Search results for: data driven

Authors: Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan

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We perform an investigation of the unclosed terms in the evolution equation of the velocity gradient tensor (VGT) in compressible decaying turbulent flow. Velocity gradients in a compressible turbulent flow field influence several important nonlinear turbulent processes like cascading and intermittency. In an attempt to understand the dynamics of the velocity gradients various researchers have tried to model the unclosed terms in the evolution equation of the VGT. The existing models proposed for these unclosed terms have limited applicability. This is mainly attributable to the complex structure of the higher order gradient terms appearing in the evolution equation of VGT. We investigate these higher order gradients using the data from direct numerical simulation (DNS) of compressible decaying isotropic turbulent flow. The gas kinetic method aided with weighted essentially non-oscillatory scheme (WENO) based flow- reconstruction is employed to generate DNS data. By applying neural-network to the DNS data, we map the structure of the unclosed higher order gradient terms in the evolution of the equation of the VGT with VGT itself. We validate our findings by performing alignment based study of the unclosed higher order gradient terms obtained using the neural network with the strain rate eigenvectors.

Keywords: compressible turbulence, neural network, velocity gradient tensor, direct numerical simulation

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Authors: Adan Valdez, Shawn Gallaher, James Morison, Jordan Aragon

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Global climate change has impacted atmospheric temperatures contributing to rising sea levels, decreasing sea ice, and increased freshening of high latitude oceans. This freshening has contributed to increased stratification inhibiting local mixing and nutrient transport and modifying regional circulations in polar oceans. In recent years, the Western Arctic has seen an increase in freshwater volume at an average rate of 397+-116 km3/year. The majority of the freshwater volume resides in the Beaufort Gyre surface lens driven by anticyclonic wind forcing, sea ice melt, and Arctic river runoff. The total climatological freshwater content is typically defined as water fresher than 34.8. The near-isothermal nature of Arctic seawater and non-linearities in the equation of state for near-freezing waters result in a salinity driven pycnocline as opposed to the temperature driven density structure seen in the lower latitudes. In this study, we investigate the relationship between freshwater content and remotely sensed dynamic ocean topography (DOT). In-situ measurements of freshwater content are useful in providing information on the freshening rate of the Beaufort Gyre; however, their collection is costly and time consuming. NASA’s Advanced Topographic Laser Altimeter System (ATLAS) derived dynamic ocean topography (DOT), and Air Expendable CTD (AXCTD) derived Freshwater Content are used to develop a linear regression model. In-situ data for the regression model is collected across the 150° West meridian, which typically defines the centerline of the Beaufort Gyre. Two freshwater content models are determined by integrating the freshwater volume between the surface and an isopycnal corresponding to reference salinities of 28.7 and 34.8. These salinities correspond to those of the winter pycnocline and total climatological freshwater content, respectively. Using each model, we determine the strength of the linear relationship between freshwater content and satellite derived DOT. The result of this modeling study could provide a future predictive capability of freshwater volume changes in the Beaufort-Chukchi Sea using non in-situ methods. Successful employment of the ICESat-2’s DOT approximation of freshwater content could potentially reduce reliance on field deployment platforms to characterize physical ocean properties.

Keywords: ICESat-2, dynamic ocean topography, freshwater content, beaufort gyre

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Authors: Omid Tavasoli, Mahmoud Ghazavi

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A numerical analysis of drivability of piles in different geometry is presented. In this paper, a three-dimensional finite difference analysis for plastic point extension and soil compaction in the effect of pile driving is analyzed. Four pile configurations such as cylindrical pile, fully tapered pile, T-C pile consists of a top tapered segment and a lower cylindrical segment and C-T pile has a top cylindrical part followed by a tapered part are investigated. All piles which driven up to a total penetration depth of 16 m have the same length with equivalent surface area and approximately with identical material volumes. An idealization for pile-soil system in pile driving is considered for this approach. A linear elastic material is assumed to model the vertical pile behaviors and the soil obeys the elasto-plastic constitutive low and its failure is controlled by the Mohr-Coulomb failure criterion. A slip which occurred at the pile-soil contact surfaces along the shaft and the toe in pile driving procedures is simulated with interface elements. All initial and boundary conditions are the same in all analyses. Quiet boundaries are used to prevent wave reflection in the lateral and vertical directions for the soil. The results obtained from numerical analyses were compared with available other numerical data and laboratory tests, indicating a satisfactory agreement. It will be shown that with increasing the angle of taper, the permanent piles toe settlement increase and therefore, the extension of plastic points increase. These are interesting phenomena in pile driving and are on the safe side for driven piles.

