Search results for: decision matrix

Authors: Endalkachew Abebe Kebede, Bojin Bojinov, Andon Vasilev Andonov, Orhan Dengiz

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Remote sensing has a potential application in assessing and monitoring the plants' biophysical properties using the spectral responses of plants and soils within the electromagnetic spectrum. However, only a few reports compare the performance of different remote sensing sensors against in-situ field spectral measurement. The current study assessed the potential applications of open data source satellite images (Sentinel 2 and Landsat 9) in estimating the biophysical properties of the wheat crop on a study farm found in the village of OvchaMogila. A Landsat 9 (30 m resolution) and Sentinel-2 (10 m resolution) satellite images with less than 10% cloud cover have been extracted from the open data sources for the period of December 2021 to April 2022. An Unmanned Aerial Vehicle (UAV) has been used to capture the spectral response of plant leaves. In addition, SpectraVue 710s Leaf Spectrometer was used to measure the spectral response of the crop in April at five different locations within the same field. The ten most common vegetation indices have been selected and calculated based on the reflectance wavelength range of remote sensing tools used. The soil samples have been collected in eight different locations within the farm plot. The different physicochemical properties of the soil (pH, texture, N, P₂O₅, and K₂O) have been analyzed in the laboratory. The finer resolution images from the UAV and the Leaf Spectrometer have been used to validate the satellite images. The performance of different sensors has been compared based on the measured leaf spectral response and the extracted vegetation indices using the five sampling points. A scatter plot with the coefficient of determination (R2) and Root Mean Square Error (RMSE) and the correlation (r) matrix prepared using the corr and heatmap python libraries have been used for comparing the performance of Sentinel 2 and Landsat 9 VIs compared to the drone and SpectraVue 710s spectrophotometer. The soil analysis revealed the study farm plot is slightly alkaline (8.4 to 8.52). The soil texture of the study farm is dominantly Clay and Clay Loam.The vegetation indices (VIs) increased linearly with the growth of the plant. Both the scatter plot and the correlation matrix showed that Sentinel 2 vegetation indices have a relatively better correlation with the vegetation indices of the Buteo dronecompared to the Landsat 9. The Landsat 9 vegetation indices somewhat align better with the leaf spectrometer. Generally, the Sentinel 2 showed a better performance than the Landsat 9. Further study with enough field spectral sampling and repeated UAV imaging is required to improve the quality of the current study.

Keywords: landsat 9, leaf spectrometer, sentinel 2, UAV

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Authors: Dovile Petkeviciute-Barysiene

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Legal technologies not only create new ways for accessing and providing legal services but also transform the role of legal practitioners. Both lawyers and users of legal services expect automated solutions to outperform people with objectivity and impartiality. Although fairness of the automated decisions is crucial, research on assessing various characteristics of automated processes related to the perceived fairness has only begun. One of the major obstacles to this research is the lack of comprehensive understanding of what legal actions are automated and could be meaningfully automated, and to what extent. Neither public nor legal practitioners oftentimes cannot envision technological input due to the lack of general without illustrative examples. The aim of this study is to map decision making stages and automation levels which are and/or could be achieved in legal actions related to pre-trial and trial processes. Major legal decisions and judgments are identified during the consultations with legal practitioners. The dual-process model of information processing is used to describe cognitive processes taking place while making legal decisions and judgments during pre-trial and trial action. Some of the existing legal technologies are incorporated into the analysis as well. Several published automation level taxonomies are considered because none of them fit well into the legal context, as they were all created for avionics, teleoperation, unmanned aerial vehicles, etc. From the information processing perspective, analysis of the legal decisions and judgments expose situations that are most sensitive to cognitive bias, among others, also help to identify areas that would benefit from the automation the most. Automation level analysis, in turn, provides a systematic approach to interaction and cooperation between humans and algorithms. Moreover, an integrated map of legal decisions and judgments, information processing characteristics, and automation levels all together provide some groundwork for the research of legal technology perceived fairness and acceptance. Acknowledgment: This project has received funding from European Social Fund (project No 09.3.3-LMT-K-712-19-0116) under grant agreement with the Research Council of Lithuania (LMTLT).

