Search results for: back propagation algorithm

Authors: Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete

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Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resources are now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to a lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN-based AUC validation platform showed that 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Keywords: CNN, geodetector, groundwater influencing factors, Groundwater potential, Gunabay watershed

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Authors: Dong Seop Lee, Byung Sik Kim

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In the 4th Industrial Revolution, various intelligent technologies have been developed in many fields. These artificial intelligence technologies are applied in various services, including disaster management. Disaster information management does not just support disaster work, but it is also the foundation of smart disaster management. Furthermore, it gets historical disaster information using artificial intelligence technology. Disaster information is one of important elements of entire disaster cycle. Disaster information management refers to the act of managing and processing electronic data about disaster cycle from its’ occurrence to progress, response, and plan. However, information about status control, response, recovery from natural and social disaster events, etc. is mainly managed in the structured and unstructured form of reports. Those exist as handouts or hard-copies of reports. Such unstructured form of data is often lost or destroyed due to inefficient management. It is necessary to manage unstructured data for disaster information. In this paper, the Optical Character Recognition approach is used to convert handout, hard-copies, images or reports, which is printed or generated by scanners, etc. into electronic documents. Following that, the converted disaster data is organized into the disaster code system as disaster information. Those data are stored in the disaster database system. Gathering and creating disaster information based on Optical Character Recognition for unstructured data is important element as realm of the smart disaster management. In this paper, Korean characters were improved to over 90% character recognition rate by using upgraded OCR. In the case of character recognition, the recognition rate depends on the fonts, size, and special symbols of character. We improved it through the machine learning algorithm. These converted structured data is managed in a standardized disaster information form connected with the disaster code system. The disaster code system is covered that the structured information is stored and retrieve on entire disaster cycle such as historical disaster progress, damages, response, and recovery. The expected effect of this research will be able to apply it to smart disaster management and decision making by combining artificial intelligence technologies and historical big data.

Keywords: disaster information management, unstructured data, optical character recognition, machine learning

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Authors: Athina Puype, Lorenzo Malerba, Nico De Wispelaere, Roumen Petrov, Jilt Sietsma

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Reduced activation ferritic/martensitic (RAFM) steels like EUROFER97 are primary candidate structural materials for first wall application in the future demonstration (DEMO) fusion reactor. Existing steels of this type obtain their functional properties by a two-stage heat treatment, which consists of an annealing stage at 980°C for thirty minutes followed by quenching and an additional tempering stage at 750°C for two hours. This thermal quench and temper (Q&T) treatment creates a microstructure of tempered martensite with, as main precipitates, M23C6 carbides, with M = Fe, Cr and carbonitrides of MX type, e.g. TaC and VN. The resulting microstructure determines the mechanical properties of the steel. The ductility is largely determined by the tempered martensite matrix, while the resistance to mechanical degradation, determined by the spatial and size distribution of precipitates and the martensite crystals, plays a key role in the high temperature properties of the steel. Unfortunately, the high temperature response of EUROFER97 is currently insufficient for long term use in fusion reactors, due to instability of the matrix phase and coarsening of the precipitates at prolonged high temperature exposure. The objective of this study is to induce grain refinement by appropriate modifications of the processing route in order to increase the high temperature strength of a lab-cast EUROFER RAFM steel grade. The goal of the work is to obtain improved mechanical behavior at elevated temperatures with respect to conventionally heat treated EUROFER97. A dilatometric study was conducted to study the effect of the annealing temperature on the mechanical properties after a Q&T treatment. The microstructural features were investigated with scanning electron microscopy (SEM), electron back-scattered diffraction (EBSD) and transmission electron microscopy (TEM). Additionally, hardness measurements, tensile tests at elevated temperatures and Charpy V-notch impact testing of KLST-type MCVN specimens were performed to study the mechanical properties of the furnace-heated lab-cast EUROFER RAFM steel grade. A significant prior austenite grain (PAG) refinement was obtained by lowering the annealing temperature of the conventionally used Q&T treatment for EUROFER97. The reduction of the PAG results in finer martensitic constituents upon quenching, which offers more nucleation sites for carbide and carbonitride formation upon tempering. The ductile-to-brittle transition temperature (DBTT) was found to decrease with decreasing martensitic block size. Additionally, an increased resistance against high temperature degradation was accomplished in the fine grained martensitic materials with smallest precipitates obtained by tailoring the annealing temperature of the Q&T treatment. It is concluded that the microstructural refinement has a pronounced effect on the DBTT without significant loss of strength and ductility. Further investigation into the optimization of the processing route is recommended to improve the mechanical behavior of RAFM steels at elevated temperatures.

Keywords: ductile-to-brittle transition temperature (DBTT), EUROFER, reduced activation ferritic/martensitic (RAFM) steels, thermal treatments

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Authors: Tingting Huang, Sha Li, Geoffrey Griffiths, Martin Lukac, Jianning Zhu

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Territorial landscapes have experienced a gradual loss of their typical characteristics during long-term human activities. In order to protect the integrity of regional landscapes, it is necessary to characterize, evaluate and protect them in a graded manner. The study takes Fujian, China, as an example and classifies the landscape characters of the site at the regional scale, middle scale, and detailed scale. A multi-scale approach combining parametric and holistic approaches is used to classify and partition the landscape character types (LCTs) and landscape character areas (LCAs) at different scales, and a multi-element landscape assessment approach is adopted to explore the conservation strategies of the landscape character. Firstly, multiple fields and multiple elements of geography, nature and humanities were selected as the basis of assessment according to the scales. Secondly, the study takes a parametric approach to the classification and partitioning of landscape character, Principal Component Analysis, and two-stage cluster analysis (K-means and GMM) in MATLAB software to obtain LCTs, combines with Canny Operator Edge Detection Algorithm to obtain landscape character contours and corrects LCTs and LCAs by field survey and manual identification methods. Finally, the study adopts the Landscape Sensitivity Assessment method to perform landscape character conservation analysis and formulates five strategies for different LCAs: conservation, enhancement, restoration, creation, and combination. This multi-scale identification approach can efficiently integrate multiple types of landscape character elements, reduce the difficulty of broad-scale operations in the process of landscape character conservation, and provide a basis for landscape character conservation strategies. Based on the natural background and the restoration of regional characteristics, the results of landscape character assessment are scientific and objective and can provide a strong reference in regional and national scale territorial spatial planning.