Keywords: pile driving, finite difference method, non-uniform piles, pile geometry, pile set, plastic points, soil compaction

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Authors: Rômulo Eustáquio Martins de Souza, Paulo Alexandre Rodrigues de Vasconcelos Figueiredo

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In recent years, there has been a surge in interest in applying artificial intelligence techniques, particularly machine learning algorithms. Machine learning is a data-analysis technique that automates the creation of analytical models, making it especially useful for designing complex situations. As a result, this technology aids in reducing human intervention while producing accurate results. This methodology is also extensively used in aerospace engineering since this is a field that encompasses several high-complexity operations, such as rocket propulsion. Rocket propulsion is a high-risk operation in which engine failure could result in the loss of life. As a result, it is critical to use computational methods capable of precisely representing the spacecraft's analytical model to guarantee its security and operation. Thus, this paper describes the use of machine learning algorithms for rocket propulsion to aid the realization that this technique is an efficient way to deal with challenging and restrictive aerospace engineering activities. The paper focuses on three machine-learning-aided rocket propulsion applications: set-point control of an expander-bleed rocket engine, supersonic retro-propulsion of a small-scale rocket, and leak detection and isolation on rocket engine data. This paper describes the data-driven methods used for each implementation in depth and presents the obtained results.

Keywords: data analysis, modeling, machine learning, aerospace, rocket propulsion

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Authors: Xiaodong Wen, Lijuan Liu, Xinfeng Sun

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A high power electrodeless plasma thruster is being developed at Lanzhou Institute of Physics. In this thruster, a rotating magnetic field (RMF) driven by two radio-frequency coils which dephased by 90 degrees are applied both for propellant ionization and plasma acceleration. In the ionization stage, a very high azimuthal current can be driven by RMF and then makes plasma forms a field reversed configuration, namely self-confined plasmoid. Profoundly understanding the transport characteristics of the plasmoid in the following acceleration stage is the key to improve the thruster performances. In this paper, a 3D MHD model is established and the influences of the RMF and an applied magnetic field on the self-confined plasmoid acceleration are investigated. The simulation results show that, by applying a RMF with strength and frequency of 250 G and 370 kHz, the plasmoid can be accelerated to an average velocity of 17 km/s at the exit of the thruster.

Keywords: electric space propulsion, field reversed configuration, rotating magnetic field, transport phenomena

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Authors: Weiliang Wen, Xinyu Guo, Ying Zhang, Jianjun Du, Boxiang Xiao

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Reconstructing and analyzing three-dimensional (3D) models from situ measured data is important for a number of researches and applications in plant science, including plant phenotyping, functional-structural plant modeling (FSPM), plant germplasm resources protection, agricultural technology popularization. It has many scales like cell, tissue, organ, plant and canopy from micro to macroscopic. The techniques currently used for data capture, feature analysis, and 3D reconstruction are quite different of different scales. In this context, morphological data acquisition, 3D analysis and modeling of plants on different scales are introduced systematically. The commonly used data capture equipment for these multiscale is introduced. Then hot issues and difficulties of different scales are described respectively. Some examples are also given, such as Micron-scale phenotyping quantification and 3D microstructure reconstruction of vascular bundles within maize stalks based on micro-CT scanning, 3D reconstruction of leaf surfaces and feature extraction from point cloud acquired by using 3D handheld scanner, plant modeling by combining parameter driven 3D organ templates. Several application examples by using the 3D models and analysis results of plants are also introduced. A 3D maize canopy was constructed, and light distribution was simulated within the canopy, which was used for the designation of ideal plant type. A grape tree model was constructed from 3D digital and point cloud data, which was used for the production of science content of 11th international conference on grapevine breeding and genetics. By using the tissue models of plants, a Google glass was used to look around visually inside the plant to understand the internal structure of plants. With the development of information technology, 3D data acquisition, and data processing techniques will play a greater role in plant science.

