecting the grammatical standards and following the presentation conventions, the choice of words can touch the reader, so the journalist must add other means than mastering of the language to create emotion. This study aims to highlight the strategies, such as rhetorical figures, the tenses, or factual data, used by journalists to create emotions for the readers. We also try, thanks to the study of the articles which were published for several days relating to the same event, to emphasize whether we can speak or not of the dissipation of emotion and the catastrophic side as the event fades away in time. The theoretical framework is offered by works on rhetorical strategies (Perelman, 1992; Amossi, 2000; Charaudeau, 2000) and on the study of emotions (Plantin, 1997, 1998, 2004; Tetu, 2004).

Keywords: disaster, earthquake, emotion, feeling

Procedia PDF Downloads 121
639 An In Situ Exploration of Practising Rugby Coaches’ Cognitions, Higher Psychological Functions and Actions Using Think Aloud Protocol

Authors: Simon Quick, John Lyle

Abstract:

Psychology-based research has been a characteristic of empirical enquiry in sport coaching for over fifty years and cognitive function is widely accepted as a fundamental component of sport coaching expertise. Within the academic literature, much empirical research on coaches’ cognitions has tended to adopt retrospective approaches, such as post-session interviews or stimulated recall, thus capturing coaches’ cognitions after the incident, training session or competition. Such methods are prone to a variety of issues, including memory decay and the reordering of accounts. The aim of this research was to overcome the limitations that exist with retrospective approaches and, rather, to capture coaching cognitions in situ using Think Aloud Protocol. Data that were captured was broken down into meaning units and analysed using a Thematic Analysis. Situated in the practice of 6 experienced rugby coaches, findings revealed that Think Aloud Protocol generated rich data, although problematic in a site of enquiry confounded by multiple social interactions and requiring coaches to provide frequent instruction and feedback. The resultant interaction between cognition and action is conceptualised by the tentative offering of a model that situates these elements in conjunction with cognitive triggers and thresholds. The implications of these findings can help academics and coaches to understand the dynamic relationship between types of coaching cognitions and the complexity of the coaching environment.

Keywords: sports coaching, Psychology, Pedagogy, cognition

Procedia PDF Downloads 54
638 A Location-based Authentication and Key Management Scheme for Border Surveillance Wireless Sensor Networks

Authors: Walid Abdallah, Noureddine Boudriga

Abstract:

Wireless sensor networks have shown their effectiveness in the deployment of many critical applications especially in the military domain. Border surveillance is one of these applications where a set of wireless sensors are deployed along a country border line to detect illegal intrusion attempts to the national territory and report this to a control center to undergo the necessary measures. Regarding its nature, this wireless sensor network can be the target of many security attacks trying to compromise its normal operation. Particularly, in this application the deployment and location of sensor nodes are of great importance for detecting and tracking intruders. This paper proposes a location-based authentication and key distribution mechanism to secure wireless sensor networks intended for border surveillance where the key establishment is performed using elliptic curve cryptography and identity-based public key scheme. In this scheme, the public key of each sensor node will be authenticated by keys that depend on its position in the monitored area. Before establishing a pairwise key between two nodes, each one of them must verify the neighborhood location of the other node using a message authentication code (MAC) calculated on the corresponding public key and keys derived from encrypted beacon messages broadcast by anchor nodes. We show that our proposed public key authentication and key distribution scheme is more resilient to node capture and node replication attacks than currently available schemes. Also, the achievement of the key distribution between nodes in our scheme generates less communication overhead and hence increases network performances.

Keywords: wireless sensor networks, border surveillance, security, key distribution, location-based

Procedia PDF Downloads 641