Keywords: parameterization, multi-scale, landscape character identify, landscape character assessment

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Authors: Manpreet Kaur, Cheol-Hong Min

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The increasing advancement in the robotics equipped with machine vision sensors applied to precision agriculture is a demanding solution for various problems in the agricultural farms. An important issue related with the machine vision system concerns crop row and weed detection. This paper proposes an automatic furrow detection system based on real-time processing for identifying crop rows in maize fields in the presence of weed. This vision system is designed to be installed on the farming vehicles, that is, submitted to gyros, vibration and other undesired movements. The images are captured under image perspective, being affected by above undesired effects. The goal is to identify crop rows for vehicle navigation which includes weed removal, where weeds are identified as plants outside the crop rows. The images quality is affected by different lighting conditions and gaps along the crop rows due to lack of germination and wrong plantation. The proposed image processing method consists of four different processes. First, image segmentation based on HSV (Hue, Saturation, Value) decision tree. The proposed algorithm used HSV color space to discriminate crops, weeds and soil. The region of interest is defined by filtering each of the HSV channels between maximum and minimum threshold values. Then the noises in the images were eliminated by the means of hybrid median filter. Further, mathematical morphological processes, i.e., erosion to remove smaller objects followed by dilation to gradually enlarge the boundaries of regions of foreground pixels was applied. It enhances the image contrast. To accurately detect the position of crop rows, the region of interest is defined by creating a binary mask. The edge detection and Hough transform were applied to detect lines represented in polar coordinates and furrow directions as accumulations on the angle axis in the Hough space. The experimental results show that the method is effective.

Keywords: furrow detection, morphological, HSV, Hough transform

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Authors: Rama Umesh Pandey, Jyoti Yadav

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Flooding is one of the waterborne disasters that causes extensive destruction in urban areas. Developing countries are at a higher risk of such damage and more than half of the global flooding events take place in Asian countries including India. Urban flooding is more of a human-induced disaster rather than natural. This is highly influenced by the anthropogenic factors, besides metrological and hydrological causes. Unplanned urbanization and poor management of cities enhance the impact manifold and cause huge loss of life and property in urban areas. It is an irony that urban areas have been facing water scarcity in summers and flooding during monsoon. This paper is an attempt to highlight the factors responsible for flooding in a city especially from an urban planning perspective and to suggest mitigating measures through spatial planning interventions. Analysis has been done in two stages; first is to assess the impacts of previous flooding events and second to analyze the factors responsible for flooding at macro and micro level in cities. Bhopal, a city in Central India having nearly two million population, has been selected for the study. The city has been experiencing flooding during heavy rains in monsoon. The factors responsible for urban flooding were identified through literature review as well as various case studies from different cities across the world and India. The factors thus identified were analyzed for both macro and micro level influences. For macro level, the previous flooding events that have caused huge destructions were analyzed and the most affected areas in Bhopal city were identified. Since the identified area was falling within the catchment of a drain so the catchment area was delineated for the study. The factors analyzed were: rainfall pattern to calculate the return period using Weibull’s formula; imperviousness through mapping in ArcGIS; runoff discharge by using Rational method. The catchment was divided into micro watersheds and the micro watershed having maximum impervious surfaces was selected to analyze the coverage and effect of physical infrastructure such as: storm water management; sewerage system; solid waste management practices. The area was further analyzed to assess the extent of violation of ‘building byelaws’ and ‘development control regulations’ and encroachment over the natural water streams. Through analysis, the study has revealed that the main issues have been: lack of sewerage system; inadequate storm water drains; inefficient solid waste management in the study area; violation of building byelaws through extending building structures ether on to the drain or on the road; encroachments by slum dwellers along or on to the drain reducing the width and capacity of the drain. Other factors include faulty culvert’s design resulting in back water effect. Roads are at higher level than the plinth of houses which creates submersion of their ground floors. The study recommends spatial planning interventions for mitigating urban flooding and strategies for management of excess rain water during monsoon season. Recommendations have also been made for efficient land use management to mitigate water logging in areas vulnerable to flooding.

Keywords: mitigating strategies, spatial planning interventions, urban flooding, violation of development control regulations

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Authors: Auwal Abubakar, Shazril Imran Shaukat, Noor Khairiah A. Karim, Mohammed Zakir Kassim, Gokula Kumar Appalanaido, Hafiz Mohd Zin

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Background: Voluntary deep inspiration breath-hold radiotherapy (vDIBH-RT) is an effective cardiac dose reduction technique during left breast radiotherapy. This study aimed to assess the accuracy of the implementation of the vDIBH technique among left breast cancer patients without the use of a special device such as a surface-guided imaging system. Methods: The vDIBH-RT technique was implemented among thirteen (13) left breast cancer patients at the Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia. Breath-hold monitoring was performed based on breath-hold skin marks and laser light congruence observed on zoomed CCTV images from the control console during each delivery. The initial setup was verified using cone beam computed tomography (CBCT) during breath-hold. Each field was delivered using multiple beam segments to allow a delivery time of 20 seconds, which can be tolerated by patients in breath-hold. The data were analysed using an in-house developed MATLAB algorithm. PTV margin was computed based on van Herk's margin recipe. Results: The setup error analysed from CBCT shows that the population systematic error in lateral (x), longitudinal (y), and vertical (z) axes was 2.28 mm, 3.35 mm, and 3.10 mm, respectively. Based on the CBCT image guidance, the Planning target volume (PTV) margin that would be required for vDIBH-RT using CCTV/Laser monitoring technique is 7.77 mm, 10.85 mm, and 10.93 mm in x, y, and z axes, respectively. Conclusion: It is feasible to safely implement vDIBH-RT among left breast cancer patients without special equipment. The breath-hold monitoring technique is cost-effective, radiation-free, easy to implement, and allows real-time breath-hold monitoring.

Keywords: vDIBH, cone beam computed tomography, radiotherapy, left breast cancer

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Authors: Kottaridi Klimentia, Demopoulos Vasilis, Sidiropoulos Anastasios, Ihara Diego, Nikolaidis Vasileios, Antonopoulos Dimitrios

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Aflatoxins are highly poisonous and carcinogenic compounds produced by species of the genus Aspergillus spp. that can infect a variety of agricultural foods, including dried figs. Biological and environmental factors, such as population, pathogenicity, and aflatoxinogenic capacity of the strains, topography, soil, and climate parameters of the fig orchards, are believed to have a strong effect on aflatoxin levels. Existing methods for aflatoxin detection and measurement, such as high performance liquid chromatography (HPLC), and enzyme-linked immunosorbent assay (ELISA), can provide accurate results, but the procedures are usually time-consuming, sample-destructive, and expensive. Predicting aflatoxin levels prior to crop harvest is useful for minimizing the health and financial impact of a contaminated crop. Consequently, there is interest in developing a tool that predicts aflatoxin levels based on topography and soil analysis data of fig orchards. This paper describes the development of a risk assessment tool for the contamination of aflatoxin on dried figs, based on the location and altitude of the fig orchards, the population of the fungus Aspergillus spp. in the soil, and soil parameters such as pH, saturation percentage (SP), electrical conductivity (EC), organic matter, particle size analysis (sand, silt, clay), the concentration of the exchangeable cations (Ca, Mg, K, Na), extractable P, and trace of elements (B, Fe, Mn, Zn and Cu), by employing machine learning methods. In particular, our proposed method integrates three machine learning techniques, i.e., dimensionality reduction on the original dataset (principal component analysis), metric learning (Mahalanobis metric for clustering), and k-nearest neighbors learning algorithm (KNN), into an enhanced model, with mean performance equal to 85% by terms of the Pearson correlation coefficient (PCC) between observed and predicted values.

