Search results for: reduced order models

Authors: Mohammad Fereidoonnezhad, Seán Leen, Stephen Nash, Patrick McGarry

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From the perspective of research innovation, the tidal energy industry is still in its early stages. While a very small number of turbines have progressed to utility-scale deployment, blade breakage is commonly reported due to the enormous hydrodynamic loading applied to devices. The aim of this study is the development of computer simulation technologies for the design of next-generation fibre-reinforced composite tidal turbines. This will require significant technical advances in the areas of tidal turbine testing and multi-scale computational modelling. The complex turbine blade profiles are designed to incorporate non-linear distributions of airfoil sections to optimize power output and self-starting capability while reducing power fluctuations. A number of candidate blade geometries are investigated, ranging from spiral geometries to parabolic geometries, with blades arranged in both cylindrical and spherical configurations on a vertical axis turbine. A combined blade element theory (BET-start-up model) is developed in MATLAB to perform computationally efficient parametric design optimisation for a range of turbine blade geometries. Finite element models are developed to identify optimal fibre-reinforced composite designs to increase blade strength and fatigue life. Advanced fluid-structure-interaction models are also carried out to compute blade deflections following design optimisation.

Keywords: tidal turbine, composite materials, fluid-structure-interaction, start-up capability

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Authors: Aydin M. Torkabadi, Ehsan Pourjavad

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A decade after the introduction of Lean in Saskatchewan’s public healthcare system, its effectiveness remains a controversial subject among health researchers, workers, managers, and politicians. Therefore, developing a framework to quantitatively assess the Lean achievements is significant. This study investigates the success of initiatives across Saskatchewan health regions by recognizing the Lean healthcare criteria, measuring the success levels, comparing the regions, and identifying the areas for improvements. This study proposes an integrated intelligent computing approach by applying Fuzzy Inference System (FIS) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FIS is used as an efficient approach to assess the Lean healthcare criteria, and TOPSIS is applied for ranking the values in regards to the level of leanness. Due to the innate uncertainty in decision maker judgments on criteria, principals of the fuzzy theory are applied. Finally, FIS-TOPSIS was established as an efficient technique in determining the lean merit in healthcare systems.

Keywords: lean healthcare, intelligent computing, fuzzy inference system, healthcare evaluation, technique for order of preference by similarity to ideal solution, multi-criteria decision making, MCDM

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Authors: Mauro Iannazzone, Carmine D'Agostino

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Off-shore wind turbines are currently considered as a reliable source of renewable energy Worldwide and especially in the UK. Most of the operational off-shore wind turbines located in shallow waters (i.e. < 30 m) are supported on monopiles. Monopiles are open-ended steel tubes with diameter ranging between 4 to 6 m. It is expected that future off-shore wind farms will be located in water depths as high as 70 m. Therefore, alternative foundation arrangements are needed. Foundations for off-shore structures normally consist of open-ended piles driven into the soil by means of impact hammers. During pile installation, the soil inside the pile may be mobilized by the increasing shear strength such as to prevent more soil from entering the pile. This phenomenon is known as soil plugging, and represents an important issue as it may change significantly the driving resistance of open-ended piles. In fact, if the plugging formation is unexpected, the installation may require more powerful and more expensive hammers. Engineers need to estimate whether the driven pile will be installed in a plugged or unplugged mode. As a consequence, a prediction of the degree of soil plugging is required in order to correctly predict the drivability of the pile. This work presents a brief review of the state-of-the-art of pile driving and approaches used to predict formation of soil plugs. In addition, a novel analytical approach is proposed, which is based on the vertical equilibrium of a plugged pile. Differently from previous studies, this research takes into account the enhancement of the stress within the soil plug. Finally, the work presents and discusses a series of experimental tests, which are carried out on small-scale models piles to validate the analytical solution.

Keywords: off-shore wind turbines, pile installation, soil plugging, wind energy

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Authors: Ola Hall, Ibrahim Wahab, Thorsteinn Rognvaldsson, Mattias Ohlsson

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The subfield of poverty and welfare estimation that applies machine learning tools and methods on satellite imagery is a nascent but rapidly growing one. This is in part driven by the sustainable development goal, whose overarching principle is that no region is left behind. Among other things, this requires that welfare levels can be accurately and rapidly estimated at different spatial scales and resolutions. Conventional tools of household surveys and interviews do not suffice in this regard. While they are useful for gaining a longitudinal understanding of the welfare levels of populations, they do not offer adequate spatial coverage for the accuracy that is needed, nor are their implementation sufficiently swift to gain an accurate insight into people and places. It is this void that satellite imagery fills. Previously, this was near-impossible to implement due to the sheer volume of data that needed processing. Recent advances in machine learning, especially the deep learning subtype, such as deep neural networks, have made this a rapidly growing area of scholarship. Despite their unprecedented levels of performance, such models lack transparency and explainability and thus have seen limited downstream applications as humans generally are apprehensive of techniques that are not inherently interpretable and trustworthy. While several studies have demonstrated the superhuman performance of AI models, none has directly compared the performance of such models and human readers in the domain of poverty studies. In the present study, we directly compare the performance of human readers and a DL model using different resolutions of satellite imagery to estimate the welfare levels of demographic and health survey clusters in Tanzania, using the wealth quintile ratings from the same survey as the ground truth data. The cluster-level imagery covers all 608 cluster locations, of which 428 were classified as rural. The imagery for the human readers was sourced from the Google Maps Platform at an ultra-high resolution of 0.6m per pixel at zoom level 18, while that of the machine learning model was sourced from the comparatively lower resolution Sentinel-2 10m per pixel data for the same cluster locations. Rank correlation coefficients of between 0.31 and 0.32 achieved by the human readers were much lower when compared to those attained by the machine learning model – 0.69-0.79. This superhuman performance by the model is even more significant given that it was trained on the relatively lower 10-meter resolution satellite data while the human readers estimated welfare levels from the higher 0.6m spatial resolution data from which key markers of poverty and slums – roofing and road quality – are discernible. It is important to note, however, that the human readers did not receive any training before ratings, and had this been done, their performance might have improved. The stellar performance of the model also comes with the inevitable shortfall relating to limited transparency and explainability. The findings have significant implications for attaining the objective of the current frontier of deep learning models in this domain of scholarship – eXplainable Artificial Intelligence through a collaborative rather than a comparative framework.

