Search results for: data mining applications and discovery

Authors: Chico Horacio Jose Sambo

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Recently neural network has gained popularity when come to solve complex nonlinear problems. Permeability is one of fundamental reservoir characteristics system that are anisotropic distributed and non-linear manner. For this reason, permeability prediction from well log data is well suited by using neural networks and other computer-based techniques. The main goal of this paper is to predict reservoir permeability from well logs data by using neural network approach. A multi-layered perceptron trained by back propagation algorithm was used to build the predictive model. The performance of the model on net results was measured by correlation coefficient. The correlation coefficient from testing, training, validation and all data sets was evaluated. The results show that neural network was capable of reproducing permeability with accuracy in all cases, so that the calculated correlation coefficients for training, testing and validation permeability were 0.96273, 0.89991 and 0.87858, respectively. The generalization of the results to other field can be made after examining new data, and a regional study might be possible to study reservoir properties with cheap and very fast constructed models.

Keywords: neural network, permeability, multilayer perceptron, well log

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Authors: Salihah Alghamdi, Surajit Ray

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Space-time data can be observed over irregularly shaped manifolds, which might have complex boundaries or interior gaps. Most of the existing methods do not consider the shape of the data, and as a result, it is difficult to model irregularly shaped data accommodating the complex domain. We used a method that can deal with space-time data that are distributed over non-planner shaped regions. The method is based on partial differential equations and finite element analysis. The model can be estimated using a penalized least squares approach with a regularization term that controls the over-fitting. The model is regularized using two roughness penalties, which consider the spatial and temporal regularities separately. The integrated square of the second derivative of the basis function is used as temporal penalty. While the spatial penalty consists of the integrated square of Laplace operator, which is integrated exclusively over the domain of interest that is determined using finite element technique. In this paper, we applied a spatio-temporal regression model with partial differential equations regularization (ST-PDE) approach to analyze a remote sensing data measuring the greenness of vegetation, measure by an index called enhanced vegetation index (EVI). The EVI data consist of measurements that take values between -1 and 1 reflecting the level of greenness of some region over a period of time. We applied (ST-PDE) approach to irregular shaped region of the EVI data. The approach efficiently accommodates the irregular shaped regions taking into account the complex boundaries rather than smoothing across the boundaries. Furthermore, the approach succeeds in capturing the temporal variation in the data.

Keywords: irregularly shaped domain, partial differential equations, finite element analysis, complex boundray

Procedia PDF Downloads 144

Authors: P. Daniels, T. Adonis, P. September-Brown, R. Comalie

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The community engagement unit at the University of the Western Cape was tasked with establishing a community engagement database. The database would store information of all community engagement projects related to the university. The wealth of knowledge obtained from the various disciplines would be used to facilitate interdisciplinary collaboration within the university, as well as facilitating community university partnership opportunities. The purpose of this qualitative study was to explore electronic data collection through the development of a database. Two types of electronic data collection platforms were used, namely online questionnaire and email. The semi structured questionnaire was used to collect data related to community engagement projects from different faculties and departments at the university. There are many benefits for using an electronic data collection platform, such as reduction of costs and time, ease in reaching large numbers of potential respondents, and the possibility of providing anonymity to participants. Despite all the advantages of using the electronic platform, there were as many challenges, as depicted in our findings. The findings suggest that certain barriers existed by using an electronic platform for data collection, even though it was in an academic environment, where knowledge and resources were in abundance. One of the challenges experienced in this process was the lack of dissemination of information via email to staff within faculties. The actual online software used for the questionnaire had its own limitations, such as only being able to access the questionnaire from the same electronic device. In a few cases, academics only completed the questionnaire after a telephonic prompt or face to face meeting about "Is higher education in South Africa ready to embrace electronic platform in data collection?"