Keywords: aflatoxins, Aspergillus spp., dried figs, k-nearest neighbors, machine learning, prediction

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Authors: Mamoon Masud, Suleman Mazhar

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Indus river basin’s irrigation system in Pakistan is extremely complex, spanning over 50,000 km. Maintenance and monitoring of this demands enormous resources. This paper describes the development of a streamlined and low-cost autonomous underwater vehicle (AUV) for the monitoring of irrigation canals including water quality monitoring and water theft detection. The vehicle is a hovering-type AUV, designed mainly for monitoring irrigation canals, with fully documented design and open source code. It has a length of 17 inches, and a radius of 3.5 inches with a depth rating of 5m. Multiple sensors are present onboard the AUV for monitoring water quality parameters including pH, turbidity, total dissolved solids (TDS) and dissolved oxygen. A 9-DOF Inertial Measurement Unit (IMU), GY-85, is used, which incorporates an Accelerometer (ADXL345), a Gyroscope (ITG-3200) and a Magnetometer (HMC5883L). The readings from these sensors are fused together using directional cosine matrix (DCM) algorithm, providing the AUV with the heading angle, while a pressure sensor gives the depth of the AUV. 2 sonar-based range sensors are used for obstacle detection, enabling the vehicle to align itself with the irrigation canals edges. 4 thrusters control the vehicle’s surge, heading and heave, providing 3 DOF. The thrusters are controlled using a proportional-integral-derivative (PID) feedback control system, with heading angle and depth being the controller’s input and the thruster motor speed as the output. A flow sensor has been incorporated to monitor canal water level to detect water-theft event in the irrigation system. In addition to water theft detection, the vehicle also provides information on water quality, providing us with the ability to identify the source(s) of water contamination. Detection of such events can provide useful policy inputs for improving irrigation efficiency and reducing water contamination. The AUV being low cost, small sized and suitable for autonomous maneuvering, water level and quality monitoring in the irrigation canals, can be used for irrigation network monitoring at a large scale.

Keywords: the autonomous underwater vehicle, irrigation canal monitoring, water quality monitoring, underwater line tracking

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Authors: Elżbieta Turska

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Introduction: One of the most difficult times in a person’s life is adolescence. The experiences in this period may shape the future life of this person to a large extent. This is the reason why many young people experience sadness, dejection, hopelessness, sense of worthlessness, as well as losing interest in various activities and social relationships, all of which are often classified as mood disorders. As many as 15-40% adolescents experience depressed moods and for most of them they resolve and are not carried into adulthood. However, (5-6%) of those affected by mood disorders develop the depressive syndrome and as many as (1-3%) develop full-blown clinical depression. Materials: A large questionnaire was given to 2508 students, aged 13–16 years old, and one of its parts was the Burns checklist, i.e. the standard test for identifying depressed mood. The questionnaire asked about many aspects of the student’s life, it included a total of 53 questions, most of which had subquestions. It is important to note that the data suffered from many problems, the most important of which were missing data and collinearity. Aim: In order to identify the correlates of mood disorders we built predictive models which were then trained and validated. Our aim was not to be able to predict which students suffer from mood disorders but rather to explore the factors influencing mood disorders. Methods: The problems with data described above practically excluded using all classical statistical methods. For this reason, we attempted to use the following Artificial Intelligence (AI) methods: classification trees with surrogate variables, random forests and xgboost. All analyses were carried out with the use of the mlr package for the R programming language. Resuts: The predictive model built by classification trees algorithm outperformed the other algorithms by a large margin. As a result, we were able to rank the variables (questions and subquestions from the questionnaire) from the most to least influential as far as protection against mood disorder is concerned. Thirteen out of twenty most important variables reflect the relationships with parents. This seems to be a really significant result both from the cognitive point of view and also from the practical point of view, i.e. as far as interventions to correct mood disorders are concerned.

Keywords: mood disorders, adolescents, family, artificial intelligence

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Authors: Larissa Bolliger, Junoš Lukan, Mitja Lustrek, Dirk De Bacquer, Els Clays

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Introduction and Aim: Chronic workplace stress and its health-related consequences like mental and cardiovascular diseases have been widely investigated. This project focuses on the sources and context of psychosocial daily workplace stress in a real-world setting. The main objective is to analyze and model real-time relationships between (1) psychosocial stress experiences within the natural work environment, (2) micro-level work activities and events, and (3) physiological signals and behaviors in office workers. Methods: An Ecological Momentary Assessment (EMA) protocol has been developed, partly building on machine learning techniques. Empatica® wristbands will be used for real-life detection of stress from physiological signals; micro-level activities and events at work will be based on smartphone registrations, further processed according to an automated computer algorithm. A field study including 100 office-based workers with high-level problem-solving tasks like managers and researchers will be implemented in Slovenia and Belgium (50 in each country). Data mining and state-of-the-art statistical methods – mainly multilevel statistical modelling for repeated data – will be used. Expected Results and Impact: The project findings will provide novel contributions to the field of occupational health research. While traditional assessments provide information about global perceived state of chronic stress exposure, the EMA approach is expected to bring new insights about daily fluctuating work stress experiences, especially micro-level events and activities at work that induce acute physiological stress responses. The project is therefore likely to generate further evidence on relevant stressors in a real-time working environment and hence make it possible to advise on workplace procedures and policies for reducing stress.

Keywords: ecological momentary assessment, real-time, stress, work

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Authors: A. Kyprianou, A. Tjirkallis

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Condition monitoring of structures in service is very important as it provides information about the risk of damage development. One of the essential constituents of structural condition monitoring is the damage detection methodology. In the context of condition monitoring of in service structures a damage detection methodology analyses data obtained from the structure while it is in operation. Usually, this means that the data could be affected by operational and environmental conditions in a way that could mask the effects of a possible damage on the data. This, depending on the damage detection methodology, could lead to either false alarms or miss existing damages. In this article a damage detection methodology that is based on the Spatio-temporal continuous wavelet transform (SPT-CWT) analysis of a sequence of experimental time responses of a cantilever beam is proposed. The cantilever is subjected to white and pink noise excitation to simulate different operating conditions. In addition, in order to simulate changing environmental conditions, the cantilever is subjected to heating by a heat gun. The response of the cantilever beam is measured by a high-speed camera. Edges are extracted from the series of images of the beam response captured by the camera. Subsequent processing of the edges gives a series of time responses on 439 points on the beam. This sequence is then analyzed using the SPT-CWT to identify damage. The algorithm proposed was able to clearly identify damage under any condition when the structure was excited by white noise force. In addition, in the case of white noise excitation, the analysis could also reveal the position of the heat gun when it was used to heat the structure. The analysis could identify the different operating conditions i.e. between responses due to white noise excitation and responses due to pink noise excitation. During the pink noise excitation whereas damage and changing temperature were identified it was not possible to clearly identify the effect of damage from that of temperature. The methodology proposed in this article for damage detection enables the separation the damage effect from that due to temperature and excitation on data obtained from measurements of a cantilever beam. This methodology does not require information about the apriori state of the structure.

