Search results for: mosaic augmentation

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

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Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: computer-aided system, detection, image segmentation, morphology

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Authors: Esther Chiramal, Kelvin Soh Boon Kai

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Explainable AI is a strong strategy implemented to understand complex black-box model predictions in a human-interpretable language. It provides the evidence required to execute the use of trustworthy and reliable AI systems. On the other hand, however, it also opens the door to locating possible vulnerabilities in an AI model. Traditional adversarial text attack uses word substitution, data augmentation techniques, and gradient-based attacks on powerful pre-trained Bidirectional Encoder Representations from Transformers (BERT) variants to generate adversarial sentences. These attacks are generally white-box in nature and not practical as they can be easily detected by humans e.g., Changing the word from “Poor” to “Rich”. We proposed a simple yet effective Grey-box cum Black-box approach that does not require the knowledge of the model while using a set of surrogate Transformer/BERT models to perform the attack using Explainable AI techniques. As Transformers are the current state-of-the-art models for almost all Natural Language Processing (NLP) tasks, an attack generated from BERT1 is transferable to BERT2. This transferability is made possible due to the attention mechanism in the transformer that allows the model to capture long-range dependencies in a sequence. Using the power of BERT generalisation via attention, we attempt to exploit how transformers learn by attacking a few surrogate transformer variants which are all based on a different architecture. We demonstrate that this approach is highly effective to generate semantically good sentences by changing as little as one word that is not detectable by humans while still fooling other BERT models.

Keywords: BERT, explainable AI, Grey-box text attack, transformer

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Authors: Waqas Mehdi, Muhammad Umar Hassan, Khadeeja Mustafa

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Objective: Post-concussion syndrome (PCS) is a medical term used to point out the complicated combination of physical, emotional, cognitive and behavioral signs and symptoms associated with Mild Traumatic Brain Injury(mTBI). This study was conducted to assess the improvement or debilitating effect of behavioral therapy in addition to the conventional treatment and to document these results for increasing the efficiency of treatment provided to such cases. Method: This was primarily an interventional prospective cohort study which was conducted in the Department of Neurosurgery, Mayo Hospital Lahore. The sample size was 200 patients who were randomly distributed into two groups. The interventional group with Cognitive behavioral therapy was added in addition to the conventional treatment regimen and the Control group receiving only conventional treatment. Results were noted initially as well as after two weeks of the follow-up period. Data were subsequently analyzed by Statistical Package for Social Sciences (SPSS) software and associations worked out. Result and conclusion: Among the patients that were given therapy sessions along with conventional medicine, there was a significant improvement in the symptoms and their overall quality of life. It is also important to notice that the time period taken for these effects to wane is cut down by psychiatric solutions too. So we can conclude that CBT sessions not only speed up recovery in patients with post-concussion syndrome they also aid in the efficiency improvement in functional capability and quality of life.

Keywords: neurosurgery, CBT, PCS, mTBI

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Adam Patterson

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This study analyzes the self-reported experiences of virtual reality users to develop insight into an optimal learning path for education within virtual reality. This research uses a sample of 1000 observations to statistically define factors influencing (i) immersion level and (ii) motion sickness rating for virtual reality experience respondents of college age. This paper recommends an efficient duration for each virtual reality session, to minimize sickness and maximize engagement, utilizing modern machine learning methods such as association rules. The goal of this research, in augmentation with previous literature, is to inform logistical decisions relating to implementation of pilot instruction for virtual reality at the collegiate level. Future research will include a Randomized Control Trial (RCT) to quantify the effect of virtual reality education on student learning outcomes and engagement measures. Current research aims to maximize the treatment effect within the RCT by optimizing the learning benefits of virtual reality. Results suggest significant gender heterogeneity amongst likelihood of reporting motion sickness. Females are 1.7 times more likely, than males, to report high levels of motion sickness resulting from a virtual reality experience. Regarding duration, respondents were 1.29 times more likely to select the lowest level of motion sickness after an engagement lasting between 24.3 and 42 minutes. Conversely, respondents between 42 to 60 minutes were 1.2 times more likely to select the higher levels of motion sickness.