Keywords: community engagement, database, data collection, electronic platform, electronic tools, knowledge sharing, university

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Authors: Divya Juneja, Sukhjeet Kaur Matharu

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Purpose: The paper is aimed at identifying the challenging factors experienced by the women entrepreneurs in India in operating their enterprises amidst the challenges posed by the COVID-19 pandemic. Methodology: The sample for the study comprised 396 women entrepreneurs from different regions of India. A purposive sampling technique was adopted for data collection. Data was collected through a self-administered questionnaire. Analysis was performed using the SPSS package for quantitative data analysis. Findings: The results of the study state that entrepreneurial characteristics, resourcefulness, networking, adaptability, and continuity have a positive influence on the resiliency of women entrepreneurs when faced with a crisis situation. Practical Implications: The findings of the study have some important implications for women entrepreneurs, organizations, government, and other institutions extending support to entrepreneurs.

Keywords: women entrepreneurs, analysis, data analysis, positive influence, resiliency

Procedia PDF Downloads 118

Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

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Authors: Tapan Jain, Davender Singh Saini

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Clustering is a useful mechanism in wireless sensor networks which helps to cope with scalability and data transmission problems. The basic aim of our research work is to provide efficient clustering using Hierarchical agglomerative clustering (HAC). If the distance between the sensing nodes is calculated using their location then it’s quantitative HAC. This paper compares the various agglomerative clustering techniques applied in a wireless sensor network using the quantitative data. The simulations are done in MATLAB and the comparisons are made between the different protocols using dendrograms.

Keywords: routing, hierarchical clustering, agglomerative, quantitative, wireless sensor network

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Authors: Sophia Shi

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Acute kidney injury (AKI) is the sudden onset of kidney damage in which the kidneys cannot filter waste from the blood, requiring emergency hospitalization. AKI patient mortality rate is high in the ICU and is virtually impossible for doctors to predict because it is so unexpected. Currently, there is no hybrid model predicting AKI that takes advantage of two types of data. De-identified patient data from the MIMIC-III database and de-identified kidney images and corresponding patient records from the Beijing Hospital of the Ministry of Health were collected. Using data features including serum creatinine among others, two numeric models using MIMIC and Beijing Hospital data were built, and with the hospital ultrasounds, an image-only model was built. Convolutional neural networks (CNN) were used, VGG and Resnet for numeric data and Resnet for image data, and they were combined into a hybrid model by concatenating feature maps of both types of models to create a new input. This input enters another CNN block and then two fully connected layers, ending in a binary output after running through Softmax and additional code. The hybrid model successfully predicted AKI and the highest AUROC of the model was 0.953, achieving an accuracy of 90% and F1-score of 0.91. This model can be implemented into urgent clinical settings such as the ICU and aid doctors by assessing the risk of AKI shortly after the patient’s admission to the ICU, so that doctors can take preventative measures and diminish mortality risks and severe kidney damage.

Keywords: Acute kidney injury, Convolutional neural network, Hybrid deep learning, Patient record data, ResNet, Ultrasound kidney images, VGG

Procedia PDF Downloads 136

Authors: A. E. Kobryn

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We present both descriptive and predictive modeling of structural properties of blends of PCBM or organic-inorganic hybrid perovskites of the type CH3NH3PbX3 (X=Cl, Br, I) with P3HT, P3BT or squaraine SQ2 dye sensitizer, including adsorption on TiO2 clusters having rutile (110) surface. In our study, we use a methodology that allows computing the microscopic structure of blends on the nanometer scale and getting insight on miscibility of its components at various thermodynamic conditions. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site representation/model (RISM) and uses the universal force field. Input parameters for RISM, such as optimized molecular geometries and charge distribution of interaction sites, are derived with the use of the density functional theory methods. To compare the diffusivity of the PCBM in binary blends with P3HT and P3BT, respectively, the study is complemented with MD simulation. A very good agreement with experiment and the reports of alternative modeling or simulation is observed for PCBM in P3HT system. The performance of P3BT with perovskites, however, seems as expected. The calculated nanoscale morphologies of blends of P3HT, P3BT or SQ2 with perovskites, including adsorption on TiO2, are all new and serve as an instrument in rational design of organic/hybrid photovoltaics. They are used in collaboration with experts who actually make prototypes or devices for practical applications.