Keywords: automation levels, information processing, legal judgment and decision making, legal technology

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Authors: A. Pascual Ramsay

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Accounts of the management of the economic and euro crisis in Spain have been dominated by an emphasis on external constraints. However, this approach leaves unanswered important questions about the role of domestic political factors. Using systematic qualitative primary research and employing elite interviewing and process tracing, this paper aims to fill this gap for the period of the Partido Socialista Obrero Español (PSOE) administration. The paper shows that domestic politics played a crucial role in the management of the crisis, most importantly by determining the shape of the measures undertaken. In its three distinct stages – downplaying/inaction, reaction/stimulus, and austerity/reform – the PSOE's response was certainly constrained by external factors, most notably EMU membership and the actions of sovereign-bond investors, the ECB and Germany. Yet while these external constraints forced the government to act, domestic political factors fundamentally shaped the content of key measures: the fiscal stimulus, the labour, financial and pension reforms, the refusal to accept a bailout or the reform of the Constitution. Seven factors were particularly influential: i) electoral and political cost, ii) party and partisanship, iii) organised interests, iv) domestic institutions, v) ideological preferences, vi) ineffective decision-making, and vii) judgement and personal characteristics of decision-makers. In conclusion, domestic politics played an important role in the management of the crisis, a role that has been underestimated by dominant approaches focusing on external constraints and weak domestic policy autonomy. The findings provide empirical evidence to support research agendas that identify significant state discretion in the face of international economic integration and an important role for domestic political factors such as institutions, material interests, partisanship and ideology in shaping economic outcomes.

Keywords: economic crisis, Euro, PSOE, Spain

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Authors: Christina Zourna, Ioanna Papavassiliou-Alexiou

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Drama in Education (DiE) is a dynamic experiential method that can be used in many interdisciplinary contexts. In the Educational and Social Policy Department, University of Macedonia, Thessaloniki, Greece, DiE is being used as a core method for developing professional competences in pre- and postgraduate courses as well as adult education training programs. In this presentation, an innovative DiE application will be described concerning the development of educational leaders’ skills necessary to meet unprecedented, unexpected challenges in the 21st century schools. In a non-threatening risk-taking no-penalty environment, future educational leaders live-in-role problems, challenges, and dilemmas before having to face similar ones in their profession. Through personal involvement, emotional engagement, and reflection, via individual and group activities, they experience the behaviour, dilemmas, decision-making processes, and informed choices of a recognized leader and are able to make connections with their own life. As pretext serves the life of Alexander the Great, the Macedonian King who defeated the vast Persian empire in the 4th century BC and, by uniting all Greeks, conquered the up-to-date known eastern world thanks to his authentic leadership skills and exceptional personality traits. Since the early years of his education mastered by the famous Greek philosopher Aristotle, Alexander proved his unique qualities by providing the world with the example of an undeniably genuine, inspirational, effective, and most recognizable authentic leader. Through questionnaires and individual interviews, participants in these workshops revealed how they developed active listening, empathy, creativity, imagination, critical strategic and out-of-the-box thinking, cooperation and own vision communicating, crisis management skills, self-efficacy, self-awareness, self-exposure, information management, negotiation and inspiration skills, enhanced sense of responsibility and commitment, and decision-making skills.

Keywords: drama in education method, educational leadership, professional competences, skills’ development

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Authors: Manana Lobzhanidze

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Reforming the pension system is urgent in Georgia due to socio-economic problems. Replacing the current pension system with a new one requires, on the one hand, an assessment of the challenges in this field and, on the other hand, a study of the best practices of foreign experience. Objectives: The aim of the research is to identify challenges in the pension reform process in Georgia, to study international experience, and to develop recommendations for the implementation of an effective pension system. Methodologies: A desk study was conducted, and methods of analysis, comparison, grouping, matrix charts, and scenario analysis were used. Findings: The advantages of accumulative pension compared to the current pension system are identified. The main challenge is the non-targeting of the pension contributions and the ineffective investment policy; the public's attitude towards the cumulative pension system is determined.