Keywords: plant, three dimensional modeling, multi-scale, plant phenotyping, three dimensional data acquisition

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Authors: Siham Rahoui, Amr Mahfouz, Amr Arisha

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In a highly competitive environment characterised by uncertainty and disruptions, such as the recent COVID-19 outbreak, supply chains (SC) face the challenge of maintaining their cost at minimum levels while continuing to provide customers with high-quality products and services. More importantly, businesses in such an economic context strive to maintain survival by keeping the cost of undertaken activities (such as logistics) low and in-house. To do so, managers need to understand the costs associated with different products and services in order to have a clear vision of the SC performance, maintain profitability levels, and make strategic decisions. In this context, SC literature explored different costing models that sought to determine the costs of undertaking supply chain-related activities. While some cost accounting techniques have been extensively explored in the SC context, more contributions are needed to explore the potential of time driven activity-based costing (TDABC). More specifically, more applications are needed in the manufacturing context of the SC, where the debate is ongoing. The aim of the study is to assess the capability of the technique to assess the operational performance of the logistics function. Through a case study methodology applied to a manufacturing company operating in the automotive industry, TDABC evaluates the efficiency of the current configuration and its logistics processes. The study shows that monitoring the process efficiency and cost efficiency leads to strategic decisions that contributed to improve the overall efficiency of the logistics processes.

Keywords: efficiency, operational performance, supply chain costing, time driven activity based costing

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Authors: Ailing Yang, Penghan Song, Aihua Cai

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The nonlinear maneuvering target tracking problem is mainly a state estimation problem when the target motion model is uncertain. Traditional solutions include Kalman filtering based on Bayesian filtering framework and extended Kalman filtering. However, these methods need prior knowledge such as kinematics model and state system distribution, and their performance is poor in state estimation of nonprior complex dynamic systems. Therefore, in view of the problems existing in traditional algorithms, a convolution LSTM target state estimation (SAConvLSTM-SE) algorithm based on Self-Attention memory (SAM) is proposed to learn the historical motion state of the target and the error distribution information measured at the current time. The measured track point data of airborne radar are processed into data sets. After supervised training, the data-driven deep neural network based on SAConvLSTM can directly obtain the target state at the next moment. Through experiments on two different maneuvering targets, we find that the network has stronger robustness and better tracking accuracy than the existing tracking methods.

Keywords: maneuvering target, state estimation, Kalman filter, LSTM, self-attention

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Authors: Dina Magdy Abdo, Ayat N. El-Shazly, Hamdy Maamoun Abdel-Ghafar, E. A. Abdel-Aal

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Forward osmosis (FO) is one of the important processes during the wastewater treatment system for environmental remediation and fresh water regeneration. Both Egypt and China are troubled by over millions of tons of wastewater every year, including domestic and industrial wastewater. However, traditional FO process in wastewater treatment usually suffers low efficiency and high energy consumption because of the continuously diluted draw solution. An additional concentration process is necessary to keep running of FO separation, causing energy waste. Based on the previous study on photothermal membrane, a sun-driven evaporation process is integrated into the draw solution side of FO system. During the sun-driven evaporation, not only the draw solution can be concentrated to maintain a stable and sustainable FO system, but fresh water can be directly separated for regeneration. Solar energy is the ultimate energy source of everything we have on Earth and is, without any doubt, the most renewable and sustainable energy source available to us. Additionally, the FO membrane process is rationally designed to limit the concentration polarization and fouling. The FO membrane’s structure and surface property will be further optimized by the adjustment of the doping ratio of controllable nano-materials, membrane formation conditions, and selection of functional groups. A novel kind of nano-composite functional separation membrane with bi-interception layers and high hydrophilicity will be developed for the application in wastewater treatment. So, herein we aim to design a new wastewater treatment system include forward osmosis with high-efficiency energy recovery via the integration of photothermal membrane.