Keywords: multiscale theory and modeling, nanoscale morphology, organic-inorganic halide perovskites, three dimensional distribution

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Authors: Taner Ersoz, Filiz Ersoz

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This study was designed enable application of multivariate technique in the interpretation of categorical data for measuring health care services satisfaction in Turkey. The data was collected from a total of 17726 respondents. The establishment of the sample group and collection of the data were carried out by a joint team from The Ministry of Health and Turkish Statistical Institute (Turk Stat) of Turkey. The multiple correspondence analysis (MCA) was used on the data of 2882 respondents who answered the questionnaire in full. The multiple correspondence analysis indicated that, in the evaluation of health services females, public employees, younger and more highly educated individuals were more concerned and complainant than males, private sector employees, older and less educated individuals. Overall 53 % of the respondents were pleased with the improvements in health care services in the past three years. This study demonstrates the public consciousness in health services and health care satisfaction in Turkey. It was found that most the respondents were pleased with the improvements in health care services over the past three years. Awareness of health service quality increases with education levels. Older individuals and males would appear to have lower expectancies in health services.

Keywords: multiple correspondence analysis, multivariate categorical data, health care services, health satisfaction survey

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Authors: Sofiane Grira, Hichem Eleuch

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We explore the analytical soliton-pair solutions for unbalanced coupling between the two coherent lights and the atomic transitions in a dissipative three-level system in lambda configuration. The two allowed atomic transitions are interacting resonantly with two laser fields. For unbalanced coupling, it is possible to derive an explicit solution for non-linear differential equations describing the soliton-pair propagation in this three-level system with the same velocity. We suppose that the spontaneous emission rates from the excited state to both ground states are the same. In this work, we focus on such case where we consider the coupling between the transitions and the optical fields are unbalanced. The existence conditions for the soliton-pair propagations are determined. We will show that there are four possible configurations of the soliton-pair pulses. Two of them can be interpreted as a couple of solitons with same directions of polarization and the other two as soliton-pair with opposite directions of polarization. Due to the fact that solitons have stable shapes while propagating in the considered media, they are insensitive to noise and dispersion. Our results have potential applications in data transfer with the soliton-pair pulses, where a dissipative three-level medium could be a realistic model for the optical communication media.

Keywords: non-linear differential equations, solitons, wave propagations, optical fiber

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Authors: Paul Dallyn, Ashraf El-Hamalawi, Alessandro Palmeri, Bob Knight

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In order to better understand the long term implications of the grout wear failure mode in large-diameter plain-sided grouted connections, a numerical model has been developed and calibrated that can take advantage of existing operational plant data to predict the wear accumulation for the actual load conditions experienced over a given period, thus limiting the need for expensive monitoring systems. This model has been derived and calibrated based on site structural condition monitoring (SCM) data and supervisory control and data acquisition systems (SCADA) data for two operational wind turbine generator substructures afflicted with this challenge, along with experimentally derived wear rates.

Keywords: grouted connection, numerical model, offshore structure, wear, wind energy

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Authors: Muhammad Hassan, Kemal Celebi

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Developing a highly efficient electrochromic energy storage device with sufficient color fluctuation and significant electrochemical performance is highly desirable for practical energy-saving applications. Here, to achieve a highly stable material with a large electrochemical storage capacity, a W₁₈O₄₉ NW/Ti₃C₂Tₓ composite has been fabricated and deposited on a pre-assembled Ag and W₁₈O₄₉ NW conductive network by Langmuir-Blodgett technique. The resulting hybrid electrode composed of 15 layers of W₁₈O₄₉ NW/Ti₃C₂Tₓ exhibits an areal capacitance of 125 mF/cm², with a fast and reversible switching response. An optical modulation of 98.2% can be maintained at a current density of 5 mAcm⁻². Using this electrode, we fabricated a bifunctional symmetric electrochromic supercapacitor device having an energy density of 10.26 μWh/cm² and a power density of 0.605 mW/cm², with high capacity retention and full columbic efficiency over 4000 charge-discharge cycles. Meanwhile, the device displays remarkable electrochromic characteristics, including fast switching time (5 s for coloring and 7 s for bleaching) and a significant coloration efficiency of 116 cm²/C with good optical modulation stability. In addition, the device exhibits remarkable mechanical flexibility and fast switching while being stable over 100 bending cycles, which is promising for real-world applications.