Keywords: spatiotemporal continuous wavelet transform, damage detection, data normalization, varying temperature

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Authors: Sohini Manna, Jong Woo Kim, Oleg Shpyrko, Eric E. Fullerton

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CVD techniques have emerged as a promising approach in the formation of a broad range of nanostructured materials. The realization of many practical applications will require efficient and economical synthesis techniques that preferably avoid the need for templates or costly single-crystal substrates and also afford process adaptability. Towards this end, we have developed a single-step route for the reduction-type synthesis of nanostructured Ni materials using a thermal CVD method. By tuning the CVD growth parameters, we can synthesize morphologically dissimilar nanostructures including single-crystal cubes and Au nanostructures which form atop untreated amorphous SiO2||Si substrates. An understanding of the new properties that emerge in these nanostructures materials and their relationship to function will lead to for a broad range of magnetostrictive devices as well as other catalysis, fuel cell, sensor, and battery applications based on high-surface-area transition-metal nanostructures. We use coherent X-ray diffraction imaging technique to obtain 3-D image and strain maps of individual nanocrystals. Coherent x-ray diffractive imaging (CXDI) is a technique that provides the overall shape of a nanostructure and the lattice distortion based on the combination of highly brilliant coherent x-ray sources and phase retrieval algorithm. We observe a fine interplay of reduction of surface energy vs internal stress, which plays an important role in the morphology of nano-crystals. The strain distribution is influenced by the metal-substrate interface and metal-air interface, which arise due to differences in their thermal expansion. We find the lattice strain at the surface of the octahedral gold nanocrystal agrees well with the predictions of the Young-Laplace equation quantitatively, but exhibits a discrepancy near the nanocrystal-substrate interface resulting from the interface. The strain in the bottom side of the Ni nanocube, which is contacted on the substrate surface is compressive. This is caused by dissimilar thermal expansion coefficients between Ni nanocube and Si substrate. Research at UCSD support by NSF DMR Award # 1411335.

Keywords: CVD, nanostructures, strain, CXRD

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Authors: Hasna Elalaoui Elabdallaoui, Abdelaziz Elfazziki, Mohamed Sadgal

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Outlines: The social economy is a moral approach to solidarity applied to the projects’ development. To reconcile economic activity and social equity, crowdfunding is as an alternative means of financing social projects. Several collaborative blockchain platforms exist. It eliminates the need for a central authority or an inconsiderate middleman. Also, the costs for a successful crowdfunding campaign are reduced, since there is no commission to be paid to the intermediary. It improves the transparency of record keeping and delegates authority to authorities who may be prone to corruption. Objectives: The objectives are: to define a software infrastructure for projects’ participatory financing within a social and solidarity economy, allowing transparent, secure, and fair management and to have a financial mechanism that improves financial inclusion. Methodology: The proposed methodology is: crowdfunding platforms literature review, financing mechanisms literature review, requirements analysis and project definition, a business plan, Platform development process and implementation technology, and testing an MVP. Contributions: The solution consists of proposing a new approach to crowdfunding based on Islamic financing, which is the principle of Mousharaka inspired by Islamic financing, which presents a financial innovation that integrates ethics and the social dimension into contemporary banking practices. Conclusion: Crowdfunding platforms need to secure projects and allow only quality projects but also offer a wide range of options to funders. Thus, a framework based on blockchain technology and Islamic financing is proposed to manage this arbitration between quality and quantity of options. The proposed financing system, "Musharaka", is a mode of financing that prohibits interests and uncertainties. The implementation is offered on the secure Ethereum platform as investors sign and initiate transactions for contributions using their digital signature wallet managed by a cryptography algorithm and smart contracts. Our proposal is illustrated by a crop irrigation project in the Marrakech region.

Keywords: social economy, Musharaka, blockchain, smart contract, crowdfunding

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Authors: Ferran Paulí Pla, Pere Palacín Farré, Albert Fornells Herrera, Pol Toldrà Fernández

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Poor image acquisition is limiting the promising growth of industrial vision in food control. In recent years, the food industry has witnessed a significant increase in the implementation of automation in quality control through artificial vision, a trend that continues to grow. During the packaging process, some defects may appear, compromising the proper sealing of the products and diminishing their shelf life, sanitary conditions and overall properties. While failure to detect a defective product leads to major losses, food producers also aim to minimize over-rejection to avoid unnecessary waste. Thus, accuracy in the evaluation of the products is crucial, and, given the large production volumes, even small improvements have a significant impact. Recently, efforts have been focused on maximizing the performance of classification neural networks; nevertheless, their performance is limited by the quality of the input data. Monochrome linear backlight systems are most commonly used for online inspections of food packaging thermo-sealing zones. These simple acquisition systems fit the high cadence of the production lines imposed by the market demand. Nevertheless, they provide a limited amount of data, which negatively impacts classification algorithm training. A desired situation would be one where data quality is maximized in terms of obtaining the key information to detect defects while maintaining a fast working pace. This work presents a backlight hyperspectral imaging system designed and implemented replicating an industrial environment to better understand the relationship between visual data quality and spectral illumination range for a variety of packed food products. Furthermore, results led to the identification of advantageous spectral bands that significantly enhance image quality, providing clearer detection of defects.

Keywords: artificial vision, food packaging, hyperspectral imaging, image acquisition, quality control

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Authors: Fouzia Arif, Arif S. Mohammad, Hifsa Altaf, Lubna Raees

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Introduction: The term "disability", refers to any condition that impedes the completion of daily tasks using traditional methods. In developing countries like Pakistan, disable population is usually excluded from the mainstream. In squatter settlements the situation is more critical. Sultanabad is one of the squatter settlements of Karachi. Purpose of case study is to improve the health of disabled children’s, and create awareness among the parents and community. Through a household visit, Shiraz, a young disabled boy of 15.5 years old was identified. Her mother articulated that her son was living normally and happily with his parents two years back. When he was 13 years old and student of class 8th, both his legs were traumatized in a Railway Train Accident while playing cricket. He got both femoral shaft fractured severely. He was taken to Jinnah Post Graduate Medical Centre (JPMC) where his left leg was amputated at above knee level and right leg was opened & fixed by reduction internally, luckily bone healed moderately with the passage of time. Methods: In Squatter settlements of Karachi Sultanabad, a survey was conducted in two sectors. Disability screening questionnaire was developed, collaboration with community through household visits, outreach sessions 23cases of disabled were identified who were socialized through sports, Musical program and get-together was organized with stockholder for creating awareness among community and parent’s. Collaboration was established with different NGOs, Government, stakeholders and community support for establishment of Physiotherapy Center. During home visit it was identified that Shiraz was on bed since last 1 year, his family could not afforded cost of physiotherapist and medical consultation due to poverty. Parents counseling was done mentioning that Shiraz needed to take treatment. After motivation his parents agreed for treatment. He was consulted by an orthopedic surgeon in AKUH, Who referred to DMC University of Health Science for rehabilitation service. There he was assessed and referred for Community Based Physiotherapy Centre Sultanabad. Physiotherapist visited home along with Coordinator for Special children and assessed him regularly, planned Physiotherapy treatment for abdominal, high muscles strutting exercise foot muscles strengthening exercise, knee mobilization weight bearing from partial to full weight gradually, also strengthen exercise were given for residual limb as the boy was dependent on it. He was also provided by an artificial leg and training was done. Result: Shiraz is now fully mobile, he can walk independently even out of home, functional ability progress improved and dependency factors reduced. It was difficult but not impossible. We all have sympathy but if we have empathy then we can rehabilitate the community in a better way. His parents are very happy and also the community is surprised to see him in such better condition. Conclusion: Combined efforts of physiotherapist, Coordinator of special children, community and parents made a drastic change in Shiraz’s case by continuously motivating him for better outcome. He is going to school regularly without support. Since he belongs to a poor family he faces financial constraints for education and clinical follow ups regularly.