Keywords: poverty prediction, satellite imagery, human readers, machine learning, Tanzania

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Authors: Muhammad Sajjad-ur-Rehman

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Pakistan was meant to be a progressive modern Muslim nation state since its inception in 1947. Its birth was a big hope for the Muslims of Sub-continent to transform their societies on Islamic lines—the promise which made them unite and vote for Pakistan during independence movement. This was the vision put forwarded by Allama Iqbal and Muhammad Ali Jinnah—the two founding fathers of Pakistan. Dwelling on interpretive/ analytical approach, this paper analyzes the thoughts and reflections of Iqbal and Jinnah to understand the issues of collective identity formation in Pakistan. It argues that there may be traced two distinct identity models in the thoughts and reflections of these two leading figures of Pakistan movement: First may be called as ‘faith-based identity model’ while the other may be named as ‘interests-based identity model’. These can also be entitled as ‘Islam-as-faith model’ and ‘Islam-as-ideology model’. Former seeks the diffusion of power by cultural/ faith based means and thus society remains independent in determining its change. While the later goes on to open and expand the power realm by maximizing the role of state in determining the social change. With the help of these models, it can better be explained that what made Pakistani society fail in the collective political identity construction, hindering thus the political potential of the society to be utilized for initiating state formation and societal growth. As a result, today, we see a state that is often rebelled and resisted on the name of ethnicity, religion and sectarianism on one hand and by the ordinary folk when and wherever possible.

Keywords: idealogy, Iqbal, Jinnah, identity

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Authors: Samuel Fekadu A., Esayas Alemayehu B., Bultum Oljira D., Seid Tiku D., Dessalegn Dadi D., Bart Van Der Bruggen A.

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The peroxi-photoelectrocoagulation process was evaluated for the removal of chemical oxygen demand (COD) and color from healthcare wastewater. A 2-level full factorial design with center points was created to investigate the effect of the process parameters, i.e., initial COD, H₂O₂, pH, reaction time and current density. Furthermore, the total energy consumption and average current efficiency in the system were evaluated. Predictive models for % COD, % color removal and energy consumption were obtained. The initial COD and pH were found to be the most significant variables in the reduction of COD and color in peroxi-photoelectrocoagulation process. Hydrogen peroxide only has a significant effect on the treated wastewater when combined with other input variables in the process like pH, reaction time and current density. In the peroxi-photoelectrocoagulation process, current density appears not as a single effect but rather as an interaction effect with H₂O₂ in reducing COD and color. Lower energy expenditure was observed at higher initial COD, shorter reaction time and lower current density. The average current efficiency was found as low as 13 % and as high as 777 %. Overall, the study showed that hybrid electrochemical oxidation can be applied effectively and efficiently for the removal of pollutants from healthcare wastewater.

Keywords: electrochemical oxidation, UV, healthcare pollutants removals, factorial design

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Authors: Adel Bouchakhchoukha, Hakim Akeb

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In this work, we propose a hybrid heuristic in order to solve the Team Orienteering Problem (TOP). Given a set of points (or customers), each with associated score (profit or benefit), and a team that has a fixed number of members, the problem to solve is to visit a subset of points in order to maximize the total collected score. Each member performs a tour starting at the start point, visiting distinct customers and the tour terminates at the arrival point. In addition, each point is visited at most once, and the total time in each tour cannot be greater than a given value. The proposed heuristic combines beam search and a local optimization strategy. The algorithm was tested on several sets of instances and encouraging results were obtained.

Keywords: team orienteering problem, vehicle routing, beam search, local search

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Authors: Kate Duggan

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Introduction: Functional Communication Training (FCT) is an approach which aims to reduce behaviours of concern by teaching more effective ways to communicate. It requires identification of the function of the behaviour of concern, through gathering information from key stakeholders and completing observations of the individual’s behaviour including antecedents to, and consequences of the behaviour. Appropriate communicative alternatives are then identified and taught to the individual using systematic instruction techniques. Behaviours of concern demonstrated by individuals with autism spectrum conditions (ASC) frequently have a communication function. When contributing to positive behavior support plans, speech and language therapists and other professionals working with individuals with ASC need to identify alternative communicative behaviours which are equally reinforcing as the existing behaviours of concern. Successful implementation of FCT is dependent on an effective ‘response match’. The new way of communicating must be equally as effective as the behaviour previously used and require the same amount or less effort from the individual. It must also be understood by the communication partners the individual encounters and be appropriate to their communicative contexts. Method: Four case studies within a non-maintained special school environment were described and analysed. A response match framework was used to identify the effectiveness of functional communication training delivered by the student’s speech and language therapist, teacher and learning support assistants. The success of systematic instruction techniques used to develop new communicative behaviours was evaluated using the CODES framework. Findings: Functional communication training can be used as part of a positive behaviour support approach for students within this setting. All case studies reviewed demonstrated ‘response success’, in that the desired response was gained from the new communicative behaviour. Barriers to the successful embedding of new communicative behaviours were encountered. In some instances, the new communicative behaviour could not be consistently understood across all communication partners which reduced ‘response recognisability’. There was also evidence of increased physical or cognitive difficulty in employing the new communicative behaviour which reduced the ‘response effectivity’. Successful use of ‘thinning schedules of reinforcement’, taught students to tolerate a delay to reinforcement once the new communication behaviour was learned.

