Search results for: cluster model approach

Authors: Alirıza Kaleli, M. Akif Ceviz, Erdoğan Güner, Köksal Erentürk

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The cyclic variations in spark ignition engines occurring especially under specific engine operating conditions make the maximum pressure variable for successive in-cylinder pressure cycles. Minimization of cyclic variations has a great importance in effectively operating near to lean limit, or at low speed and load. The cyclic variations may reduce the power output of the engine, lead to operational instabilities, and result in undesirable engine vibrations and noise. In this study, spark timing is controlled in order to reduce the cyclic variations in spark ignition engines. Firstly, an ARMAX model has developed between spark timing and maximum pressure using system identification techniques. By using this model, the maximum pressure of the next cycle has been predicted. Then, self-tuning minimum variance controller has been designed to change the spark timing for consecutive cycles of the first cylinder of test engine to regulate the in-cylinder maximum pressure. The performance of the proposed controller is illustrated in real time and experimental results show that the controller has a reliable effect on cycle to cycle variations of maximum cylinder pressure when the engine works under low speed conditions.

Keywords: cyclic variations, cylinder pressure, SI engines, self tuning controller

Procedia PDF Downloads 468

Authors: Ana Magana

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Youth are entering the juvenile justice system at alarming rates. For the youth of color entering the system, the outcomes are far worse than for their white counterparts. In fact, the youth of color are more likely to be arrested and sentenced for longer periods of time than white youth. Race disproportionality in the juvenile justice system is evident, but what happens to the youth that exit the juvenile justice system? Who supports them after they are incarcerated and who can prevent them from re-offending? There are several diversion programs that have been implemented in the US to aid the reduction of juvenile incarceration and help reduce recidivism. The program interviewed for this study is a community-based diversion program (CBDP). The CBDP is a pre-filing diversion non-profit organization based in South Seattle. The objective of this exploratory research study is to provide a space and platform for the CBDP team to speak about their background experiences and the influence their background has on their current approach and practice with juveniles. A qualitative, exploratory study was conducted. Interviews were conducted with staff and provided oral consent. The interview included six open-ended, semi-structured questions. Interviews were digitally recoded and transcribed. The aim of this study was to understand how the influence of the participant’s backgrounds and previous experiences impact their current practice approaches with the CBDP youth and young adults. Ecological systems theory was the guiding framework for analysis. After careful analysis, three major themes emerged: 1) strong influence of participant’s background, 2) participants belonging to community and 3) strong self-identity with the CBDP. Within these three themes, subthemes were developed based on participant’s responses. It was concluded that the participant’s approach is influenced by their background experiences. This corresponds to the ecological systems theory and the community-based lens which underscores theoretical analysis. The participant’s approach is grounded in interpersonal relationships within the client’s systems, meaning that the participants understand and view their clients within an ecological systems perspective. When choosing participants that reflect the population being served, the clients receive a balanced, inclusive and caring approach. Youth and young adults are searching for supportive adults to be there for them, it is essential for diversion programs to provide a space for shared background experiences and have people that hold similar identities. Grassroots organizations such as CBDP have the tools and experience to work with marginalized populations that are constantly being passed on. While articles and studies focus on the reduction of recidivism and re-offending it is important to question the reasons behind this data. For instance, there can be a reduction in statistics, but at whose expense. Are the youth and young adults truly being supported? Or is it just a requirement that they are completing in order to remove their charge? This research study can serve as the beginning of a series of studies conducted at CBDP to further understand and validate the need to employ individuals with similar backgrounds as the participants CBDP serves.

Keywords: background experience, diversion, ecological systems theory, relationships

Procedia PDF Downloads 134

Authors: Muhammad Abdullah, Muhammad Atiq Ur Rehman Tariq, Faraz Ul Haq

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In developed countries, sustainability measures are widely accepted and acknowledged as crucial for addressing environmental concerns. Hydroponics, a soilless cultivation technique, has emerged as a potentially sustainable solution as it can reduce water consumption, land use, and environmental impacts. However, hydroponics may not be economically viable, especially when using reclaimed water, which may entail additional costs and risks. This study aims to address the critical question of whether hydroponics using reclaimed water can achieve a balance between sustainability and financial viability. Life Cycle Assessment (LCA) and Life Cycle Cost (LCC) will be integrated to assess the potential of hydroponics whether it is environmentally sustainable and economically viable. Life cycle assessment, or LCA, is a methodology for assessing environmental impacts associated with all the stages of the life cycle of a commercial product, process, or service. While Life Cycle Cost (LCC) is an approach that assesses the total cost of an asset over its life cycle, including initial capital costs and maintenance costs. The expected benefits of this study include supporting evidence-based decision-making for policymakers, farmers, and stakeholders involved in agriculture. By quantifying environmental impacts and economic costs, this research will facilitate informed choices regarding the adoption of hydroponics with reclaimed water. It is believed that the outcomes of this research work will help to achieve a sustainable approach to agricultural production, aligning with sustainability goals while considering economic factors by adopting hydroponic technique.

Keywords: hydroponic, life cycle assessment, life cycle cost, sustainability

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Authors: Fei Gao, Rodolfo C. Raga Jr.