Keywords: pension reform, challenges, international practice, opportunity for development

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Authors: Iman Taraghi, Sandra Paszkiewicz, Daria Pawlikowska, Anna Szymczyk, Izabela Irska, Rafal Stanik, Amelia Linares, Tiberio A. Ezquerra, Elżbieta Piesowicz

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Currently, the massive use of thermoplastic materials in industrial applications causes huge amounts of polymer waste. The poly (ethylene glycol-co-1,4-cyclohexanedimethanol terephthalate) (PET-G) has been widely used in food packaging and polymer foils. In this research, the PET-G foils have been recycled and reused as a matrix to combine with different types of nanofillers such as carbon nanotubes, graphene nanoplatelets, and nanosized carbon black. The mechanical and electrical properties, as well as thermal stability and thermal conductivity of the PET-G, improved along with the addition of the aforementioned nanofillers and hybrid system of them.

Keywords: polymer hybrid nanocomposites, carbon nanofillers, recycling, physical performance

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Authors: Wang Yichen, Haruka Yamashita

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In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: recurrent neural network, players lineup, basketball data, decision making model

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Authors: Mayuri Gandhi, Nayan Agrawal, Harshita Bhatia

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In the present work, cerium fluoride (CeF3) was selected as the host material because of its high density, fast response and high radiation resistance, efficient absorption and energy transfer by host (to activator). For the synthesis of CeF3 nanoparticles doped with Sm3+ ion, co-precipitation route was employed. Thus for optimum results, concentration dependent studies of the fluorescence of Sm3+ was carried out. The photoluminescence gave emissions in both visible as well as the NIR region and therefore it can have its application in solar cells, where it can absorb a large spectrum of energy. CeF3:Sm3+ nanoparticles were carefully incorporated in a suitable polymer matrix in order to demonstrate a variety of applications to improve the performance of the polymer materials and use it to develop high grade optoelectronic devices such as LEDs, security labelling, lasers, displays, biological imaging, etc.

Keywords: bioimaging, cerium fluoride, NIR emission, samarium

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Authors: M. Miljkovic, A. Urakpo, M. Simic-Koumoutsaris

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Benefit-risk assessment (BRA) is a valuable tool that takes place in multiple stages during a medicine's lifecycle, and this assessment can be conducted in a variety of ways. The aim was to summarize current BRA methods used during approval decisions and in post-approval settings and to see possible future directions. Relevant reviews, recommendations, and guidelines published in medical literature and through regulatory agencies over the past five years have been examined. BRA implies the review of two dimensions: the dimension of benefits (determined mainly by the therapeutic efficacy) and the dimension of risks (comprises the safety profile of a drug). Regulators, industry, and academia have developed various approaches, ranging from descriptive textual (qualitative) to decision-analytic (quantitative) models, to facilitate the BRA of medicines during the product lifecycle (from Phase I trials, to authorization procedure, post-marketing surveillance and health technology assessment for inclusion in public formularies). These approaches can be classified into the following categories: stepwise structured approaches (frameworks); measures for benefits and risks that are usually endpoint specific (metrics), simulation techniques and meta-analysis (estimation techniques), and utility survey techniques to elicit stakeholders’ preferences (utilities). All these approaches share the following two common goals: to assist this analysis and to improve the communication of decisions, but each is subject to its own specific strengths and limitations. Before using any method, its utility, complexity, the extent to which it is established, and the ease of results interpretation should be considered. Despite widespread and long-time use, BRA is subject to debate, suffers from a number of limitations, and currently is still under development. The use of formal, systematic structured approaches to BRA for regulatory decision-making and quantitative methods to support BRA during the product lifecycle is a standard practice in medicine that is subject to continuous improvement and modernization, not only in methodology but also in cooperation between organizations.