Keywords: augmentative and alternative communication, autism spectrum conditions, behaviours of concern, functional communication training

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Authors: Said Alshukri, Mazhar Hussain Malik

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Green hydrogen is hydrogen produced using renewable energy sources. In the last few years, Oman aimed to reduce its dependency on fossil fuels. Recently, the hydrogen economy has become a global trend, and many countries have started to investigate the feasibility of implementing this sector. Oman created an alliance to establish the policy and rules for this sector. With motivation coming from both global and local interest in green hydrogen, this paper investigates the potential of producing hydrogen from wind and solar energies in three different locations in Oman, namely Duqm, Salalah, and Sohar. By using machine learning-based software “WEKA” and local metrological data, the project was designed to figure out which location has the highest wind and solar energy potential. First, various supervised models were tested to obtain their prediction accuracy, and it was found that the Random Forest (RF) model has the best prediction performance. The RF model was applied to 2021 metrological data for each location, and the results indicated that Duqm has the highest wind and solar energy potential. The system of one wind turbine in Duqm can produce 8335 MWh/year, which could be utilized in the water electrolysis process to produce 88847 kg of hydrogen mass, while a solar system consisting of 2820 solar cells is estimated to produce 1666.223 MWh/ year which is capable of producing 177591 kg of hydrogen mass.

Keywords: green hydrogen, machine learning, wind and solar energies, WEKA, supervised models, random forest

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Authors: Sadaf H. Khan, Jyoti Kumar

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Sick Building Syndrome (SBS) is a situation in which inhabitants of a building develop illness symptoms or get infected with a chronic disease as a result of the building in which they reside or work. Certain symptoms tend to get more severe as an individual spends more time in the building; however, they generally improve with time or even disappear when they leave that space. Though ‘Design of Built-Spaces’ is a crucial factor in regulating these symptoms but it still needs to be identified further as to what specific design features of a ‘Built-Space’ trigger sick building syndrome (SBS). Much of the research work present to date is focused on the physiological or physical sickness caused due to inappropriate built-space design. In this paper, the psychological aspects of sick building syndrome (SBS) will be investigated across the adult population, more specifically graduate students in India trying to settle in back to their previous physical work environments, i.e., campus, classrooms, hostels, after a very long hold which lasted more than a year due to lockdowns during Covid-19 crisis all over the world. The study will follow an analytical approach and the data will be collected through self-reported online surveys. The purpose of this study is to enquire causal agents, diagnosable symptoms and remedial design of built spaces which can enhance the productive level of built environments and better facilitate the inhabitants by improving their psychological wellbeing, which is the most uprising concern. The fact that SBS symptoms can be studied only within the initial few weeks as an occupant starts interacting with a built-environment and leaves as the occupant leaves that space or zone, the post-lockdown incoming of students back to their respective campuses provides an opportunity to clearly draw multiple conclusions of the relationship that exist between the Design of Built-Spaces and Psychological Sickness Syndrome associated with it. The study will be one of a kind approach for understanding and formulating methods to improve psychological wellbeing within a built-setting by better identifying factors associated with these psychological symptoms, including anxiety, mental fatigue, reduced attention span and reduced memory span as refined symptoms of SBS discussed in 1987 by Molhave within his study.

Keywords: built-environment psychology, built-space design, healthcare architecture, psychological wellbeing

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Authors: Richard Sogah

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The significance of transport infrastructure investments for economic growth and development has been central to the World Bank’s strategy for poverty reduction. Among the conventional surface transport infrastructures, road infrastructure is significant in facilitating the movement of human capital goods and services. When transport projects (i.e., roads, super-highways) are implemented, they come along with some negative social values (costs), such as increased noise and air pollution for local residents living near these facilities, displaced individuals, etc. However, these projects also facilitate better utilization of existing capital stock and generate other observable benefits that can be easily quantified. For example, the improvement or construction of roads creates employment, stimulates revenue generation (toll), reduces vehicle operating costs and accidents, increases accessibility, trade expansion, safety improvement, etc. Aside from these benefits, travel time savings (TTSs) which are the major economic benefits of urban and inter-urban transport projects and therefore integral in the economic assessment of transport projects, are often overlooked and omitted when estimating the benefits of transport projects, especially in developing countries. The absence of current and reliable domestic travel data and the inability of replicated models from the developed world to capture the actual value of travel time savings due to the large unemployment, underemployment, and other labor-induced distortions has contributed to the failure to assign value to travel time savings when estimating the benefits of transport schemes in developing countries. This omission of the value of travel time savings from the benefits of transport projects in developing countries poses problems for investors and stakeholders to either accept or dismiss projects based on schemes that favor reduced vehicular operating costs and other parameters rather than those that ease congestion, increase average speed, facilitate walking and handloading, and thus save travel time. Given the complex reality in the estimation of the value of travel time savings and the presence of widespread informal labour activities in Sub-Saharan Africa, we construct a “nationally ranked distribution of time values” and estimate the value of travel time savings based on the area beneath the distribution. Compared with other approaches, our method captures both formal sector workers and individuals/people who work outside the formal sector and hence changes in their time allocation occur in the informal economy and household production activities. The dataset for the estimations is sourced from the World Bank, the International Labour Organization, etc.

Keywords: road infrastructure, transport projects, travel time savings, congestion, Sub-Sahara Africa

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Authors: Yonwaba Atyosi, Moses Cho, Abel Ramoelo, Nobuhle Majozi, Cecilia Masemola, Yoliswa Mkhize