Keywords: forword, membrane, solar, water treatment

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

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

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

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Authors: Irna Farikhah

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Converting low-grade waste heat into useful energy such as sound energy which can then be used to generate acoustic power in a thermoacoustic engine has become an attracting issue for researchers. The generated power in thermoacoustic engine can be used for driving a thermoacoustic cooler when they are installed in a tube. This cooler system can be called as a heat-driven thermoacoustic cooler. In this study, low heating temperature of the engine is discussed. In addition, having high efficiency of the whole cooler is also essential. To design a thermoacoustic cooler having high efficiency with using low-grade waste heat for the engine, the effect of mean pressure is investigated. By increasing the mean pressure, the heating temperature to generate acoustic power can be decreased from 557 °C to 300 °C. Moreover, the efficiency of the engine and cooler regenerators attain 67% and 47% of the upper limit values, respectively and 49% of the acoustical work generated by the engine regenerator is utilized in the cooler regenerator. As a result, the efficiency of the whole cooler becomes 15% of the upper limit value.

Keywords: cooler, mean pressure, performance, thermoacoustic

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Bylhah Mugotitsa, Jim Todd, Agnes Kiragga, Jay Greenfield, Evans Omondi, Lukoye Atwoli, Reinpeter Momanyi

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The INSPIRE network aims to build an open, ethical, sustainable, and FAIR (Findable, Accessible, Interoperable, Reusable) data science platform, particularly for longitudinal mental health (MH) data. While studies have been done at the clinical and population level, there still exists limitations in data and research in LMICs, which pose a risk of underrepresentation of mental disorders. It is vital to examine the existing longitudinal MH data, focusing on how FAIR datasets are. This landscape analysis aimed to provide both overall level of evidence of availability of longitudinal datasets and degree of consistency in longitudinal studies conducted. Utilizing prompters proved instrumental in streamlining the analysis process, facilitating access, crafting code snippets, categorization, and analysis of extensive data repositories related to depression, anxiety, and psychosis in Africa. While leveraging artificial intelligence (AI), we filtered through over 18,000 scientific papers spanning from 1970 to 2023. This AI-driven approach enabled the identification of 228 longitudinal research papers meeting inclusion criteria. Quality assurance revealed 10% incorrectly identified articles and 2 duplicates, underscoring the prevalence of longitudinal MH research in South Africa, focusing on depression. From the analysis, evaluating data and metadata adherence to FAIR principles remains crucial for enhancing accessibility and quality of MH research in Africa. While AI has the potential to enhance research processes, challenges such as privacy concerns and data security risks must be addressed. Ethical and equity considerations in data sharing and reuse are also vital. There’s need for collaborative efforts across disciplinary and national boundaries to improve the Findability and Accessibility of data. Current efforts should also focus on creating integrated data resources and tools to improve Interoperability and Reusability of MH data. Practical steps for researchers include careful study planning, data preservation, machine-actionable metadata, and promoting data reuse to advance science and improve equity. Metrics and recognition should be established to incentivize adherence to FAIR principles in MH research

Keywords: longitudinal mental health research, data sharing, fair data principles, Africa, landscape analysis