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This research proposal will ascertain the major risk factors for diabetes and to design a predictive model for risk assessment. The project aims to improve diabetes early detection and management by utilizing data science techniques, which may improve patient outcomes and healthcare efficiency. The phase relation values of each attribute were used to analyze and choose the attributes that might influence the examiner's survival probability using Diabetes Health Indicators Dataset from Kaggle’s data as the research data. We compare and evaluate eight machine learning algorithms. Our investigation begins with comprehensive data preprocessing, including feature engineering and dimensionality reduction, aimed at enhancing data quality. The dataset, comprising health indicators and medical data, serves as a foundation for training and testing these algorithms. A rigorous cross-validation process is applied, and we assess their performance using five key metrics like accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). After analyzing the data characteristics, investigate their impact on the likelihood of diabetes and develop corresponding risk indicators.

Keywords: diabetes, risk factors, predictive model, risk assessment, data science techniques, early detection, data analysis, Kaggle

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Authors: Wang Shuen-Tai, Lin Yu-Ching, Chang Hsi-Ya

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Cloud computing is the innovative and leading information technology model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort. In this paper, we aim at the development of workflow management system for cloud computing platforms based on our previous research on the dynamic allocation of the cloud computing resources and its workflow process. We took advantage of the HTML 5 technology and developed web-based workflow interface. In order to enable the combination of many tasks running on the cloud platform in sequence, we designed a mechanism and developed an execution engine for workflow management on clouds. We also established a prediction model which was integrated with job queuing system to estimate the waiting time and cost of the individual tasks on different computing nodes, therefore helping users achieve maximum performance at lowest payment. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for cloud computing platform. This development also helps boost user productivity by promoting a flexible workflow interface that lets users design and control their tasks' flow from anywhere.

Keywords: web-based, workflow, HTML5, Cloud Computing, Queuing System

Procedia PDF Downloads 297

Authors: Zoran Jovanovic, Zoran Masonicic, S. Dragutinovic, Z. Sakota

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In this paper, results concerning flame propagation of various fuels in a particular combustion chamber with four tilted valves were elucidated. Flame propagation was represented by the evolution of spatial distribution of temperature in various cut-planes within combustion chamber while the flame front location was determined by dint of zones with maximum temperature gradient. The results presented are only a small part of broader on-going scrutinizing activity in the field of multidimensional modeling of reactive flows in combustion chambers with complicated geometries encompassing various models of turbulence, different fuels and combustion models. In the case of turbulence two different models were applied i.e. standard k-ε model of turbulence and k-ξ-f model of turbulence. In this paper flame propagation results were analyzed and presented for two different hydrocarbon fuels, such as CH4 and C8H18. In the case of combustion all differences ensuing from different turbulence models, obvious for non-reactive flows are annihilated entirely. Namely the interplay between fluid flow pattern and flame propagation is invariant as regards turbulence models and fuels applied. Namely the interplay between fluid flow pattern and flame propagation is entirely invariant as regards fuel variation indicating that the flame propagation through unburned mixture of CH4 and C8H18 fuels is not chemically controlled.

Keywords: automotive flows, flame propagation, combustion modelling, CNG

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Authors: Mojtaba Mahmoodian, Joshua Phelan, Mehdi Shahparvari

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With a large percentage of countries’ total infrastructure expenditure attributed to water network maintenance, it is essential to optimise maintenance strategies to rehabilitate or replace underground pipes before failure occurs. The aim of this paper is to provide water utility managers with a maintenance management approach for underground water pipes, subject to external loading and material corrosion, to give the lowest life cycle cost over a predetermined time period. This reliability-based maintenance management methodology details the optimal years for intervention, the ideal number of maintenance activities to perform before replacement and specifies feasible renewal options and intervention prioritisation to minimise the life cycle cost. The study was then extended to include feasible renewal methods by determining the structural condition index and potential for soil loss, then obtaining the failure impact rating to assist in prioritising pipe replacement. A case study on optimisation of maintenance plans for the Melbourne water pipe network is considered in this paper to evaluate the practicality of the proposed methodology. The results confirm that the suggested methodology can provide water utility managers with a reliable systematic approach to determining optimum maintenance plans for pipe networks.

Keywords: water pipe networks, maintenance management, reliability analysis, optimum maintenance plan

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Authors: Oibar Martinez, Clara Oliver, Jose Miguel Miranda

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The study of dispersion phenomena in electromagnetic waves reflected by conductors at infrared and lower frequencies is a topic which finds a number of applications. We aim to explain in this work what are the most relevant ones and how this phenomenon is modeled from both optics and electromagnetics points of view. We also explain here how the amplitude of an electromagnetic wave reflected by a lossy conductor could depend on both the frequency of the incident wave, as well as on the electrical properties of the conductor, and we illustrate this phenomenon with a practical example. The mathematical analysis made by a specialist in electromagnetics or a microwave engineer is apparently very different from the one made by a specialist in optics. We show here how both approaches lead to the same physical result and what are the key concepts which enable one to understand that despite the differences in the equations the solution to the problem happens to be the same. Our study starts with an analysis made by using the complex refractive index and the reflectance parameter. We show how this reflectance has a dependence with the square root of the frequency when the reflecting material is a good conductor, and the frequency of the wave is low enough. Then we analyze the same problem with a less known approach, which is based on the reflection coefficient of the electric field, a parameter that is most commonly used in electromagnetics and microwave engineering. In summary, this paper presents a mathematical study illustrated with a worked example which unifies the modeling of dispersion effects made by specialists in optics and the one made by specialists in electromagnetics. The main finding of this work is that it is possible to reproduce the dependence of the Fresnel reflectance with frequency from the intrinsic impedance of the reflecting media.