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Spectroscopy has been widely used to understand the hyperspectral remote sensing of soils. Accurate and efficient measurement of soil moisture is essential for precision agriculture. The aim of this study was to understand the spectral behavior of soil at different soil water content levels and identify the significant spectral bands for soil moisture content retrieval at field-scale. The study consisted of 60 soil samples from a maize farm, divided into four different treatments representing different moisture levels. Spectral signatures were measured for each sample in laboratory under artificial light using an Analytical Spectral Device (ASD) spectrometer, covering a wavelength range from 350 nm to 2500 nm, with a spectral resolution of 1 nm. The results showed that the absorption features at 1450 nm, 1900 nm, and 2200 nm were particularly sensitive to soil moisture content and exhibited strong correlations with the water content levels. Continuum removal was developed in the R programming language to enhance the absorption features of soil moisture and to precisely understand its spectral behavior at different water content levels. Statistical analysis using partial least squares regression (PLSR) models were performed to quantify the correlation between the spectral bands and soil moisture content. This study provides insights into the spectral behavior of soil at different water content levels and identifies the significant spectral bands for soil moisture content retrieval. The findings highlight the potential of spectroscopy for non-destructive and rapid soil moisture measurement, which can be applied to various fields such as precision agriculture, hydrology, and environmental monitoring. However, it is important to note that the spectral behavior of soil can be influenced by various factors such as soil type, texture, and organic matter content, and caution should be taken when applying the results to other soil systems. The results of this study showed a good agreement between measured and predicted values of Soil Moisture Content with high R2 and low root mean square error (RMSE) values. Model validation using independent data was satisfactory for all the studied soil samples. The results has significant implications for developing high-resolution and precise field-scale soil moisture retrieval models. These models can be used to understand the spatial and temporal variation of soil moisture content in agricultural fields, which is essential for managing irrigation and optimizing crop yield.

Keywords: soil moisture content retrieval, precision agriculture, continuum removal, remote sensing, machine learning, spectroscopy

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Authors: Shun-Qi Zhang, Shu-Yang Zhang, Min Chen

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Applying strong electric field on piezoelectric actuators, on one hand very significant electroelastic material nonlinear effects will occur, on the other hand piezo plates and shells may undergo large displacements and rotations. In order to give a precise prediction of piezolaminated smart structures under large electric field, this paper develops a finite element (FE) model accounting for both electroelastic material nonlinearity and geometric nonlinearity with large rotations based on the first order shear deformation (FSOD) hypothesis. The proposed FE model is applied to analyze a piezolaminated semicircular shell structure.

Keywords: smart structures, piezolamintes, material nonlinearity, strong electric field

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Authors: Sairah Bashir, Liu Yang, John Liggat, James Thomason

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Glass fibre is currently used as reinforcement in over 90% of all fibre-reinforced composites produced. The high rigidity and chemical resistance of these composites are required for optimum performance but unfortunately results in poor recyclability; when such materials are no longer fit for purpose, they are frequently deposited in landfill sites. Recycling technologies, for example, thermal treatment, can be employed to address this issue; temperatures typically between 450 and 600 °C are required to allow degradation of the rigid polymeric matrix and subsequent extraction of fibrous reinforcement. However, due to the severe thermal conditions utilised in the recycling procedure, glass fibres become too weak for reprocessing in second-life composite materials. In addition, more stringent legislation is being put in place regarding disposal of composite waste, and so it is becoming increasingly important to develop long-term recycling solutions for such materials. In particular, the development of a cost-effective method to regenerate strength of thermally recycled glass fibres will have a positive environmental effect as a reduced volume of composite material will be destined for landfill. This research study has demonstrated the positive impact of sodium hydroxide (NaOH) and potassium hydroxide (KOH) solution, prepared at relatively mild temperatures and at concentrations of 1.5 M and above, on the strength of heat-treated glass fibres. As a result, alkaline treatments can potentially be implemented to glass fibres that are recycled from composite waste to allow their reuse in second-life materials. The optimisation of the strength recovery process is being conducted by varying certain reaction parameters such as molarity of alkaline solution and treatment time. It is believed that deep V-shaped surface flaws exist commonly on severely damaged fibre surfaces and are effectively removed to form smooth, U-shaped structures following alkaline treatment. Although these surface flaws are believed to be present on glass fibres they have not in fact been observed, however, they have recently been discovered in this research investigation through analytical techniques such as AFM (atomic force microscopy) and SEM (scanning electron microscopy). Reaction conditions such as molarity of alkaline solution affect the degree of etching of the glass fibre surface, and therefore the extent to which fibre strength is recovered. A novel method in determining the etching rate of glass fibres after alkaline treatment has been developed, and the data acquired can be correlated with strength. By varying reaction conditions such as alkaline solution temperature and molarity, the activation energy of the glass etching process and the reaction order can be calculated respectively. The promising results obtained from NaOH and KOH treatments have opened an exciting route to strength regeneration of thermally recycled glass fibres, and the optimisation of the alkaline treatment process is being continued in order to produce recycled fibres with properties that match original glass fibre products. The reuse of such glass filaments indicates that closed-loop recycling of glass fibre reinforced composite (GFRC) waste can be achieved. In fact, the development of a closed-loop recycling process for GFRC waste is already underway in this research study.

Keywords: glass fibers, glass strengthening, glass structure and properties, surface reactions and corrosion

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Authors: Riccardo Fratini, Riccardo Santini, Jacopo Serafini, Massimo Gennaretti, Stefano Panzieri