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Authors: Patricia Kopetz

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Too often our youngest children with disabilities receive diagnostic labels and accompanying treatment plans based upon perceptions that the children are of limited aptitude and/or ambition. However, children of varied-ability levels who are diagnosed with ‘multiple disabilities,’ can participate and excel in school-based instruction that aligns with their desires, interests, and fortitude – criteria components not foretold by scores on standardized assessments. The paper represents theoretical work in Special Education Innovative Instruction, and includes presenting research materials, some developed by the author herself. The majority of students with disabilities are now served in general education settings in the United States, embracing inclusive practices in our schools. ‘There is now a stronger call for special education to step up and improve efficiency, implement evidence-based practices, and provide greater accountability on key performance indicators that support successful academic and post-school outcomes for students with disabilities.’ For example, in the United States, the Office of Special Education Programs (OSEP) is focusing on results-driven indicators to improve outcomes for students with disabilities. School personnel are appreciating the implications of research-driven approaches for students diagnosed with multiple disabilities, and aim to align their practices toward such focus. The paper presented will provide updates on current theoretical principles and perspectives, and explore advancements in latest, evidence-based and results-driven instructional practices that can motivate children with multiple disabilities to advance their skills and engage in learning activities that as nonconventional, innovative, and proven successful.

Keywords: childhood special education, educational technology , innovative instruction, multiple disabilities

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Authors: Glebert C. Dadol, Pamela Mae L. Ucab, Camila Flor Y. Lobarbio, Noel Peter B. Tan

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Water scarcity is a global problem and membrane-based desalination technologies are one of the promising solutions to this problem. In this study, a passive solar-driven membrane distiller was fabricated and tested for its desalination performance. The distiller was composed of a TiNOX plate solar absorber, cellulose-based upper and lower hydrophilic layers, a hydrophobic middle layer, and aluminum heatsinks. The effect of the middle layer material and thickness on the desalination performance was investigated in terms of distillate productivity and salinity. The materials used for the middle layer were a screen mesh (2 mm, 4 mm, 6 mm thickness) to generate an air gap, a PTFE membrane (0.3 mm thickness)), and a combination of the screen mesh and the PTFE membrane (2.3 mm total thickness). Salt water (35 g/L NaCl) was desalinated using the distiller at a rooftop setting at the University of San Carlos, Cebu City, Philippines. The highest distillate productivity of 1.08 L/m2-h was achieved using a 2-mm screen mesh (air gap) but it also resulted in a high distillate salinity of 25.20 g/L. Increasing the thickness of the air gap lowered the distillate salinity but also decreased the distillate productivity. The lowest salinity of 1.07 g/L was achieved using a 6-mm air gap but the productivity was reduced to 0.08 L/m2-h. The use of the hydrophobic PTFE membrane increased the productivity (0.44 L/m2-h) compared to a 6-mm air gap but produced a distillate with high salinity (16.68 g/L). When using a combination of the screen mesh and the PTFE membrane, the productivity was 0.13 L/m2-h and a distillate salinity of 1.61 g/L. The distiller with a thick air gap as the middle layer can deliver a distillate with low salinity and is preferred over a thin hydrophobic PTFE membrane. The use of a combination of the air gap and PTFE membrane slightly increased the productivity with comparable distillate salinity. Modifications and optimizations to the distiller can be done to improve further its performance.

Keywords: desalination, membrane distillation, passive solar-driven membrane distiller, solar distillation

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Authors: Hebberly Ahatlan

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The process of digitizing energy systems involves transforming traditional energy infrastructure into interconnected, data-driven systems that enhance efficiency, sustainability, and responsiveness. As smart grids become increasingly integral to the efficient distribution and management of electricity from both fossil and renewable energy sources, the energy industry faces strategic challenges associated with digitalization and interoperability — particularly in the context of modern energy business models, such as virtual power plants (VPPs). The critical challenge in modern smart grids is to seamlessly integrate diverse technologies and systems, including virtualization, grid computing and service-oriented architecture (SOA), across the entire energy ecosystem. Achieving this requires addressing issues like semantic interoperability, IT/OT convergence, and digital asset scalability, all while ensuring security and risk management. This paper proposes a four-layer digitalization framework to tackle these challenges, encompassing persistent data protection, trusted key management, secure messaging, and authentication of IoT resources. Data assets generated through this framework enable AI systems to derive insights for improving smart grid operations, security, and revenue generation. Furthermore, this paper also proposes a Trusted Energy Interoperability Alliance as a universal guiding standard in the development of this digitalization framework to support more dynamic and interoperable energy markets.