Keywords: applications and integration of e-education, practices and cases in e-education, systems and technologies in e-education, technology adoption and diffusion of e-learning

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Authors: Nathaniel Carpenter

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Web-enabled systems for supporting youth mental health management in Australia are pioneering in their field; however, with their success, these systems are experiencing exponential growth in demand which is straining an already stretched service. Supporting youth mental is critical as the lack of support is associated with significant and lasting negative consequences. To meet this growing demand, and provide critical support, investigations are needed on evaluating and improving existing online support services. Improvements should focus on developing frameworks capable of augmenting and scaling service provisions. There are few investigations informing best-practice frameworks when implementing e-mental health support systems for youth mental health; there are fewer which implement machine learning or artificially intelligent systems to facilitate the delivering of services. This investigation will use a case study methodology to highlight the design features which are important for systems to enable young people to self-manage their mental health. The investigation will also highlight the current information system challenges, to include challenges associated with service quality, provisioning, and scaling. This work will propose methods of meeting these challenges through improved design, service augmentation and automation, service quality, and through artificially intelligent inspired solutions. The results of this study will inform a framework for supporting youth mental health with intelligent and scalable web-enabled technologies to support an ever-growing user base.

Keywords: artificial intelligence, information systems, machine learning, youth mental health

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Authors: Florence Awde

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In 2023, it was expected 350 parents in Arizona would have a child receive a cancer diagnosis (Welcome Arizona Cancer Foundation For Children, n.d.). The news of a child’s diagnosis with cancer can be overwhelming and confusing, especially for those lucky enough to lack a personal tie to the disease that takes approximately 1800 children’s lives each year in the United States (Deegan et al., n.d.). A parent’s beliefs, attitudes, and understandings surrounding cancer are vital for medical staff to provide adequate and culturally competent care for each patient, especially across cultural and ethnic lines in regions housing multicultural populations. Arizona's cultural/linguistic mosaic houses many White and Latino populations and English and Spanish speakers. Variations in insurance coverage, from those insured through public insurance programs (e.g., Medicaid) or private insurance plans (e.g., employee-sponsored insurance) versus those uninsured, also factor into health-seeking attitudes and behaviors. To further understand parental attitudes, understandings, and beliefs towards childhood cancer, 22 parents (11 of Latino ethnicity, 11 of White ethnicity) were interviewed on these facets of childhood cancer, despite 21 of the 22 never having a child receive a cancer diagnosis. The exploration of these perceptions across ethnic lines revealed a higher report of fear-orientated beliefs amongst Latino parents--hypothesized to be rooted in the starkly contrasting lack of belief in the possibility of recovering for children with cancer, compared to their white counterparts who displayed more optimism in the recovery process. Further, this study’s results lay the foundation for future scholarship to explore avenues of information dispersal to Latino parents that correct misconceptions of health outcomes and enable earlier intervention to be possible, ultimately correlating to better health and treatment outcomes by increasing parental health literacy rates for childhood cancer in the Phoenix Metropolitan.

Keywords: Childhood Cancer, Parental Beliefs, Parental Attitudes, Parental Understandings, Phoenix Metropolitan, Culturally Competent Care, Health Disparities, Health Inequities

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Authors: Ammani K., Glory M.