Keywords: MXene, nanowires, supercapacitor, ion diffusion, electrochromic, coloration efficiency

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Authors: Shadia Al-Bahlani, Khadija Al-Bulushi, Zuweina Al-Hadidi, Buthaina Al-Dhahl, Nadia Al-Abri

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Breast cancer is the most common cancer in women worldwide. Triple-Negative Breast Cancer (TNBC) is an aggressive type of breast cancer, which is defined by the absence of Estrogen (ER), Progesterone (PR) and human epidermal growth factor (Her-2) receptors. The calpain system plays an important role in many cellular processes including apoptosis, necrosis, cell signaling and proliferation. However, the role of calpain in cisplatin (CDDP)-induced apoptosis in TNBC cells is not fully understood. Here, TNBC (MDA-MB231) cells were treated with different concentration of CDDP (0, 20 & 40 µM) and calpain activation and apoptosis were measured by western blot and Hoechst Stain respectively. In addition, calpain modulation by either activation and/or inhibition and its effect on CDDP-induced apoptosis were assessed by the same above approaches. Our findings showed that CDDP induced endoplasmic reticulum stress and thus Calcium release and subsequently activate calpain α-fodrin cleavage indicated by the increase in GRP78 and Calmodulin protein expression and respectively in MDA-MB231 cells. It also induced apoptosis as measured by Hoechst stain and caspase-12 cleavage. Calpain activation by both Cyclopiazonic acid and Thapsigargin showed similar effect and enhanced the sensitivity of these cells to CDDP treatment. On the other hand, calpain inhibition by either specific siRNA and/or exogenous inhibitor (Calpeptin) had an adverse effect where it attenuated calpain activation and thus CDDP- induced apoptosis in these cells. Altogether, these findings suggested that calpain activation play an essential role in sensitizing the TNBC cells to CDDP-induced apoptosis. This might lead to the discovery of novel treatment to over this aggressive type of breast cancer.

Keywords: calpain, cisplatin, apoptosis, breast cancer

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: Voss Harrigan, Korey Carter, Mohammad Reza Shaeri

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Detrimental effects of lunar dust on space hardware, spacesuits, and astronauts’ health have been already identified during Apollo missions. Developing effective dust mitigation technologies is critically important for successful space exploration and related missions in NASA applications. In this study, an electrostatic cleaning system (ECS) integrated with a negatively ionized Thunderon brush was developed to mitigate small-sized lunar dust particles with diameters ranging from 0.04 µm to 35 µm, and the mean and median size of 7 µm and 5 µm, respectively. It was found that the frequency pulses of the negative ion generator caused particles to stick to the Thunderon bristles and repel between the pulses. The brush was used manually to ensure that particles were removed from areas where the ECS failed to mitigate the lunar simulant. The acquired data demonstrated that the developed system removed over 91-96% of the lunar dust particles. The present study was performed as a proof-of-concept to enhance the cleaning performance of ECSs by integrating a brushing process. Suggestions were made to further improve the performance of the developed technology through future research.

Keywords: lunar dust mitigation, electrostatic cleaning system, Brushing, Thunderon brush, cleaning rate

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Authors: Dulanjali Ranasinghe, Isuru Supasan, Kaushalya Premachandra, Ranjan Dissanayake, Ajith Rajapaksha, Eustace Fernando

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Proteomics studies of organisms are considered to be significantly information-rich compared to their genomic counterparts because proteomes of organisms represent the expressed state of all proteins of an organism at a given time. In modern top-down and bottom-up proteomics workflows, the primary analysis methods employed are gel–based methods such as two-dimensional (2D) electrophoresis and mass spectrometry based methods. Machine learning (ML) and artificial intelligence (AI) have been used increasingly in modern biological data analyses. In particular, the fields of genomics, DNA sequencing, and bioinformatics have seen an incremental trend in the usage of ML and AI techniques in recent years. The use of aforesaid techniques in the field of proteomics studies is only beginning to be materialised now. Although there is a wealth of information available in the scientific literature pertaining to proteomics workflows, no comprehensive review addresses various aspects of the combined use of proteomics and machine learning. The objective of this review is to provide a comprehensive outlook on the application of machine learning into the known proteomics workflows in order to extract more meaningful information that could be useful in a plethora of applications such as medicine, agriculture, and biotechnology.