Keywords: dispersion, electromagnetic waves, microwaves, optics

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Authors: Rylle Evan Gabriel Zamora, Micah Dennise Malia, Abygail Deniese Villabona

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The primary motivators of altruism are usually viewed as mutually exclusive. In this study, we wanted to know if the two primary motivators, reciprocity and empathy, can work together in motivating altruism. Therefore, we wanted to find out if there is a significant interaction of effects between reciprocity and empathy. To show how this may occur, we devised the combined altruism model, which is based on Batson’s empathy altruism hypothesis. A sample of 120, 6th-grade students were randomly selected and then randomly assigned to four treatment groups. A 2x2 between subjects’ design was used, which had empathy and reciprocity as independent variables, and altruism as the dependent variable. The study made use of materials that were effort based, where subjects were required to complete a task or a puzzle to help a person in a given scenario, two videos, one to prime empathy were also used. This along with Witt & Boleman’s adapted Self-Reported Altruism Scale was used to determine an individual’s altruism. It was found that both variables were significant in motivating altruism, with empathy being the greater of the two. However, there was no significant interaction of effects between the two variables. To explain why this occurred, we turned to the combined altruism model, where it was found that when empathically primed, we tend to not think of ourselves when helping others. Future studies could focus on other variables, especially age which is said to be one of the greatest factors that influenced the results of the experiment.

Keywords: reciprocity, empathy, altruism, experimental psychology, social psychology

Procedia PDF Downloads 239

Authors: Jonghyuk Yoon, Hyoungwoon Song

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Recently, there are lots of increasing interest in a smart farming that represents application of modern Information and Communication Technologies (ICT) into agriculture since it provides a methodology to optimize production efficiencies by managing growing conditions of crops automatically. In order to obtain high performance and stability for smart greenhouse, it is important to identify the effect of various working parameters such as capacity of ventilation fan, vent opening area and etc. In the present study, a 3-dimensional CFD (Computational Fluid Dynamics) simulation for single-span greenhouse was conducted using the commercial program, Ansys CFX 18.0. The numerical simulation for single-span greenhouse was implemented to figure out the internal thermal and flow characteristics. In order to numerically model solar radiation that spread over a wide range of wavelengths, the multiband model that discretizes the spectrum into finite bands of wavelength based on Wien’s law is applied to the simulation. In addition, absorption coefficient of vinyl varied with the wavelength bands is also applied based on Beer-Lambert Law. To validate the numerical method applied herein, the numerical results of the temperature at specific monitoring points were compared with the experimental data. The average error rates (12.2~14.2%) between them was shown and numerical results of temperature distribution are in good agreement with the experimental data. The results of the present study can be useful information for the design of various greenhouses. This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Advanced Production Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA)(315093-03).

Keywords: single-span greenhouse, CFD (computational fluid dynamics), solar radiation, multiband model, absorption coefficient

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Authors: Amirhossein Mohajerzadeh, Abolghasem Mohajerzadeh

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In this paper, target parameter is estimated with desirable precision in hierarchical wireless sensor networks (WSN) while the proposed algorithm also tries to prolong network lifetime as much as possible, using efficient data collecting algorithm. Target parameter distribution function is considered unknown. Sensor nodes sense the environment and send the data to the base station called fusion center (FC) using hierarchical data collecting algorithm. FC builds underlying phenomena based on collected data. Considering the aggregation level, x, the goal is providing the essential infrastructure to find the best value for aggregation level in order to prolong network lifetime as much as possible, while desirable accuracy is guaranteed (required sample size is fully depended on desirable precision). First, the sample size calculation algorithm is discussed, second, the average queue length based on M/M[x]/1/K queue model is determined and it is used for energy consumption calculation. Nodes can decrease transmission cost by aggregating incoming data. Furthermore, the performance of the new algorithm is evaluated in terms of lifetime and estimation accuracy.

Keywords: aggregation, estimation, queuing, wireless sensor network

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Authors: Arthur D. Wiedemann, Christopher Fuller, Kyle A. Pascioni

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With the introduction of electric motors, small unmanned aerial vehicle designers have to consider trade-offs between acoustic noise and thrust generated. Currently, there are few low-computational tools available for predicting acoustic noise emitted by a propeller into the far-field. Artificial neural networks offer a highly non-linear and adaptive model for predicting isolated and interactive tonal noise. But neural networks require large data sets, exceeding practical considerations in modeling experimental results. A methodology known as physics guided machine learning has been applied in this study to reduce the required data set to train the network. After building and evaluating several neural networks, the best model is investigated to determine how the network successfully predicts the acoustic waveform. Lastly, a post-network transfer function is developed to remove discontinuity from the predicted waveform. Overall, methodologies from physics guided machine learning show a notable improvement in prediction performance, but additional loss functions are necessary for constructing predictive networks on small datasets.