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Demands of traditional herbal medicines are increasing day by day over the world. Considering the growing demand of medicinal plants in curative treatments and the role of VAM fungi in augmentation of the production of active secondary metabolites by the medicinal plants, the present work has been undertaken to survey the mycorrhizal status in 30 different medicinal plants belonging to various families from Krishna district, Andhra Pradesh. The roots were collected carefully and stained by the Phillips & Hayman technique. Basing on the occurrence of vesicles and arbuscules, categorized into four grades; Excellent: mycelia, vesicles or arbuscules present more than 75% of root bits, Good: mycelia, vesicles or arbuscules present 50-75% in surface of root bits, moderate: mycelia, vesicles or arbuscules present 25-50% in surface of root bits, and poor: mycelia, vesicles or arbuscules present 1-25% in surface of root bits. The study reveals that the roots of all plants were colonized by AM fungi. Percentage of root colonization by AM fungi was more in Aloe vera, Phylanthus emblica, Azadiracta indica and least in plants such as Aerva lanata, Vinca rosea, Crotalaria verrucosa among the 30 medicinal plants in present study. The enhancement of growth and vigour and increased production of bioactive compounds of the medicinal plants is desirable which may be achieved by inoculation of the roots with Arbuscular mycorrhizal fungi. There is a steady increase in the cultivation of medicinal plants to maintain a steady supply to support the increasing demand but corresponding researches of VAM fungi and their association in medicinal plants have received very little attention as compared to the studies on forest species and field crops. So a vast research on this field is necessary for a better tomorrow.

Keywords: Arbuscular mycorrhizae, colonization, categories, medicinal plants

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Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

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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: Haydar Kepekçi, Baha Zafer, Hasan Rıza Güven

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Noise disturbance is one of the major factors considered in the fast development of aircraft technology. This paper reviews the flow field, which is examined on the 2D NACA0015 and 3D NACA0012 blade profile using SST k-ω turbulence model to compute the unsteady flow field. We inserted the time-dependent flow area variables in Ffowcs-Williams and Hawkings (FW-H) equations as an input and Sound Pressure Level (SPL) values will be computed for different angles of attack (AoA) from the microphone which is positioned in the computational domain to investigate effect of augmentation of unsteady 2D and 3D airfoil region noise level. The computed results will be compared with experimental data which are available in the open literature. As results; one of the calculated Cp is slightly lower than the experimental value. This difference could be due to the higher Reynolds number of the experimental data. The ANSYS Fluent software was used in this study. Fluent includes well-validated physical modeling capabilities to deliver fast, accurate results across the widest range of CFD and multiphysics applications. This paper includes a study which is on external flow over an airfoil. The case of 2D NACA0015 has approximately 7 million elements and solves compressible fluid flow with heat transfer using the SST turbulence model. The other case of 3D NACA0012 has approximately 3 million elements.

Keywords: 3D blade profile, noise disturbance, aeroacoustics, Ffowcs-Williams and Hawkings (FW-H) equations, k-ω-SST turbulence model

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Authors: Aminath Waseela, Vinesh Chandra, Shaun Nykvist,

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The success of technology integration initiatives hinges on the knowledge and skills of teachers to effectively integrate technology in classroom teaching. Consequently, gaining an understanding of teachers' technology knowledge and its integration can provide useful insights on strategies that can be adopted to enhance teaching and learning, especially in developing country contexts where research is scant. This paper extends existing knowledge on teachers' use of technology by developing a conceptual framework that recognises how three key types of knowledge; content, pedagogy, technology, and their integration are at the crux of teachers' technology use while at the same time is amenable to empirical studies. Although the aforementioned knowledge is important for effective use of technology that can result in enhanced student engagement, literature on how this knowledge leads to effective technology use and enhanced student engagement is limited. Thus, this theoretical paper proposes a framework to explore teachers' knowledge through the lens of the Technological Pedagogical and Content Knowledge (TPACK); the integration of technology in classroom teaching through the Substitution Augmentation Modification and Redefinition (SAMR) model and how this affects students' learning through the Bloom's Digital Taxonomy (BDT) lens. Studies using this framework could inform the design of professional development to support teachers to develop skills for effective use of available technology that can enhance student learning engagement.

