Search results for: particle image velocimetry (PIV)

Authors: Seyedeh Rozhano Azimi Hashemi

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There is no doubt that ‘Art’ is a social phenomena and cinema is the most social kind of art. Hence, it’s clear that we can analyze the relation’s of cinema and art from different aspects. In this paper sociological cinema will be investigated which, is a subdivision of sociological art. This term will be discussed by two main approaches. One of these approaches is focused on the effects of cinema on the society, which is known as “Effects Theory” and the second one, which is dealing with the reflection of social issues in cinema is called ” Reflection Theory”. "Reflect theory" approach, unlike "Effects theory" is considering movies as documents, in which social life is reflected, and by analyzing them, the changes and tendencies of a society are understood. Criticizing these approaches to cinema and society doesn’t mean that they are not real. Conversely, it proves the fact that for better understanding of cinema and society’s relation, more complicated models are required, which should consider two aspects. First, they should be bilinear and they should provide a dynamic and active relation between cinema and society, as for the current concept social life and cinema have bi-linear effects on each other, and that’s how they fit in a dialectic and dynamic process. Second, it should pay attention to the role of inductor elements such as small social institutions, marketing, advertisements, cultural pattern, art’s genres and popular cinema in society. In the current study, image of middle class in cinema of Iran and changing the role of women in cinema and society which were two bold issue that cinema and society faced since 1979 revolution till 80s are analyzed. Films as an artwork on one hand, are reflections of social changes and with their effects on the society on the other hand, are trying to speed up the trends of these changes. Cinema by the illustration of changes in ideologies and approaches in exaggerated ways and through it’s normalizing functions, is preparing the audiences and public opinions for the acceptance of these changes. Consequently, audience takes effect from this process, which is a bi-linear and interactive process.

Keywords: Iranian Cinema, Cinema and Society, Middle Class, Woman’s Role

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Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

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Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

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Authors: José María Agugusto-landa, Inmaculada García-Martínez, Lara Checa Domene, Óscar Gavín Chocano

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Self-concept is the perception that a person has of himself, of his abilities, skills, traits, and values. Self-concept is composed of different dimensions, such as academic self-concept, physical self-concept, social self-concept, emotional self-concept, and family self-concept. The relationship between the dimensions of self-concept and mental health and academic performance among future teachers is a topic of interest for educational psychology. Some studies have found that: (i) There is a positive relationship between general self-concept, academic self-concept and academic performance, that is, students who have a more positive image of themselves tend to get better grades and be more motivated to learn. (ii) There is a positive relationship between emotional intelligence, physical self-concept and healthy habits, that is, students who regulate and understand their emotions better have a higher satisfaction with their physical appearance and follow a more balanced diet and a higher physical activity. As for gender differences in the dimensions of self-concept among future teachers, some studies have found that: (i) Girls tend to have a higher self-concept in the social, family and verbal dimensions, that is, they perceive themselves as more capable of relating to others, communicating effectively and receiving support from their family. (ii) Boys tend to have a higher self-concept in the physical, emotional and mathematical dimensions, that is, they perceive themselves as more capable of performing physical activities, controlling their emotions and solving mathematical problems. (iii) There are no significant differences between general self-concept and academic self-concept according to gender, that is, both girls and boys have a similar perception of their global worth and academic competence.

Keywords: preservice teachers, self-concept, academic performance, mental wellbeing

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Authors: Lakmali Anthony, Madeline Gillies

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Purpose: Long-term survivorship in colorectal cancer (CRC) is increasing as mortality decreases, leading to increased focus on patient-reported outcomes such as quality of life (QoL). CRC patients often have decreased QoL even after treatment is complete. This systematic review of the literature aims to identify psychosocial factors associated with decreased QoL in post-treatment CRC patients. Methodology: This systematic review was performed in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses recommendations. The search was conducted in MEDLINE, EMBASE, and PsychINFO using MeSH headings. The two authors screened studies for relevance and extracted data. Results: Seventeen studies were identified, including 6,272 total participants (mean = 392, 58% male) with a mean age of 60.6 years. The European Organisation for Research and Treatment of Cancer QLQ-C30 was the most common measure of QoL (n=14, 82.3%). Most studies (n=15, 88.2%) found that emotional distress correlated with poor global QoL. This was most commonly measured with the Hospital Anxiety & Depression Scale (n=11, 64.7%). Other psychosocial factors associated with QoL were lack of social support, body image, and financial difficulties. Clinicopathologic determinants included presence of stoma and metastasis. Conclusion: This systematic review provides a summary of the psychosocial determinants of poor QoL in post-treatment CRC patients, as well as the most commonly reported measures of these. An understanding of these potentially modifiable determinants of poor outcome is pivotal to the provision of quality, patient-centred care in surgical oncology.

