Search results for: data mining applications and discovery

Authors: M. Regis, E. Marin, S. Fusi, M. Pressacco, L. Fedrizzi

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Electron Beam Melting (EBM) process was used to prepare porous scaffolds with controlled porosity to ensure optimal levels of osteointegration for different trabeculae sizes. Morphological characterization by means of SEM analyses was carried out to assess pore dimensions; tensile, compression and adhesion tests have been carried out to determine the mechanical behavior. The results indicate that EBM process allows the creation of regular and repeatable porous scaffolds. Mechanical properties greatly depend on pore dimension and on bulk-pore ratio. Adhesion resistance meets the normative requirements, and the overall performance of the produced structures is compatible with potential orthopaedic applications.

Keywords: additive manufacturing, orthopaedic implants, osteointegration, trabecular structures

Procedia PDF Downloads 325

Authors: Usama Massoud, Abeer A. Kenawy, El-Said A. Ragab, Abbas M. Abbas, Heba M. El-Kosery

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Vertical Electrical Sounding (VES) and Transient Electro Magnetic (TEM) survey have been applied for characterizing the groundwater aquifers at El Sadat industrial area. El-Sadat city is one of the most important industrial cities in Egypt. It has been constructed more than three decades ago at about 80 km northwest of Cairo along the Cairo–Alexandria desert road. Groundwater is the main source of water supplies required for domestic, municipal, and industrial activities in this area due to the lack of surface water sources. So, it is important to maintain this vital resource in order to sustain the development plans of this city. In this study, VES and TEM data were identically measured at 24 stations along three profiles trending NE–SW with the elongation of the study area. The measuring points were arranged in a grid like pattern with both inter-station spacing and line–line distance of about 2 km. After performing the necessary processing steps, the VES and TEM data sets were inverted individually to multi-layer models, followed by a joint inversion of both data sets. Joint inversion process has succeeded to overcome the model-equivalence problem encountered in the inversion of individual data set. Then, the joint models were used for the construction of a number of cross sections and contour maps showing the lateral and vertical distribution of the geo-electrical parameters in the subsurface medium. Interpretation of the obtained results and correlation with the available geological and hydrogeological information revealed TWO aquifer systems in the area. The shallow Pleistocene aquifer consists of sand and gravel saturated with fresh water and exhibits large thickness exceeding 200 m. The deep Pliocene aquifer is composed of clay and sand and shows low resistivity values. The water bearing layer of the Pleistocene aquifer and the upper surface of Pliocene aquifer are continuous and no structural features have cut this continuity through the investigated area.

Keywords: El Sadat city, joint inversion, VES, TEM

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Authors: Laslouni Warda, M. Azzaz

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The intermetallic-based on transition metal compounds present interesting magnetic properties for the technological applications (permanent magnets, magnetic recording…). Cu70 Fe18Co12 and Cu70 Fe30 nanostructured with crystallite size vary from 10 a 12 nanometers have been developed by a mechanical milling method. For Cu-Fe samples, the iron and copper distribution was clear. The distribution showed a homogeneous distribution of iron and copper in a Cu-Fe obtained after 36 h milling. The structural properties have been performed with X-ray diffraction. With increasing milling times, Fe and Co diffuse into the Cu matrix, which accelerates the formation of the magnetic nanostructure Cu- Fe-Co and Cu-Fe alloys. The magnetic behavior is investigated using Vibrating Sample Magnetometer (VSM). The two alloys nanocrystals possess ferromagnetic character at room temperature

Keywords: Cu-Fe-Co, Cu-Fe, nanocrystals, SEM, hysteresis loops, VSM, anisotropy theory

Procedia PDF Downloads 338

Authors: Boris M. Fedorov, Liudmila P. Goncharenko, Sergey A. Sybachin, Natalia A. Mamedova, Ekaterina V. Makarenkova, Saule Rakhimova

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In modern economic conditions, the question of the possibility of correct recognition of a signature on digital documents in order to verify the expression of will or confirm a certain operation is relevant. The additional complexity of processing lies in the dynamic variability of the signature for each individual, as well as in the way information is processed because the signature refers to biometric data. The article discusses the issues of using artificial intelligence models in order to improve the quality of signature confirmation in document recognition. The analysis of several possible options for using the model is carried out. The results of the study are given, in which it is possible to correctly determine the authenticity of the signature on small samples.