Keywords: information and communication technology, ICT, in-service training, small island developing states, SIDS, student engagement, technology integration, technology professional development training, technological pedagogical and content knowledge, TPACK

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Authors: Monika Haanpää

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Well-functioning workplaces are often characterized by good co-operation, adequate flow of information, open interaction between workers and a supportive work environment. The workplace is a mosaic of human personalities and the influx of people, who speak different languages and who are from different cultural backgrounds, may bring about new challenges and enrich this environment. However, this influx of people could also pose a problem as the adaptation of immigrant people to new terms of work may depend heavily on the level of language skills, the stage of culture shock, professional identity, and personality. Migration is not a rare phenomenon in Finland anymore; nobody is surprised to see people from different countries and different backgrounds in the schools, on the streets or in shops. However, this does not mean that immigration is an easy process for people coming from other countries. The experience of workers, with diverse language and backgrounds, has rarely been researched, particularly from the superior's point of view. In addition, the vast majority of researchers have paid more attention to multicultural kindergartens in terms of immigrant children and their families. Hence, there is a need to show the problem which exists in the recruitment of the increasing number of workers who come from different countries. Opinions about kindergartens, as multicultural workplaces, have been gathered through interviews with immigrant workers responsible for education. In addition, a questionnaire for native Finnish workers and superiors in kindergartens was carried out. The collected material has been analyzed qualitatively, focusing on topics such as: the kindergarten as a multicultural workplace, factors influencing career success of workers with diverse language and cultural backgrounds, the social relations in the multicultural workplaces and teachers’ changing professional identity. The results of the research provided a novel aspect of the multicultural workplace and emphasized a dependency of immigrant workers’ on language skills in Finnish; affecting professional success. In addition, they showed the good relations between other native Finnish co-workers and superiors. The results also illustrate why writing skills in Finnish are so important in kindergartens. Part of the investigation also questions some results of the research i.e. which is more important in the kindergarten as a multicultural workplace: personality, good professional skills or good language skills.

Keywords: kindergarten, multicultural workplace, social relations at work, work satisfaction

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Authors: Tabti Lahouaria, Kahlouche Salem, Benadda Belkacem, Beldjilali Bilal

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The European Geostationary Navigation Overlay Service (EGNOS) provides an augmentation signal to GPS (Global Positioning System) single point positioning. Presently EGNOS provides data correction and integrity information using the GPS L1 (1575.42 MHz) frequency band. The main objective of this system is to provide a better real-time positioning precision than using GPS only. They are expected to be used with single-frequency code observations. EGNOS offers navigation performance for an open service (OS), in terms of precision and availability this performance gradually degrades as moving away from the service area. For accurate system performance, the service will become less and less available as the user moves away from the EGNOS service. The improvement in position solution is investigated using the two collocated dual frequency GPS, where no EGNOS Ranging and Integrity Monitoring Station (RIMS) exists. One of the pseudo-range was kept as GPS stand-alone and the other was corrected by EGNOS to estimate the planimetric and altimetric precision for different dates. It is found that precision in position improved significantly in the second due to EGNOS correction. The performance of EGNOS system in the north of Algeria is also investigated in terms of integrity. The results show that the horizontal protection level (HPL) value is below 18.25 meters (95%) and the vertical protection level (VPL) is below 42.22 meters (95 %). These results represent good integrity information transmitted by EGNOS for APV I service. This service is thus compliant with the aviation requirements for Approaches with Vertical Guidance (APV-I), which is characterised by 40 m HAL (horizontal alarm limit) and 50 m VAL (vertical alarm limit).

Keywords: EGNOS, GPS, positioning, integrity, protection level

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Authors: Mohammed A. Elhaj, Jamal S. Yassin

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In the present time, energy crises are considered a severe problem across the world. For the protection of global environment and maintain ecological balance, energy saving is considered one of the most vital issues from the view point of fuel consumption. As the industrial sectors everywhere continue efforts to improve their energy efficiency, recovering waste heat losses provides an attractive opportunity for an emission free and less costly energy resource. In the other hand the using of solar energy has become more insistent particularly after the high gross of prices and running off the conventional energy sources. Therefore, it is essential that we should endeavor for waste heat recovery as well as solar energy by making significant and concrete efforts. For these reasons this investigation is carried out to study and analyze the performance of a power plant working by a combined cycle in which Heat Recovery System Generator (HRSG) gets its energy from the waste heat of a gas turbine unit. Evaluation of the performance of the plant is based on different thermal efficiencies of the main components in addition to the second law analysis considering the exergy destructions for the whole components. The contribution factors including the solar as well as the wasted energy are considered in the calculations. The final results have shown that there is significant exergy destruction in solar concentrator and the combustion chamber of the gas turbine unit. Other components such as compressor, gas turbine, steam turbine and heat exchangers having insignificant exergy destruction. Also, solar energy can contribute by about 27% of the input energy to the plant while the energy lost with exhaust gases can contribute by about 64% at maximum cases.