Keywords: aeroacoustics, machine learning, propeller, rotor, neural network, physics guided machine learning

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Nanadcha Sinjindawong

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This article discusses the design process of a community mall called Castle of The Mall-dern. The concept behind this mall is to combine elements of a medieval castle with modern architecture. The author aims to create a building that fits into the surroundings while also providing users with the vibes of the ancient era. The total area used for the mall is 4,000 square meters, with three floors. The first floor is 1,500 square meters, the second floor is 1,750 square meters, and the third floor is 750 square meters. Research Aim: The aim of this research is to design a community mall that sells ancient clothes and accessories, and to combine sustainable architectural design with the ideas of ancient architecture in an urban area with convenient transportation. Methodology: The research utilizes qualitative research methods in architectural design. The process begins with calculating the given area and dividing it into different zones. The author then sketches and draws the plan of each floor, adding the necessary rooms based on the floor areas mentioned earlier. The program "SketchUp" is used to create an online 3D model of the community mall, and a physical model is built for presentation purposes on A1 paper, explaining all the details. Findings: The result of this research is a community mall with various amenities. The first floor includes retail shops, clothing stores, a food center, and a service zone. Additionally, there is an indoor garden with a fountain and a tree for relaxation. The second and third floors feature a void in the middle, with a few stores, cafes, restaurants, and studios on the second floor. The third floor is home to the administration and security control room, as well as a community gathering area designed as a public library with a café inside. Theoretical Importance: This research contributes to the field of sustainable architectural design by combining ancient architectural ideas with modern elements. It showcases the potential for creating buildings that blend historical aesthetics with contemporary functionality. Data Collection and Analysis Procedures: The data for this research is collected through a combination of area calculation, sketching, and building a 3D model. The analysis involves evaluating the design based on the allocated area, zoning, and functional requirements for a community mall. Question Addressed: The research addresses the question of how to design a community mall with a theme of ancient Medieval and Victorian eras. It explores how to combine sustainable architectural design principles with historical aesthetics to create a functional and visually appealing space. Conclusion: In conclusion, this research successfully designs a community mall called “Castle of The Mall-dern” that incorporates elements of Medieval and Victorian architecture. The building encompasses various zones, including retail shops, restaurants, community gathering areas, and service zones. It also features an interior garden and a public library within the mall. The research contributes to the field of sustainable architectural design by showcasing the potential for combining ancient architectural ideas with modern elements in an urban setting.

Keywords: 3D model, community mall, modern architecture, medieval architecture

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Authors: Mohammad Ahmad, Sander Gersen, Erwin Wilbers

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Carbon capture, transport and underground storage have become a major solution to reduce CO2 emissions from power plants and other large CO2 sources. A big part of this captured CO2 stream is transported at high pressure dense phase conditions and stored in offshore underground depleted oil and gas fields. CO2 is also transported in offshore pipelines to be used for enhanced oil and gas recovery. The captured CO2 stream with impurities may contain water that causes severe corrosion problems, flow assurance failure and might damage valves and instrumentations. Thus, free water formation should be strictly prevented. The purpose of this work is to study the solubility of water in pure CO2 and in CO2 mixtures under real pipeline pressure (90-150 bar) and temperature operation conditions (5-35°C). A set up was constructed to generate experimental data. The results show the solubility of water in CO2 mixtures increasing with the increase of the temperature or/and with the increase in pressure. A drop in water solubility in CO2 is observed in the presence of impurities. The data generated were then used to assess the capabilities of two mixture models: the GERG-2008 model and the EOS-CG model. By generating the solubility data, this study contributes to determine the maximum allowable water content in CO2 pipelines.

Keywords: carbon capture and storage, water solubility, equation of states, fluids engineering

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Authors: Nikhil Kumar, Asish Bandyopadhyay

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Laser welding process is a capable technology for forming the automobile, microelectronics, marine and aerospace parts etc. In the present work, a mathematical and statistical approach is adopted to study the laser welding of AISI 304 stainless steel. A robotic control 500 W pulsed Nd:YAG laser source with 1064 nm wavelength has been used for welding purpose. Butt joints are made. The effects of welding parameters, namely; laser power, scanning speed and pulse width on the seam width and depth of penetration has been investigated using the empirical models developed by response surface methodology (RSM). Weld quality is directly correlated with the weld geometry. Twenty sets of experiments have been conducted as per central composite design (CCD) design matrix. The second order mathematical model has been developed for predicting the desired responses. The results of ANOVA indicate that the laser power has the most significant effect on responses. Microstructural analysis as well as hardness of the selected weld specimens has been carried out to understand the metallurgical and mechanical behaviour of the weld. Average micro-hardness of the weld is observed to be higher than the base metal. Higher hardness of the weld is the resultant of grain refinement and δ-ferrite formation in the weld structure. The result suggests that the lower line energy generally produce fine grain structure and improved mechanical properties than the high line energy. The combined effects of input parameters on responses have been analyzed with the help of developed 3-D response surface and contour plots. Finally, multi-objective optimization has been conducted for producing weld joint with complete penetration, minimum seam width and acceptable welding profile. Confirmatory tests have been conducted at optimum parametric conditions to validate the applied optimization technique.