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This paper presents a nonlinear differential model, for a three-bladed horizontal axis wind turbine (HAWT) suited for control applications. It is based on a 8-dofs, lumped parameters structural dynamics coupled with a quasi-steady sectional aerodynamics. In particular, using the Euler-Lagrange Equation (Energetic Variation approach), the authors derive, and successively validate, such model. For the derivation of the aerodynamic model, the Greenbergs theory, an extension of the theory proposed by Theodorsen to the case of thin airfoils undergoing pulsating flows, is used. Specifically, in this work, the authors restricted that theory under the hypothesis of low perturbation reduced frequency k, which causes the lift deficiency function C(k) to be real and equal to 1. Furthermore, the expressions of the aerodynamic loads are obtained using the quasi-steady strip theory (Hodges and Ormiston), as a function of the chordwise and normal components of relative velocity between flow and airfoil Ut, Up, their derivatives, and section angular velocity ε˙. For the validation of the proposed model, the authors carried out open and closed-loop simulations of a 5 MW HAWT, characterized by radius R =61.5 m and by mean chord c = 3 m, with a nominal angular velocity Ωn = 1.266rad/sec. The first analysis performed is the steady state solution, where a uniform wind Vw = 11.4 m/s is considered and a collective pitch angle θ = 0.88◦ is imposed. During this step, the authors noticed that the proposed model is intrinsically periodic due to the effect of the wind and of the gravitational force. In order to reject this periodic trend in the model dynamics, the authors propose a collective repetitive control algorithm coupled with a PD controller. In particular, when the reference command to be tracked and/or the disturbance to be rejected are periodic signals with a fixed period, the repetitive control strategies can be applied due to their high precision, simple implementation and little performance dependency on system parameters. The functional scheme of a repetitive controller is quite simple and, given a periodic reference command, is composed of a control block Crc(s) usually added to an existing feedback control system. The control block contains and a free time-delay system eτs in a positive feedback loop, and a low-pass filter q(s). It should be noticed that, while the time delay term reduces the stability margin, on the other hand the low pass filter is added to ensure stability. It is worth noting that, in this work, the authors propose a phase shifting for the controller and the delay system has been modified as e^(−(T−γk)), where T is the period of the signal and γk is a phase shifting of k samples of the same periodic signal. It should be noticed that, the phase shifting technique is particularly useful in non-minimum phase systems, such as flexible structures. In fact, using the phase shifting, the iterative algorithm could reach the convergence also at high frequencies. Notice that, in our case study, the shifting of k samples depends both on the rotor angular velocity Ω and on the rotor azimuth angle Ψ: we refer to this controller as a spatial repetitive controller. The collective repetitive controller has also been coupled with a C(s) = PD(s), in order to dampen oscillations of the blades. The performance of the spatial repetitive controller is compared with an industrial PI controller. In particular, starting from wind speed velocity Vw = 11.4 m/s the controller is asked to maintain the nominal angular velocity Ωn = 1.266rad/s after an instantaneous increase of wind speed (Vw = 15 m/s). Then, a purely periodic external disturbance is introduced in order to stress the capabilities of the repetitive controller. The results of the simulations show that, contrary to a simple PI controller, the spatial repetitive-PD controller has the capability to reject both external disturbances and periodic trend in the model dynamics. Finally, the nominal value of the angular velocity is reached, in accordance with results obtained with commercial software for a turbine of the same type.

Keywords: wind turbines, aeroelasticity, repetitive control, periodic systems

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Authors: Sara Saboonian, Pierre Filion

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The dispersed-suburban model has been the dominant one across North America for the past seventy years, characterized by automobile reliance, low density, and land-use specialization. Two planning models have emerged as possible alternatives to address the ills inflicted by this development pattern. First, there is intensification, which promotes efficient infrastructure by connecting high-density, multi-functional, and walkable nodes with public transit services within the suburban landscape. Second is green infrastructure, which provides environmental health and human well-being by preserving and restoring ecosystem services. This research studies incompatibilities and the possibility of amalgamating the two alternatives in an attempt to develop a comprehensive alternative to suburban model that advocates density, multi-functionality and transit- and pedestrian-conduciveness, with measures capable of mitigating the adverse environmental impacts of compactness. The research investigates three Canadian urban growth centers, where intensification is the current planning practice, and the awareness of green infrastructure benefits is on the rise. However, these three centers are contrasted by their development stage, the presence or absence of protected natural land, their environmental approach, and their adverse environmental consequences according to the planning cannons of different periods. The methods include reviewing the literature on green infrastructure planning, criticizing the Ontario provincial plans for intensification, surveying residents’ preferences for alternative models, and interviewing officials who deal with the local planning for the centers. Moreover, the research draws on recalling debates between New Urbanism and Landscape/Ecological Urbanism. The case studies expose the difficulties in creating urban growth centres that accommodate green infrastructure while adhering to intensification principles. First, the dominant status of intensification and the obstacles confronting intensification have monopolized the planners’ concerns. Second, the tension between green infrastructure and intensification explains the absence of the green infrastructure typologies that correspond to intensification-compatible forms and dynamics. Finally, the lack of highlighted social-economic benefits of green infrastructure reduces residents’ participation. Moreover, the results from the research provide insight into predominating urbanization theories, New Urbanism and Landscape/Ecological Urbanism. In order to understand political, planning, and ecological dynamics of such blending, dexterous context-specific planning is required. Findings suggest the influence of the following factors on amalgamating intensification and green infrastructure. Initially, producing ecosystem services-based justifications for green infrastructure development in the intensification context provides an expert-driven backbone for the implementation programs. This knowledge-base should be translated to effectively imbue different urban stakeholders. Moreover, due to the limited greenfields in intensified areas, spatial distribution and development of multi-level corridors such as pedestrian-hospitable settings and transportation networks along green infrastructure measures are required. Finally, to ensure the long-term integrity of implemented green infrastructure measures, significant investment in public engagement and education, as well as clarification of management responsibilities is essential.

Keywords: ecosystem services, green infrastructure, intensification, planning

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Authors: James Jin, Weisi Xia, Runzhe Gao, Alain Vandal, Darren Svirkis, Andrew Hill

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Background: Post-haemorrhoidectomy pain is concerned by patients/clinicians. Minimizing the postoperation pain is highly interested clinically. Combinations of topical cream targeting three hypothesised post-haemorrhoidectomy pain mechanisms were developed and their effectiveness were evaluated. Specifically, a multi-centred double-blinded randomized clinical trial (RCT) was conducted in adults undergoing excisional haemorrhoidectomy. The primary analysis was conveyed on the data collected to evaluate the effectiveness of the combinations of topical cream targeting three hypothesized pain mechanisms after the operations. Methods: 192 patients were randomly allocated to 4 arms (each arm has 48 patients), and each arm was provided with pain cream 10% metronidazole (M), M and 2% diltiazem (MD), M with 4% lidocaine (ML), or MDL, respectively. Patients were instructed to apply topical treatments three times a day for 7 days, and record outcomes for 14 days after the operations. The primary outcome was VAS pain on day 4. Covariates and models were selected in the blind review stage. Multiple imputations were applied for the missingness. LMER, GLMER models together with natural splines were applied. Sandwich estimators and Wald statistics were used. P-values < 0.05 were considered as significant. Conclusions: The addition of topical lidocaine or diltiazem to metronidazole does not add any benefit. ML had significantly better pain and recovery scores than combination MDL. Multimodal topical analgesia with ML after haemorrhoidectomy could be considered for further evaluation. Further trials considering only 3 arms (M, ML, MD) might be worth exploring.