Keywords: signature recognition, biometric data, artificial intelligence, neural networks

Procedia PDF Downloads 151

Authors: Elif D. Oguz Erkal, David Krackhardt, Erica Cochran-Hameen

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Even though social network analysis (SNA) is an abundantly studied concept for many organizations and industries, a clear SNA approach to the project teams has not yet been adopted by the construction industry. The main challenges for performing SNA in construction and the apparent reason for this gap is the unique and complex structure of each construction project, the comparatively high circulation of project team members/contributing parties and the variety of authentic problems for each project. Additionally, there are stakeholders from a variety of professional backgrounds collaborating in a high-stress environment fueled by time and cost constraints. Within this case study on Project RE, a design & build project performed at the Urban Design Build Studio of Carnegie Mellon University, social network analysis of the project design team will be performed with the main goal of applying social network theory to construction project environments. The research objective is to determine a correlation between the network of how individuals relate to each other on one’s perception of their own professional strengths and weaknesses and the communication patterns within the team and the group dynamics. Data is collected through a survey performed over four rounds conducted monthly, detailed follow-up interviews and constant observations to assess the natural alteration in the network with the effect of time. The data collected is processed by the means of network analytics and in the light of the qualitative data collected with observations and individual interviews. This paper presents the full ethnography of this construction design team of fourteen architecture students based on an elaborate social network data analysis over time. This study is expected to be used as an initial step to perform a refined, targeted and large-scale social network data collection in construction projects in order to deduce the impacts of social networks on project performance and suggest better collaboration structures for construction project teams henceforth.

Keywords: construction design teams, construction project management, social network analysis, team collaboration, network analytics

Procedia PDF Downloads 203

Authors: Bharath R., Tharun Sai N., Bhuvan G.

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This project focuses on developing a Parallel Computing method aimed at optimizing matrix multiplication through GPU acceleration. Addressing algorithmic challenges, GPU programming intricacies, and integration issues, the project aims to enhance efficiency and scalability. The methodology involves algorithm design, GPU programming, and optimization techniques. Future plans include advanced optimizations, extended functionality, and integration with high-level frameworks. User engagement is emphasized through user-friendly interfaces, open- source collaboration, and continuous refinement based on feedback. The project's impact extends to significantly improving matrix multiplication performance in scientific computing and machine learning applications.

Keywords: matrix multiplication, parallel processing, cuda, performance boost, neural networks

Procedia PDF Downloads 63

Authors: Wondmyibza Tsegaye Bayou, Johannes C. Nonner, Joost Heijkers

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This study aimed to identify and estimate dynamic groundwater seepage rates using four comparative methods; the Darcian approach, the water balance approach, the tracer method, and modeling. The theoretical background to these methods is put together in this study. The methodology was applied to a case study area at Zegveld following the advice of the Water Board Stichtse Rijnlanden. Data collection has been from various offices and a field campaign in the winter of 2008/09. In this complex confining layer of the study area, the location of the phreatic groundwater table is at a shallow depth compared to the piezometric water level. Data were available for the model years 1989 to 2000 and winter 2008/09. The higher groundwater table shows predominately-downward seepage in the study area. Results of the study indicated that net recharge to the groundwater table (precipitation excess) and the ditch system are the principal sources for seepage across the complex confining layer. Especially in the summer season, the contribution from the ditches is significant. Water is supplied from River Meije through a pumping system to meet the ditches' water demand. The groundwater seepage rate was distributed unevenly throughout the study area at the nature reserve averaging 0.60 mm/day for the model years 1989 to 2000 and 0.70 mm/day for winter 2008/09. Due to data restrictions, the seepage rates were mainly determined based on the Darcian method. Furthermore, the water balance approach and the tracer methods are applied to compute the flow exchange within the ditch system. The site had various validated groundwater levels and vertical flow resistance data sources. The phreatic groundwater level map compared with TNO-DINO groundwater level data values overestimated the groundwater level depth by 28 cm. The hydraulic resistance values obtained based on the 3D geological map compared with the TNO-DINO data agreed with the model values before calibration. On the other hand, the calibrated model significantly underestimated the downward seepage in the area compared with the field-based computations following the Darcian approach.

Keywords: groundwater seepage, phreatic water table, piezometric water level, nature reserve, Zegveld, The Netherlands