Keywords: ANOVA, laser welding, modeling and optimization, response surface methodology

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: Zhenzhen Xu, Chunfu Shao, Shengyou Wang, Chunjiao Dong

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To improve the satisfaction of residents' travel, this paper analyzes the characteristics and influencing factors of urban residents' travel behavior. First, a Multinominal Logit Model (MNL) model is built to analyze the characteristics of residents' travel behavior, reveal the influence of individual attributes, family attributes and travel characteristics on the choice of travel mode, and identify the significant factors. Then put forward suggestions for policy improvement. Finally, Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) models are introduced to evaluate the policy effect. This paper selects Futian Street in Futian District, Shenzhen City for investigation and research. The results show that gender, age, education, income, number of cars owned, travel purpose, departure time, journey time, travel distance and times all have a significant influence on residents' choice of travel mode. Based on the above results, two policy improvement suggestions are put forward from reducing public transportation and non-motor vehicle travel time, and the policy effect is evaluated. Before the evaluation, the prediction effect of MNL, SVM and MLP models was evaluated. After parameter optimization, it was found that the prediction accuracy of the three models was 72.80%, 71.42%, and 76.42%, respectively. The MLP model with the highest prediction accuracy was selected to evaluate the effect of policy improvement. The results showed that after the implementation of the policy, the proportion of public transportation in plan 1 and plan 2 increased by 14.04% and 9.86%, respectively, while the proportion of private cars decreased by 3.47% and 2.54%, respectively. The proportion of car trips decreased obviously, while the proportion of public transport trips increased. It can be considered that the measures have a positive effect on promoting green trips and improving the satisfaction of urban residents, and can provide a reference for relevant departments to formulate transportation policies.

Keywords: neural network, travel characteristics analysis, transportation choice, travel sharing rate, traffic resource allocation

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Authors: Jamshaid Ul Rahman, Faiza Makhdoom, Dianchen Lu

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Activation functions play a decisive role in determining the capacity of Deep Neural Networks (DNNs) as they enable neural networks to capture inherent nonlinearities present in data fed to them. The prior research on activation functions primarily focused on the utility of monotonic or non-oscillatory functions, until Growing Cosine Unit (GCU) broke the taboo for a number of applications. In this paper, a Convolutional Neural Network (CNN) model named as ASU-CNN is proposed which utilizes recently designed activation function ASU across its layers. The effect of this non-monotonic and oscillatory function is inspected through feature map visualizations from different convolutional layers. The optimization of proposed network is offered by Adam with a fine-tuned adjustment of learning rate. The network achieved promising results on both training and testing data for the classification of CIFAR-10. The experimental results affirm the computational feasibility and efficacy of the proposed model for performing tasks related to the field of computer vision.

Keywords: amplifying sine unit, activation function, convolutional neural networks, oscillatory activation, image classification, CIFAR-10

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Authors: Alaaeddeen M. Seufi

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Many have been published on therapeutic derivatives from living organisms including insects. In addition to traditional maggot therapy, more than 900 therapeutic products were isolated from insects. Most people look at insects as enemies and others believe that insects are friends. Many beneficial insects rather than Honey Bees, Silk Worms and Shellac insect could insure human-insect friendship. In addition, insects could be MicroFactories, Biosensors or Bioreactors. InsectFarm is an amazing example of the applied research that transfers insects from laboratory to market by Prof Mircea Ciuhrii and co-workers. They worked for 18 years to derive therapeutics from insects. Their research resulted in production of more than 30 commercial medications derived from insects (e.g. Imunomax, Noblesse, etc.). Two general approaches were followed to discover drugs from living organisms. Some laboratories preferred biochemical approach to purify components of the innate immune system of insects and insect metabolites as well. Then the purified components could be tested for many therapeutic trials. Other researchers preferred molecular approach based on proteomic studies. Components of the innate immune system of insects were then tested for their medical activities. Our Laboratory team preferred to induce insect immune system (using oral, topical and injection routes of administration), then a transcriptomic study was done to discover the induced genes and to identify specific biomarkers that can help in drug discovery. Biomarkers play an important role in medicine and in drug discovery and development as well. Optimum biomarker development and application will require a team approach because of the multifaceted nature of biomarker selection, validation, and application. This team uses several techniques such as pharmacoepidemiology, pharmacogenomics, and functional proteomics; bioanalytical development and validation; modeling and simulation to improve and refine drug development. Our Achievements included the discovery of four components of the innate immune system of Spodoptera littoralis and Musca domestica. These components were designated as SpliDef (defesin), SpliLec (lectin), SpliCec (cecropin) and MdAtt (attacin). SpliDef, SpliLec and MdAtt were confirmed as antimicrobial peptides, while SpliCec was additionally confirmed as anticancer peptide. Our current research is going on to achieve something in antioxidants and anticoagulants from insects. Our perspective is to achieve something in the mass production of prototypes of our products and to reach it to the commercial level. These achievements are the integrated contributions of everybody in our team staff.

Keywords: AMPs, insect, innate immunitty, therappeutics

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Authors: Choi Younshick, Lee Wonseok, Lee Jaemeun, Park Sun-Hyun, Kim Sunwoung, Park Sua, Kim Eun Ho, Kim Jong-Ki

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Iron accumulation in the brain accelerates Alzheimer’s disease progression. To cure iron toxicity, we assessed the therapeutic effects of noncontact transcranial electric field stimulation to the brain on toxic iron deposits in either the Aβ-fibril structure or the Aβ plaque in a mouse model of Alzheimer’s disease (AD). A capacitive electrode-based alternating electric field (AEF) was applied to a suspension of magnetite (Fe₃O₄) to measure the field-sensitized electro-Fenton effect and resultant reactive oxygen species (ROS) generation. The increase in ROS generation compared to the untreated control was both exposure-time and AEF-frequency dependent. The frequency-specific exposure of AEF to 0.7–1.4 V/cm on a magnetite-bound Aβ-fibril or a transgenic Alzheimer’s disease (AD) mouse model revealed the removal of intraplaque ferrous magnetite iron deposit and Aβ-plaque burden together at the same time compared to the untreated control. The results of the behavioral tests show an improvement in impaired cognitive function following AEF treatment on the AD mouse model. Western blot assay found some disease-modifying biological responses, including down-regulating ferroptosis, neuroinflammation and reactive astrocytes that eventually made cognitive improvement feasible. Tissue clearing and 3D-imaging analysis revealed no induced damage to the neuronal structures of normal brain tissue following AEF treatment. In conclusion, our results suggest that the effective degradation of magnetite-bound amyloid fibrils or plaques in the AD brain by the electro-Fenton effect from electric field-sensitized magnetite offers a potential electroceutical treatment option for AD.