Keywords: solar energy, environment, efficiency, waste heat, steam generator, performance, exergy destruction

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Authors: Khiddir Iddris, Martin Oteng – Ababio, Andreas Bürkert, Christoph Scherrer, Katharina Hemmler

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Agbogbloshie, as an informal settlement and economy where the e-waste sector thrives, has become a global hub of complex urban contestations involving sociocultural, economic, and environmental dimensions due to the implication that e-waste and informal economic patterns have on livelihoods, urbanisation, development and sustainability. Multi-author collaborations have produced an ever-growing body of literature on Agbogbloshie and the informal e-waste economy. There is, however, a dearth of an assessment of Agbogbloshie as an urban informal settlement's intricate nexus of socioecological contestations. We address this gap by systematising, from literature, the context knowledge, navigating the complex terrain of Agbogbloshie's challenges, and employing a multidimensional lens to unravel the sociocultural intricacies, economic dynamics, and environmental complexities shaping its identity. A systematic critical review approach was espoused, with a pragmatic consolidation of content analysis and controversy mapping, grounded on the concept of ‘sustainable rurbanism,’ highlighted core themes and identified contrasting viewpoints. An analytical framework is presented. Five categories – geohistorical, sociocultural, economic, environmental and future trends - are proposed as an approach to systematising the literature. The review finds that the sociocultural dimension unveils a mosaic of cultural amalgamation, communal identity, and tensions impacting community cohesion. The analysis of economic intricacies reveals the prevalence of informal economies sustaining livelihoods yet entrenching economic disparities and marginalisation. Environmental scrutiny exposes the grim realities of e-waste disposal, pollution, and land use conflicts. The findings suggest that there is a high resilience within the community and the potential for sustainable trajectories. Theoretical and conceptual synergy is limited. This review provides a comprehensive exploration, offering insights and directions for future research, policy formulation, and community-driven interventions aimed at fostering sustainable transformations in Agbogbloshie and analogous urban contexts.

Keywords: Agbogbloshie, economic complexities, environmental challenges, resilience, sociocultural dynamics, sustainability, urban informal settlement

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: K. Nikilsha, Lakshmi Manohar, Debayan Chatterjee

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Landscape is a setting that informs the way of life of a set of people, and the repository of intangible values and human meanings that nurture our very existence. Along with the linkage that it forms with our lives, it can be argued that landscape and memory cannot be separated, as landscape is the nucleus of our memories. In this context, this paper studies landscape evolution of a region with unique geographic setting, where the dependency of the inhabitants on its resources, led to the formation of certain peculiar beliefs and taboos that formed the basis of a set of unwritten rules and guidelines which they still follow as a part of their lifestyle. One such example is Kuttanad, a low lying region in Kerala which is a complex mosaic of fragmented agricultural landscape incorporating coastal backwaters, rivers, marshes, paddy fields and water channels. The more the physical involvement with the resources, the more was the inhabitants attachment towards it. This attachment of the inhabitants to the place is very strong because the creation of this land was the result of the toil of the low caste labourers who strived day and night to create Kuttanad, which was reclaimed from water with the help of the finance supplied by their landlords. However, the greatest challenge faced by them is posed by the forces of water in the form of floods. As this land is fed by five rivers, even the slight variation in rainfall in its watershed area can cause a large imbalance in the water level causing the reclaimed land to be inundated. The effects of climate change including increase in rainfall, rise in sea level and change of seasons can act as a catalyst to this damage. Hasty urbanization has led to the conversion of paddy fields to housing plots and coconut/plantain fields giving no regard to the traditional systems which had once respected nature and combated floods and draughts through the various cultural practices and taboos practiced by the people. Thus it is essential to look back at the landscape evolution of Kuttanad and to recognise methods used traditionally in the region to establish a cultural landscape, and to understand how climate change and urbanisation shall pose a challenge to the existing landscape and lifestyle. This research also explores the possibilities of alternative and sustainable approaches for resilient urban development learned from Kuttanad as a case study.