Procedia PDF Downloads 91

Authors: Eric Bang

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Interactions between receptors and ligands are crucial for many essential biological processes, including neurotransmission and metabolism. Ligand-receptor analyses that examine cell behavior and interactions often utilize cell type-specific RNA expressions from single-cell RNA sequencing (scRNA-seq) data. Using CellPhoneDB, a public repository consisting of ligands, receptors, and ligand-receptor interactions, the cell-cell interactions were explored in a specific scRNA-seq dataset from kidney tissue and portrayed the results with dot plots and heat maps. Depending on the type of cell, each ligand-receptor pair was aligned with the interacting cell type and calculated the positori probabilities of these associations, with corresponding P values reflecting average expression values between the triads and their significance. Using single-cell data (sample kidney cell references), genes in the dataset were cross-referenced with ones in the existing CellPhoneDB dataset. For example, a gene such as Pleiotrophin (PTN) present in the single-cell data also needed to be present in the CellPhoneDB dataset. Using the single-cell transcriptomics data via slide-seq and reference data, the CellPhoneDB program defines cell types and plots them in different formats, with the two main ones being dot plots and heat map plots. The dot plot displays derived measures of the cell to cell interaction scores and p values. For the dot plot, each row shows a ligand-receptor pair, and each column shows the two interacting cell types. CellPhoneDB defines interactions and interaction levels from the gene expression level, so since the p-value is on a -log10 scale, the larger dots represent more significant interactions. By performing an interaction analysis, a significant interaction was discovered for myeloid and T-cell ligand-receptor pairs, including those between Secreted Phosphoprotein 1 (SPP1) and Fibronectin 1 (FN1), which is consistent with previous findings. It was proposed that an effective protocol would involve a filtration step where cell types would be filtered out, depending on which ligand-receptor pair is activated in that part of the tissue, as well as the incorporation of the CellPhoneDB data in a streamlined workflow pipeline. The filtration step would be in the form of a Python script that expedites the manual process necessary for dataset filtration. Being in Python allows it to be integrated with the CellPhoneDB dataset for future workflow analysis. The manual process involves filtering cell types based on what ligand/receptor pair is activated in kidney cells. One limitation of this would be the fact that some pairings are activated in multiple cells at a time, so the manual manipulation of the data is reflected prior to analysis. Using the filtration script, accurate sorting is incorporated into the CellPhoneDB database rather than waiting until the output is produced and then subsequently applying spatial data. It was envisioned that this would reveal wherein the cell various ligands and receptors are interacting with different cell types, allowing for easier identification of which cells are being impacted and why, for the purpose of disease treatment. The hope is this new computational method utilizing spatially explicit ligand-receptor association data can be used to uncover previously unknown specific interactions within kidney tissue.

Keywords: bioinformatics, Ligands, kidney tissue, receptors, spatial transcriptome

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Authors: Néstor Iván Rojas, Wilson Medina Sierra

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Managing spatial information is described through a Geographic Information System (GIS), for some neighborhoods in the city of Tunja, in relation to the structural typology of the buildings. The use of GIS provides tools that facilitate the capture, processing, analysis and dissemination of cartographic information, product quality evaluation of the classification of buildings. Allows the development of a method that unifies and standardizes processes information. The project aims to generate a geographic database that is useful to the entities responsible for planning and disaster prevention and care for vulnerable populations, also seeks to be a basis for seismic vulnerability studies that can contribute in a study of urban seismic microzonation. The methodology consists in capturing the plat including road naming, neighborhoods, blocks and buildings, to which were added as attributes, the product of the evaluation of each of the housing data such as the number of inhabitants and classification, year of construction, the predominant structural systems, the type of mezzanine board and state of favorability, the presence of geo-technical problems, the type of cover, the use of each building, damage to structural and non-structural elements . The above data are tabulated in a spreadsheet that includes cadastral number, through which are systematically included in the respective building that also has that attribute. Geo-referenced data base is obtained, from which graphical outputs are generated, producing thematic maps for each evaluated data, which clearly show the spatial distribution of the information obtained. Using GIS offers important advantages for spatial information management and facilitates consultation and update. Usefulness of the project is recognized as a basis for studies on issues of planning and prevention.

Keywords: microzonation, buildings, geo-processing, cadastral number

Procedia PDF Downloads 339

Authors: Annika Stechemesser, Rachel Pounds, Emma Lucas, Chris Dawson, Julia Lipecki, Pavle Vrljicak, Jan Brosens, Sean Kehoe, Jason Yap, Lawrence Young, Sascha Ott