Keywords: colorectal cancer, cancer surgery, quality of life, oncology, social determinants

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Authors: S. Baississe, S. Godbane, A. Lekbir

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The objective of our work is to develop an ice cream from a fermented cream, skim milk and other ingredients and follow the evolution of its physicochemical properties, biochemical and microstructure of the products obtained. Our cream is aerated with the manufacturing steps start with a homogenizing follow different ingredients by heating to 40°C emulsion, the preparation is then subjected to a heat treatment at 65°C for 30 min, before being stored in the cold at 4°C for a few hours. This conservation promotes crystallization of the material during the globular stage of maturation of the cream. The emulsifying agent moves gradually absorbed on the surface of fat globules homogeneous, which results in reduced protein stability. During the expansion, the collusion of destabilizing fat globules in the aqueous phase favours their coalescence. During the expansion, the collusion of destabilized fat globules in the aqueous phase favours their coalescence. The stabilizing agent increases the viscosity of the aqueous phase and the drainage limit interaction with the proteins of the aqueous phase and the protein absorbed on fat globules. The cutting improved organoleptic property of our cream is made by the use of three dyes and aromas. The products obtained undergo physicochemical analyses (pH, conductivity and acidity), biochemical (moisture, % dry matter and fat in %), and finally in the microscopic observation of the microstructure and the results obtained by analysis of the image processing software. The results show a remarkable evolution of physicochemical properties (pH, conductivity and acidity), biochemical (moisture, fat and non-fat) and microstructure of the products developed in relation to the raw material (skim milk) and the intermediate product (fermented cream).

Keywords: ice cream, sour cream, physicochemical, biochemical, microstructure

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Authors: Masood Varshosaz, Kamyar Hasanpour

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In recent years, we have seen growing interest in recognizing humans in drone images for post-disaster search and rescue operations. Deep learning algorithms have shown great promise in this area, but they often require large amounts of labeled data to train the models. To keep the data acquisition cost low, augmentation techniques can be used to create additional data from existing images. There are many techniques of such that can help generate variations of an original image to improve the performance of deep learning algorithms. While data augmentation is potentially assumed to improve the accuracy and robustness of the models, it is important to ensure that the performance gains are not outweighed by the additional computational cost or complexity of implementing the techniques. To this end, it is important to evaluate the impact of data augmentation on the performance of the deep learning models. In this paper, we evaluated the most currently available 2D data augmentation techniques on a standard convolutional network which was trained for recognizing humans in drone images. The techniques include rotation, scaling, random cropping, flipping, shifting, and their combination. The results showed that the augmented models perform 1-3% better compared to a base network. However, as the augmented images only contain the human parts already visible in the original images, a new data augmentation approach is needed to include the invisible parts of the human body. Thus, we suggest a new method that employs simulated 3D human models to generate new data for training the network.

Keywords: human recognition, deep learning, drones, disaster mitigation

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Authors: Reza Ziaie Moayed, Saeideh Mohammadi

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Soft soil is used in many of civil engineering projects like coastal, marine and road projects. Because of low shear strength and stiffness of soft soils, large settlement and low bearing capacity will occur under superstructure loads. This will make the civil engineering activities more difficult and costlier. In the case of soft soils, improvement is a suitable method to increase the shear strength and stiffness for engineering purposes. In recent years, the artificial cementation of soil by cement and lime has been extensively used for soft soil improvement. Cement stabilization is a well-established technique for improving soft soils. Artificial cementation increases the shear strength and hardness of the natural soils. On the other hand, in soft soils, the use of piles to transfer loads to the depths of ground is usual. By using cement treated soil around the piles, high bearing capacity and low settlement in piles can be achieved. In the present study, lateral bearing capacity of short piles in cemented soils is investigated by numerical approach. For this purpose, three dimensional (3D) finite difference software, FLAC 3D is used. Cement treated soil has a strain hardening-softening behavior, because of breaking of bonds between cement agent and soil particle. To simulate such behavior, strain hardening-softening soil constitutive model is used for cement treated soft soil. Additionally, conventional elastic-plastic Mohr Coulomb constitutive model and linear elastic model are used for stress-strain behavior of natural soils and pile. To determine the parameters of constitutive models and also for verification of numerical model, the results of available triaxial laboratory tests on and insitu loading of piles in cement treated soft soil are used. Different parameters are considered in parametric study to determine the effective parameters on the bearing of the piles on cemented treated soils. In the present paper, the effect of various length and height of the artificial cemented area, different diameter and length of the pile and the properties of the materials are studied. Also, the effect of choosing a constitutive model for cemented treated soils in the bearing capacity of the pile is investigated.

Keywords: bearing capacity, cement-treated soils, FLAC 3D, pile

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Authors: Atiq Zaman

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The high contamination rate in the recycling waste stream is one of the major problems in Australia. In addition, a lack of reliable waste data makes it even more difficult for designing and implementing an effective waste management plan. This article conceptualizes the opportunity to improve resource productivity by integrating smart resource tracking system (SRTS) into the Australian household waste management system. The application of the smart resource tracking system will be implemented through the following ways: (i) mobile application-based resource tracking system used to measure the household’s material flow; (ii) RFID, smart image and weighing system used to track waste generation, recycling and contamination; (iii) informing and motivating manufacturer and retailers to improve their problematic products’ packaging; and (iv) ensure quality and reliable data through open-sourced cloud data for public use. The smart mobile application, imaging, radio-frequency identification (RFID) and weighing technologies are not new, but the very straightforward idea of using these technologies in the household resource consumption, waste bins and collection trucks will open up a new era of accurately measuring and effectively managing our waste. The idea will bring the most urgently needed reliable, data and clarity on household consumption, recycling behaviour and waste management practices in the context of available local infrastructure and policies. Therefore, the findings of this study would be very important for decision makers to improve resource productivity in the waste industry by using smart resource tracking system.

