Search results for: time series fractal analysis

Authors: C. Pacheco, A. Cipriano

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A new concept of response system is proposed for filling the gap that exists in reducing vulnerability during immediate response to natural disasters. Real Time Early Response Systems (RTERSs) incorporate real time information as feedback data for closing control loop and for generating real time situation assessment. A review of the state of the art works that fit the concept of RTERS is presented, and it is found that they are mainly focused on manmade disasters. At the same time, in response phase of natural disaster management many works are involved in creating early warning systems, but just few efforts have been put on deciding what to do once an alarm is activated. In this context a RTERS arises as a useful tool for supporting people in their decision making process during natural disasters after an event is detected, and also as an innovative context for applying well-known automation technologies and automatic control concepts and tools.

Keywords: disaster management, emergency response system, natural disasters, real time

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Authors: Mario Isaza-Ruget, Claudia C. Colmenares-Mejia, Nancy Yomayusa, Camilo A. González, Andres Cely, Jossie Murcia

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Quantification of risks associated with complications development from chronic kidney disease (CKD) through accurate survival models can help with patient management. A retrospective cohort that included patients diagnosed with CKD from a primary care program and followed up between 2013 and 2018 was carried out. Time-dependent and static covariates associated with demographic, clinical, and laboratory factors were included. Deep Learning (DL) survival analyzes were developed for three CKD outcomes: CKD stage progression, >25% decrease in Estimated Glomerular Filtration Rate (eGFR), and Renal Replacement Therapy (RRT). Models were evaluated and compared with Random Survival Forest (RSF) based on concordance index (C-index) metric. 2.143 patients were included. Two models were developed for each outcome, Deep Neural Network (DNN) model reported C-index=0.9867 for CKD stage progression; C-index=0.9905 for reduction in eGFR; C-index=0.9867 for RRT. Regarding the RSF model, C-index=0.6650 was reached for CKD stage progression; decreased eGFR C-index=0.6759; RRT C-index=0.8926. DNN models applied in survival analysis context with considerations of longitudinal covariates at the start of follow-up can predict renal stage progression, a significant decrease in eGFR and RRT. The success of these survival models lies in the appropriate definition of survival times and the analysis of covariates, especially those that vary over time.

Keywords: artificial intelligence, chronic kidney disease, deep neural networks, survival analysis

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Authors: Amir Hedjripour

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The required dewatering configuration for a contaminated sediment dam is discussed to meet no-spill criteria for a defined Average Recurrence Interval (ARI). There is an option for the sediment dam to pump the contaminated water to another storage facility before its capacity is exceeded. The system is subjected to a range of storm durations belonging to the design ARI with concurrent dewatering to the other storage facility. The model is set up in 1-minute time intervals and temporal patterns of storm events are used to de-segregate the total storm depth into partial durations. By running the model for selected storm durations, the maximum water volume in the dam is recorded as the critical volume, which indicates the required storage capacity for that storm duration. Runoff from upstream catchment and the direct rainfall over the dam open area are calculated by taking into account the time of concentration for the catchment. Total 99 different storm durations from 5 minutes to 72 hours were modelled together with five dewatering scenarios from 50 l/s to 500 l/s. The optimised dam/pump configuration is selected by plotting critical points for all cases and storage-dewatering envelopes. A simple economic analysis is also presented in the paper using Present-Value (PV) analysis to assist with the financial evaluation of each configuration and selection of the best alternative.

Keywords: contaminated water, optimisation, pump, sediment dam

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Authors: Hassan Al Salman, Ahmed Al Ghafli

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In this study we consider a nonlinear in time finite element approximation of a reaction diffusion system of lambda-omega type. We use a fixed point theorem to prove existence of the approximations. Then, we derive some essential stability estimates and discuss the uniqueness of the approximations. Also, we prove an optimal error bound in time for d=1, 2 and 3 space dimensions. Finally, we present some numerical experiments to verify the theoretical results.