Keywords: benefit-risk assessment, benefit-risk profile, product lifecycle, quantitative methods, structured approaches

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Authors: Farah Islam, Md. Monirul Islam, Mosammat Salma Akter, Goutam Kumar Kundu

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The continuous variability of climate and the risk associated with it have a significant impact on the fisheries leading to a global concern for about half a billion fishery-based livelihoods. Though in the context of Bangladesh mounting evidence on the impacts of climate change on fishery-based livelihoods or their socioeconomic conditions are present, the country’s inland fisheries sector remains in a negligible corner as compared to the coastal areas which are spotted on the highlight due to its higher vulnerability to climatic hazards. The available research on inland fisheries, particularly river fisheries, has focussed mainly on fish production, pollution, fishing gear, fish biodiversity and livelihoods of the fishers. This study assesses the impacts of climate variability and changes on the Jamuna (a transboundary river called Brahmaputra in India) River fishing communities and their coping and adaptation strategies. This study has used primary data collected from Kalitola Ghat and Debdanga fishing communities of the Jamuna River during May, August and December 2015 using semi-structured interviews, oral history interviews, key informant interviews, focus group discussions and impact matrix as well as secondary data. This study has found that both communities are exposed to storms, floods and land erosions which impact on fishery-based livelihood assets, strategies, and outcomes. The impact matrix shows that human and physical capitals are more affected by climate hazards which in turn affect financial capital. Both communities have been responding to these exposures through multiple coping and adaptation strategies. The coping strategies include making dam with soil, putting jute sac on the yard, taking shelter on boat or embankment, making raised platform or ‘Kheua’ and involving with temporary jobs. While, adaptation strategies include permanent migration, change of livelihood activities and strategies, changing fishing practices and making robust houses. The study shows that migration is the most common adaptation strategy for the fishers which resulted in mostly positive outcomes for the migrants. However, this migration has impacted negatively on the livelihoods of existing fishers in the communities. In sum, the Jamuna river fishing communities have been impacted by several climatic hazards and they have traditionally coped with or adapted to the impacts which are not sufficient to maintain sustainable livelihoods and fisheries. In coming decades, this situation may become worse as predicted by latest scientific research and an enhanced level of response would be needed.

Keywords: climatic hazards, impacts and adaptation, fisherfolk, the Jamuna River

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Authors: Zahid Hussain

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Provincial Disaster Management Authority (PDMA) Punjab was established under NDM Act 2010 and now working under Senior Member Board of Revenue, deals with the whole spectrum of disasters including preparedness, mitigation, early warning, response, relief, rescue, recovery and rehabilitation. The District Disaster Management Authorities (DDMA) are acting as implementing arms of PDMA in the districts to respond any disaster. DDMAs' role is very important in disaster mitigation, response and recovery as they are the first responder and closest tier to the community. Keeping in view the significant role of DDMAs, technical and human resource capacity are need to be checked. For calculating the technical efficiencies of District Disaster Management Authority (DDMA) in Punjab, three inputs like number of labour, the number of transportation and number of equipment, two outputs like relief assistance and the number of rescue and 25 districts as decision making unit have been selected. For this purpose, 8 years secondary data from 2005 to 2012 has been used. Data Envelopment Analysis technique has been applied. DEA estimates the relative efficiency of peer entities or entities performing the similar tasks. The findings show that all decision making unit (DMU) (districts) are inefficient on techonological and scale efficiency scale while technically efficient on pure and total factor productivity efficiency scale. All DMU are found technically inefficient only in the year 2006. Labour and equipment were not efficiently used in the year 2005, 2007, 2008, 2009 and 2012. Furthermore, only three years 2006, 2010 and 2011 show that districts could not efficiently use transportation in a disaster situation. This study suggests that all districts should curtail labour, transportation and equipment to be efficient. Similarly, overall all districts are not required to achieve number of rescue and relief assistant, these should be reduced.

Keywords: DEA, DMU, PDMA, DDMA

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Authors: Hadia Rafi

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To make any decision in any work/task/project it involves many factors that needed to be looked. The analytic Hierarchy process (AHP) is based on the judgments of experts to derive the required results this technique measures the intangibles and then by the help of judgment and software analysis the comparisons are made which shows how much a certain element/unit leads another. AHP includes how an inconsistent judgment should be made consistent and how the judgment should be improved when possible. The Priority scales are obtained by multiplying them with the priority of their parent node and after that they are added.