Keywords: femoral shaft fracture, trauma, orthopedic surgeon, physiotherapy treatment

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Authors: Parimalah Velo, Ahmad Zakaria

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Introduction: Monte Carlo simulations of preclinical imaging systems allow opportunity to enable new research that could range from designing hardware up to discovery of new imaging application. The simulation system which could accurately model an imaging modality provides a platform for imaging developments that might be inconvenient in physical experiment systems due to the expense, unnecessary radiation exposures and technological difficulties. The aim of present study is to validate the Monte Carlo simulation of thyroid phantom imaging using Geant4-GATE for Siemen’s e-cam single head gamma camera. Upon the validation of the gamma camera simulation model by comparing physical characteristic such as energy resolution, spatial resolution, sensitivity, and dead time, the GATE simulation of thyroid phantom imaging is carried out. Methods: A thyroid phantom is defined geometrically which comprises of 2 lobes with 80mm in diameter, 1 hot spot, and 3 cold spots. This geometry accurately resembling the actual dimensions of thyroid phantom. A planar image of 500k counts with 128x128 matrix size was acquired using simulation model and in actual experimental setup. Upon image acquisition, quantitative image analysis was performed by investigating the total number of counts in image, the contrast of the image, radioactivity distributions on image and the dimension of hot spot. Algorithm for each quantification is described in detail. The difference in estimated and actual values for both simulation and experimental setup is analyzed for radioactivity distribution and dimension of hot spot. Results: The results show that the difference between contrast level of simulation image and experimental image is within 2%. The difference in the total count between simulation and actual study is 0.4%. The results of activity estimation show that the relative difference between estimated and actual activity for experimental and simulation is 4.62% and 3.03% respectively. The deviation in estimated diameter of hot spot for both simulation and experimental study are similar which is 0.5 pixel. In conclusion, the comparisons show good agreement between the simulation and experimental data.

Keywords: gamma camera, Geant4 application of tomographic emission (GATE), Monte Carlo, thyroid imaging

Procedia PDF Downloads 271

Authors: Jelena Maletkovic

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Background: Endogenous Cushing’s syndrome is a rare disease, but iatrogenic or drug related Cushing syndrome from glucocorticoid products is commonly seen in clinical practice. With high dose and long term use of glucocorticoids, patients can develop isolated hypothalamic-pituitary-adrenal (HPA) suppression, or HPA axis suppression can be accompanied by overt iatrogenic Cushing’s syndrome. This is a rare case where severe Cushing’s syndrome developed from an unknown medication and was followed by severe and prolonged adrenal insufficiency and multiple potentially fatal complications. Case: This is a 37-year-old woman who is presented to Emergency Room (ER) with shortness of breath and chest pain. Four months prior to this presentation the patient was a generally healthy woman who was looking for improvement in her appearance and visited local Rejuvenation Clinic. After initial consultation with a nurse, she was contacted by a physician over the phone and was advised to start taking multiple injectable medications that will arrive by mail. Medications without any labels on bottles were delivered and the patient started daily intramuscular injections. Over the next two months, she noticed rounding of her face and swelling around her eyes. She gained 20 pounds, mostly abdominal fat and became extremely fatigued. Her muscles on legs were visibly decreasing in size and she felt significant muscle weakness. Unexplained bruising occurred. She started growing hair on face and developed secondary amenorrhea. New severe back pain started. She developed depression and headaches. Finally, over a few days, a number of red-purple stretch marks that were sensitive and painful appeared over her abdomen, upper part of arms and legs. She then became suspicious that these dramatic symptoms are caused by injectable medication and she discontinued injections. Over the next few days she presented to ER with low blood pressure and oxygen saturation of 75%. Studies revealed extensive pneumonia as well as multiple pulmonary emboli. Her white blood count was elevated with 32 000 and she also had acute kidney failure on admission. She was treated for sepsis and was also given stress dose steroids. Steroids were tapered over 48 hours and discontinued. After being discharged to home, on her first visit to endocrinology clinic she had undetectable ACTH of < 2pg/mL and undetectable 8am cortisol of < 0.2mcg/dL. She did not respond to an intramuscular injection of cosyntropin 250mcg and her repeated cortisol after 60 minutes was only 1mcg/dL. The patient was diagnosed with adrenal insufficiency and was started on hydrocortisone 20mg+10mg. It took close to 2 years of slow tapering for recovery of this patient’s HPA axis and resolve all the sequelae from Cushing’s syndrome. Conclusion: Misuse and abuse of glucocorticoids have been present almost since these medications were discovered. This is a rare case where not only severe Cushing’s syndrome in full clinical picture developed but also the patient suffered multiple potentially fatal complications and prolonged adrenal insufficiency. Visits to herbal, rejuvenation, esthetic, and similar clinics are becoming more and more popular and physicians need to be aware of possible non-benign nature of medications that their patients may be using.

Keywords: iatrogenic, Cushing's syndrome, adrenal crisis, steroid abuse

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Authors: Sabina-Adriana Floria, Marius Gavrilescu, Florin Leon, Silvia Curteanu, Costel Anton

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Neuroevolution is a subfield of artificial intelligence used to solve various problems in different application areas. Specifically, neuroevolution is a technique that applies biologically inspired methods to generate neural network architectures and optimize their parameters automatically. In this paper, we use different biologically inspired optimization algorithms in an ensemble strategy with the aim of training multilayer perceptron neural networks, resulting in regression models used to simulate the industrial chemical process of obtaining bricks from silicone-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. In addition, the initial conditions that were taken into account during the design and commissioning of the installation can change over time, which leads to the need to add new mixes to adjust the operating conditions for the desired purpose, e.g., material properties and energy saving. The present approach follows the study by simulation of a process of obtaining bricks from silicone-based materials, i.e., the modeling and optimization of the process. Optimization aims to determine the working conditions that minimize the emissions represented by nitrogen monoxide. We first use a search procedure to find the best values for the parameters of various biologically inspired optimization algorithms. Then, we propose an adaptive ensemble strategy that uses only a subset of the best algorithms identified in the search stage. The adaptive ensemble strategy combines the results of selected algorithms and automatically assigns more processing capacity to the more efficient algorithms. Their efficiency may also vary at different stages of the optimization process. In a given ensemble iteration, the most efficient algorithms aim to maintain good convergence, while the less efficient algorithms can improve population diversity. The proposed adaptive ensemble strategy outperforms the individual optimizers and the non-adaptive ensemble strategy in convergence speed, and the obtained results provide lower error values.