Keywords: smart devices, mobile application, smart sensors, resource tracking, waste management, resource productivity

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Authors: Denise C. I. Maior-Barron

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This conference presentation proposes to discuss the Japanese tourist perception of Marie Antoinette, at the heritage site which represents the home par excellence of the last Queen of France: Petit Trianon, Versailles. The underpinning analysis has a two-fold aim of firstly identifying the elements that contributed at the said perception and secondly of placing this in the wider context of tabi (travel) culture. The contribution of the presentation lies in its relevance to the analysis of postmodern trends of Japanese travel culture in relation to the consumption of European cultural heritage, through an insight into Japanese contemporary perception of heritage sites and their associated historical figures subject to controversy. Based upon the author’s doctoral studies field research at Petit Trianon - survey led in situ between 2010-2012, applied with the questionnaire method on a total of 307 respondents out of which 53 Japanese nationals - the media sources that were revealed to have had a direct influence on these nationals’ perception of Marie Antoinette, were Riyoko Ikeda’s shōjo manga La Rose de Versailles (1972) and Sofia Coppola’s film Marie-Antoinette (2006). The interpretation of the survey results through an assessment of visitor discourse determined the research methodology to be qualitative as opposed to quantitative, thus what confirmed the empirical hypothesis of the survey was a pattern of perception instead of percentages. Consequently, the interpretation focused on the answers to the questions relating to the image of Marie Antoinette in relation to historical knowledge, cultural background and last but not least media influences.

Keywords: cultural heritage, manga, film, tabi

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Authors: Adel Menchawy, Noha Labib

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Many cities have quarters that confer on them sense of identity and place through its cultural history. They are often vital part of the cities charm and appeal, their functional and visual qualities are important to the city’s image and identity. Brownfield sites present an important part of our built landscape. They provide tangible and intangible links to our past and have great potential to play significant roles in the future of our cities, towns and rural environments. Brownfield sites are places that were previously industrial factories or areas that might have had waste kept at that location or been exposed to many types of hazards. Thus its redevelopment revitalizes and strengthens towns and communities as it helps in economic growth, builds community pride and protects public health and the environment Three case studies are discussed in this paper; the first one is the city of Sterling which was developed and revitalized entirely and became a city with identity after it was derelict, the Second is the city of Castlefield with was a place no one was eager to visit now it became a touristic area. And finally the city of Cleveland which adopted a strategy that transferred it from being a polluted, derelict place into a mixed use development city Brownfield revitalization offers a great opportunity to transfer the city from being derelict, useless and contaminated into a place where tourists would love to come. Also it will increase the economy of the place, increase the social level, it can improve energy efficiency, reduce natural consumption, clean air, water and land and take advantage of existing buildings and sites and transfers them into an adaptive reuse after being remediated

Keywords: Brownfield Revitalization, Sustainable Brownfield, Historical conservation, Adaptive reuse

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Authors: Mazifah Simis, Azahan Awang, Kadir Arifin

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The brownfield regeneration is being implemented in developped countries. However, as a group 1 developing country in the South East Asia, the rapid development and increasing number of urban population in Malaysia have urged the needs to incorporate the brownfield regeneration into its physical planning development. The increasing number of urban ex-landfills is seen as a new resource that could overcome the issues of inadequate urban green space provisions. With regards to the new development approach in urban planning, this perception study aims to identify the sustainable planning approach based on what the stakeholders have in mind. Respondents consist of 375 local communities within four urban ex-landfill areas and 61 landscape architect and town planner officers in the Malaysian Local Authorities. Three main objectives are set to be achieved, which are (i) to identify ex-landfill issues that need to be overcome prior to the ex-landfill redevelopment (ii) to identify the most suitable types of ex-landfill redevelopment, and (iii) to identify the priority function for ex-landfill redevelopment as the public parks. From the data gathered through the survey method, the order of priorities based on stakeholders' perception was produced. The results show different perception among the stakeholders, but they agreed to the development of the public park as the main development. Hence, this study attempts to produce an integrated approach as a model for sustainable ex-landfill redevelopment that could be accepted by the stakeholders as a beneficial future development that could change the image of 296 ex-landfills in Malaysia into the urban public parks by the year 2020.