Keywords: AHP, priority scales, parent node, software analysis

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: K. R. Phaneesh, Anirudh Bhat, G. Mukherjee, K. T. Kashyap

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Extensive Monte Carlo Potts model simulations were performed on 2D square lattice to investigate the effects of simulated higher temperatures effects on grain growth kinetics. A range of simulation temperatures (KTs) were applied on a matrix of size 10002 with Q-state 64, dispersed with a wide range of second phase particles, ranging from 0.001 to 0.1, and then run to 100,000 Monte Carlo steps. The average grain size, the largest grain size and the grain growth exponent were evaluated for all particle fractions and simulated temperatures. After evaluating several growth parameters, the critical temperature for a square lattice, with eight nearest neighbors, was found to be KTs = 0.4.

Keywords: average grain size, critical temperature, grain growth exponent, Monte Carlo steps

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Authors: Ammar Maziz, Mostapha Tarfaoui, Said Rechak

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The high mechanical performance of composite pipes can be adversely affected by their low resistance to impact loads. Loads in dynamic origin are dangerous and cause consequences on the operation of pipes because the damage is often not detected and can affect the structural integrity of composite pipes. In this work, an advanced 3-D finite element (FE) model, based on the use of intralaminar damage models was developed and used to predict damage under low-velocity impact. The performance of the numerical model is validated with the confrontation with the results of experimental tests. The results show that at low impact energy, the damage happens mainly by matrix cracking and delamination. The model capabilities to simulate the low-velocity impact events on the full-scale composite structures were proved.

Keywords: composite materials, low velocity impact, FEA, dynamic behavior, progressive damage modeling

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Authors: O. Hassoon, M. Tarfoui, A. El Malk

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Fiber Bragg optic sensor embedded in composite material to detect and monitor the damage which is occur in composite structure. In this paper we deal with the mode-Ι delamination to determine the resistance of material to crack propagation, and use the coupling mode theory and T-matrix method to simulating the FBGs spectrum for both uniform and non-uniform strain distribution. The double cantilever beam test which is modeling in FEM to determine the Longitudinal strain, there are two models which are used, the first is the global half model, and the second the sub-model to represent the FBGs with refine mesh. This method can simulate the damage in the composite structure and converting the strain to wavelength shifting of the FBG spectrum.

Keywords: fiber bragg grating, delamination detection, DCB, FBG spectrum, structure health monitoring

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Mohammed Fayez Al Rez

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Nowadays, there is an unmet clinical need for new small-diameter vascular grafts to overcome the drawbacks of traditional methods used for treatment of widespread cardiovascular diseases. Vascular tissue engineering (VTE) is a promising approach that can be utilized to develop viable vascular grafts by in vitro seeding of functional cells onto a scaffold allowing them to attach, proliferate and differentiate. To achieve this purpose, the scaffold should provide cells with the initial necessary extracellular matrix environment and structure until being able to reconstruct the required vascular tissue. Therefore, producing scaffolds with suitable features is crucial for guiding cells properly to develop the desired tissue-engineered vascular grafts for clinical applications. The main objective of this work is fabrication and characterization of tubular small-diameter ( < 6 mm) bilayered scaffolds for VTE. The scaffolds were prepared via mixing electrospinning approach of biodegradable poly(ε-caprolactone) (PCL) polymer – due to its favorable physicochemical properties – to mimic the natural environment-extracellular matrix. Firstly, tubular nanofibrous construct with inner diameter of 3, 4 or 5 mm was electrospun as inner layer, and secondly, microfibrous construct was electrospun as outer layer directly on the first produced inner layer. To improve the biological properties of PCL, a group of the electrospun scaffolds was immersed in type-1 collagen solution. The morphology and structure of the resulting fibrous scaffolds were investigated by scanning electron microscope. The electrospun nanofibrous inner layer contained fibers measuring 219±35 nm in diameter, while the electrospun microfibrous outer layer contained fibers measuring 1011 ± 150 nm. Furthermore, mechanical, thermal and physical tests were conducted with both electrospun bilayered scaffold types where revealed improved properties. Biological investigations using endothelial, smooth muscle and fibroblast cell line showed good biocompatibility of both tested electrospun scaffolds. Better attachment and proliferation were obviously found when cells were cultured on the scaffolds immersed with collagen due to increasing the hydrophilicity of the PCL. The easy, inexpensive and versatile electrospinning approach used in this work was able to successfully produce double layered tubular elastic structures containing both nanofibers and microfibers to imitate the native vascular structure. The PCL – as a suitable and approved biomaterial for many biomedical and tissue engineering applications – can ensure favorable mechanical properties of scaffolds used for VTE. The VTE approach using electrospun bilayered scaffolds offers optimal solutions and holds significant promises for treatment of many cardiovascular diseases.