Keywords: ecological conservation, landscape and ecological engineering, landscape evolution, man-made landscapes

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Authors: Hande Yesil, Seda Aycan Altan, M. Vehbi Bal, Alper Uyar, O. Cumhur Sipahi

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Purpose: Fixed partial dentures (FPDs) must fulfill functional requirements such as phonetics, chewing efficiency and esthetics especially in the anterior region. A convex type tissue surface is usually recommended for pontics of FPDs. That pontic design also provides suitable oral hygiene and ease of cleaning. However, high esthetic requirements and correct emergence profile are not always achievable because of the convex shape of adjacent soft tissues. Therefore, the ovate type pontic which fulfills the high esthetic demands of the patients may be a good alternative to the modified ridge lap pontic design. Clinical Report: A female patient referred with the complaint of anterior upper edentulousness. In the oral examination it was determined that teeth 11, 12, 21, 22 were deficient. A thick and convex gingival tissue that may cause aesthetic problems was also observed.. Periodontal augmentation surgery was performed to ensure proper papillary configuration and gingival contour. An interim removable partial denture (IRPD) which applied pressure to operated gingival tissues was fabricated postoperatively. The IRPD was used for 4 weeks and after completion of tissue sculpting, the permanent FPD with an ovate pontic was fabricated and cemented. After a follow-up period of 6 months, not any esthetical and hygienic problem was detected and the patient was satisfied with her prosthesis. Conclusion: It was concluded that shaping of gingival contours with IRPD and use of a FPD with ovate pontic fulfills all esthetic and hygienic requirements.

Keywords: interim appliance, ovate pontic, tissue sculpturing, fixed partial denture

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Authors: Edvard Kokanyan, Narine Babajanyan, Ninel Kokanyan, Marco Bazzan

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Lithium niobate (LN) crystals, renowned for their exceptional nonlinear optical, electro-optical, piezoelectric, and photorefractive properties, stand as foundational materials in diverse fields of study and application. While they have long been utilized in frequency converters of laser radiation, electro-optical modulators, and holographic information recording media, LN crystals doped with rare earth ions represent a compelling frontier for modern compact devices. These materials exhibit immense potential as key components in infrared lasers, optical sensors, self-cooling systems, and radiation balanced laser setups. In this study, we present the successful synthesis of Ho-doped lithium niobate (LN:Ho) thin films on sapphire substrates employing the Sol-Gel technique. The films exhibit a strong crystallographic orientation along the perpendicular direction to the substrate surface, with X-ray diffraction analysis confirming the predominant alignment of the film's "c" axis, notably evidenced by the intense (006) reflection peak. Further characterization through Raman spectroscopy, employing a confocal Raman microscope (LabRAM HR Evolution) with exciting wavelengths of 532 nm and 785 nm, unraveled intriguing insights. Under excitation with a 785 nm laser, Raman scattering obeyed selection rules, while employing a 532 nm laser unveiled additional forbidden lines reminiscent of behaviors observed in bulk LN:Ho crystals. These supplementary lines were attributed to luminescence induced by excitation at 532 nm. Leveraging data from anti-Stokes Raman lines facilitated the disentanglement of luminescence spectra from the investigated samples. Surface scanning affirmed the uniformity of both structure and luminescence across the thin films. Notably, despite the robust orientation of the "c" axis perpendicular to the substrate surface, Raman signals indicated a stochastic distribution of "a" and "b" axes, validating the mosaic structure of the films along the mentioned axis. This study offers valuable insights into the structural properties of Ho-doped lithium niobate thin films, with the observed luminescence behavior holding significant promise for potential applications in optoelectronic devices.