Keywords: brownfield regeneration, ex-landfill redevelopment, integrated approach, stakeholders' perception

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Authors: Iwan Cony Setiadi, Aulia M. T. Nasution

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The multispectral imaging (MSI) technique has been used for skin analysis, especially for distant mapping of in-vivo skin chromophores by analyzing spectral data at each reflected image pixel. For ergonomic purpose, our multispectral imaging system is decomposed in two parts: a light source compartment based on LED with 11 different wavelenghts and a monochromatic 8-Bit CCD camera with C-Mount Objective Lens. The software based on GUI MATLAB to control the system was also developed. Our system provides 11 monoband images and is coupled with a software reconstructing hyperspectral cubes from these multispectral images. In this paper, we proposed a new method to build a hyperspectral reflectance cube based on artificial neural network algorithm. After preliminary corrections, a neural network is trained using the 32 natural color from X-Rite Color Checker Passport. The learning procedure involves acquisition, by a spectrophotometer. This neural network is then used to retrieve a megapixel multispectral cube between 380 and 880 nm with a 5 nm resolution from a low-spectral-resolution multispectral acquisition. As hyperspectral cubes contain spectra for each pixel; comparison should be done between the theoretical values from the spectrophotometer and the reconstructed spectrum. To evaluate the performance of reconstruction, we used the Goodness of Fit Coefficient (GFC) and Root Mean Squared Error (RMSE). To validate reconstruction, the set of 8 colour patches reconstructed by our MSI system and the one recorded by the spectrophotometer were compared. The average GFC was 0.9990 (standard deviation = 0.0010) and the average RMSE is 0.2167 (standard deviation = 0.064).

Keywords: multispectral imaging, reflectance cube, spectral reconstruction, artificial neural network

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Authors: Salah Hadjout, Mohamed Zouidi

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In Algeria, several works carried out in recent years have shown the importance of fusarium head blight in durum wheat. Indeed, this disease is caused by a complex of Fusarium genus pathogens. The research carried out reports that F. culmorum is the main species infecting cereals. These informations motivated our interest in the field evaluation of the behavior of some durum wheat genotypes (parental varieties and lines) with regard to fusarium head blight, mainly caused by four F. culmorum isolates. Our research work focused on following the evolution of symptom development throughout the grain filling, after artificial inoculation of ears by Fusarium isolates in order to establish a first image on the differences in genotype behavior to fusarium haed blight. Field disease assessment criteria are: disease assessment using a grading scale, thousand grain weight measurement and AUDPC. The results obtained revealed that the varieties and lines resulting from crosses had a quite different level of sensitivity to F. culmorum species and no genotype showed complete resistance in our culture conditions. Among the material tested, some lines showed higher resistance than their parents. The results also show a slight behavioral variability also linked to the aggressiveness of the Fusarium species studied in this work. Our results open very important research perspectives on fusarium head blight, in particular the search for toxins produced by Fusarium species.

Keywords: fusarium head blight, durum wheat, Fusarium culmorum, field disease assessment criteria, Algeria

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifyig the red blood cells as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-Malaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Keywords: red blood cells, classification, radial basis function neural networks, suport vector machine, k-nearest neighbors algorithm

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Yingisani Chabalala, Elhadi Adam, Khalid Adem Ali

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Mapping smallholder fruit plantations using optical data is challenging due to morphological landscape heterogeneity and crop types having overlapped spectral signatures. Furthermore, cloud covers limit the use of optical sensing, especially in subtropical climates where they are persistent. This research assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for mapping fruit trees and co-existing land-use types by using support vector machine (SVM) and random forest (RF) classifiers independently. These classifiers were also applied to fused data from the two sensors. Feature ranks were extracted using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. Based on RF MDA and FVS, the SVM classifier resulted in relatively high classification accuracy with overall accuracy (OA) = 0.91.6% and kappa coefficient = 0.91% when applied to the fused satellite data. Application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; OA = 87.01%, Kappa coefficient = 87%, respectively. Results also indicated that the optimal spectral bands for fruit tree mapping are green (B3) and SWIR_2 (B10) for S2, whereas for S1, the vertical-horizontal (VH) polarization band. Including the textural metrics from the VV channel improved crop discrimination and co-existing land use cover types. The fusion approach proved robust and well-suited for accurate smallholder fruit plantation mapping.

Keywords: smallholder agriculture, fruit trees, data fusion, precision agriculture

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Authors: Nazari Maryam, Gorji Saman

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For most competitions, athletes usually engage in a process called rapid weight loss (RWL) and subsequent rapid weight gain (RWG) in the days preceding the event. Besides the perfection of performance, weight regulation mediates a self-image of being “a real athlete” which is mentally important as a part of the pre-competition preparation. This feeling enhances the focus and commitment of the athlete. There is a large body of evidence that weight loss, particularly in combat sports, results in several health benefits. However, intentional weight loss beyond normal levels might have unknown negative special effects on the immune system. As the results show, a high prevalence (50%) of RWL is happening among combat athletes. It seems that energy deprivation and intense exercise to reach RWL results in altered blood cell distribution through modification of body composition that, in turn, changes B and T-Lymphocyte and/or CD4 T-Helper response. Moreover, it may diminish IgG antibody levels and modulate IgG glycosylation after this course. On the other hand, some studies show suppression of signaling and regulation of IgE antibody and chemokine production are responsible for immunodeficiency following a period of low-energy availability. Some researchers hypothesize that severe glutamine depletion, which occurs during exercise and calorie restriction, is responsible for this immune system weakness. However, supplementation by this amino acid is not prescribed yet. Therefore, weight loss is achieved not only through chronic strategies (body fat losses) but also through acute manipulations prior to competition should be supervised by a sports nutritionist to minimize side effects on the immune system and other body systems.