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Advances in technology make it now possible to retrieve the genetic information of thousands of single cancerous cells. One of the key challenges in single cell analysis of cancerous tissue is to determine the number of different cell types and their characteristic genes within the sample to better understand the tumors and their reaction to different treatments. For this analysis to be possible, it is crucial to filter out background noise as it can severely blur the downstream analysis and give misleading results. In-depth analysis of the state-of-the-art filtering methods for single cell data showed that they do, in some cases, not separate noisy and normal cells sufficiently. We introduced an algorithm that filters and clusters single cell data simultaneously without relying on certain genes or thresholds chosen by eye. It detects communities in a Shared Nearest Neighbor similarity network, which captures the similarities and dissimilarities of the cells by optimizing the modularity and then identifies and removes vertices with a weak clustering belonging. This strategy is based on the fact that noisy data instances are very likely to be similar to true cell types but do not match any of these wells. Once the clustering is complete, we apply a set of evaluation metrics on the cluster level and accept or reject clusters based on the outcome. The performance of our algorithm was tested on three datasets and led to convincing results. We were able to replicate the results on a Peripheral Blood Mononuclear Cells dataset. Furthermore, we applied the algorithm to two samples of ovarian cancer from the same patient before and after chemotherapy. Comparing the standard approach to our algorithm, we found a hidden cell type in the ovarian postchemotherapy data with interesting marker genes that are potentially relevant for medical research.

Keywords: cancer research, graph theory, machine learning, single cell analysis

Procedia PDF Downloads 118

Authors: Shuhua Lai, Kairui Chen

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In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper.

Keywords: coarsening, mesh clustering, shape approximation, mesh simplification

Procedia PDF Downloads 385

Authors: Chaudhary Muhammad Aqdus Ilyas, Matthias Rehm, Kamal Nasrollahi, Thomas B. Moeslund

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This paper presents the investigation of data collection procedures, associated with robots when placed with traumatic brain injured (TBI) patients for rehabilitation purposes through facial expression and mood analysis. Rehabilitation after TBI is very crucial due to nature of injury and variation in recovery time. It is advantageous to analyze these emotional signals in a contactless manner, due to the non-supportive behavior of patients, limited muscle movements and increase in negative emotional expressions. This work aims at the development of framework where robots can recognize TBI emotions through facial expressions to perform rehabilitation tasks by physical, cognitive or interactive activities. The result of these studies shows that with customized data collection strategies, proposed framework identify facial and emotional expressions more accurately that can be utilized in enhancing recovery treatment and social interaction in robotic context.

Keywords: computer vision, convolution neural network- long short term memory network (CNN-LSTM), facial expression and mood recognition, multimodal (RGB-thermal) analysis, rehabilitation, robots, traumatic brain injured patients

Procedia PDF Downloads 159

Authors: M. T. Alodat

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In this paper, we propose a generalization to the Gaussian process regression(GPR) model called the extended skew Gaussian process for regression(ESGPr) model. The ESGPR model works better than the GPR model when the errors are skewed. We derive the predictive distribution for the ESGPR model at a new input. Also we apply the ESGPR model to FOREX data and we find that it fits the Forex data better than the GPR model.

Keywords: extended skew normal distribution, Gaussian process for regression, predictive distribution, ESGPr model

Procedia PDF Downloads 559

Authors: Aliya Grig, Konstantin Sokolov, Igor Shatalin

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The report describes the methodology for collecting data and building an ML model for determining the personality psychotype using profiling and personality traits methods based on several short messages of a user communicating on an arbitrary topic with a chitchat bot. In the course of the experiments, the minimum amount of text was revealed to confidently determine aspects of personality. Model accuracy - 85%. Users' language of communication is English. AI for a personalized communication with a user based on his mood, personality, and current emotional state. Features investigated during the research: personalized communication; providing empathy; adaptation to a user; predictive analytics. In the report, we describe the processes that captures both structured and unstructured data pertaining to a user in large quantities and diverse forms. This data is then effectively processed through ML tools to construct a knowledge graph and draw inferences regarding users of text messages in a comprehensive manner. Specifically, the system analyzes users' behavioral patterns and predicts future scenarios based on this analysis. As a result of the experiments, we provide for further research on training AI models to be empathetic, creating personalized communication for a user

Keywords: AI, empathetic, chatbot, AI models

Procedia PDF Downloads 96

Authors: Rahil Samani, Saeed Soleimani, Ehsan Adib, Majid Pahlevani

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This paper presents an inverting high step-up DC/DC converter. Basically, this high step-up DC/DC converter is an appealing interface for solar applications. The proposed topology takes advantage of using coupled inductors. Due to the leakage inductances of these coupled inductors, the power MOSFET has the zero current switching (ZCS) condition, which results in decreased switching losses. This will substantially improve the overall efficiency of the power converter. Furthermore, employing coupled inductors has led to a higher voltage gain. Theoretical analysis and experimental results of a 100W 20V/220V prototype are presented to verify the superior performance of the proposed DC/DC converter.