Keywords: electroceutical, intraplaque magnetite, alzheimer’s disease, transcranial electric field, electro-fenton effect

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Authors: Augustine Okolie, Johannes Müller, Mirjam Kretzchmar

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We adopt a maximum-likelihood framework to estimate parameters of a stochastic susceptible-infected-recovered (SIR) model with contact tracing on a rooted random tree. Given the number of detectees per index case, our estimator allows to determine the degree distribution of the random tree as well as the tracing probability. Since we do not discover all infectees via contact tracing, this estimation is non-trivial. To keep things simple and stable, we develop an approximation suited for realistic situations (contract tracing probability small, or the probability for the detection of index cases small). In this approximation, the only epidemiological parameter entering the estimator is the basic reproduction number R0. The estimator is tested in a simulation study and applied to covid-19 contact tracing data from India. The simulation study underlines the efficiency of the method. For the empirical covid-19 data, we are able to compare different degree distributions and perform a sensitivity analysis. We find that particularly a power-law and a negative binomial degree distribution meet the data well and that the tracing probability is rather large. The sensitivity analysis shows no strong dependency on the reproduction number.

Keywords: stochastic SIR model on graph, contact tracing, branching process, parameter inference

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Authors: Hsiang-Yu Yuan, Marc Baguelin, Kin O. Kwok, Nimalan Arinaminpathy, Edwin Leeuwen, Steven Riley

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Traditional syndromic surveillance for influenza has substantial public health value in characterizing epidemics. Because the relationship between syndromic incidence and the true infection events can vary from one population to another and from one year to another, recent studies rely on combining serological test results with syndromic data from traditional surveillance into epidemic models to make inference on epidemiological processes of influenza. However, despite the widespread availability of serological data, epidemic models have thus far not explicitly represented antibody titre levels and their correspondence with immunity. Most studies use dichotomized data with a threshold (Typically, a titre of 1:40 was used) to define individuals as likely recently infected and likely immune and further estimate the cumulative incidence. Underestimation of Influenza attack rate could be resulted from the dichotomized data. In order to improve the use of serosurveillance data, here, a refinement of the concept of the stratified immunity within an epidemic model for influenza transmission was proposed, such that all individual antibody titre levels were enumerated explicitly and mapped onto a variable scale of susceptibility in different age groups. Haemagglutination inhibition titres from 523 individuals and 465 individuals during pre- and post-pandemic phase of the 2009 pandemic in Hong Kong were collected. The model was fitted to serological data in age-structured population using Bayesian framework and was able to reproduce key features of the epidemics. The effects of age-specific antibody boosting and protection were explored in greater detail. RB was defined to be the effective reproductive number in the presence of stratified immunity and its temporal dynamics was compared to the traditional epidemic model using use dichotomized seropositivity data. Deviance Information Criterion (DIC) was used to measure the fitness of the model to serological data with different mechanisms of the serological response. The results demonstrated that the differential antibody response with age was present (ΔDIC = -7.0). The age-specific mixing patterns with children specific transmissibility, rather than pre-existing immunity, was most likely to explain the high serological attack rates in children and low serological attack rates in elderly (ΔDIC = -38.5). Our results suggested that the disease dynamics and herd immunity of a population could be described more accurately for influenza when the distribution of immunity was explicitly represented, rather than relying only on the dichotomous states 'susceptible' and 'immune' defined by the threshold titre (1:40) (ΔDIC = -11.5). During the outbreak, RB declined slowly from 1.22[1.16-1.28] in the first four months after 1st May. RB dropped rapidly below to 1 during September and October, which was consistent to the observed epidemic peak time in the late September. One of the most important challenges for infectious disease control is to monitor disease transmissibility in real time with statistics such as the effective reproduction number. Once early estimates of antibody boosting and protection are obtained, disease dynamics can be reconstructed, which are valuable for infectious disease prevention and control.

Keywords: effective reproductive number, epidemic model, influenza epidemic dynamics, stratified immunity

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Authors: Juliani Rianto, Emma Lumby, Tracey Smith

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Cancer patients’ survival are growing with the new approach and therapy in oncology medicine. Cancer is now a new chronic disease, and rehabilitation program has become an ongoing program as part of cancer care. The focus of Cancer rehabilitation is maximising person’s physical and emotional function, stabilising general health and reducing unnecessary hospital admission. In Australia there are 150000 newly diagnosed cancer every year, and the most common Cancer are prostate, Breast, Colorectal, Melanoma and Lung Cancer. Through referral from the oncology team, we recruited cancer patient into our cancer rehabilitation program. Patients are assessed by our multi-disciplinary team including rehabilitation specialist, physiotherapist, occupational therapist, dietician, exercise physiologist, and psychologist. Specific issues are identified, including pain, side effect of chemo and radiation therapy and mental well-being. The goals were identified and reassessed every fortnight. Common goals including nutritional status, improve endurance and exercise performance, working on balance and mobility, improving emotional and vocational state, educational program for insomnia and tiredness, and reducing hospitalisation are identified and assessed. Patients are given 2 hours exercise program twice a week for 6 weeks with focus on aerobic and weight exercises and education sessions. Patients are generally benefited from the program. The quality of life is improved, support and interaction from the therapist has played an important factor in directing patient for their goals.