Keywords: optimization, biologically inspired algorithm, neuroevolution, ensembles, bricks, emission minimization

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Authors: Periklis Brakatsoulas

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Recent empirical research shows a growing interest in investment decision-making under market anomalies that contradict the rational paradigm. Momentum is undoubtedly one of the most robust anomalies in the empirical asset pricing research and remains surprisingly lucrative ever since first documented. Although predominantly phenomena identified across equities, momentum premia are now evident across various asset classes. Yet few many attempts are made so far to provide traders a diversified portfolio of strategies across different assets and markets. Moreover, literature focuses on patterns from past returns rather than mechanisms to signal future price directions prior to momentum runs. The aim of this paper is to develop a diversified portfolio approach to price distortion signals using daily position data on stocks, credit derivatives, and bonds. An algorithm allocates assets periodically, and new investment tactics take over upon price momentum signals and across different ranking groups. We focus on momentum life cycles, trend reversals, and price acceleration signals. The main effort here concentrates on the density, time span and maturity of momentum phenomena to identify consistent patterns over time and measure the predictive power of buy-sell signals generated by these anomalies. To tackle this, we propose a two-stage modelling process. First, we generate forecasts on core macroeconomic drivers. Secondly, satellite models generate market risk forecasts using the core driver projections generated at the first stage as input. Moreover, using a combination of the ARFIMA and FIGARCH models, we examine the dependence of consecutive observations across time and portfolio assets since long memory behavior in volatilities of one market appears to trigger persistent volatility patterns across other markets. We believe that this is the first work that employs evidence of volatility transmissions among derivatives, equities, and bonds to identify momentum life cycle patterns.

Keywords: forecasting, long memory, momentum, returns

Procedia PDF Downloads 102

Authors: Saraswati Kerketta

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Tea industry is one of the oldest organised sector of India. It employs roughly 1.5 million people directly. Since the last decade Indian tea industry, especially in the northern region is experiencing worst crisis in the post-independence period. Due to many reason the prices of tea show steady decline. The workers are paid one of the lowest wage in tea industry in the world (1.5$ a day) below the UN's $2 a day for extreme poverty. The workers rely on addition benefits from plantation which includes food, housing and medical facilities. These have been effective means of enslavement of generations of labourers by the owners. There is hardly any change in the tea estates where the owners determine the fate of workers. When the tea garden is abandoned or is closed all the facilities disappear immediately. The workers are the descendants of tribes from central India also known as 'tea tribes'. Alienated from their native place, the geographical and social isolation compounded their vulnerability of these people. The economy of the region being totally dependent on tea has resulted in absolute unemployment for the workers of these tea gardens. With no other livelihood and no land to grow food, thousands of workers faced hunger and starvation. The Plantation Labour Act which ensures the decent working and living condition is violated continuously. The labours are forced to migrate and are also exposed to the risk of human trafficking. Those who are left behind suffers from starvation, malnutrition and disease. The condition in the sick tea plantation is no better. Wage are not paid regularly, subsidised food, fuel are also not supplied properly. Health care facilities are in very bad shape. Objectives: • To study the socio-cultural and demographic characteristics of the tea garden labourers in the study area. • To examine the social situation of workers in sick estates in dooars region. • To assess the magnitude of deprivation the impact of economic crisis on abandoned and closed tea estates in the region. Data Base: The study is based on data collected from field survey. Methods: Quantative: Cross-Tabulation, Regression analysis. Qualitative: Household Survey, Focussed Group Discussion, In-depth interview of key informants. Findings: Purchasing power parity has declined since in last three decades. There has been many fold increase in migration. Males migrates long distance towards central and west and south India. Females and children migrates both long and short distance. No one has reported to migrate back to the place of origin of their ancestors. Migrant males work mostly as construction labourers and as factory workers whereas females and children work as domestic help and construction labourers. In about 37 cases either they haven't contacted their families in last six months or are not traceable. The families with single earning members are more likely to migrate. Burden of disease and the duration of sickness, abandonment and closure of plantation are closely related. Death tolls are likely to rise 1.5 times in sick tea gardens and three times in closed tea estates. Sixty percent of the people are malnourished in the sick tea gardens and more than eighty five per cent in abandoned and sick tea gardens.

Keywords: migration, trafficking, starvation death, tea garden workers

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Authors: Hendri Purwadi, Christine McCloud

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Triage is a complex decision-making process that aims to categorize a victim’s level of acuity and the need for medical assistance. Two common triage systems have been widely used in Mass Casualty Incidents (MCIs) and disaster situation are START (Simple triage algorithm and rapid treatment) and SALT (sort, asses, lifesaving, intervention, and treatment/transport). There is currently controversy regarding the effectiveness of SALT over START triage system. This systematic review aims to investigate and compare the effectiveness between SALT and START triage system in disaster and MCIs setting. Literatures were searched via systematic search strategy from 2009 until 2019 in PubMed, Cochrane Library, CINAHL, Scopus, Science direct, Medlib, ProQuest. This review included simulated-based and medical record -based studies investigating the accuracy and applicability of SALT and START triage systems of adult and children population during MCIs and disaster. All type of studies were included. Joana Briggs institute critical appraisal tools were used to assess the quality of reviewed studies. As a result, 1450 articles identified in the search, 10 articles were included. Four themes were identified by review, they were accuracy, under-triage, over-triage and time to triage per individual victim. The START triage system has a wide range and inconsistent level of accuracy compared to SALT triage system (44% to 94. 2% of START compared to 70% to 83% of SALT). The under-triage error of START triage system ranged from 2.73% to 20%, slightly lower than SALT triage system (7.6 to 23.3%). The over-triage error of START triage system was slightly greater than SALT triage system (START ranged from 2% to 53% compared to 2% to 22% of SALT). The time for applying START triage system was faster than SALT triage system (START was 70-72.18 seconds compared to 78 second of SALT). Consequently; The START triage system has lower level of under-triage error and faster than SALT triage system in classifying victims of MCIs and disaster whereas SALT triage system is known slightly more accurate and lower level of over-triage. However, the magnitude of these differences is relatively small, and therefore the effect on the patient outcomes is not significance. Hence, regardless of the triage error, either START or SALT triage system is equally effective to triage victims of disaster and MCIs.