Keywords: reaction diffusion system, finite element approximation, fixed point theorem, an optimal error bound

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Authors: Iliya Bitrus Abaje

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This study examines the evidence of climate change scenario in Kaduna State from the analysis of temperature and rainfall data (1976-2015) from three meteorological stations along a geographic transect from the southern part to the northern part of the State. Different statistical methods were used in determining the changes in both the temperature and rainfall series. The result of the linear trend lines revealed a mean increase in average temperature of 0.73oC for the 40 years period of study in the State. The plotted standard deviation for the temperature anomalies generally revealed that years of temperatures above the mean standard deviation (hotter than the normal conditions) in the last two decades (1996-2005 and 2006-2015) were more than those below (colder than the normal condition). The Cramer’s test and student’s t-test generally revealed an increasing temperature trend in the recent decades. The increased in temperature is an evidence that the earth’s atmosphere is getting warmer in recent years. The linear trend line equation of the annual rainfall for the period of study showed a mean increase of 316.25 mm for the State. Findings also revealed that the plotted standard deviation for the rainfall anomalies, and the 10-year non-overlapping and 30-year overlapping sub-periods analysis in all the three stations generally showed an increasing trend from the beginning of the data to the recent years. This is an evidence that the study area is now experiencing wetter conditions in recent years and hence climate change. The study recommends diversification of the economic base of the populace with emphasis on moving away from activities that are sensitive to temperature and rainfall extremes Also, appropriate strategies to ameliorate the scourge of climate change at all levels/sectors should always take into account the recent changes in temperature and rainfall amount in the area.

Keywords: anomalies, linear trend, rainfall, temperature

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Authors: T. P. Oladele, C. A. Snyders, S. M. Bradshaw, G. Akdogan

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The effect of temperature, contact time and agitation during pre-treatment was investigated on the elution of gold from granular activated carbon at fixed caustic-cyanide concentration and elution conditions. It was shown that there are interactions between parameters during pre-treatment. At 80oC, recovery is independent of the contact time while the maximum recovery is obtained in the absence of agitation (0rpm). Increase in agitation speed from 0 rev/min to 1200 rev/min showed a decrease in recovery of approximately 20 percent at 80°C. Recovery with increased time from 15 minutes to 45 minutes is only pronounced at 25°C with approximately 4 percent increase at all agitation speeds. The results from elution recovery are aimed to give insight into the mechanisms of pre-treatment under the combinations of the chosen parameters.

Keywords: gold, temperature, contact time, agitation speed, recovery

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Authors: Mohammed Umar Manko

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A series of batch experiments were conducted in order to investigate the feasibility of Balanitesaegyptiaca seeds based activated carbon as compared with industrial activated carbon for the removal of chromium and lead ions from aqueous solution by the adsorption process within 30 to 150 minutes contact time. The activated samples were prepared using zinc chloride and tetraoxophophate(VI) acid. The results obtained showed that the activated carbon of Balanitesaegyptiaca seeds studied had relatively high adsorption capacities for these heavy metal ions compared with industrial Activated Carbon. The percentage removal of Cr (VI) and lead (II) ions by the three activated carbon samples were 64%, 70% and 71%; 60%, 66% and 60% respectively. Adsorption equilibrium was established in 90 minutes for the heavy metal ions. The equilibrium data fitted the pseudo second order out of the pseudo first, pseudo second, Elovich ,Natarajan and Khalaf models tested. The investigation also showed that the adsorbents can effectively remove metal ions from similar wastewater and aqueous media.

Keywords: activated carbon, pseudo second order, chromium, lead, Elovich model

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Jairo Alejandro Fernandez Ortega

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Objective: Regarding effects of training velocity on strength in the functional condition of older adults controversy exists. The purpose of this study was to examine the effects of a twelve-week strength training program (PE) performed at high speed (GAV) versus a traditionally executed program (GBV), on functional performance, maximum strength and muscle power in a group of older adult women. Methodology: 86 women aged between 60-81 years participated voluntarily in the study and were assigned randomly to the GAV (three series at 40% 1RM at maximum speed, with maximum losses of 10% speed) or to the GBV (three series with three sets at 70% of 1RM). Both groups performed three weekly trainings. The maximum strength of upper and lower limbs (1RM), prehensile strength, walking speed, maximum power, mean propulsive velocity (MPV) and functional performance (senior fitness test) were evaluated before and after the PE. Results: Significant improvements were observed (p < 0.05) in all the tests in the two groups after the twelve weeks of training. However, the results of GAV were significantly (P < 0.05) higher than those of the GBV, in the tests of agility and dynamic equilibrium, stationary walking, sitting and standing, walking speed over 4 and 6 meters, MPV and peak power. In the tests of maximum strength and prehensile force, the differences were not significant. Conclusion: Strength training performed at high speeds seems to have a better effect on functional performance and muscle power than strength training performed at low speed.