Keywords: athletes, immune system, rapid weight loss, weight loss strategies

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Authors: A. González-Caro, A. M. Merino-Lechuga, D. Suescum-Morales, E. Fernández-Ledesma, J. R. Jiménez, J. M. Fernández-Rodríguez

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Climate change is one of the most significant issues today. Since the 19th century, the rise in temperature has not only been due to natural change, but also to human activities, which have been the main cause of climate change, mainly due to the burning of fossil fuels such as coal, oil and gas. The boom in the construction sector in recent years is also one of the main contributors to CO₂ emissions into the atmosphere; for example, for every tonne of cement produced, 1 tonne of CO₂ is emitted into the atmosphere. Most of the research being carried out in this sector is focused on reducing the large environmental impact generated during the manufacturing process of building materials. In detail, this research focuses on the recovery of waste from olive oil mills. Spain is the world's largest producer of olive oil, and this sector generates a large amount of waste and by-products such as olive pits, “alpechín” or “alpeorujo”. This olive stone by means of a pyrosilisis process gives rise to the production of active carbon. The process causes the carbon to develop many internal spaces. This study is based on the manufacture of porous mortars with Portland cement and natural limestone sand, with an addition of 5% and 10% of activated carbon. Two curing environments were used: i) dry chamber, with a humidity of 65 ± 10% and temperature of 21 ± 2 ºC and an atmospheric CO₂ concentration (approximately 0.04%); ii) accelerated carbonation chamber, with a humidity of 65 ± 10% and temperature of 21 ± 2 ºC and an atmospheric CO₂ concentration of 5%. In addition to eliminating waste from an industry, the aim of this study is to reduce atmospheric CO₂. For this purpose, first, a physicochemical and mineralogical characterisation of all raw materials was carried out, using techniques such as fluorescence and X-ray diffraction. The particle size and specific surface area of the activated carbon were determined. Subsequently, tests were carried out on the hardened mortar, such as thermogravimetric analysis (to determine the percentage of CO₂ capture), as well as mechanical properties, density, porosity, and water absorption. It was concluded that the activated carbon acts as a sink for CO₂, causing it to be trapped inside the voids. This increases CO₂ capture by 300% with the addition of 10% activated carbon at 7 days of curing. There was an increase in compressive strength of 17.5% with the CO₂ chamber after 7 days of curing using 10% activated carbon compared to the dry chamber.

Keywords: olive stone, activated carbon, porous mortar, CO₂ capture, economy circular

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Authors: Ni̇sa Yeni̇kalayci, Türkan Aybi̇ke Akarca

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The ability of correlation the number and space relations, one of the basic scientific process skills, is being used in the transformation of a two-dimensional object into a three-dimensional image or in the expression of symmetry axes of the object. With this research, it is aimed to determine the ability of science students to establish number and space relations. The research was carried out with a total of 90 students studying in the first semester of the Science Education program of a state university located in the Turkey’s Black Sea Region in the fall semester of 2017-2018 academic year. An ‘Integrative Perception Test (IPT)’ was designed by the researchers to collect the data. Within the scope of IPT, the courses and workbooks specific to the field of science were scanned and the ones without symmetrical structure from the visual items belonging to the ‘Physics - Chemistry – Biology’ sub-fields were selected and listed. During the application, it was expected that students would imagine and draw images of the missing half of the visual items that were given incomplete in the first place. The data obtained from the test in which there are 30 images or pictures in total (f Physics = 10, f Chemistry = 10, f Biology = 10) were analyzed descriptively based on the drawings created by the students as ‘complete (2 points), incomplete/wrong (1 point), empty (0 point)’. For the teaching of new concepts in small aged groups, images or pictures showing symmetrical structures and similar applications can also be used.

Keywords: integrative perception, number and space relations, science education, scientific process skills

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Authors: Ahmet Erten, Adil Mustafa, Ayşenur Eser, Özlem Yalçın

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We present a new viscometer based on a microfluidic chip with elastic high aspect ratio micropillar arrays. The displacement of pillar tips in flow direction can be used to analyze viscosity of liquid. In our work, Computational Fluid Dynamics (CFD) is used to analyze pillar displacement of various micropillar array configurations in flow direction at different viscosities. Following CFD optimization, micro-CNC based rapid prototyping is used to fabricate molds for microfluidic chips. Microfluidic chips are fabricated out of polydimethylsiloxane (PDMS) using soft lithography methods with molds machined out of aluminum. Tip displacements of micropillar array (300 µm in diameter and 1400 µm in height) in flow direction are recorded using a microscope mounted camera, and the displacements are analyzed using image processing with an algorithm written in MATLAB. Experiments are performed with water-glycerol solutions mixed at 4 different ratios to attain 1 cP, 5 cP, 10 cP and 15 cP viscosities at room temperature. The prepared solutions are injected into the microfluidic chips using a syringe pump at flow rates from 10-100 mL / hr and the displacement versus flow rate is plotted for different viscosities. A displacement of around 1.5 µm was observed for 15 cP solution at 60 mL / hr while only a 1 µm displacement was observed for 10 cP solution. The presented viscometer design optimization is still in progress for better sensitivity and accuracy. Our microfluidic viscometer platform has potential for tailor made microfluidic chips to enable real time observation and control of viscosity changes in biological or chemical reactions.