Keywords: coupled inductors, high step-up DC/DC converter, zero-current switching, Cuk converter, SEPIC converter

Procedia PDF Downloads 725

Authors: Şafak Aksoy, Ali Kurşun, Erhan Çetin, Mustafa Reşit Haboğlu

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In this study, thermo elastic stress analysis is performed on a cylinder made of laminated isotropic materials under thermomechanical loads. Laminated cylinders have many applications such as aerospace, automotive and nuclear plant in the industry. These cylinders generally performed under thermomechanical loads. Stress and displacement distribution of the laminated cylinders are determined using by analytical method both thermal and mechanical loads. Based on the results, materials combination plays an important role on the stresses distribution along the radius. Variation of the stresses and displacements along the radius are presented as graphs. Calculations program are prepared using MATLAB® by authors.

Keywords: isotropic materials, laminated cylinders, thermoelastic stress, thermomechanical load

Procedia PDF Downloads 418

Authors: Majeda Hammoud

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Background: Anemia during pregnancy increases the risk of delivery by cesarean section, low birth weight, preterm birth, perinatal mortality, stillbirth, and maternal mortality. In this study, we aimed to assess the prevalence of anemia in pregnant women and its associated factors in the Kuwait birth cohort study. Methods: The Kuwait birth cohort (N=1108) was a prospective cohort study in which pregnant women were recruited in the third trimester. Data were collected through personal interviews with mothers who attend antenatal care visits, including data on socio-economic status and lifestyle factors. Blood samples were taken after the recruitment to measure multiple laboratory indicators. Clinical data were extracted from the medical records by a clinician including data on comorbidities. Anemia was defined as having Hemoglobin (Hb) <110 g/L with further classification as mild (100-109 g/L), moderate (70-99 g/L), or severe (<70 g/L). Predictors of anemia were classified as underlying or direct factors, and logistic regression was used to investigate their association with anemia. Results: The mean Hb level in the study group was 115.21 g/L (95%CI: 114.56- 115.87 g/L), with significant differences between age groups (p=0.034). The prevalence of anemia was 28.16% (95%CI: 25.53-30.91%), with no significant difference by age group (p=0.164). Of all 1108 pregnant women, 8.75% had moderate anemia, and 19.40% had mild anemia, but no pregnant women had severe anemia. In multivariable analysis, getting pregnant while using contraception, adjusted odds ratio (AOR) 1.73(95%CI:1.01-2.96); p=0.046 and current use of supplements, AOR 0.50 (95%CI: 0.26-0.95); p=0.035 were significantly associated with anemia (underlying factors). From the direct factors group, only iron and ferritin levels were significantly associated with anemia (P<0.001). Conclusion: Although the severe form of anemia is low among pregnant women in Kuwait, mild and moderate anemia remains a significant health problem despite free access to antenatal care.

Keywords: anemia, pregnancy, hemoglobin, ferritin

Procedia PDF Downloads 56

Authors: Xiangtuo Chen, Paul-Henry Cournéde

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Crop yield prediction is a paramount issue in agriculture. The main idea of this paper is to find out efficient way to predict the yield of corn based meteorological records. The prediction models used in this paper can be classified into model-driven approaches and data-driven approaches, according to the different modeling methodologies. The model-driven approaches are based on crop mechanistic modeling. They describe crop growth in interaction with their environment as dynamical systems. But the calibration process of the dynamic system comes up with much difficulty, because it turns out to be a multidimensional non-convex optimization problem. An original contribution of this paper is to propose a statistical methodology, Multi-Scenarios Parameters Estimation (MSPE), for the parametrization of potentially complex mechanistic models from a new type of datasets (climatic data, final yield in many situations). It is tested with CORNFLO, a crop model for maize growth. On the other hand, the data-driven approach for yield prediction is free of the complex biophysical process. But it has some strict requirements about the dataset. A second contribution of the paper is the comparison of these model-driven methods with classical data-driven methods. For this purpose, we consider two classes of regression methods, methods derived from linear regression (Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression) and machine learning methods (Random Forest, k-Nearest Neighbor, Artificial Neural Network and SVM regression). The dataset consists of 720 records of corn yield at county scale provided by the United States Department of Agriculture (USDA) and the associated climatic data. A 5-folds cross-validation process and two accuracy metrics: root mean square error of prediction(RMSEP), mean absolute error of prediction(MAEP) were used to evaluate the crop prediction capacity. The results show that among the data-driven approaches, Random Forest is the most robust and generally achieves the best prediction error (MAEP 4.27%). It also outperforms our model-driven approach (MAEP 6.11%). However, the method to calibrate the mechanistic model from dataset easy to access offers several side-perspectives. The mechanistic model can potentially help to underline the stresses suffered by the crop or to identify the biological parameters of interest for breeding purposes. For this reason, an interesting perspective is to combine these two types of approaches.