Keywords: power training, resistance exercise, aging, strength, physical performance, high-velocity, resistance training

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Authors: N. Gayathri Devi, K. Batri

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The advent of technology has increased the traffic hazards and the road accidents take place. Collision detection system in automobile aims at reducing or mitigating the severity of an accident. This project aims at avoiding Vehicle head on collision by means of collision detection algorithm. This collision detection algorithm predicts the collision and the avoidance or minimization have to be done within few seconds on confirmation. Under critical situation collision minimization is made possible by turning the vehicle to the desired turn radius so that collision impact can be reduced. In order to avoid the collision completely, the turning of the vehicle should be achieved at reduced speed in order to maintain the stability.

Keywords: collision avoidance system, time to collision, time to turn, turn radius

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Authors: S. K. Khosrowshahi, E. Güler

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This study investigates the reinforcement function of geosynthetics on the shear strength and strain profile of sand. Conducting a series of simple shear tests, the shearing behavior of the samples under static and cyclic loads was evaluated. Three different types of geosynthetics including geotextile and geonets were used as the reinforcement materials. An image processing analysis based on the optical flow method was performed to measure the lateral displacements and estimate the shear strains. It is shown that besides improving the shear strength, the geosynthetic reinforcement leads a remarkable reduction on the shear strains. The improved layer reduces the required thickness of the soil layer to resist against shear stresses. Consequently, the geosynthetic reinforcement can be considered as a proper approach for the sustainable designs, especially in the projects with huge amount of geotechnical applications like subgrade of the pavements, roadways, and railways.

Keywords: image processing, soil reinforcement, geosynthetics, simple shear test, shear strain profile

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Authors: Wiseman Emmanuel Nkhomah, Chiyembekezo Kachimanga, Julia Huggins, Fabien Munyaneza

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Background: Partners In Health – Malawi introduced one of Operational Researches called Primary Health Care (PHC) Surveys in 2020, which seeks to assess progress of delivery of care in the district. The study consists of 5 long surveys, namely; Facility assessment, General Patient, Provider, Sick Child, Antenatal Care (ANC), primarily conducted in 4 health facilities in Neno district. These facilities include Neno district hospital, Dambe health centre, Chifunga and Matope. Usually, these annual surveys are conducted from January, and the target is to present final report by June. Once data is collected and analyzed, there are a series of reviews that take place before reaching final report. In the first place, the manual process took over 9 months to present final report. Initial findings reported about 76.9% of the data that added up when cross-checked with paper-based sources. Purpose: The aim of this approach is to run away from manually pulling the data, do fresh analysis, and reporting often associated not only with delays in reporting inconsistencies but also with poor quality of data if not done carefully. This automation approach was meant to utilize features of new technologies to create visualizations, reports, and dashboards in Power BI that are directly fished from the data source – CommCare hence only require a single click of a ‘refresh’ button to have the updated information populated in visualizations, reports, and dashboards at once. Methodology: We transformed paper-based questionnaires into electronic using CommCare mobile application. We further connected CommCare Mobile App directly to Power BI using Application Program Interface (API) connection as data pipeline. This provided chance to create visualizations, reports, and dashboards in Power BI. Contrary to the process of manually collecting data in paper-based questionnaires, entering them in ordinary spreadsheets, and conducting analysis every time when preparing for reporting, the team utilized CommCare and Microsoft Power BI technologies. We utilized validations and logics in CommCare to capture data with less errors. We utilized Power BI features to host the reports online by publishing them as cloud-computing process. We switched from sharing ordinary report files to sharing the link to potential recipients hence giving them freedom to dig deep into extra findings within Power BI dashboards and also freedom to export to any formats of their choice. Results: This data automation approach reduced research timelines from the initial 9 months’ duration to 5. It also improved the quality of the data findings from the original 76.9% to 98.9%. This brought confidence to draw conclusions from the findings that help in decision-making and gave opportunities for further researches. Conclusion: These results suggest that automating the research data process has the potential of reducing overall amount of time spent and improving the quality of the data. On this basis, the concept of data automation should be taken into serious consideration when conducting operational research for efficiency and decision-making.