Keywords: disaster, effectiveness, mass casualty incidents, START triage system, SALT triage system

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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: Tugba Altin

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In a time characterized by escalating climate change impacts, communities globally face extreme events with deep-reaching tangible and intangible consequences. At the intersection of these phenomena lies the profound impact on the cultural and emotional connections that individuals forge with their environments. This study casts a spotlight on the Lytton Creek Fire of 2021, showcasing it as an exemplar of both the visible destruction brought by such events and the more covert yet deeply impactful disturbances to place attachment (PA). Defined as the emotional and cognitive bond individuals form with their surroundings, PA is critical in comprehending how such catastrophic events reshape cultural identity and the bond with the land. Against the stark backdrop of the Lytton Creek Fire's devastation, the research seeks to unpack the multilayered dynamics of PA amidst the tangible wreckage and the intangible repercussions such as emotional distress and disrupted cultural landscapes. Delving deeper, it examines how affected populations renegotiate their affiliations with these drastically altered environments, grappling with both the tangible loss of their homes and the intangible challenges to solace, identity, and community cohesion. This exploration is instrumental in the broader climate change narrative, as it offers crucial insights into how these personal-place relationships can influence and shape climate adaptation and recovery strategies. Departing from traditional data collection methodologies, this study adopts an interpretive phenomenological approach enriched by hermeneutic insights and places the experiences of the Lytton community and its co-researchers at its core. Instead of conventional interviews, innovative methods like walking audio sessions and photo elicitation are employed. These techniques allow participants to immerse themselves back into the environment, reviving and voicing their memories and emotions in real-time. Walking audio captures reflections on spatial narratives after the trauma, whereas photo voices encapsulate the intangible emotions, presenting a visual representation of place-based experiences. Key findings emphasize the indispensability of addressing both the tangible and intangible traumas in community recovery efforts post-disaster. The profound changes to the cultural landscape and the subsequent shifts in PA underscore the need for holistic, culturally attuned, and emotionally insightful adaptation strategies. These strategies, rooted in the lived experiences and testimonies of the affected individuals, promise more resonant and effective recovery efforts. The research further contributes to climate change discourse, highlighting the intertwined pathways of tangible reconstruction and the essentiality of emotional and cultural rejuvenation. Furthermore, the use of participatory methodologies in this inquiry challenges traditional research paradigms, pointing to potential evolutionary shifts in qualitative research norms. Ultimately, this study underscores the need for a more integrative approach in addressing the aftermath of environmental disasters, ensuring that both physical and emotional rebuilding are given equal emphasis.

Keywords: place attachment, community recovery, disaster reponse, sensory responses, intangible traumas, visual methodologies

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Authors: C. Yin, B. Zhang, Y. Liu, J. Cai

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Airborne EM (AEM) is an effective geophysical exploration tool, especially suitable for ridged mountain areas. In these areas, topography will have serious effects on AEM system responses. However, until now little study has been reported on topographic effect on airborne EM systems. In this paper, an edge-based unstructured finite-element (FE) method is developed for 3D topographic modeling for both frequency and time-domain airborne EM systems. Starting from the frequency-domain Maxwell equations, a vector Helmholtz equation is derived to obtain a stable and accurate solution. Considering that the AEM transmitter and receiver are both located in the air, the scattered field method is used in our modeling. The Galerkin method is applied to discretize the Helmholtz equation for the final FE equations. Solving the FE equations, the frequency-domain AEM responses are obtained. To accelerate the calculation speed, the response of source in free-space is used as the primary field and the PARDISO direct solver is used to deal with the problem with multiple transmitting sources. After calculating the frequency-domain AEM responses, a Hankel’s transform is applied to obtain the time-domain AEM responses. To check the accuracy of present algorithm and to analyze the characteristic of topographic effect on airborne EM systems, both the frequency- and time-domain AEM responses for 3 model groups are simulated: 1) a flat half-space model that has a semi-analytical solution of EM response; 2) a valley or hill earth model; 3) a valley or hill earth with an abnormal body embedded. Numerical experiments show that close to the node points of the topography, AEM responses demonstrate sharp changes. Special attentions need to be paid to the topographic effects when interpreting AEM survey data over rugged topographic areas. Besides, the profile of the AEM responses presents a mirror relation with the topographic earth surface. In comparison to the topographic effect that mainly occurs at the high-frequency end and early time channels, the EM responses of underground conductors mainly occur at low frequencies and later time channels. For the signal of the same time channel, the dB/dt field reflects the change of conductivity better than the B-field. The research of this paper will serve airborne EM in the identification and correction of the topographic effects.

Keywords: 3D, Airborne EM, forward modeling, topographic effect

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Authors: Narayan Moger, Divya B., Preethi Jambagi, Krishnaveni C. K., Apsana M. R., B. R. Patil, Basvaraj Bagewadi

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Salinity is one of the major threats to world food security. Considering the requirement for salt tolerant crop plants in the present study was undertaken to clone and transferred the salt tolerant ectA gene from marine ecosystem into agriculture crop system to impart salinity tolerance. Ectoine is the compatible solute which accumulates in the cell membrane, is known to be involved in salt tolerance activity in most of the Halophiles. The present situation is insisting to development of salt tolerant transgenic lines to combat abiotic stress. In this background, the investigation was conducted to develop transgenic tomato lines by cloning and transferring of ectA gene is an ectoine derivative capable of enzymatic action for the production of acetyl-diaminobutyric acid. The gene ectA is involved in maintaining the osmotic balance of plants. The PCR amplified ectA gene (579bp) was cloned into T/A cloning vector (pTZ57R/T). The construct pDBJ26 containing ectA gene was sequenced by using gene specific forward and reverse primers. Sequence was analyzed using BLAST algorithm to check similarity of ectA gene with other isolates. Highest homology of 99.66 per cent was found with ectA gene sequences of isolates Halomonas elongata with the available sequence information in NCBI database. The ectA gene was further sub cloned into pRI101-AN plant expression vector and transferred into E. coli DH5α for its maintenance. Further pDNM27 was mobilized into A. tumefaciens LBA4404 through tri-parental mating system. The recombinant Agrobacterium containing pDNM27 was transferred into tomato plants through In planta plant transformation method. Out of 300 seedlings, co-cultivated only twenty-seven plants were able to well establish under the greenhouse condition. Among twenty-seven transformants only twelve plants showed amplification with gene specific primers. Further work must be extended to evaluate the transformants at T1 and T2 generations for ectoine accumulation, salinity tolerance, plant growth and development and yield.