Keywords: crop yield prediction, crop model, sensitivity analysis, paramater estimation, particle swarm optimization, random forest

Procedia PDF Downloads 235

Authors: Rezaul K. Sk., Mohammad Ashiq, Avinash K. Srivastava

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The use of radiation shielding technology ranging from EMI to high energy gamma rays in various areas such as devices, medical science, defense, nuclear power plants, medical diagnostics etc. is increasing all over the world. However, exposure to different radiations such as X-ray, gamma ray, neutrons and EMI above the permissible limits is harmful to living beings, the environment and sensitive laboratory equipment. In order to solve this problem, there is a need to develop effective radiation shielding materials. Conventionally, lead and lead-based materials are used in making shielding materials, as lead is cheap, dense and provides very effective shielding to radiation. However, the problem associated with the use of lead is its toxic nature and carcinogenic. So, to overcome these drawbacks, there is a great need for lead-free radiation shielding materials and that should also be economically sustainable. Therefore, it is necessary to look for the synthesis of radiation-shielding glass by using other heavy metal oxides (HMO) instead of lead. The lead-free BaO-doped TeZnCaB glass systems have been synthesized by the traditional melt-quenching method. X-ray diffraction analysis confirmed the glassy nature of the synthesized samples. The densities of the developed glass samples were increased by doping the BaO concentration, ranging from 4.292 to 4.725 g/cm3. The vibrational and bending modes of the BaO-doped glass samples were analyzed by Raman spectroscopy, and FTIR (Fourier-transform infrared spectroscopy) was performed to study the functional group present in the samples. UV-visible characterization revealed the significance of optical parameters such as Urbach’s energy, refractive index and optical energy band gap. The indirect and direct energy band gaps were decreased with the BaO concentration whereas the refractive index was increased. X-ray attenuation measurements were performed to determine the radiation shielding parameters such as linear attenuation coefficient (LAC), mass attenuation coefficient (MAC), half value layer (HVL), tenth value layer (TVL), mean free path (MFP), attenuation factor (Att%) and lead equivalent thickness of the lead-free BaO-doped TeZnCaB glass system. It was observed that the radiation shielding characteristics were enhanced with the addition of BaO content in the TeZnCaB glass samples. The glass samples with higher contents of BaO have the best attenuation performance. So, it could be concluded that the addition of BaO into TeZnCaB glass samples is a significant technique to improve the radiation shielding performance of the glass samples. The best lead equivalent thickness was 2.626 mm, and these glasses could be good materials for medical diagnostics applications.

Keywords: heavy metal oxides, lead-free, melt-quenching method, x-ray attenuation

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Authors: Asikhia Olubusayo Aduke

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Students’ inability to set data-based (specific, measurable, attainable, reliable, and time-bound) personal improvement goals threatens their academic success. Hence, the study aimed to investigate year-one students’ academic goal-setting practices at Lagos State University of Education, Nigeria. Descriptive survey research was used in carrying out this study. The study population consisted of 3,101 year-one students of the University. A sample size of five hundred (501) participants was selected through a proportional and simple random sampling technique. The Formative Goal Setting Questionnaire (FGSQ) developed by Research Collaboration (2015) was adapted and used as an instrument for the study. Two main research questions were answered, while two null hypotheses were formulated and tested for the study. The study revealed higher data-based goals for all students than personal improvement goals. Nevertheless, data-based and personal improvement goal-setting for female students was higher than for male students. One sample test statistic and Anova used to analyse data for the two hypotheses also revealed that the mean difference between male and female year one students’ data-based and personal improvement goal-setting formation was statistically significant (p < 0.05). This means year one students’ data-based and personal improvement goals showed significant gender differences. Based on the findings of this study, it was recommended, among others, that therapeutic techniques that can help to change students’ faulty thinking and challenge their lack of desire for personal improvement should be sought to treat students who have problems with setting high personal improvement goals. Counsellors also need to advocate continued research into how to increase the goal-setting ability of male students and should focus more on counselling male students’ goal-setting ability. The main contributions of the study are higher institutions must prioritize early intervention in first-year students' academic goal setting. Researching gender differences in this practice reveals a crucial insight: male students often lag behind in setting meaningful goals, impacting their motivation and performance. Focusing on this demographic with data-driven personal improvement goals can be transformative. By promoting goal setting that is specific, measurable, and focused on self-growth (rather than competition), male students can unlock their full potential. Researchers and counselors play a vital role in detecting and supporting students with lower goal-setting tendencies. By prioritizing this intervention, we can empower all students to set ambitious, personalized goals that ignite their passion for learning and pave the way for academic success.