Keywords: reporting, decision-making, power BI, commcare, data automation, visualizations, dashboards

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Authors: S. Vavias, T. R. Brewer, T. S. Farewell

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Coastal floods have been identified as an important natural hazard that can cause significant damage to the populated built-up areas, related infrastructure and also ecosystems and habitats. This study attempts to fill the gap associated with the development of preliminary assessments of coastal flood vulnerability for compliance with the EU Directive on the Assessment and Management of Flood Risks (2007/60/EC). In this context, a methodology has been created by taking into account three major parameters; the maximum wave run-up modelled from historical weather observations, the highest tide according to historic time series, and the sea level rise projections due to climate change. A high resolution digital terrain model (DTM) derived from LIDAR data has been used to integrate the estimated flood events in a GIS environment. The flood vulnerability map created shows potential risk areas and can play a crucial role in the coastal zone planning process. The proposed method has the potential to be a powerful tool for policy and decision makers for spatial planning and strategic management.

Keywords: coastal floods, vulnerability mapping, climate change, extreme weather events

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Authors: Elene Tatunashvili, Bun Chan, Philippe E. Nashar, Christopher S. P. McErlean

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The reduction of aryl diazonium salts is one of the most efficient ways to generate aryl radicals for use in a wide range of transformations, including Sandmeyer-type reactions, Meerwein arylations of olefins and Gomberg-Bachmann-Hey arylations of heteroaromatic systems. The aryl diazonium species can be reduced electrochemically, by UV irradiation, inner-sphere and outer-sphere single electron transfer processes (SET) from metal salts, SET from photo-excited organic catalysts or fragmentation of adducts with weak bases (acetate, hydroxide, etc.). This paper details an approach for the metal-free reduction of aryl diazonium salts, which facilitates the efficient synthesis of various aromatic compounds under exceedingly mild reaction conditions. By measuring the oxidation potential of a number of organic molecules, a series of nucleophiles were identified that reduce aryl diazonium salts via the addition-fragmentation mechanism. This approach leads to unprecedented operational simplicity: The reactions are very rapid and proceed in the open air; there is no need for external irradiation or heating, and the process is compatible with a large number of radical reactions. We illustrate these advantages by using the addition-fragmentation strategy to regioselectively arylate a series of heterocyclic compounds, to synthesize ketones by arylation of silyl enol ethers, and to synthesize benzothiophene and phenanthrene derivatives by radical annulation reactions.

Keywords: diazonium salts, hantzsch esters, oxygen, radical reactions, synthetic methods

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Authors: Majid Farsadrooh, Mehran Feizi-Dehnayebi

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Serum albumin is the most abundant protein that is present in the circulatory system of a wide variety of organisms. Although it is a significant macromolecule, it can contribute to osmotic blood pressure and also, plays a superior role in drug disposition and efficiency. Molecular docking simulation can improve in silico drug design and discovery procedures to propound a lead compound and develop it from the discovery step to the clinic. In this study, the molecular docking simulation was applied to select a lead molecule through an investigation of the interaction of the two anticancer drugs (Alitretinoin and Abemaciclib) with Human Serum Albumin (HSA). Then, a series of new compounds (a-e) were suggested using lead molecule modification. Density functional theory (DFT) including MEP map and HOMO-LUMO analysis were used for the newly proposed compounds to predict the reactivity zones on the molecules, stability, and chemical reactivity. DFT calculation illustrated that these new compounds were stable. The estimated binding free energy (ΔG) values for a-e compounds were obtained as -5.78, -5.81, -5.95, -5,98, and -6.11 kcal/mol, respectively. Finally, the pharmaceutical properties and toxicity of these new compounds were estimated through OSIRIS DataWarrior software. The results indicated no risk of tumorigenic, irritant, or reproductive effects and mutagenicity for compounds d and e. As a result, compounds d and e, could be selected for further study as potential therapeutic candidates. Moreover, employing molecular docking simulation with the prediction of pharmaceutical properties helps to discover new potential drug compounds.