Keywords: digitalization, IT/OT convergence, semantic interoperability, VPP, energy blockchain

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Authors: Kassahun Tilahun

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Most of the studies available on adult psychological well-being have been centered on Western countries. However, psychological well-being does not have the same meaning across the world. The Euro-American and African conceptions and experiences of psychological well-being differ systematically. As a result, questions like, how do people living in developing African countries, like Ethiopia, report their psychological well-being; what would the context-specific prominent determinants of their psychological well-being be, needs a definitive answer. This study was, therefore, aimed at developing a new theory that would address these socio-cultural issues of psychological well-being. Consequently, data were obtained through interview and open ended questionnaire. A total of 438 adults, working in governmental and non-governmental organizations situated in Addis Ababa, participated in the study. Appropriate qualitative method of data analysis, i.e. thematic content analysis, was employed for analyzing the data. The thematic analysis involves a type of abductive analysis, driven both by theoretical interest and the nature of the data. Reliability and credibility issues were addressed appropriately. The finding identified five major categories of themes, which are viewed as essential in determining the conceptions and experiences of psychological well-being of Ethiopian adults. These were; socio-cultural harmony, social cohesion, security, competence and accomplishment, and the self. Detailed discussion on the rational for including these themes was made and appropriate positive psychology interventions were proposed. Researchers are also encouraged to expand this qualitative research and in turn develop a suitable instrument taping the psychological well-being of adults with different sociocultural orientations.

Keywords: sociocultural, psychological, well-being Ethiopia, adults

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Authors: Belmiloud Mohamed Amine, Sad Chemloul Nord-Eddine

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In this study we investigated numerically heat transfer by mixed convection coupled to radiation in a square cavity; the upper horizontal wall is movable. The purpose of this study is to see the influence of the emissivity and the varying of the Richardson number on the variation of the average Nusselt number. The vertical walls of the cavity are differentially heated, the left wall is maintained at a uniform temperature higher than the right wall, and the two horizontal walls are adiabatic. The finite volume method is used for solving the dimensionless governing equations. Emissivity values used in this study are ranged between 0 and 1, the Richardson number in the range 0.1 to10. The Rayleigh number is fixed to Ra = 10000 and the Prandtl number is maintained constant Pr = 0.71. Streamlines, isothermal lines and the average Nusselt number are presented according to the surface emissivity. The results of this study show that the Richardson number and emissivity affect the average Nusselt number.

Keywords: mixed convection, square cavity, wall emissivity, lid-driven, numerical study

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Authors: Feng Yang

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Machine learning (ML), especially deep learning (DL), has been extensively applied to many applications in recently years and gained great success in solving different problems, including scientific problems. However, conventional ML/DL methodologies are purely data-driven which have the limitations, such as need of ample amount of labelled training data, lack of consistency to physical principles, and lack of generalizability to new problems/domains. Recently, there is a growing consensus that ML models need to further take advantage of prior knowledge to deal with these limitations. Physics-informed machine learning, aiming at integration of physics/domain knowledge into ML, has been recognized as an emerging area of research, especially in the recent 2 to 3 years. In this work, physics-informed ML, specifically physics-informed neural network (NN), is employed and implemented to estimate the displacements at x, y, z directions in a solid mechanics problem that is controlled by equilibrium equations with boundary conditions. By incorporating the physics (i.e. the equilibrium equations) into the learning process of NN, it is showed that the NN can be trained very efficiently with a small set of labelled training data. Experiments with different settings of the NN model and the amount of labelled training data were conducted, and the results show that very high accuracy can be achieved in fulfilling the equilibrium equations as well as in predicting the displacements, e.g. in setting the overall displacement of 0.1, a root mean square error (RMSE) of 2.09 × 10−4 was achieved.