Keywords: proteomics, machine learning, gel-based proteomics, mass spectrometry

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Authors: Burcu Guvenek, Duygu Baysal Kurt

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The impact of foreign trade on economic growth has been discussed since the Classical Economists. Today, foreign trade has become more important for the country's economy with the increasing globalization. When it comes to foreign trade, policies which may vary from country to country and from time to time as protectionism or free trade are implemented. In general, the positive effect of foreign trade on economic growth is alleged. However, as studies supporting this general acceptance take place in the economics literature, there are also studies in the opposite direction. In this paper, the impact of foreign trade on economic growth will be investigated with the help of panel data analysis. For this research, 24 OECD countries’ GDP and foreign trade data, including the period of 1990 and 2010, will be used.

Keywords: foreign trade, economic growth, OECD countries, panel data analysis

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Authors: Ajay Panchal

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This research introduces the Law of Activity Dominancy (LAD), a foundational theory in neuroscience postulating that simultaneous brain-regulated activities cannot share identical brain wave frequencies. The study explores the LAD through comprehensive observational and statistical analyses, illustrating its applicability across all cognitive functions. Utilizing brain wave frequency data across diverse scenarios, the research derives probabilistic models to predict the likelihood of concurrent cognitive activities. The LAD theory's predictive power extends to all neurological conditions, offering insights into Alzheimer's disease, major depressive disorder, epilepsy, schizophrenia, anxiety disorders, OCD, ASD, ADHD etc... By analyzing EEG patterns, the research demonstrates how overlapping brain wave frequencies disrupt specific cognitive and motor functions, aligning with clinical observations. This study also outlines the statistical properties of the LAD, presenting equations to calculate activity probabilities and emphasizing its utility in personalizing cognitive assessments, early disease detection, tailored therapies, optimizing cognitive performance etc... By bridging theoretical neuroscience with practical applications, the research establishes the LAD as a pivotal framework for understanding and enhancing human cognitive functions.

Keywords: Ajay Panchal, cognitive neuroscience, computational neuroscience, theoretical neuroscience

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Authors: Ma. Rosario Concepcion O. Ang, Luis Caezar Ian K. Panganiban, Charmyne B. Mamador, Oliver Dan G. De Luna, Michael D. Bausas, Joselito P. Cruz

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PhilSHORE is a multi-site, multi-device and multi-criteria decision support tool designed to support the development of tidal current energy in the Philippines. Its platform is based on Geographic Information Systems (GIS) which allows for the collection, storage, processing, analyses and display of geospatial data. Combining GIS tools with open source web development applications, PhilSHORE becomes a webGIS-based marine spatial planning tool. To date, PhilSHORE displays output maps and graphs of power and energy density, site suitability and site-device analysis. It enables stakeholders and the public easy access to the results of tidal current energy resource assessments and site suitability analyses. Results of the initial development shows PhilSHORE is a promising decision support tool for ORE project developments.

Keywords: gis, site suitability analysis, tidal current energy resource assessment, webgis

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Authors: Y. B. Kirankumar, J. D. Mallapur

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Wireless Sensor Network (WSN) is an emerging technology, which has great invention for various low cost applications both for mass public as well as for defence. The wireless sensor communication technology allows random participation of sensor nodes with particular applications to take part in the network, which results in most of the uncovered simulation area, where fewer nodes are located at far distances. The drawback of such network would be that the additional energy is spent by the nodes located in a pattern of dense location, using more number of nodes for a smaller distance of communication adversely in a region with less number of nodes and additional energy is again spent by the source node in order to transmit a packet to neighbours, thereby transmitting the packet to reach the destination. The proposed work is intended to develop Energy Efficient Node Placement Algorithm (EENPA) in order to place the sensor node efficiently in simulated area, where all the nodes are equally located on a radial path to cover maximum area at equidistance. The total energy consumed by each node compared to random placement of nodes is less by having equal burden on fewer nodes of far location, having distributed the nodes in whole of the simulation area. Calculating the network lifetime also proves to be efficient as compared to random placement of nodes, hence increasing the network lifetime, too. Simulation is been carried out in a qualnet simulator, results are obtained on par with random placement of nodes with EENP algorithm.