Keywords: drug design, anticancer, computational studies, DFT analysis

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Authors: M. Hadjisoteriou, A. Kyriacou Petrou

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This paper will discuss ideas of social sustainability in urban design and human behavior in multicultural contested sites. It will focus on the potential of the re-reading of the “site” through mapping that acts as a research methodology and will discuss the chosen site of Mouttalos, Cyprus as a place of multiple identities. Through a methodology of mapping using a bottom up approach, a process of disassembling derives that acts as a mechanism to re-examine space and place by searching for the invisible and the non-measurable, understanding the site through its detailed inhabitation patterns. The significance of this study lies in the use of mapping as an active form of thinking rather than a passive process of representation that allows for a new site to be discovered, giving multiple opportunities for adaptive urban strategies and socially engaged design approaches. We will discuss the above thematic based on the chosen contested site of Mouttalos, a small Turkish Cypriot neighbourhood, in the old centre of Paphos (Ktima), SW of Cyprus. During the political unrest, between Greek and Turkish Cypriot communities, in 1963, the area became an enclave to the Turkish Cypriots, excluding any contact with the rest of the area. Following the Turkish invasion of 1974, the residents left their homes, plots and workplaces, resettling in the North of Cyprus. Greek Cypriot refugees moved into the area. The presence of the Greek Cypriot refugees is still considered to be a temporary resettlement. The buildings and the residents themselves exist in a state of uncertainty. The site is documented through a series of parallel investigations into the physical conditions and history of the site. Research methodologies use the process of mapping to expose the complex and often invisible layers of information that coexist. By registering the site through the subjective experiences, and everyday stories of inhabitants, a series of cartographic recordings reveals the space between: happening and narrative and especially space between different cultures and religions. Research put specific emphasis on engaging the public, promoting social interaction, identifying spatial patterns of occupation by previous inhabitants through social media. Findings exposed three main areas of interest. Firstly we identified inter-dependent relationships between permanence and temporality, characterised by elements such us, signage through layers of time, past events and periodical street festivals, unfolding memory and belonging. Secondly issues of co-ownership and occupation, found through particular narratives of exchange between the two communities and through appropriation of space. Finally formal and informal inhabitation of space, revealed through the presence of informal shared back yards, alternative paths, porous street edges and formal and informal landmarks. The importance of the above findings, was achieving a shift of focus from the built infrastructure to the soft network of multiple and complex relations of dependence and autonomy. Proposed interventions for this contested site were informed and led by a new multicultural identity where invisible qualities were revealed though the process of mapping, taking on issues of layers of time, formal and informal inhabitation and the “permanence of the temporary”.

Keywords: contested sites, mapping, social sustainability, temporary urban strategies

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Authors: Yu-Wen Chen, Szu-Wei Huang, Chung-Hsiang Mu, Kelvin Cheng

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This paper developed a data-driven based model to deal with the causality between the Continuous Emission Monitoring System (CEMS, by Environmental Protection Administration, Taiwan) in industrial factories, and the air quality around environment. Compared to the heavy burden of traditional numerical models of regional weather and air pollution simulation, the lightweight burden of the proposed model can provide forecasting hourly with current observations of weather, air pollution and emissions from factories. The observation data are included wind speed, wind direction, relative humidity, temperature and others. The observations can be collected real time from Open APIs of civil IoT Taiwan, which are sourced from 439 weather stations, 10,193 qualitative air stations, 77 national quantitative stations and 140 CEMS quantitative industrial factories. This study completed a causal inference engine and gave an air pollution forecasting for the next 12 hours related to local industrial factories. The outcomes of the pollution forecasting are produced hourly with a grid resolution of 1km*1km on IIoTC (Industrial Internet of Things Cloud) and saved in netCDF4 format. The elaborated procedures to generate forecasts comprise data recalibrating, outlier elimination, Kriging Interpolation and particle tracking and random walk techniques for the mechanisms of diffusion and advection. The solution of these equations reveals the causality between factories emission and the associated air pollution. Further, with the aid of installed real-time flue emission (Total Suspension Emission, TSP) sensors and the mentioned forecasted air pollution map, this study also disclosed the converting mechanism between the TSP and PM2.5/PM10 for different region and industrial characteristics, according to the long-term data observation and calibration. These different time-series qualitative and quantitative data which successfully achieved a causal inference engine in cloud for factory management control in practicable. Once the forecasted air quality for a region is marked as harmful, the correlated factories are notified and asked to suppress its operation and reduces emission in advance.