Keywords: cancer, exercises, benefit, mental health

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Authors: Michael Kenomore, Mohamed Hassan, Amjad Shah, Hom Dhakal

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Shale gas reservoirs have been of greater importance compared to shale oil reservoirs since 2009 and with the current nature of the oil market, understanding the technical and economic performance of shale gas reservoirs is of importance. Using the Barnett shale as a case study, an economic model was developed to quantify the effect of finding and development costs and gas prices on the basins in the Barnett shale using net present value as an evaluation parameter. A rate of return of 20% and a payback period of 60 months or less was used as the investment hurdle in the model. The Barnett was split into four basins (Strawn Basin, Ouachita Folded Belt, Forth-worth Syncline and Bend-arch Basin) with analysis conducted on each of the basin to provide a holistic outlook. The dataset consisted of only horizontal wells that started production from 2008 to at most 2015 with 1835 wells coming from the strawn basin, 137 wells from the Ouachita folded belt, 55 wells from the bend-arch basin and 724 wells from the forth-worth syncline. The data was analyzed initially on Microsoft Excel to determine the estimated ultimate recoverable (EUR). The range of EUR from each basin were loaded in the Palisade Risk software and a log normal distribution typical of Barnett shale wells was fitted to the dataset. Monte Carlo simulation was then carried out over a 1000 iterations to obtain a cumulative distribution plot showing the probabilistic distribution of EUR for each basin. From the cumulative distribution plot, the P10, P50 and P90 EUR values for each basin were used in the economic model. Gas production from an individual well with a EUR similar to the calculated EUR was chosen and rescaled to fit the calculated EUR values for each basin at the respective percentiles i.e. P10, P50 and P90. The rescaled production was entered into the economic model to determine the effect of the finding and development cost and gas price on the net present value (10% discount rate/year) as well as also determine the scenario that satisfied the proposed investment hurdle. The finding and development costs used in this paper (assumed to consist only of the drilling and completion costs) were £1 million, £2 million and £4 million while the gas price was varied from $2/MCF-$13/MCF based on Henry Hub spot prices from 2008-2015. One of the major findings in this study was that wells in the bend-arch basin were least economic, higher gas prices are needed in basins containing non-core counties and 90% of the Barnet shale wells were not economic at all finding and development costs irrespective of the gas price in all the basins. This study helps to determine the percentage of wells that are economic at different range of costs and gas prices, determine the basins that are most economic and the wells that satisfy the investment hurdle.

Keywords: shale gas, Barnett shale, unconventional gas, estimated ultimate recoverable

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Authors: Manam Hanfi

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Pakistan is a victim of terrorism since 9/11 attacks. Since then it is a home for violence and extremism. One of the major reasons behind rising violence and extremism in Pakistan is radicalization. Pakistan has seen and suffered from the modification of terrorism from old to new. In new terrorism, the terrorist organizations incorporated internet to disseminate propaganda, to recruit and train people. The study focuses on the relationship between Pakistan and new terrorism and examines how the internet is being used by terrorist organizations. The study investigates radicalization through social media by terrorist organizations in Pakistan with the help of case studies. The study suggests five ways to counter radicalization, including, counter narrative on social media, content analysis of the data on the internet, curriculum and madrassa reforms, teaching peace education in the educational institutions and use of technical software such as eGLYPH to quickly remove violent data from social media. Lastly, the research attempted to contribute in counter-radicalization by combining the media dependency model and ideas for counter-radicalization. The dependency model elaborates the impact of mass media content on the audience. If media dependency is high, it will cause cognitive, affective and behavioral changes. In order to counter radicalization through social media, it is important to make cognitive, affective and behavioral changes with the help of counter-radicalization suggestions.

Keywords: counter radicalization, extremism, social media, terrorism

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Authors: Abdulrahman S. Alqahtani

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The revolution of computing and networks could revolutionise terrorism in the same way that it has brought about changes in other aspects of life. The modern technological era has faced countries with a new set of security challenges. There are many states and potential adversaries who have the potential and capacity in cyberspace, which makes them able to carry out cyber-attacks in the future. Some of them are currently conducting surveillance, gathering and analysis of technical information, and mapping of networks and nodes and infrastructure of opponents, which may be exploited in future conflicts. This poster presents the results of the quantitative study (survey) to test the validity of the proposed theoretical framework for the cyber terrorist threats. This theoretical framework will help to in-depth understand these new digital terrorist threats. It may also be a practical guide for managers and technicians in critical infrastructure, to understand and assess the threats they face. It might also be the foundation for building a national strategy to counter cyberterrorism. In the beginning, it provides basic information about the data. To purify the data, reliability and exploratory factor analysis, as well as confirmatory factor analysis (CFA) were performed. Then, Structural Equation Modelling (SEM) was utilised to test the final model of the theory and to assess the overall goodness-of-fit between the proposed model and the collected data set.