Keywords: academic goal setting, counselling, practice, university, year one students

Procedia PDF Downloads 66

Authors: Chieh-Chun Chang, Cheng-Ting Shih, Yan-Lin Liu, Shu-Jun Chang, Jay Wu

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With the widespread use of digital mammography (DM), radiation dose evaluation of breasts has become important. X-ray spectra are one of the key factors that influence the absorbed dose of glandular tissue. In this study, we estimated the X-ray spectrum of DM using the expectation maximization (EM) algorithm with the transmission measurement data. The interpolating polynomial model proposed by Boone was applied to generate the initial guess of the DM spectrum with the target/filter combination of Mo/Mo and the tube voltage of 26 kVp. The Monte Carlo N-particle code (MCNP5) was used to tally the transmission data through aluminum sheets of 0.2 to 3 mm. The X-ray spectrum was reconstructed by using the EM algorithm iteratively. The influence of the initial guess for EM reconstruction was evaluated. The percentage error of the average energy between the reference spectrum inputted for Monte Carlo simulation and the spectrum estimated by the EM algorithm was -0.14%. The normalized root mean square error (NRMSE) and the normalized root max square error (NRMaSE) between both spectra were 0.6% and 2.3%, respectively. We conclude that the EM algorithm with transmission measurement data is a convenient and useful tool for estimating x-ray spectra for DM in clinical practice.

Keywords: digital mammography, expectation maximization algorithm, X-Ray spectrum, X-Ray

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Authors: Ioana Pisica, Dimitrios Gkritzapis

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The significance of UAS in scientific applications has been amply demonstrated in recent years. The combinations of portability and quasi-static positioning by means of flying in close loop path make them versatile and efficient in the inspection of power systems infrastructure. In this paper, we critically assess several platforms and sensor capabilities to identify their pros and cons in relation to the power systems assets to be monitored. In this respect, it is paramount the flights to be conducted by using UAS which bear certain suitable features, such as responsive and easy control, video capturing in real time, autonomous routing of pre-planned flight programming with differentiating payloads. The outcome of this research is a set of optimal requirements for power system assets monitoring with UAS.

Keywords: platforms, power system, sensors, UAVs

Procedia PDF Downloads 288

Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Ramin Javadzadeh

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The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

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Authors: Kohkan Shamsi

Abstract:

Background: MRI and PET are now extensively utilized in Alzheimer's disease (AD) trials for patient eligibility, efficacy assessment, and safety evaluations but including imaging in AD trials impacts site selection process, patient recruitment, and patient retention. Methods: PET/MRI are performed at baseline and at multiple follow-up timepoints. This requires prospective site imaging qualification, evaluation of phantom data, training and continuous monitoring of machines for acquisition of standardized and consistent data. This also requires prospective patient/caregiver training as patients must go to multiple facilities for imaging examinations. We will share our experience form one of the largest AD programs. Lesson learned: Many neurological diseases have a similar presentation as AD or could confound the assessment of drug therapy. The inclusion of wrong patients has ethical and legal issues, and data could be excluded from the analysis. Centralized eligibility evaluation read process will be discussed. Amyloid related imaging abnormalities (ARIA) were observed in amyloid-β trials. FDA recommended regular monitoring of ARIA. Our experience in ARIA evaluations in large phase III study at > 350 sites will be presented. Efficacy evaluation: MRI is utilized to evaluate various volumes of the brain. FDG PET or amyloid PET agents has been used in AD trials. We will share our experience about site and central independent reads. Imaging logistic issues that need to be handled in the planning phase will also be discussed as it can impact patient compliance thereby increasing missing data and affecting study results. Conclusion: imaging must be prospectively planned to include standardizing imaging methodologies, site selection process and selecting assessment criteria. Training should be transparently conducted and documented. Prospective patient/caregiver awareness of imaging requirement is essential for patient compliance and reduction in missing imaging data.

Keywords: Alzheimer's disease, ARIA, MRI, PET, patient recruitment, retention

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Authors: Ayse Ozturk Samur, Gozde Inal Kiziltepe

Abstract:

No matter what level of education it is, starting a school is an exciting process as it includes new experiences. In this process, child steps into a different environment and institution except from the family institution which he was born into and feels secure. That new environment is different from home; it is a social environment which has its own rules, and involves duties and responsibilities that should be fulfilled and new vital experiences. The children who have a positive attitude towards school and like school are more enthusiastic and eager to participate in classroom activities. Moreover, a close relationship with the teacher enables the child to have positive emotions and ideas about the teacher and school and helps children adapt to school easily. In this study, it is aimed to identify children’s perceptions of academic competence, attitudes towards school and ideas about their teachers. In accordance with the aim a mixed method that includes both qualitative and quantitative data collection methods are used. The study is supported with qualitative data after collecting quantitative data. The study group of the research consists of randomly chosen 250 children who are 60-72 month old and attending a preschool institution in a city center located West Anatolian region of Turkey. Quantitative data was collected using Feelings about School scale. The scale consists of 12 items and 4 dimensions; school, teacher, mathematic, and literacy. Reliability and validity study for the scale used in the study was conducted by the researchers with 318 children who were 60-72 months old. For content validity experts’ ideas were asked, for construct validity confirmatory factor analysis was utilized. Reliability of the scale was examined by calculating internal consistency coefficient (Cronbach alpha). At the end of the analyses it was found that FAS is a valid and reliable instrument to identify 60-72 month old children’ perception of their academic competency, attitude toward school and ideas about their teachers. For the qualitative dimension of the study, semi-structured interviews were done with 30 children aged 60-72 month. At the end of the study, it was identified that children’s’ perceptions of their academic competencies and attitudes towards school was medium-level and their ideas about their teachers were high. Based on the semi structured interviews done with children, it is identified that they have a positive perception of school and teacher. That means quantitatively gathered data is supported by qualitatively collected data.

Keywords: feelings, preschool education, school, teacher, thoughts

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Authors: Zeinab Sanaee, Hossein Jafaripour

Abstract:

Copper is an excellent conductive material that is widely used in the energy devices such as Lithium-ion batteries and supercapacitors as the current collector. On the other hand, copper oxide nanowires have been used in these applications as potential electrode material. In this paper, nanowires of Copper and Copper oxide have been synthesized through a simple and time and cost-effective approach. The thermally grown Copper oxide nanowires have been converted into Copper nanowires through annealing in the Hydrogen atmosphere in a DC-PECVD system. To have a proper Copper nanostructure formation, an Au nanolayer was coated on the surface of Copper oxide nanowires. The results show the successful achievement of Copper nanowires without deformation or cracking. These structures have a great potential for Lithium-ion batteries and supercapacitors.

Keywords: Copper, Copper oxide, nanowires, Hydrogen annealing, Lithium ion battery

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Authors: Hamed Alipour-Banaei, Farhad Mehdizadeh

Abstract:

We report the design and characterization of ultra high quality factor filter based on one-dimensional photonic-crystal Thue-Morse sequence structure. The behavior of aperiodic array of photonic crystal structure is numerically investigated and we show that by changing the angle of incident wave, desired wavelengths could be tuned and a tunable filter is realized. Also it is shown that high quality factor filter be achieved in the telecommunication window around 1550 nm, with a device based on Thue-Morse structure. Simulation results show that the proposed structure has a quality factor more than 100000 and it is suitable for DWDM communication applications.

Keywords: Thue-Morse, filter, quality factor, photonic

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Authors: Veena Chaudhary, Rakesh P. Gakkhar

Abstract:

In this study, energy and exergy analysis are applied to the experimental data of an internal combustion engine operating on conventional diesel cycle. The experimental data are collected using an engine unit which enables accurate measurements of fuel flow rate, combustion air flow rate, engine load, engine speed and all relevant temperatures. First and second law efficiencies are calculated for different engine speed and compared. Results indicate that the first law (energy) efficiency is maximum at 1700 rpm whereas exergy efficiency is maximum and exergy destruction is minimum at 1900 rpm.

Keywords: diesel engine, exergy destruction, exergy efficiency, second law of thermodynamics

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Authors: Shin-Shin Kao, Hsiu-Chunj Pan

Abstract:

Given a graph G. A cycle of G is a sequence of vertices of G such that the first and the last vertices are the same. A hamiltonian cycle of G is a cycle containing all vertices of G. The graph G is k-ordered (resp. k-ordered hamiltonian) if for any sequence of k distinct vertices of G, there exists a cycle (resp. hamiltonian cycle) in G containing these k vertices in the specified order. Obviously, any cycle in a graph is 1-ordered, 2-ordered and 3-ordered. Thus the study of any graph being k-ordered (resp. k-ordered hamiltonian) always starts with k = 4. Most studies about this topic work on graphs with no real applications. To our knowledge, the chordal ring families were the first one utilized as the underlying topology in interconnection networks and shown to be 4-ordered [1]. Furthermore, based on computer experimental results in [1], it was conjectured that some of them are 4-ordered hamiltonian. In this paper, we intend to give some possible directions in proving the conjecture.

Keywords: Hamiltonian cycle, 4-ordered, Chordal rings, 3-regular

Procedia PDF Downloads 442