Keywords: continuous emission monitoring system, total suspension particulates, causal inference, air pollution forecast, IoT

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Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi

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MANET for Mobile Ad hoc NETwork is a collection of wireless mobile nodes that communicates with each other without using any existing infrastructure, access point or centralized administration, due to the higher mobility and limited radio transmission range, routing is an important issue in ad hoc network, so in order to ensure reliable and efficient route between to communicating nodes quickly, an appropriate routing protocol is needed. In this paper, we present the performance analysis of two mobile ad hoc network routing protocols namely DSR and OLSR using NS2.34, the performance is determined on the basis of packet delivery ratio, throughput, average jitter and end to end delay with varying pause time.

Keywords: DSR, OLSR, quality of service, routing protocols, MANET

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Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Hamid Goharnejad, Amir Hossein Eghbali

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Recent investigations have demonstrated the global sea level rise due to climate change impacts. In this study climate changes study the effects of increasing water level in the strait of Hormuz. The probable changes of sea level rise should be investigated to employ the adaption strategies. The climatic output data of a GCM (General Circulation Model) named CGCM3 under climate change scenario of A1b and A2 were used. Among different variables simulated by this model, those of maximum correlation with sea level changes in the study region and least redundancy among themselves were selected for sea level rise prediction by using stepwise regression. One models of Discrete Wavelet artificial Neural Network (DWNN) was developed to explore the relationship between climatic variables and sea level changes. In these models, wavelet was used to disaggregate the time series of input and output data into different components and then ANN was used to relate the disaggregated components of predictors and predictands to each other. The results showed in the Shahid Rajae Station for scenario A1B sea level rise is among 64 to 75 cm and for the A2 Scenario sea level rise is among 90 to 105 cm. Furthermore the result showed a significant increase of sea level at the study region under climate change impacts, which should be incorporated in coastal areas management.

Keywords: climate change scenarios, sea-level rise, strait of Hormuz, forecasting

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Authors: Praveen Chandramenon, Andrew Gascoyne, Fideline Tchuenbou-Magaia

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Aquaponics offers a simple conclusive solution to the food and environmental crisis of the world. This approach combines the idea of Aquaculture (growing fish) to Hydroponics (growing vegetables and plants in a soilless method). Smart Aquaponics explores the use of smart technology including artificial intelligence and IoT, to assist farmers with better decision making and online monitoring and control of the system. Identification of different growth stages of Swiss Chard plants and predicting its harvest time is found to be important in Aquaponic yield management. This paper brings out the comparative analysis of a standard Aquaponics with a Vermiponics (Aquaponics with worms), which was grown in the controlled environment, by implementing IoT and deep learning-based growth stage segregation and harvest time prediction of Swiss Chards before and after applying an optimal freshwater replenishment. Data collection, Growth stage classification and Harvest Time prediction has been performed with and without water replenishment. The paper discusses the experimental design, IoT and sensor communication with architecture, data collection process, image segmentation, various regression and classification models and error estimation used in the project. The paper concludes with the results comparison, including best models that performs growth stage segregation and harvest time prediction of the Aquaponic and Vermiponic testbed with and without freshwater replenishment.