Keywords: energy, WSN, wireless sensor network, energy approach

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Authors: Enerit Sacdanaku, Idriz Haxhiu

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This study was conducted in the area of Vlora Bay, Albania. Data about Sea Turtles Caretta caretta and Chelonia mydas, belonging to two periods of time (1984–1991; 2008–2014) are given. All data gathered were analyzed using recent methodologies. For all turtles captured (as by catch), the Curve Carapace Length (CCL) and Curved Carapace Width (CCW) were measured. These data were statistically analyzed, where the mean was 67.11 cm for CCL and 57.57 cm for CCW of all individuals studied (n=13). All untagged individuals of marine turtles were tagged using metallic tags (Stockbrand’s titanium tag) with an Albanian address. Sex was determined and resulted that 45.4% of individuals were females, 27.3% males and 27.3% juveniles. All turtles were studied for the presence of the epibionts. The area of Vlora Bay is used from marine turtles (Caretta caretta) as a migratory corridor to pass from the Mediterranean to the northern part of the Adriatic Sea.

Keywords: Caretta caretta, Chelonia mydas, CCL, CCW, tagging, Vlora Bay

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Authors: Ed de Jong

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Avantium is a leading technology development company and a frontrunner in renewable chemistry. Avantium develops disruptive technologies that enable the production of sustainable high value products from renewable materials and actively seek out collaborations and partnerships with like-minded companies and academic institutions globally, to speed up introductions of chemical innovations in the marketplace. In addition, Avantium helps companies to accelerate their catalysis R&D to improve efficiencies and deliver increased sustainability, growth, and profits, by providing proprietary systems and services to this regard. Many chemical building blocks and materials can be produced from biomass, nowadays mainly from 1st generation based carbohydrates, but potential for competition with the human food chain leads brand-owners to look for strategies to transition from 1st to 2nd generation feedstock. The use of non-edible lignocellulosic feedstock is an equally attractive source to produce chemical intermediates and an important part of the solution addressing these global issues (Paris targets). Avantium’s Dawn Technology™ separates the glucose, mixed sugars, and lignin available in non-food agricultural and forestry residues such as wood chips, wheat straw, bagasse, empty fruit bunches or corn stover. The resulting very pure lignin is dense in energy and can be used for energy generation. However, such a material might preferably be deployed in higher added value applications. Bitumen, which is fossil based, are mostly used for paving applications. Traditional hot mix asphalt emits large quantities of the GHG’s CO₂, CH₄, and N₂O, which is unfavorable for obvious environmental reasons. Another challenge for the bitumen industry is that the petrochemical industry is becoming more and more efficient in breaking down higher chain hydrocarbons to lower chain hydrocarbons with higher added value than bitumen. This has a negative effect on the availability of bitumen. The asphalt market, as well as governments, are looking for alternatives with higher sustainability in terms of GHG emission. The usage of alternative sustainable binders, which can (partly) replace the bitumen, contributes to reduce GHG emissions and at the same time broadens the availability of binders. As lignin is a major component (around 25-30%) of lignocellulosic material, which includes terrestrial plants (e.g., trees, bushes, and grass) and agricultural residues (e.g., empty fruit bunches, corn stover, sugarcane bagasse, straw, etc.), it is globally highly available. The chemical structure shows resemblance with the structure of bitumen and could, therefore, be used as an alternative for bitumen in applications like roofing or asphalt. Applications such as the use of lignin in asphalt need both fundamental research as well as practical proof under relevant use conditions. From a fundamental point of view, rheological aspects, as well as mixing, are key criteria. From a practical point of view, behavior in real road conditions is key (how easy can the asphalt be prepared, how easy can it be applied on the road, what is the durability, etc.). The paper will discuss the fundamentals of the use of lignin as bitumen replacement as well as the status of the different demonstration projects in Europe using lignin as a partial bitumen replacement in asphalts and will especially present the results of using Dawn Technology™ lignin as partial replacement of bitumen.