Keywords: electrospinning, poly(ε-caprolactone) (PCL), tissue-engineered vascular graft, tubular bilayered scaffolds, vascular cells

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Authors: Siavash Eftekharifar, Tohid Yousefi Rezaii, Mahdi Shamsi

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The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method.

Keywords: compressed sensing, ECG compression, Gaussian kernel, sparse representation

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Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Eman AbdelSabour

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Sustainability is increasingly being considered as a critical aspect in shaping the urban environment. It works as an invention development basis for global urban growth. Currently, different models and structures impact the means of interpreting the criteria that would be included in defining a sustainable city. There is a collective need to improve the growth path to an extremely durable path by presenting different suggestions regarding multi-scale initiatives. The global rise in urbanization has led to increased demand and pressure for better urban planning choice and scenarios for a better sustainable urban alternative. The need for an assessment tool at the urban scale was prompted due to the trend of developing increasingly sustainable urban development (SUD). The neighborhood scale is being managed by a growing research committee since it seems to be a pertinent scale through which economic, environmental, and social impacts could be addressed. Although neighborhood design is a comparatively old practice, it is in the initial years of the 21st century when environmentalists and planners started developing sustainable assessment at the neighborhood level. Through this, urban reality can be considered at a larger scale whereby themes which are beyond the size of a single building can be addressed, while it still stays small enough that concrete measures could be analyzed. The neighborhood assessment tool has a crucial role in helping neighborhood sustainability to perform approach and fulfill objectives through a set of themes and criteria. These devices are also known as neighborhood assessment tool, district assessment tool, and sustainable community rating tool. The primary focus of research has been on sustainability from the economic and environmental aspect, whereas the social, cultural issue is rarely focused. Therefore, this research is based on Doha, Qatar, the current urban conditions of the neighborhoods is discussed in this study. The research problem focuses on the spatial features in relation to the socio-cultural aspects. This study is outlined in three parts; the first section comprises of review of the latest use of wellbeing assessment methods to enhance decision process of retrofitting physical features of the neighborhood. The second section discusses the urban settlement development, regulations and the process of decision-making rule. An analysis of urban development policy with reference to neighborhood development is also discussed in this section. Moreover, it includes a historical review of the urban growth of the neighborhoods as an atom of the city system present in Doha. Last part involves developing quantified indicators regarding subjective well-being through a participatory approach. Additionally, applying GIS will be utilized as a visualizing tool for the apparent Quality of Life (QoL) that need to develop in the neighborhood area as an assessment approach. Envisaging the present QoL situation in Doha neighborhoods is a process to improve current condition neighborhood function involves many days to day activities of the residents, due to which areas are considered dynamic.

Keywords: neighborhood, subjective wellbeing, decision support tools, Doha, retrofiring

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Authors: Launcelot McGrath, Xiaoxiao Liu, Colin Flanagan

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It is widely recognised that a comprehensive understanding of physiological data is a vital aid to the anaesthesiologist in monitoring and maintaining the well-being of a patient undergoing surgery. Bio signal analysis is one of the most important topics that researchers have tried to develop over the last century to understand numerous human diseases. There are tremendous biological signals during anaesthesia, and not all of them are important, which to choose to observe is a significant decision. It is important that the anaesthesiologist understand both the signals themselves, and the limitations introduced by the processes of acquisition. In this article, we provide an all-sided overview of different types of biological signals as well as the mechanisms applied to acquire them.