Keywords: aquaponics, deep learning, internet of things, vermiponics

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Authors: Haleh Rezaei Zanjirabadi, Fatemeh Saberi, Bahman Rahimzadeh, Fariborz Masoudi, Mohammad Rahgosha

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In this study, apatite minerals from the iron-phosphate deposit of Yazd have been investigated within the microcontinent zone of Iran in the Zagros structural zone. The geological units in the Esfordi area belong to the pre-Cambrian to lower-Cambrian age, consisting of a succession of carbonate rocks (dolomite), shale, tuff, sandstone, and volcanic rocks. In addition to the mentioned sedimentary and volcanic rocks, the granitoid mass of Bahabad, which is the largest intrusive mass in the region, has intruded into the eastern part of this series and has caused its metamorphism and alteration. After collecting the available data, various samples of Esfordi’s apatite were prepared, and their mineralogy and crystallography were investigated using laboratory methods such as petrographic microscopy, Raman spectroscopy, EDS, and SEM. In non-destructive Raman spectroscopy, the molecular structure of apatite minerals was revealed in four distinct spectral ranges. Initially, the spectra of phosphate and aluminum bonds with O2HO, OH, were observed, followed by the identification of Cl, OH, Al, Na, Ca and hydroxyl units depending on the type of apatite mineral family. In SEM analysis, based on various shapes and different phases of apatites, their constituent major elements were identified through EDS, indicating that the samples from the Esfordi mining area exhibit a dense and coherent texture with smooth surfaces. Based on the elemental analysis results by EDS, the apatites in the Esfordi area are classified into the calcic apatite group.

Keywords: petrology, apatite, Esfordi, EDS, SEM, Raman spectroscopy

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Authors: Nellija Titova

Abstract:

Latvia, Lithuania, and Estonia, as Baltic Countries, have gone throughout transformational changes since 90s leading to the high level of economic development. As countries departing Soviet Union with industrialization policy moved to service economies, the issues of intangibles, human capital, structural capital, and innovation capital have gained impetus. Following the growing demand of practitioners and later academia, intellectual capital as a discipline, which appeared in 90s, became fundamental nowadays. Aim of the paper is to analyze the Baltic companies entering stock markets at Nasdaq Baltic from the perspective of Intellectual Capital. Methodology of the research is based on a longitudinal analysis of the companies using composite ratios of Intellectual Capital and Business performance in the period 2012-2019. Data for 2020 as COVID year) were excluded from the analysis. Findings allow concluding there is a pattern of influence and companies clearly experience the systemic impact of IC on business performance, identifying also time effect investing in intangibles.

Keywords: intellectual capital, impact analysis, longitudinal effect, composite ratios

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Authors: Mustafa Tufekci, Caner Guven

Abstract:

In Automotive Industry, sliding door systems that are also used as body closures, are safety members. Extreme product tests are realized to prevent failures in a design process, but these tests realized experimentally result in high costs. Finite element analysis is an effective tool used for the design process. These analyses are used before production of a prototype for validation of design according to customer requirement. In result of this, the substantial amount of time and cost is saved. Finite element model is created for geometries that are designed in 3D CAD programs. Different element types as bar, shell and solid, can be used for creating mesh model. The cheaper model can be created by the selection of element type, but combination of element type that was used in model, number and geometry of element and degrees of freedom affects the analysis result. Sliding door system is a good example which used these methods for this study. Structural analysis was realized for sliding door mechanism by using FE models. As well, physical tests that have same boundary conditions with FE models were realized. Comparison study for these element types, were done regarding test and analyses results then the optimum combination was achieved.

Keywords: finite element analysis, sliding door mechanism, element type, structural analysis

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Authors: A. Y. Mukhtar, J. B. Munyakazi, R. Ouifki

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Over the past few decades, the unprecedented increase in mobility has raised considerable concern about the relationship between mobility and vector-borne diseases and malaria in particular. Thus, one can claim that human mobility is one of the contributing factors to the resurgence of malaria. To assess human mobility on malaria burden among hosts, we formulate a movement-based model on a network of patches. We then extend human multi-group SEIAR deterministic epidemic models into a system of stochastic differential equations (SDEs). Our quantitative stochastic model which is expressed in terms of average rates of movement between compartments is fitted to time-series data (weekly malaria data of 2011 for each patch) using the maximum likelihood approach. Using the metapopulation (multi-group) model, we compute and analyze the basic reproduction number. The result shows that human movement is sufficient to preserve malaria disease firmness in the patches with the low transmission. With these results, we concluded that the sensitivity of malaria to the human mobility is turning to be greatly important over the implications of future malaria control in South Sudan.