Keywords: biorefinery, wood fractionation, lignin, asphalt, bitumen, sustainability

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Authors: Warnakulasuriya Arachichige Malisha Ann Rosary Fernando, Udalamatta Gamage Omila Chalanka Jinadasa, Bihini Pabasara Amandi Amarasinghe, Manul Thisuraka Mandalawatta, Uthpala Samarakoon, Manori Gamage

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The surge in sexually transmitted diseases (STDs) has become a critical public health crisis demanding urgent attention and action. Like many other nations, Sri Lanka is grappling with a significant increase in STDs due to a lack of education and awareness regarding their dangers. Presently, the available applications for tracking and managing STDs cover only a limited number of easily detectable infections, resulting in a significant gap in effectively controlling their spread. To address this gap and combat the rising STD rates, it is essential to leverage technology and data. Employing technology to enhance the tracking and management of STDs is vital to prevent their further propagation and to enable early intervention and treatment. This requires adopting a comprehensive approach that involves raising public awareness about the perils of STDs, improving access to affordable healthcare services for early detection and treatment, and utilizing advanced technology and data analysis. The proposed mobile application aims to cater to a broad range of users, including STD patients, recovered individuals, and those unaware of their STD status. By harnessing cutting-edge technologies like image detection, symptom-based identification, prevention methods, doctor and clinic recommendations, and virtual counselor chat, the application offers a holistic approach to STD management. In conclusion, the escalating STD rates in Sri Lanka and across the globe require immediate action. The integration of technology-driven solutions, along with comprehensive education and healthcare accessibility, is the key to curbing the spread of STDs and promoting better overall public health.

Keywords: STD, machine learning, NLP, artificial intelligence

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Authors: Amita Singha

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The project describes the scope of magnetic braking. The potential applications of the braking system can be a de-accelerating system to increase the safety of an elevator or any guided rail transportation system.

Keywords: boost and buck converter, electromagnet, elevator, ferromagnetic material, sensor, solenoid, timer

Procedia PDF Downloads 442

Authors: Adrian Ionut Nicoara, Denisa Ionela Ene, Alina Maria Holban, Cristina Busuioc

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At present, the development of materials with biomedical applications is a domain of interest that will produce a full series of benefits in engineering and medicine. In this sense, it is required to use a natural material, and this paper is focused on the development of a composite material based on bacterial cellulose – hydroxyapatite and silver nanoparticles with applications in hard tissue. Bacterial cellulose own features like biocompatibility, non-toxicity character and flexibility. Moreover, the bacterial cellulose can be conjugated with different forms of active silver to possess antimicrobial activity. Hydroxyapatite is well known that can mimic at a significant level the activity of the initial bone. The material was synthesized by using an ultrasound probe and finally characterized by several methods. Thereby, the morphological properties were analyzed by using Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM). Because the synthesized material has medical application in restore the tissue and to fight against microbial invasion, the samples were tested from the biological point of view by evaluating the biodegradability in phosphate-buffered saline (PBS) and simulated body fluid (SBF) and moreover the antimicrobial effect was performed on Gram-positive bacterium Staphylococcus aureus, Gram-negative bacterium Escherichia coli, and fungi Candida albicans. The results reveal that the obtained material has specific characteristics for bone regeneration.

Keywords: bacterial cellulose, biomaterials, hydroxyapatite, scaffolds materials

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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: Amon Olimov, Umarov Jamshid, Dae-Ho Kim, Chol-U Lee, Ryum-Duck Oh

Abstract:

This paper proposes IoT virtualization platform based incident information system. IoT information based environment is the platform that was developed for the purpose of collecting a variety of data by managing regionally scattered IoT devices easily and conveniently in addition to analyzing data collected from roads. Moreover, this paper configured the platform for the purpose of providing incident information based on sensed data. It also provides the same input/output interface as UNIX and Linux by means of matching IoT devices with the directory of file system and also the files. In addition, it has a variety of approaches as to the devices. Thus, it can be applied to not only incident information but also other platforms. This paper proposes the incident information system that identifies and provides various data in real time as to urgent matters on roads based on the existing USN/M2M and IoT visualization platform.