Keywords: RCT, primary analysis, multiple imputation, pain scores, haemorrhoidectomy, analgesia, lmer

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Authors: J. G. Batista, K. N. Sousa, ¬J. L. Nunes, R. L. S. Sousa, G. A. P. Thé

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This work deals with parameter identification of permanent magnet motors, a class of ac motor which is particularly important in industrial automation due to characteristics like applications high performance, are very attractive for applications with limited space and reducing the need to eliminate because they have reduced size and volume and can operate in a wide speed range, without independent ventilation. By using experimental data and genetic algorithm we have been able to extract values for both the motor inductance and the electromechanical coupling constant, which are then compared to measured and/or expected values.

Keywords: modeling, AC servomotor, permanent magnet synchronous motor-PMSM, genetic algorithm, vector control, robotic manipulator, control

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Heba M. Jahin, Ali F. Bakr, Zeyad T. Elsayad

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As smart spaces can be defined as any working environment which integrates embedded computers, information appliances and multi-modal sensors to remain focused on the interaction between the users, their activity, and their behavior in the space; hence, smart space must be aware of their contexts and automatically adapt to their changing context-awareness, by interacting with their physical environment through natural and multimodal interfaces. Also, by serving the information used proactively. This paper suggests a dynamic framework through the architectural design process of the space based on the principles of computational methods and context-awareness principles to help in creating a field of changes and modifications. It generates possibilities, concerns about the physical, structural and user contexts. This framework is concerned with five main processes: gathering and analyzing data to generate smart design scenarios, parameters, and attributes; which will be transformed by coding into four types of models. Furthmore, connecting those models together in the interaction model which will represent the context-awareness system. Then, transforming that model into a virtual and ambient environment which represents the physical and real environments, to act as a linkage phase between the users and their activities taking place in that smart space . Finally, the feedback phase from users of that environment to be sure that the design of that smart space fulfill their needs. Therefore, the generated design process will help in designing smarts spaces that can be adapted and controlled to answer the users’ defined goals, needs, and activity.

Keywords: computational methods, context-awareness, design process, smart spaces

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Authors: Dimitra Marini, Konstantinos Bakogiannis, Spyros Polychronopoulos, Georgios Kouroupetroglou

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Archaemusicology cannot entirely depend on the study of the excavated ancient musical instruments as most of the time their condition is not ideal (i.e., missing/eroded parts) and moreover, because of the concern damaging the originals during the experiments. Researchers, in order to overcome the above obstacles, build replicas. This technique is still the most popular one, although it is rather expensive and time-consuming. Throughout the last decades, the development of physical modeling techniques has provided tools that enable the study of musical instruments through their digitally simulated models. This is not only a more cost and time-efficient technique but also provides additional flexibility as the user can easily modify parameters such as their geometrical features and materials. This paper thoroughly describes the steps to create a physical model of a woodwind ancient Greek instrument, Plagiaulos. This instrument could be considered as the ancestor of the modern flute due to the common geometry and air-jet excitation mechanism. Plagiaulos is comprised of a single resonator with an open end and a number of tone holes. The combination of closed and open tone holes produces the pitch variations. In this work, the effects of all the instrument’s components are described by means of physics and then simulated based on digital waveguides. The synthesized sound of the proposed model complies with the theory, highlighting its validity. Further, the synthesized sound of the model simulating the Plagiaulos of Koile (2nd century BCE) was compared with its replica build in our laboratory by following the scientific methodologies of archeomusicology. The aforementioned results verify that robust dynamic digital tools can be introduced in the field of computational, experimental archaemusicology.

Keywords: archaeomusicology, digital waveguides, musical acoustics, physical modeling

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Authors: Jyoti Bharti, M. K. Gupta, Astha Jain

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This paper proposes a new improved approach for false positives filtering of detected face images on OpenCV’s Viola Jones Algorithm In this approach, for Filtering of False Positives, Skin Detection in two colour spaces i.e. HSV (Hue, Saturation and Value) and YCrCb (Y is luma component and Cr- red difference, Cb- Blue difference) is used. As a result, it is found that false detection has been reduced. Our proposed method reaches the accuracy of about 98.7%. Thus, a better recognition rate is achieved.

Keywords: face detection, Viola Jones, false positives, OpenCV

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Authors: Mohammadjavad Sotoudeheian