Keywords: basic reproduction number, malaria, maximum likelihood, movement, stochastic model

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Authors: Nuray Uçar, Mervin Ölmez, Özge Alptoğa, Nilgün K. Yavuz, Ayşen Önen

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In recent years, research on continuous graphene oxide fibers has been intensified. Therefore, many factors of production stages are being studied. In this study, the effect of exfoliation time and presence of activated carbon particle (ACP) on graphene oxide fiber’s properties has been analyzed. It has been seen that cross-sectional appearance of sample with ACP is harsh and porous because of ACP. The addition of ACP did not change the electrical conductivity. However, ACP results in an enormous decrease of mechanical properties. Longer exfoliation time results to higher crystallinity degree, C/O ratio and less d space between layers. The breaking strength and electrical conductivity of sample with less exfoliation time is some higher than sample with high exfoliation time.

Keywords: activated carbon, coagulation by wet spinning, exfoliation, graphene oxide fiber

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Jaqueline Bohn Donada

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The significance of Euripides’ Medea for contemporary literature is noticeable. Even if the bulk of Classical Reception studies does not tend to look carefully and consistently to the literature produced outside the Anglophone world, Brazilian literature offers abundant materials for such studies. Indeed, a certain Classical background can be observed in Brazilian literature at least since 1975 when Gota d’Água [The Final Straw, in English], a play that recreates the story of Medea and sets it in a favela in Rio de Janeiro. Also worthy of notice is Ivo Bender’s Trilogia Perversa [Perverse Trilogy, in English], a series of three historical plays set in Southern Brazil and based on Aeschylus’ Oresteia and on Euripides’ Iphigenia in Aulis published in the 1980s. Since then, a number of works directly inspired by the plays of Aeschylus, Sophocles and Euripides have been published, not to mention several adaptations of Homer’s two epic poems. This paper proposes a comparative analysis of two such works: Grace Passô’s 2017 play Mata teu Pai [Kill your father, in English] and Marcia Lima’s 2019 play Medeia Negra [Black Medea, in English] from the perspective of Classical Reception Studies in an intersection with feminist literary criticism. The paper intends to look at the endurance of Euripides’ character in contemporary Brazilian literature with a focus on how the character seems to have acquired special relevance to the treatment of pressing issues of the twenty-first century. Whereas Grace Passô’s play sets Medea at the center of a group of immigrant women, Marcia Limma has the character enact the dilemmas of incarcerated women in Brazil. The hypothesis that this research aims at testing is that both artists preserve the pathos of Euripides’s original character at the same time that they recreate his Medea in concrete circumstances of Brazilian contemporary social reality. At the end, the research aims at stating the significance of the Medea theme to contemporary Brazilian literature.

Keywords: Euripides, Medea, Grace Passô, Marcia Limma, Brazilian literature

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Authors: Neelum Maqsood

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This paper is a study of how institutional structures shape the policy-making activities of female legislators. The literature on this area indicates that if there is an institution created by men to secure elite interests, women will face constraints in legislative activities. This paper will analyze the institutional setting in Pakistan and document the conditions women face that both restrict or enable them from representing the general interests of other women. The main experimental design depends on the variation of international scrutiny that Pakistan faces in two different time periods that will be classified as high international scrutiny and low international scrutiny. A high international scrutiny period is one where Pakistan comes under the international lens because of a domestic event that has international ramifications, for example, in terms of gender equality. The argument is that women parliamentarians receive different treatment in periods of high international scrutiny. As Pakistan comes under scrutiny, women will be more active in their legislative activities than in low international scrutiny, as male parliamentarians will be less likely to influence or restrain women’s activities. Using this variation, the trends in memberships and support functions given to women in these two time periods will be studied. The second variation will comprise the analysis of male and female assignments, training, and funding on general seats across time, which will require data collection over this time of 12-15 years, including the years during the war when Pakistan was under high international scrutiny.

Keywords: female representation, gender equality, democratic institutions, quota seats

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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