Keywords: general biosignals, anaesthesia, biological, electroencephalogram

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Authors: G. Traini, V. Giacometti, R. Mirabelli, V. Patera, D. Pinci, A. Sarti, A. Sciubba, M. Marafini

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The MONDO (MOnitor for Neutron Dose in hadrOntherapy) project aims a precise characterisation of the secondary fast and ultrafast neutrons produced in particle therapy treatments. The detector is composed of a matrix of scintillating fibres (250 um) readout by CMOS Digital-SPAD based sensors. Recoil protons from n-p elastic scattering are detected and used to track neutrons. A prototype was tested with proton beams (Trento Proton Therapy Centre): efficiency, light yield, and track-reconstruction capability were studied. The results of a MonteCarlo FLUKA simulation used to evaluated double scattering efficiency and expected backgrounds will be presented.

Keywords: secondary neutrons, particle therapy, tracking, elastic scattering

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Authors: R. L. M. Paiva, M. R. Capri, D. R. Mulinari, C. F. Bandeira, S. R. Montoro

Abstract:

Natural fibers are used in polymer composites to improve mechanical properties, substituting inorganic reinforcing agents produced by non renewable resources. The present study investigates the tensile, flexural and impact behaviors of sugarcane bagasse fibers-polypropylene composite as a function of volume fraction. The surface of the fibers was modified by mercerization treatments to improve the wetting behavior of the apolar polypropylene. The treatment characterization was obtained by infrared spectroscopy and scanning electron microscopy. Results evidence that a good adhesion interfacial between fibers-matrix causing an increase strength and modulus flexural as well as impact strength in the modified fibers/PP composites when compared to the pure PP and unmodified fibers reinforced composites.

Keywords: sugarcane bagasse, polymer composites, mechanical properties, fibers

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Authors: B. J. Sandesh, Saurabha Jirgi, S. Vidya, Prakash Eljer, Gowri Srinivasa

Abstract:

In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users.

Keywords: video indexing and retrieval, lecture videos, content based video search, multimodal indexing

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Authors: Hireni Mankodi

Abstract:

The textile preforms for thermoplastic composite play a key role in providing the mechanical properties and gives the idea about preparing combination of yarn from Glass, Basalt, Carbon as reinforcement and PP, PET, Nylon as thermoplastic matrix at yarn stage for preforms to improve the quality and performance of laminates. The main objectives of this work are to develop the hybrid yarn using different yarn manufacturing process and prepare different performs using hybrid yarns. It has been observed that the glass/pp combination give homogeneous distribution in yarn. The proportion varied to optimize the glass/pp composition. The different preform has been prepared with combination of hybrid yarn, PP, glass combination. Further studies will investigate the effect of glass content in fabric, effect of weave, warps and filling density, number of layer plays significant role in deciding mechanical properties of thermoplastic laminates.

Keywords: thermoplastic, preform, laminates, hybrid yarn, glass

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Authors: O. D. Arbeláez-Echeverri, E. Restrepo-Parra, J. C. Riano-Rojas

Abstract:

A two dimensional geometric model of Bit Patterned Media is proposed, the model is based on the crystal structure of the materials commonly used to produce the nano islands in bit patterned materials and the possible defects that may arise from the interaction between the nano islands and the matrix material. The dynamic magnetic properties of the material are then computed using time aware integration methods for the multi spin Hamiltonian. The Hamiltonian takes into account both the spatial and topological disorder of the sample as well as the high perpendicular anisotropy that is pursued when building bit patterned media. The main finding of the research was the possibility of replicating the results of previous experiments on similar materials and the ability of computing the switching field distribution given the geometry of the material and the parameters required by the model.

Keywords: nanostructures, Monte Carlo, pattern media, magnetic properties

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Authors: Mohamed Barbary, Mohamed H. Abd El-Azeem

Abstract:

Maneuvering extended object tracking (M-EOT) using high-resolution inverse synthetic aperture radar (ISAR) observations has been gaining momentum recently. This work presents a new robust implementation of the multiple models (MM) multi-Bernoulli (MB) filter for M-EOT, where the M-EOT’s ISAR observations are characterized using a skewed (SK) non-symmetrically normal distribution. To cope with the possible abrupt change of kinematic state, extension, and observation distribution over an extended object when a target maneuvers, a multiple model technique is represented based on MB-track-before-detect (TBD) filter supported by SK-sub-random matrix model (RMM) or sub-ellipses framework. Simulation results demonstrate this remarkable impact.