Keywords: deep learning, neural network, physics-informed machine learning, solid mechanics

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Authors: D. Amaranatha Reddy

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Highly efficient and stable co-catalysts materials is of great important for boosting photo charge carrier’s separation, transportation efficiency, and accelerating the catalytic reactive sites of semiconductor photocatalysts. As a result, it is of decisive importance to fabricate low price noble metal free co-catalysts with high catalytic reactivity, but it remains very challenging. Considering this challenge here, dual metal organic frame work derived N-Doped Fe3C nanocages have been rationally designed and decorated with ultrathin ZnIn2S4 nanosheets for efficient photocatalytic hydrogen generation. The fabrication strategy precisely integrates co-catalyst nanocages with ultrathin two-dimensional (2D) semiconductor nanosheets by providing tightly interconnected nano-junctions and helps to suppress the charge carrier’s recombination rate. Furthermore, constructed highly porous hybrid structures expose ample active sites for catalytic reduction reactions and harvest visible light more effectively by light scattering. As a result, fabricated nanostructures exhibit superior solar driven hydrogen evolution rate (9600 µmol/g/h) with an apparent quantum efficiency of 3.6 %, which is relatively higher than the Pt noble metal co-catalyst systems and earlier reported ZnIn2S4 based nanohybrids. We believe that the present work promotes the application of sulfide based nanostructures in solar driven hydrogen production.

Keywords: photocatalysis, water splitting, hydrogen fuel production, solar-driven hydrogen

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Authors: Gaurav Kumar Sinha

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In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: The Danh Phan

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House price forecasting is a main topic in the real estate market research. Effective house price prediction models could not only allow home buyers and real estate agents to make better data-driven decisions but may also be beneficial for the property policymaking process. This study investigates the housing market by using machine learning techniques to analyze real historical house sale transactions in Australia. It seeks useful models which could be deployed as an application for house buyers and sellers. Data analytics show a high discrepancy between the house price in the most expensive suburbs and the most affordable suburbs in the city of Melbourne. In addition, experiments demonstrate that the combination of Stepwise and Support Vector Machine (SVM), based on the Mean Squared Error (MSE) measurement, consistently outperforms other models in terms of prediction accuracy.

Keywords: house price prediction, regression trees, neural network, support vector machine, stepwise

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Authors: Yuta Moriyama, Tsuyoshi Yamazaki, Etsuo Morishita

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Several aerodynamic devices currently attract engineers and research students. The plasma actuator is one of them, and it is very effective to control the flow. The actuator recovers a separated flow to an attached one. The actuator is also inversely applied to a spoiler. The model aircraft might be controlled by this actuator. We develop a model aircraft with the plasma actuator. Another interesting device is the Wells turbine which rotates in one direction. The present authors propose a bicycle with the Wells turbine in the wheels. Power reduction is measured when the turbine is driven by an electric motor at the exit of a wind tunnel. Several Watts power reduction might be possible. This means that the torque of the bike can be augmented by the turbine in the cross wind. These devices are tested in the wind tunnel with a three-component balance and the aerodynamic forces and moment are obtained. In this paper, we introduce these devices and their aerodynamic characteristics. The control force and moment of the plasma actuator are clarified and the power reduction of the bicycle is quantified.

Keywords: aerodynamics, model aircraft, plasma actuator, Wells turbine

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Authors: Kakali Bhadra

Abstract:

Binding of small molecules to DNA and recently to RNA, continues to attract considerable attention for developing effective therapeutic agents for control of gene expression. This work focuses towards understanding interaction of harmalol, a dihydro beta-carboline alkaloid, with different nucleic acid motifs viz. double stranded CT DNA, single stranded A-form poly(A), double-stranded A-form of poly(C)·poly(G) and clover leaf tRNAphe by different spectroscopic, calorimetric and molecular modeling techniques. Results of this study converge to suggest that (i) binding constant varied in the order of CT DNA > poly(C)·poly(G) > tRNAphe > poly(A), (ii) non-cooperative binding of harmalol to poly(C)·poly(G) and poly(A) and cooperative binding with CT DNA and tRNAphe, (iii) significant structural changes of CT DNA, poly(C)·poly(G) and tRNAphe with concomitant induction of optical activity in the bound achiral alkaloid molecules, while with poly(A) no intrinsic CD perturbation was observed, (iv) the binding was predominantly exothermic, enthalpy driven, entropy favoured with CT DNA and poly(C)·poly(G) while it was entropy driven with tRNAphe and poly(A), (v) a hydrophobic contribution and comparatively large role of non-polyelectrolytic forces to Gibbs energy changes with CT DNA, poly(C)·poly(G) and tRNAphe, and (vi) intercalated state of harmalol with CT DNA and poly(C)·poly(G) structure as revealed from molecular docking and supported by the viscometric data. Furthermore, with competition dialysis assay it was shown that harmalol prefers hetero GC sequences. All these findings unequivocally pointed out that harmalol prefers binding with ds CT DNA followed by ds poly(C)·poly(G), clover leaf tRNAphe and least with ss poly(A). The results highlight the importance of structural elements in these natural beta-carboline alkaloids in stabilizing different DNA and RNA of various motifs for developing nucleic acid based better therapeutic agents.

Keywords: calorimetry, docking, DNA/RNA-alkaloid interaction, harmalol, spectroscopy

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Authors: Jiangxia Han, Liang Xue, Keda Chen

Abstract:

Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

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Authors: Sandeep Sharma, Sarabjit Singh

Abstract:

As per the user demand and growth trends of large free data the storage solutions are now becoming more challenge-able to protect, store and to retrieve data. The days are not so far when the storage companies and organizations are start saying 'no' to store our valuable data or they will start charging a huge amount for its storage and protection. On the other hand as per the environmental conditions it becomes challenge-able to maintain and establish new data warehouses and data centers to protect global warming threats. A challenge of small data is over now, the challenges are big that how to manage the exponential growth of data. In this paper we have analyzed the growth trend of big data and its future implications. We have also focused on the impact of the unstructured data on various concerns and we have also suggested some possible remedies to streamline big data.

Keywords: big data, unstructured data, volume, variety, velocity

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Authors: Sachin Sharma

Abstract:

A wireless sensor network (WSN) consists of a large number of sensor nodes which are deployed over an area to perform local computations based on information gathered from the surroundings. With the increasing demand for real-time applications in WSN, real-time critical events anticipate an efficient quality-of-service (QoS) based routing for data delivery from the network infrastructure. Hence, maximizing the lifetime of the network through minimizing the energy is an important challenge in WSN; sensors cannot be easily replaced or recharged due to their ad-hoc deployment in a hazardous environment. Considerable research has been focused on developing robust energy efficient QoS based routing protocols. The main focus of this article is primarily on periodical cycling schemes which represent the most compatible technique for energy saving and we also focus on the data-driven approaches that can be used to improve the energy efficiency. Finally, we will make a review on some communication protocols proposed for sensor networks.

Keywords: energy efficient, quality of service, wireless sensor networks, MAC

Procedia PDF Downloads 313

Authors: Jamil Ahmed

Abstract:

Access control models aim to decide whether a user should be denied or granted access to the user‟s requested activity. Various access control models have been established and proposed. The most prominent of these models include role-based, attribute-based, policy based access control models as well as role-centric attribute based access control model. In this paper, a novel access control model is presented called “Role centric Attribute based Policy Driven Access Control (RAPDAC) model”. RAPDAC incorporates the concept of “policy” in the “role centric attribute based access control model”. It leverages the concept of "policy‟ by precisely combining the evaluation of conditions, attributes, permissions and roles in order to allow authorization access. This approach allows capturing the "access control policy‟ of a real time application in a well defined manner. RAPDAC model allows making access decision at much finer granularity as illustrated by the case study of a real time library information system.

Keywords: authorization, access control model, role based access control, attribute based access control

Procedia PDF Downloads 131