Keywords: salinity, computable solutes, ectA, transgenic, in planta transformation

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Authors: Fulya Asena Uluç, Ehsan Tuzcuoğlu, Songül Bayraktar, Burak Koca, Alper Gürarslan

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Microplastics are contaminants that are widely distributed in the environment with a detrimental ecological effect. Besides this, recent research has proved the existence of microplastics in human blood and organs. Microplastics in the environment can be divided into two main categories: primary and secondary microplastics. Primary microplastics are plastics that are released into the environment as microscopic particles. On the other hand, secondary microplastics are the smaller particles that are shed as a result of the consumption of synthetic materials in textile products as well as other products. Textiles are the main source of microplastic contamination in aquatic ecosystems. Laundry of synthetic textiles (34.8%) accounts for an average annual discharge of 3.2 million tons of primary microplastics into the environment. Recently, microfiber shedding from laundry research has gained traction. However, no comprehensive study was conducted from the standpoint of rinsing parameters during laundry to analyze microfiber shedding. The purpose of the present study is to quantify microfiber shedding from fabric under different rinsing conditions and determine the effective rinsing parameters on microfiber release in a laundry environment. In this regard, a parametric study is carried out to investigate the key factors affecting the microfiber release from a front-load washing machine. These parameters are the amount of water used during the rinsing step and the spinning speed at the end of the washing cycle. Minitab statistical program is used to create a design of the experiment (DOE) and analyze the experimental results. Tests are repeated twice and besides the controlled parameters, other washing parameters are kept constant in the washing algorithm. At the end of each cycle, released microfibers are collected via a custom-made filtration system and weighted with precision balance. The results showed that by increasing the water amount during the rinsing step, the amount of microplastic released from the washing machine increased drastically. Also, the parametric study revealed that increasing the spinning speed results in an increase in the microfiber release from textiles.

Keywords: front load, laundry, microfiber, microfiber release, microfiber shedding, microplastic, pollution, rinsing parameters, sustainability, washing parameters, washing machine

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Authors: Zdzislaw Kaminski, Zbigniew Czyz, Tytus Tulwin

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This paper describes the geometrical model, algorithm and CFD simulation of an airflow around a Vertical Axis Wind Turbine rotor. A solver, ANSYS Fluent, was applied for the numerical simulation. Numerical simulation, unlike experiments, enables us to validate project assumptions when it is designed to avoid a costly preparation of a model or a prototype for a bench test. This research focuses on the rotor designed according to patent no PL 219985 with its blades capable of modifying their working surfaces, i.e. absorbing wind kinetic energy. The operation of this rotor is based on a regulation of blade angle α between the top and bottom parts of blades mounted on an axis. If angle α increases, the working surface which absorbs wind kinetic energy also increases. CFD calculations enable us to compare aerodynamic characteristics of forces acting on rotor working surfaces and specify rotor operation parameters like torque or turbine assembly power output. This paper is part of the research to improve an efficiency of a rotor assembly and it contains investigation of the impact of a blade angle of wind turbine working blades on the power output as a function of rotor torque, specific rotational speed and wind speed. The simulation was made for wind speeds ranging from 3.4 m/s to 6.2 m/s and blade angles of 30°, 60°, 90°. The simulation enables us to create a mathematical model to describe how aerodynamic forces acting each of the blade of the studied rotor are generated. Also, the simulation results are compared with the wind tunnel ones. This investigation enables us to estimate the growth in turbine power output if a blade angle changes. The regulation of blade angle α enables a smooth change in turbine rotor power, which is a kind of safety measures if the wind is strong. Decreasing blade angle α reduces the risk of damaging or destroying a turbine that is still in operation and there is no complete rotor braking as it is in other Horizontal Axis Wind Turbines. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: computational fluid dynamics, mathematical model, numerical analysis, power, renewable energy, wind turbine

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Authors: Ksenija Taški-Ajdukovic, Nevena Nagl, Živko Ćurčić, Dario Danojević

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Information about genetic diversity of sugar beet parental populations is of a great importance for hybrid breeding programs. The aim of this research was to evaluate genetic diversity among and within populations and lines of diploid sugar beet pollinators, by using SSR markers. As plant material were used eight pollinators originating from three USDA-ARS breeding programs and four pollinators from Institute of Field and Vegetable Crops, Novi Sad. Depending on the presence of self-fertility gene, the pollinators were divided into three groups: autofertile (inbred lines), autosterile (open-pollinating populations), and group with partial presence of autofertility gene. A total of 40 SSR primers were screened, out of which 34 were selected for the analysis of genetic diversity. A total of 129 different alleles were obtained with mean value 3.2 alleles per SSR primer. According to the results of genetic variability assessment the number and percentage of polymorphic loci was the maximal in pollinators NS1 and tester cms2 while effective number of alleles, expected heterozygosis and Shannon’s index was highest in pollinator EL0204. Analysis of molecular variance (AMOVA) showed that 77.34% of the total genetic variation was attributed to intra-varietal variance. Correspondence analysis results were very similar to grouping by neighbor-joining algorithm. Number of groups was smaller by one, because correspondence analysis merged IFVCNS pollinators with CZ25 into one group. Pollinators FC220, FC221 and C 51 were in the next group, while self-fertile pollinators CR10 and C930-35 from USDA-Salinas were separated. On another branch were self-sterile pollinators ЕL0204 and ЕL53 from USDA-East Lansing. Sterile testers cms1 and cms2 formed separate group. The presented results confirmed that SSR analysis can be successfully used in estimation of genetic diversity within and among sugar beet populations. Since the tested pollinator differed considering the presence of self-fertility gene, their heterozygosity differed as well. It was lower in genotypes with fixed self-fertility genes. Since the most of tested populations were open-pollinated, which rarely self-pollinate, high variability within the populations was expected. Cluster analysis grouped populations according to their origin.

Keywords: auto fertility, genetic diversity, pollinator, SSR, sugar beet

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Authors: Ugwu O. C., Mamah R. O., Awudu W. S.

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This work is aimed at enhancing signal reception on a mobile radio network and minimizing outage probability in a mobile radio network using adaptive beamforming antenna arrays. In this research work, an empirical real-time drive measurement was done in a cellular network of Globalcom Nigeria Limited located at Ikeja, the headquarters of Lagos State, Nigeria, with reference base station number KJA 004. The empirical measurement includes Received Signal Strength and Bit Error Rate which were recorded for exact prediction of the signal strength of the network as at the time of carrying out this research work. The Received Signal Strength and Bit Error Rate were measured with a spectrum monitoring Van with the help of a Ray Tracer at an interval of 100 meters up to 700 meters from the transmitting base station. The distance and angular location measurements from the reference network were done with the help Global Positioning System (GPS). The other equipment used were transmitting equipment measurements software (Temsoftware), Laptops and log files, which showed received signal strength with distance from the base station. Results obtained were about 11% from the real-time experiment, which showed that mobile radio networks are prone to signal failure and can be minimized using an Adaptive Beamforming Antenna Array in terms of a significant reduction in Bit Error Rate, which implies improved performance of the mobile radio network. In addition, this work did not only include experiments done through empirical measurement but also enhanced mathematical models that were developed and implemented as a reference model for accurate prediction. The proposed signal models were based on the analysis of continuous time and discrete space, and some other assumptions. These developed (proposed) enhanced models were validated using MATLAB (version 7.6.3.35) program and compared with the conventional antenna for accuracy. These outage models were used to manage the blocked call experience in the mobile radio network. 20% improvement was obtained when the adaptive beamforming antenna arrays were implemented on the wireless mobile radio network.

Keywords: beamforming algorithm, adaptive beamforming, simulink, reception

Procedia PDF Downloads 41