Keywords: lithium niobate, Sol-Gel, luminescence, Raman spectroscopy

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Authors: Nadia Allouache, Mohamed Belmedani

Abstract:

Energy source is a sustainable, totally inexhaustible and environmentally friendly alternative to the fossil fuels available. It is a renewable and economical energy that can be harnessed sustainably over the long term and thus stabilizes energy costs. Solar cooling technologies have been developed to decrease the augmentation electricity consumption for air conditioning and to displace the peak load during hot summer days. A numerical analysis of thermal and solar performances of an annular finned adsorber, which is the most important component of the adsorption solar refrigerating system, is considered in this work. Different adsorbent/adsorbate pairs, such as activated carbon AC35/methanol, activated carbon AC35/ethanol, and activated carbon BPL/Ammoniac, are undertaken in this study. The modeling of the adsorption cooling machine requires the resolution of the equation describing the energy and mass transfer in the tubular finned adsorber. The Wilson and Dubinin- Astakhov models of the solid-adsorbate equilibrium are used to calculate the adsorbed quantity. The porous medium and the fins are contained in the annular space, and the adsorber is heated by solar energy. Effects of key parameters on the adsorbed quantity and on the thermal and solar performances are analysed and discussed. The AC35/methanol pair is the best pair compared to BPL/Ammoniac and AC35/ethanol pairs in terms of system performance. The system performances are sensitive to the fin geometry. For the considered data measured for clear type days of July 2023 in Algeria and Morocco, the performances of the cooling system are very significant in Algeria.

Keywords: activated carbon AC35-methanol pair, activated carbon AC35-ethanol pair, activated carbon BPL-ammoniac pair, annular finned adsorber, performance coefficients, numerical analysis, solar cooling system

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Authors: Nitin Kumar

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Purpose: The current research work focuses mainly on evolving a delivery system for ginseng extract (GE), which in turn will ameliorate the neuroprotective potential by means of enhancing the ginsenoside (Rb1) bio-availability (BA). For more noteworthy enhancement in oral bioavailability (OBA) along with pharmacological properties, the drug carriers’ performance can be strengthened by utilizing phytosomes-loaded microspheres (PM) delivery system. Methods: For preparing the disparate phytosome complexes (F1, F2, and F3), an aqueous extract of ginseng roots (GR) along with phospholipids were reacted in disparate ratio. Considering the outcomes, F3 formulation (spray-dried) was chosen for preparing the phytosomes powder (PP), PM, and extract microspheres (EM). PM was made by means of loading of F3 into Gum Arabic (GA) in addition to maltodextrin polymer mixture, whereas EM was prepared by means of the addition of extract directly into the same polymer mixture. For investigating the neuroprotective effect (NPE) in addition to their pharmacokinetic (PK) properties, PP, PM, and EM formulations were assessed. Results: F3 formulation gave enhanced entrapment efficiency (EE) (i.e., 50.61%) along with good homogeneity of spherical shaped particle size (PS) (42.58 ± 1.4 nm) with least polydispersity index (PDI) (i.e., 0.193 ± 0.01). The sustained release (up to 24 h) of ginsenoside Rb1 (GRb1) is revealed by the dissolution study of PM. A significantly (p < 0.05) greater anti-oxidant (AO) potential of PM can well be perceived as of the diminution in the lipid peroxidase level in addition to the rise in the glutathione superoxide dismutase (SOD) in addition to catalase levels. It also showed a greater neuroprotective potential exhibiting significant (p < 0.05) augmentation in the nociceptive threshold together with the diminution in damage to nerves. A noteworthy enhancement in the relative BA (157.94%) of GRb1 through the PM formulation can well be seen in the PK studies. Conclusion: It is exhibited that the PM system is an optimistic and feasible strategy to enhance the delivery of GE for the effectual treatment of neuropathic pain.

Keywords: ginseng, neuropathic, phytosome, pain

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Authors: R. Adwaith, J. Gopinath, Vasantha Kohila B., R. Chandru, Arul Prakash R.