Keywords: Computational Fluid Dynamics (CFD), high aspect ratio, micropillar array, viscometer

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Authors: Nataša Todorović, Jovana Nikolov, Ivana Stojković, Milan Vraneš, Jovana Panić, Slobodan Gadžurić

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The detection of ²¹⁰Pb levels in aquatic environments evokes interest in various scientific studies. Its precise determination is important not only for the radiological assessment of drinking waters but also ²¹⁰Pb, and ²¹⁰Po distribution in the marine environment are significant for the assessment of the removal rates of particles from the ocean and particle fluxes during transport along the coast, as well as particulate organic carbon export in the upper ocean. Measurement techniques for ²¹⁰Pb determination, gamma spectrometry, alpha spectrometry, or liquid scintillation counting (LSC) are either time-consuming or demand expensive equipment or complicated chemical pre-treatments. However, one other possibility is to measure ²¹⁰Pb on an LS counter if it is in equilibrium with its progeny ²¹⁰Bi - through the Cherenkov counting method. It is unaffected by the chemical quenching and assumes easy sample preparation but has the drawback of lower counting efficiencies than standard LSC methods, typically from 10% up to 20%. The aim of the presented research in this paper is to investigate the possible increment of detection efficiency of Cherenkov counting during ²¹⁰Pb/²¹⁰Bi detection on an LS counter Quantulus 1220. Considering naturally low levels of ²¹⁰Pb in aqueous samples, the addition of ionic liquids to the counting vials with the analysed samples has the benefit of detection limit’s decrement during ²¹⁰Pb quantification. Our results demonstrated that ionic liquid, 1-butyl-3-methylimidazolium salicylate, is more efficient in Cherenkov counting efficiency increment than the previously explored 2-hydroxypropan-1-amminium salicylate. Consequently, the impact of a few other ionic liquids that were synthesized with the same cation group (1-butyl-3-methylimidazolium benzoate, 1-butyl-3-methylimidazolium 3-hydroxybenzoate, and 1-butyl-3-methylimidazolium 4-hydroxybenzoate) was explored in order to test their potential influence on Cherenkov counting efficiency. It was confirmed that, among the explored ones, only ionic liquids in the form of salicylates exhibit a wavelength shifting effect. Namely, the addition of small amounts (around 0.8 g) of 1-butyl-3-methylimidazolium salicylate increases the detection efficiency from 16% to >70%, consequently reducing the detection threshold by more than four times. Moreover, the addition of ionic liquids could find application in the quantification of other radionuclides besides ²¹⁰Pb/²¹⁰Bi via Cherenkov counting method.

Keywords: liquid scintillation counting, ionic liquids, Cherenkov counting, ²¹⁰PB/²¹⁰BI in water

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Authors: Zeinab Mirabulqasemi, Gholamali Hatam

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This article discusses the significance of mural paintings in Iranian culture, particularly within the context of religious tombs known as Imamzadehs. These tombs, dedicated to Shiite imams and other revered religious figures, serve as important religious and communal spaces. The tradition of tomb construction evolved from early Islamic practices, gradually transforming burial sites into places of worship. In the Gilan region of Iran, these tombs hold a revered status, serving as focal points for religious observances and social gatherings. The murals adorning these tombs often depict religious motifs, with a particular emphasis on events like the Day of Judgment and the martyrdom of the Imams, notably the saga of Ashura. These paintings also reflect the community's social perspectives and historical allegiances. Various architectural styles are employed in constructing these tombs, including Islamic, traditional, local, and aesthetic architecture. However, the region's climate poses challenges to the preservation of these structures and their murals. Despite these challenges, efforts are made to document and preserve these artworks to ensure their accessibility for future generations. This research also studies tomb paintings by adopting a multifaceted approach, including library research, image analysis, and field research. Finally, it examines the portrayal of significant figures such as the Shiite imams, prophets, and Imamzadehs within these murals, highlighting their thematic significance and cultural importance.