Keywords: cyberterrorism, critical infrastructure, , national security, theoretical framework, terrorism

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Authors: Yunqi Wang

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Benefiting from the demographic dividend, China has enjoyed export-led economic growth since 1978. While Lewis's model marks the structural transformation from the low-wage 'subsistence' sector to the 'modern sector' as the end of labour surplus, the Chinese government seems eager to extend such benefit by promoting a series of fertility encouragement policies, contrasting to its firm and strict birth control since last century. Based on a Attride-Stirling’s thematic analysis of interviews with unmarried female millennials in China, this paper argues that the young female generation responded to current fertility policies negatively, where the policy ineffectiveness and irresponsiveness have further worsened their marriage and childbirth reluctance. Instead of focusing on changes in wage level, this research contributes a qualitative perspective to the existing theoretical debate on the Lewis turning point, implying an inevitable end of demographic dividend in China. Highlighting the greater focus on female consciousness among the younger generation, it also suggests a policy orientation towards resolving outdated social norms to accommodate the rising female consciousness since millennials will become the childbirth mainstay in forthcoming years.

Keywords: lewis model, fertility policy, demographic dividend, one-child policy

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Authors: Asma Rebaya, Kaouther Gasmi, Imen Amari, Salem Hasnaoui

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Over these last years, applications such as telecommunications, signal processing, digital communication with advanced features (Multi-antenna, equalization..) witness a rapid evaluation accompanied with an increase of user exigencies in terms of latency, the power of computation… To satisfy these requirements, the use of hardware/software systems is a common solution; where hardware is composed of multi-cores and software is represented by models of computation, synchronous data flow (SDF) graph for instance. Otherwise, the most of the embedded system designers utilize Simulink for modeling. The issue is how to simplify the c code generation, for a multi-cores platform, of an application modeled by Simulink. To overcome this problem, we propose a workflow allowing an automatic transformation from the Simulink model to the SDF graph and providing an efficient schedule permitting to optimize the number of cores and to minimize latency. This workflow goes from a Simulink application and a hardware architecture described by IP.XACT language. Based on the synchronous and hierarchical behavior of both models, the Simulink block diagram is automatically transformed into an SDF graph. Once this process is successfully achieved, the scheduler calculates the optimal cores’ number needful by minimizing the maximum density of the whole application. Then, a core is chosen to execute a specific graph task in a specific order and, subsequently, a compatible C code is generated. In order to perform this proposal, we extend Preesm, a rapid prototyping tool, to take the Simulink model as entry input and to support the optimal schedule. Afterward, we compared our results to this tool results, using a simple illustrative application. The comparison shows that our results strictly dominate the Preesm results in terms of number of cores and latency. In fact, if Preesm needs m processors and latency L, our workflow need processors and latency L'< L.

Keywords: hardware/software system, latency, modeling, multi-cores platform, scheduler, SDF graph, Simulink model, workflow

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Authors: Balarabe Z. Ahmad, Anne-Lorène Vernay

Abstract:

The emergence of electricity in Nigeria was dated back to 1896. The power plants have the potential to generate 12,522 MW of electric power. Whereas current dispatch is about 4,000 MW, access to electrification is about 60%, with consumption at 0.14 MWh/capita. The government embarked on energy reforms to mitigate energy poverty. The reform targeted the provision of electricity access to 75% of the population by 2020 and 90% by 2030. Growth of total electricity demand by a factor of 5 by 2035 had been projected. This means that Nigeria will require almost 530 TWh of electricity which can be delivered through generators with a capacity of 65 GW. Analogously, the geographical location of Nigeria has placed it in an advantageous position as the source of solar energy; the availability of a high sunshine belt is obvious in the country. The implication is that the far North, where energy poverty is high, equally has about twice the solar radiation as against southern Nigeria. Hence, the chance of generating solar electricity is 66% possible at 11850 x 103 GWh per year, which is one hundred times the current electricity consumption rate in the country. Harvesting these huge potentials may be a mirage if the entrepreneurs in the solar panel business are left with the conventional business models that are not uncertainty resilient. Currently, business entities in RE in Nigeria are uncertain of; accessing the national grid, purchasing potentials of cooperating organizations, currency fluctuation and interest rate increases. Uncertainties such as the security of projects and government policy are issues entrepreneurs must navigate to remain sustainable in the solar panel industry in Nigeria. The aim of this paper is to identify how entrepreneurial firms consider uncertainties in developing workable business models for commercializing solar energy projects in Nigeria. In an attempt to develop a novel business model, the paper investigated how entrepreneurial firms assess and navigate uncertainties. The roles of key stakeholders in helping entrepreneurs to manage uncertainties in the Nigeria RE sector were probed in the ongoing study. The study explored empirical uncertainties that are peculiar to RE entrepreneurs in Nigeria. A mixed-mode of research was embraced using qualitative data from face-to-face interviews conducted on the Solar Energy Entrepreneurs and the experts drawn from key stakeholders. Content analysis of the interview was done using Atlas. It is a nine qualitative tool. The result suggested that all stakeholders are required to synergize in developing an uncertainty resilient business model. It was opined that the RE entrepreneurs need modifications in the business recommendations encapsulated in the energy policy in Nigeria to strengthen their capability in delivering solar energy solutions to the yawning Nigerians.

Keywords: uncertainties, entrepreneurial, business model, solar-panel

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