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Heart failure (HF) prevalence, as a global cardiovascular problem, is increasing gradually. A variety of molecular mechanisms contribute to HF. Proteins involved in cardiac contractility regulation, such as ion channels and calcium handling proteins, are altered. Additionally, epigenetic modifications and gene expression can lead to altered cardiac function. Moreover, inflammation and oxidative stress contribute to HF. The progression of HF can be attributed to mitochondrial dysfunction that impairs energy production and increases apoptosis. Molecular mechanisms such as these contribute to the development of cardiomyocyte defects and HF and can be therapeutically targeted. The heart's contractile function is controlled by cardiomyocytes. Calpain, and its related molecules, including Bax, VEGF, and AMPK, are among the proteins involved in regulating cardiomyocyte function. Apoptosis is facilitated by Bax. Cardiomyocyte apoptosis is regulated by this protein. Furthermore, cardiomyocyte survival, contractility, wound healing, and proliferation are all regulated by VEGF, which is produced by cardiomyocytes during inflammation and cytokine stress. Cardiomyocyte proliferation and survival are also influenced by AMPK, an enzyme that plays an active role in energy metabolism. They all play key roles in apoptosis, angiogenesis, hypertrophy, and metabolism during myocardial inflammation. The role of calpains has been linked to several molecular pathways. The calpain pathway plays an important role in signal transduction and apoptosis, as well as autophagy, endocytosis, and exocytosis. Cell death and survival are regulated by these calcium-dependent cysteine proteases that cleave proteins. As a result, protein fragments can be used for various cellular functions. By cleaving adhesion and motility proteins, calcium proteins also contribute to cell migration. HF may be brought about by calpain-mediated pathways. Many physiological processes are mediated by the calpain molecular pathways. Signal transduction, cell death, and cell migration are all regulated by these molecular pathways. Calpain is activated by calcium binding to calmodulin. In the presence of calcium, calmodulin activates calpain. Calpains are stimulated by calcium, which increases matrix metalloproteinases (MMPs). In order to develop novel treatments for these diseases, we must understand how this pathway works. A variety of myocardial remodeling processes involve calpains, including remodeling of the extracellular matrix and hypertrophy of cardiomyocytes. Calpains also play a role in maintaining cardiac homeostasis through apoptosis and autophagy. The development of HF may be in part due to calpain-mediated pathways promoting cardiomyocyte death. Numerous studies have suggested the importance of the Ca2+ -dependent protease calpain in cardiac physiology and pathology. Therefore, it is important to consider this pathway to develop and test therapeutic options in humans that targets calpain in HF. Apoptosis, autophagy, endocytosis, exocytosis, signal transduction, and disease progression all involve calpain molecular pathways. Therefore, it is conceivable that calpain inhibitors might have therapeutic potential as they have been investigated in preclinical models of several conditions in which the enzyme has been implicated that might be treated with them. Ca 2+ - dependent proteases and calpains contribute to adverse ventricular remodeling and HF in multiple experimental models. In this manuscript, we will discuss the calpain molecular pathway's important roles in HF development.

Keywords: calpain, heart failure, autophagy, apoptosis, cardiomyocyte

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Rani Danavath, V. B. Narsimha

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Cloud computing is emerging as a new paradigm of large scale distributed computing. Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., three service models, and four deployment networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics models. Load balancing is one of the main challenges in cloud computing, which is required to distribute the dynamic workload across multiple nodes, to ensure that no single node is overloaded. It helps in optimal utilization of resources, enhancing the performance of the system. The goal of the load balancing is to minimize the resource consumption and carbon emission rate, that is the direct need of cloud computing. This determined the need of new metrics energy consumption and carbon emission for energy-efficiency load balancing techniques in cloud computing. Existing load balancing techniques mainly focuses on reducing overhead, services, response time and improving performance etc. In this paper we introduced a Technique for energy-efficiency, but none of the techniques have considered the energy consumption and carbon emission. Therefore, our proposed work will go towards energy – efficiency. So this energy-efficiency load balancing technique can be used to improve the performance of cloud computing by balancing the workload across all the nodes in the cloud with the minimum resource utilization, in turn, reducing energy consumption, and carbon emission to an extent, which will help to achieve green computing.

Keywords: cloud computing, distributed computing, energy efficiency, green computing, load balancing, energy consumption, carbon emission

Procedia PDF Downloads 454

Authors: Ajit Brindhaban

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Background and Purpose: Computed Tomography (CT) scans have become the largest source of radiation in radiological imaging. The purpose of this study was to compare the quality of pediatric Computed Tomography (CT) images reconstructed using Filtered Back Projection (FBP) with images reconstructed using different strengths of Iterative Reconstruction (IR) technique, and to perform a feasibility study to assess the use of IR techniques as a dose reduction tool. Materials and Methods: An anthropomorphic phantom representing a 5-year old child was scanned, in two stages, using a Siemens Somatom CT unit. In stage one, scans of the head, chest and abdomen were performed using standard protocols recommended by the scanner manufacturer. Images were reconstructed using FBP and 5 different strengths of IR. Contrast-to-Noise Ratios (CNR) were calculated from average CT number and its standard deviation measured in regions of interest created in the lungs, bone, and soft tissues regions of the phantom. Paired t-test and the one-way ANOVA were used to compare the CNR from FBP images with IR images, at p = 0.05 level. The lowest strength value of IR that produced the highest CNR was identified. In the second stage, scans of the head was performed with decreased mA(s) values relative to the increase in CNR compared to the standard FBP protocol. CNR values were compared in this stage using Paired t-test at p = 0.05 level. Results: Images reconstructed using IR technique had higher CNR values (p < 0.01.) in all regions compared to the FBP images, at all strengths of IR. The CNR increased with increasing IR strength of up to 3, in the head and chest images. Increases beyond this strength were insignificant. In abdomen images, CNR continued to increase up to strength 5. The results also indicated that, IR techniques improve CNR by a up to factor of 1.5. Based on the CNR values at strength 3 of IR images and CNR values of FBP images, a reduction in mA(s) of about 20% was identified. The images of the head acquired at 20% reduced mA(s) and reconstructed using IR at strength 3, had similar CNR as FBP images at standard mA(s). In the head scans of the phantom used in this study, it was demonstrated that similar CNR can be achieved even when the mA(s) is reduced by about 20% if IR technique with strength of 3 is used for reconstruction. Conclusions: The IR technique produced better image quality at all strengths of IR in comparison to FBP. IR technique can provide approximately 20% dose reduction in pediatric head CT while maintaining the same image quality as FBP technique.

Keywords: filtered back projection, image quality, iterative reconstruction, pediatric computed tomography imaging

Procedia PDF Downloads 152

Authors: Tai-Yi Yu, Tai-Kue Yu

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Using global climate change as its main theme, this study establishes a model for understanding the environmental behavior of college students. It examines their beliefs about the environment, sustainability, and social impact. Theories about values, beliefs, norms, and planned behaviors helped establish the path relations among various latent variables, which include the students’ values regarding sustainability, environmental concern, social impact, perceived risk, environmental attitude, and behavioral intention. Personality traits were used as moderator variables in order to analyze their role in influencing environmental behaviors. The components-based partial least square (PLS) method was adopted, and the measurements and structural models were analyzed using the SmartPLS software. The proposed model complies with various test standards, including individual item reliability, composite reliability, average variance extracted, goodness-of-fit, and cross-validated redundancy. When college students are taught the concept of environmental sustainability, sustainability becomes an environmental attitude for them, and they are more likely to uphold an ethic of sustainability. The more an individual perceives the risks of global climate change, the stronger her emotional connection to the issue becomes. This positively affects the environmental attitude of college student, pushes them to participate more proactively in improvement activities, and encourages them to display their behavioral intention to improve global climate change. When considering the interaction effect among four latent variables (values regarding sustainability, social impact, environmental concern, and perceived risk), this study found that personality traits have a moderate effect on environmental attitude.