Keywords: maneuvering extended objects, ISAR, skewed normal distribution, sub-RMM, MM-MB-TBD filter

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Authors: Sima Ajami, Batool Akbari

Abstract:

Background: Radio frequency identification (RFID) system has been successfully applied to the areas of manufacturing, supply chain, agriculture, transportation, healthcare, and services. The RFID is already used to track and trace the victims in a disaster situation. Data can be collected in real time and be immediately available to emergency personnel and saves time by the RFID. Objectives: The aim of this study was, first, to identify stakeholders and customers for rescuing earthquake victims, second, to list key internal and external factors to use RFID to track earthquake victims, finally, to assess SWOT for rescuers' viewpoint. Materials and Methods: This study was an applied and analytical study. The study population included scholars, experts, planners, policy makers and rescuers in the "red crescent society of Isfahan province", "disaster management Isfahan province", "maintenance and operation department of Isfahan", "fire and safety services organization of Isfahan municipality", and "medical emergencies and disaster management center of Isfahan". After that, researchers held a workshop to teach participants about RFID and its usages in tracking earthquake victims. In the meanwhile of the workshop, participants identified, listed, and weighed key internal factors (strengths and weaknesses; SW) and external factors (opportunities and threats; OT) to use RFID in tracking earthquake victims. Therefore, participants put weigh strengths, weaknesses, opportunities, and threats (SWOT) and their weighted scales were calculated. Then, participants' opinions about this issue were assessed. Finally, according to the SWOT matrix, strategies to solve the weaknesses, problems, challenges, and threats through opportunities and strengths were proposed by participants. Results: The SWOT analysis showed that the total weighted score for internal and external factors were 3.91 (Internal Factor Evaluation) and 3.31 (External Factor Evaluation) respectively. Therefore, it was in a quadrant SO strategies cell in the SWOT analysis matrix and aggressive strategies were resulted. Organizations, scholars, experts, planners, policy makers and rescue workers should plan to use RFID technology in order to save more victims and manage their life. Conclusions: Researchers suppose to apply SO strategies and use a firm’s internal strength to take advantage of external opportunities. It is suggested, policy maker should plan to use the most developed technologies to save earthquake victims and deliver the easiest service to them. To do this, education, informing, and encouraging rescuers to use these technologies is essential. Originality/ Value: This study was a research paper that showed how RFID can be useful to track victims in earthquake.

Keywords: frequency identification system, strength, weakness, earthquake, victim

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Authors: Snigdha Sahai, Devaki Chikkavenkatappa Yellappa

Abstract:

This study outlines approaches for testing and validating stochastic models used in epidemiology, focusing on the integration and functional testing of simulation code. It details methods for combining simple functions into comprehensive simulations, distinguishing between deterministic and stochastic components, and applying tests to ensure robustness. Techniques include isolating stochastic elements, utilizing large sample sizes for validation, and handling special cases. Practical examples are provided using R code to demonstrate integration testing, handling of incorrect inputs, and special cases. The study emphasizes the importance of both functional and defensive programming to enhance code reliability and user-friendliness.

Keywords: computational epidemiology, epidemiology, public health, infectious disease modeling, statistical analysis, health data analysis, disease transmission dynamics, predictive modeling in health, population health modeling, quantitative public health, random sampling simulations, randomized numerical analysis, simulation-based analysis, variance-based simulations, algorithmic disease simulation, computational public health strategies, epidemiological surveillance, disease pattern analysis, epidemic risk assessment, population-based health strategies, preventive healthcare models, infection dynamics in populations, contagion spread prediction models, survival analysis techniques, epidemiological data mining, host-pathogen interaction models, risk assessment algorithms for disease spread, decision-support systems in epidemiology, macro-level health impact simulations, socioeconomic determinants in disease spread, data-driven decision making in public health, quantitative impact assessment of health policies, biostatistical methods in population health, probability-driven health outcome predictions

Procedia PDF Downloads 6