Keywords: incident information system, IoT, virtualization platform, USN, M2M

Procedia PDF Downloads 354

Authors: Dustin Schöder

Abstract:

The potential benefits of the utilization of highly automated and autonomous trucks in logistics operations are the subject of interest to the entire logistics industry. The benefits of the use of these new technologies were scientifically investigated and implemented in roadmaps. So far, reliable data and experiences from real life use cases are still limited. A German research consortium of both academics and industry developed a highly automated (SAE level 4) vehicle for yard operations at railports and container depots. After development and testing, a several month field test at the DUSS Terminal in Ulm-Dornstadt (Germany) and the nearby DB Intermodal Services Container Depot in Ulm-Dornstadt was conducted. The truck was piloted in a shuttle service between both sites. In a holistic automation approach, the vehicle was integrated into a digital communication platform so that the truck could move autonomously without a driver and his manual interactions with a wide variety of stakeholders. The main goal is to investigate the effects of highly automated trucks in the key processes of container loading, unloading and container relocation on holistic railport yard operation. The field test data were used to investigate changes in process efficiency of key processes of railport and container yard operations. Moreover, effects on the capacity utilization and potentials for smothering peak workloads were analyzed. The results state that process efficiency in the piloted use case was significantly higher. The reason for that could be found in the digitalized data exchange and automated dispatch. However, the field test has shown that the effect is greatly varying depending on the ratio of highly automated and manual trucks in the yard as well as on the congestion level in the loading area. Furthermore, the data confirmed that under the right conditions, the capacity utilization of highly automated trucks could be increased. In regard to the potential for smothering peak workloads, no significant findings could be made based on the limited requirements and regulations of railway operation in Germany. In addition, an empirical survey among railport managers, operational supervisors, innovation managers and strategists (n=15) within the logistics industry in Germany was conducted. The goal was to identify key characteristics of future railports and terminals as well as requirements that railports will have to meet in the future. Furthermore, the railport processes where automation and autonomization make the greatest impact, as well as hurdles and challenges in the introduction of new technologies, have been surveyed. Hence, further potential use cases of highly automated and autonomous applications could be identified, and expectations have been mapped. As a result, a highly detailed and practice-based roadmap towards a ‘terminal 4.0’ was developed.

Keywords: highly automated driving, autonomous driving, SAE level 4, railport operations, container depot, intermodal logistics, potentials of autonomization

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Authors: Chaya B. M., C. Dhanush, Inti Sai Srikar, Akula Pavan Parvatalu, Chirag Gowda R

Abstract:

Recently, there has been a lot of interest in µ-LED technology because of its exceptional qualities, including auto emission, high visibility, low consumption of power, rapid response and longevity. Light-emitting diodes (LED) using III-nitride, such as lighting sources, visible light communication (VLC) devices, and high-power devices, are finding increasing use as miniaturization technology advances. The use of micro-LED displays in place of traditional display technologies like liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) is one of the most prominent recent advances, which may even represent the next generation of displays. The development of fully integrated, multifunctional devices and the incorporation of extra capabilities into micro-LED displays, such as sensing, light detection, and solar cells, are the pillars of advanced technology. Due to the wide range of applications for micro-LED technology, the effectiveness and dependability of these devices in numerous harsh conditions are becoming increasingly important. Enough research has been conducted to overcome the under-effectiveness of micro-LED devices. In this paper, different Micro LED design structures are proposed in order to achieve optimized optical properties. In order to attain improved external quantum efficiency (EQE), devices' light extraction efficiency (LEE) has also been boosted.

Keywords: finite difference time domain, light out coupling efficiency, far field intensity, power density, quantum efficiency, flat panel displays

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

Abstract:

This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

Procedia PDF Downloads 151