Keywords: cultural ritual, Shiite imams, mural, belief foundations, religious paintings

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Authors: Svetlana Petlitckaia, Toussaint Barboni, Paul-Antoine Santoni

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Plastic is a revolutionary material. However, the pollution caused by plastics damages the environment, human health and the economy of different countries. It is important to find new ways to recycle and reuse plastic material. The use of waste materials as filler and as a matrix for composite materials is receiving increasing attention as an approach to increasing the economic value of streams. In this study, a new composite material based on high-density polyethylene (HDPE) and polypropylene (PP) wastes from bottle caps and cork powder from unused cork (virgin cork), which has a high capacity for thermal insulation, was developed. The composites were prepared with virgin and modified cork. The composite materials were obtained through twin-screw extrusion and injection molding. The composites were produced with proportions of 0 %, 5 %, 10 %, 15 %, and 20 % of cork powder in a polymer matrix with and without coupling agent and flame retardant. These composites were investigated in terms of mechanical, structural and thermal properties. The effect of cork fraction, particle size and the use of flame retardant on the properties of composites were investigated. The properties of samples elaborated with the polymer and the cork were compared to them with the coupling agent and commercial flame retardant. It was observed that the morphology of HDPE/cork and PP/cork composites revealed good distribution and dispersion of cork particles without agglomeration. The results showed that the addition of cork powder in the polymer matrix reduced the density of the composites. However, the incorporation of natural additives doesn’t have a significant effect on water adsorption. Regarding the mechanical properties, the value of tensile strength decreases with the addition of cork powder, ranging from 30 MPa to 19 MPa for PP composites and from 19 MPa to 17 MPa for HDPE composites. The value of thermal conductivity of composites HDPE/cork and PP/ cork is about 0.230 W/mK and 0.170 W/mK, respectively. Evaluation of the flammability of the composites was performed using a cone calorimeter. The results of thermal analysis and fire tests show that it is important to add flame retardants to improve fire resistance. The samples elaborated with the coupling agent and flame retardant have better mechanical properties and fire resistance. The feasibility of the composites based on cork and PP and HDPE wastes opens new ways of valorizing plastic waste and virgin cork. The formulation of composite materials must be optimized.

Keywords: composite materials, cork and polymer wastes, flammability, modificated cork

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Authors: Dusanee Suwankhong, Pranee Liamputtong

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Breast surgery leaves undesirable scars to all women who experienced mastectomy, despite the fact that this could be a principle approach to save one life. This paper explores how Thai women living with breast cancer perceived and experienced a scarred body after breast surgery. In-depth interviews and drawing methods were employed among 20 women diagnosed with breast cancer. The interviewed data were analysed using thematic analysis method. The results showed that all women with breast cancer who underwent breast surgery perceived and experienced scar as a persisting and visible side-effect. This disfigurement appearance presented a negative image of feminine identity and led to emotional burdens among women. They responded to being scarred in different ways relating to their perceptions of body and changes. The older group had less embarrassed feelings towards being scarred comparing to the younger one. All women tried to seek means to cope with such physical impairment and keep balance life related to their condition. For example, they relied on Buddhism practice and tried to heal the keloid using natural products. Scars appeared to be an unpleasant effect for women who underwent breast mastectomy. Nurses and health care professionals in the local health service sectors need to pay close attention to how the women see the scarred body and their experiences of living with the distorted feminine appearance, and to provide sensitive support that meets the needs of these vulnerable women. The suitable supports can reduce the sense of embarrassment and increase their sense of self-confidence about their social femininity.

Keywords: breast surgery, emotional response, qualitative study, scars, Thai women

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Authors: Jacintha Menezes, Ramalingam Dharmalingam, Palaiahnakote Shivakumara

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In the recent years, Omani acid lime cultivation and production has been affected by Citrus greening or Huanglongbing (HLB) disease. HLB disease is one of the most destructive diseases for citrus, with no remedies or countermeasures to stop the disease. Currently used Polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA) HLB detection tests require lengthy and labor-intensive laboratory procedures. Furthermore, the equipment and staff needed to carry out the laboratory procedures are frequently specialized hence making them a less optimal solution for the detection of the disease. The current research uses hyperspectral imaging technology for automatic detection of citrus trees with HLB disease. Omani citrus tree leaf images were captured through portable Specim IQ hyperspectral camera. The research considered healthy, nutrition deficient, and HLB infected leaf samples based on the Polymerase chain reaction (PCR) test. The highresolution image samples were sliced to into sub cubes. The sub cubes were further processed to obtain RGB images with spatial features. Similarly, RGB spectral slices were obtained through a moving window on the wavelength. The resized spectral-Spatial RGB images were given to Convolution Neural Networks for deep features extraction. The current research was able to classify a given sample to the appropriate class with 92.86% accuracy indicating the effectiveness of the proposed techniques. The significant bands with a difference in three types of leaves are found to be 560nm, 678nm, 726 nm and 750nm.