Keywords: partial least square, personality traits, social impact, environmental concern, perceived risk

Procedia PDF Downloads 432

Authors: M. O. Adeniyi, R. T. Akinnubi

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The parameterization of turbulent heat fluxes is needed for modeling land-atmosphere interactions in Global Climate Models (GCMs). However, current GCMs still have difficulties with producing reliable turbulent heat fluxes for humid tropical regions, which may be due to inadequate parameterization of the roughness lengths for momentum (z0m) and heat (z0h) transfer. These roughness lengths are usually expressed in term of excess resistance factor (κB^(-1)), and this factor is used to account for different resistances for momentum and heat transfers. In this paper, a more appropriate excess resistance factor (〖 κB〗^(-1)) suitable for low wind speed condition was developed and incorporated into the aerodynamic resistance approach (ARA) in the GCMs. Also, the performance of various standard GCMs κB^(-1) schemes developed for high wind speed conditions were assessed. Based on the in-situ surface heat fluxes and profile measurements of wind speed and temperature from Nigeria Micrometeorological Experimental site (NIMEX), new κB^(-1) was derived through application of the Monin–Obukhov similarity theory and Brutsaert theoretical model for heat transfer. Turbulent flux parameterizations with this new formula provides better estimates of heat fluxes when compared with others estimated using existing GCMs κB^(-1) schemes. The derived κB^(-1) MBE and RMSE in the parameterized QH ranged from -1.15 to – 5.10 Wm-2 and 10.01 to 23.47 Wm-2, while that of QE ranged from - 8.02 to 6.11 Wm-2 and 14.01 to 18.11 Wm-2 respectively. The derived 〖 κB〗^(-1) gave better estimates of QH than QE during daytime. The derived 〖 κB〗^(-1)=6.66〖 Re〗_*^0.02-5.47, where Re_* is the Reynolds number. The derived κB^(-1) scheme which corrects a well documented large overestimation of turbulent heat fluxes is therefore, recommended for most regional models within the tropic where low wind speed is prevalent.

Keywords: humid, tropic, excess resistance factor, overestimation, turbulent heat fluxes

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Authors: Emma Serwaa Obobisaa, Haibo Chen

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International trade and outward foreign direct investment are important factors which are generally recognized in the economic growth and development. Though several scholars have struggled to reveal the influence of trade and outward foreign direct investment (FDI) on economic growth, most studies utilized common econometric models such as vector autoregression and aggregated the variables, which for the most part prompts, however, contradictory and mixed results. Thus, there is an exigent need for the precise study of the trade and FDI effect of economic growth while applying strong econometric models and disaggregating the variables into its separate individual variables to explicate their respective effects on economic growth. This will guarantee the provision of policies and strategies that are geared towards individual variables to ensure sustainable development and growth. This study, therefore, seeks to examine the causal effect of China-Africa trade and Outward Foreign Direct Investment on the economic growth of Africa using a robust and recent econometric approach such as system dynamics model. Our study impanels and tests an ensemble of a group of vital variables predominant in recent studies on trade-FDI-economic growth causality: Foreign direct ınvestment, international trade and economic growth. Our results showed that the system dynamics method provides accurate statistical inference regarding the direction of the causality among the variables than the conventional method such as OLS and Granger Causality predominantly used in the literature as it is more robust and provides accurate, critical values.

Keywords: economic growth, outward foreign direct investment, system dynamics model, international trade

Procedia PDF Downloads 112

Authors: Eric Gismondi, Aurelie Bigot-Clivot

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Endocrine disruptors (EDCs) are well known to disrupt the development and the reproduction of exposed organisms. Although this point has been studied in vertebrate models, the limited knowledge of the endocrine system of invertebrates makes the evaluation of EDCs effects difficult. However, invertebrates represent the major part of aquatic ecosystems, such as amphipods Gammaridea, which are crucial for their functioning (e.g., litter degradation, food resource). Moreover, gammarids are hosts of parasites such as vertically-transmitted microsporidia (microsporidia VT), which could be confounding factors in assessment of EDC effects. Indeed, some microsporidia VT could have endocrine effects by their own present in the host since it was observed for example, a feminization of juvenile males, which become phenotypic females. This work evaluated the impact of ethinylestradiol (EE₂, estrogenic), cyproterone acetate (CPA, anti-androgenic), 4-hydroxytamoxifen (4HT, anti-estrogenic) and 17α-methyltestosterone (17MT - androgenic), on the 20-hydroxyecdysone concentration (i.e. 20HE - molt process) and the vitellogenin gene expression (i.e. reproduction) in the freshwater amphipod Gammarus pulex, after a 96h laboratory exposure. In addition, the presence of microsporidia VT was verified in order to analyze the effect of this confounding factor. Results of this study shown that, although endocrine systems of invertebrates and vertebrates are different, EDCs proved in vertebrates could also affect biological functions hormonally controlled in invertebrates. Indeed, the molt process of crustaceans was disrupted in the first stage (i.e. 20-HE concentration) and therefore, could affect, at the long term, the population dynamic. In addition, it was observed that G. pulex was differently impacted according to the gender and parasitism, which underline the importance to take into account these confounding factors to better evaluate the EDCs impact on invertebrate populations.

Keywords: endocrine disruption, gammarus sp., molt, parasitism

Procedia PDF Downloads 171