Keywords: huanglongbing (HLB), hyperspectral imaging (HSI), · omani citrus, CNN

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Authors: Vaia N. Koukou, Niki D. Martini, George P. Fountos, Christos M. Michail, Athanasios Bakas, Ioannis S. Kandarakis, George C. Nikiforidis

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Various substitute materials of calcifications are used in phantom measurements and simulation studies in mammography. These include calcium carbonate, calcium oxalate, hydroxyapatite and aluminum. The aim of this study is to compare the contrast-to-noise ratio (CNR) values of the different calcification types using the dual energy method. The constructed calcification phantom consisted of three different calcification types and thicknesses: hydroxyapatite, calcite and calcium oxalate of 100, 200, 300 thicknesses. The breast tissue equivalent materials were polyethylene and polymethyl methacrylate slabs simulating adipose tissue and glandular tissue, respectively. The total thickness was 4.2 cm with 50% fixed glandularity. The low- (LE) and high-energy (HE) images were obtained from a tungsten anode using 40 kV filtered with 0.1 mm cadmium and 70 kV filtered with 1 mm copper, respectively. A high resolution complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) X-ray detector was used. The total mean glandular dose (MGD) and entrance surface dose (ESD) from the LE and HE images were constrained to typical levels (MGD=1.62 mGy and ESD=1.92 mGy). On average, the CNR of hydroxyapatite calcifications was 1.4 times that of calcite calcifications and 2.5 times that of calcium oxalate calcifications. The higher CNR values of hydroxyapatite are attributed to its attenuation properties compared to the other calcification materials, leading to higher contrast in the dual energy image. This work was supported by Grant Ε.040 from the Research Committee of the University of Patras (Programme K. Karatheodori).

Keywords: calcification materials, CNR, dual energy, X-rays

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Authors: Nubwimana Rachel, Mugabowindekwe Maurice

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Trees play a crucial role in maintaining biodiversity and providing various ecological services. Traditional methods of counting trees are time-consuming, and there is a need for more efficient techniques. However, deep learning makes it feasible to identify the multi-scale elements hidden in aerial imagery. This research focuses on the application of deep learning techniques for tree detection and counting in both forest and non-forest areas through the exploration of the deep learning application for automated tree detection and counting using satellite imagery. The objective is to identify the most effective model for automated tree counting. We used different deep learning models such as YOLOV7, SSD, and UNET, along with Generative Adversarial Networks to generate synthetic samples for training and other augmentation techniques, including Random Resized Crop, AutoAugment, and Linear Contrast Enhancement. These models were trained and fine-tuned using satellite imagery to identify and count trees. The performance of the models was assessed through multiple trials; after training and fine-tuning the models, UNET demonstrated the best performance with a validation loss of 0.1211, validation accuracy of 0.9509, and validation precision of 0.9799. This research showcases the success of deep learning in accurate tree counting through remote sensing, particularly with the UNET model. It represents a significant contribution to the field by offering an efficient and precise alternative to conventional tree-counting methods.

Keywords: remote sensing, deep learning, tree counting, image segmentation, object detection, visualization

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Authors: Sourav Ghosh, Antony Gomes

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Since 1894 anti-snake venom serum (ASVS) is the only available treatment against snake envenomation, although there are many side effects and limitations. The need for a supportive treatment was felt for a long time to overcome the side effects and limitations of ASVS. Andrographolide conjugated with gold nanoparticle (A-GNP) has been found to antagonize viper venom-induced local damages. The present study was aimed to study the protective efficacy of A-GNP against Viper venom-induced inflammatory response and organ toxicity in animal model. Ethical clearance was obtained from animal experiments. Physico-chemical characterization of A-GNP was done by DLS (diameter and zeta potential), FE-SEM and XRD. Swiss albino male mice were divided into 4 groups: Gr.1-Sham control, Gr.2- Russell’s Viper venom (RVV) control, Gr.3- andrographolide treated and Gr.4- A-GNP treated. The 1/5th minimum lethal dose of RVV (500µg/kg, s.c.) was induced in animals of group 2, 3 & 4 animals, followed by treatment with andrographolide (100mg/kg, i.p.) and A-GNP (100mg/kg, i.v.) in group 3 & 4 animals, respectively. Blood was collected after 18 h, serum was prepared, and inflammatory markers (IL 1β, 6, 17a, 10, TNF α) and biochemical markers (AST, ACP, LDH, urea, creatinine) were assessed. Values were expressed as mean±SEM (n=4), one way ANOVA was done, P<0.05 was considered as statistically significant. DLS size showed the hydrodynamic diameter of A-GNP to be 230-260nm with polydispersity index of 0.103 and zeta potential was -18.32mV. XRD data confirmed the presence of crystalline gold in A-GNP, and FESEM indicated the presence of nearly spherical particle with size18-24nm.Treatment with A-GNP significantly decreased viper venom-induced proinflammatory markers (IL 1β, 6, 17, TNF α) increased anti-inflammatory markers (IL 10) and decreased organ toxicity markers (AST, ACP, LDH, urea, creatinine) in animal model. Venom neutralization efficacy of A-GNP was > andrographolide, which confirmed the increased efficacy of andrographolide after gold nanoparticle conjugation. Venom neutralization by A-GNP was due to anti-oxidant/anti-inflammatory activity of andrographolide, which showed increased efficacy after gold nanoparticle tagging. Thus, A-GNP may serve as a supportive therapy in snake-bite (against inflammatory response and organ toxicity) subject to further detail studies.

Keywords: andrographolide, gold nanoparticle, inflammatory response, organ toxicity, snake venom, snake venom neutralization, viper venom

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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