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The turbofan engine is a type of air breathing engine that is widely used in aircraft propulsion produces thrust mainly from the mass-flow of air bypassing the engine core. The present research has developed an effective method numerically by increasing the thrust generated from the bypass air. This thrust increase is brought about by heating the walls of the bypass valve from the combustion chamber using convective heat transfer method. It is achieved computationally by the use external heat to enhance the velocity of bypass air of turbofan engines. The bypass valves are either heated externally using multicell tube resistor which convert electricity generated by dynamos into heat or heat is transferred from the combustion chamber. This increases the temperature of the flow in the valves and thereby increase the velocity of the flow that enters the nozzle of the engine. As a result, mass-flow of air passing the core engine for producing more thrust can be significantly reduced thereby saving considerable amount of Jet fuel. Numerical analysis has been carried out on a scaled down version of a typical turbofan bypass valve, where the valve wall temperature has been increased to 700 Kelvin. It is observed from the analysis that, the exit velocity contributing to thrust has significantly increased by 10 % due to the heating of by-pass valve. The degree of optimum increase in the temperature, and the corresponding effect in the increase of jet velocity is calculated to determine the operating temperature range for efficient increase in velocity. The technique used in the research increases the thrust by using heated by-pass air without extracting much work from the fuel and thus improve the efficiency of existing turbofan engines. Dimensional analysis has been carried to prove the accuracy of the results obtained numerically.

Keywords: turbofan engine, bypass valve, multi-cell tube, convective heat transfer, thrust

Procedia PDF Downloads 358

Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Mark Andrew

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Geopolitical tensions are at a thirty-year high, and the pace of technological innovation is driving asymmetry in force capabilities between nation states and between non-state actors. Technology futures are a vital component of defence capability growth, and investments in technology futures need to be informed by accurate and reliable forecasts of the options for ‘systems of systems’ innovation, development, and deployment. This paper describes a method for forecasting technology futures developed through an analysis of four key systems’ development stages, namely: technology domain categorisation, scanning results examining novel systems’ signals and signs, potential system-of systems’ implications in warfare theatres, and political ramifications in terms of funding and development priorities. The method has been applied to several technology domains, including physical systems (e.g., nano weapons, loitering munitions, inflight charging, and hypersonic missiles), biological systems (e.g., molecular virus weaponry, genetic engineering, brain-computer interfaces, and trans-human augmentation), and information systems (e.g., sensor technologies supporting situation awareness, cyber-driven social attacks, and goal-specification challenges to proliferation and alliance testing). Although the current application of the method has been team-centred using paper-based rapid prototyping and iteration, the application of autonomous language models (such as GPT-3) is anticipated as a next-stage operating platform. The importance of forecasting accuracy and reliability is considered a vital element in guiding technology development to afford stronger contingencies as ideological changes are forecast to expand threats to ecology and earth systems, possibly eclipsing the traditional vulnerabilities of nation states. The early results from the method will be subjected to ground truthing using longitudinal investigation.

Keywords: forecasting, technology futures, uncertainty, complexity

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Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

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Authors: Abdolrasoul Daneshjoo

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The purpose of this research was to study the effect of aerobic exercises with 75% heart beats on the amount of urea, uric acid and creatine in blood of Iranian soccer national U-23 players. 27 players were selected according to the following demographic specifications: age: 21.4±1.60 years old; weight: 68±9.4 kg; height: 174.2±8.6 cm. Urea, uric acid and creatine in blood are considered as dependent variations where as 40 minutes running on a track with maximum 75% heart beats are independent variations. Heart beat and blood pressure in rest time, age, height, and weight are considered as the controlled variations. Maximum heart beats are recorded under maximum exercises (8 minutes and 150-250 watt energy) on ergo meter. Then, in order to determine independent variations, 75% maximum heart beats are considered for each player. Blood is taken twice (before and after determining independence variation). Moreover, the players are given a few instructions to be fulfilled 24 hours before the main exercises. Laboratory analysis method for blood urea sample is deacetyl ammoniom, for uric acid Karvy test and for creatine pyric acid. 'T' formula is applied for analyzing statistical data in dependent groups with degree of freedom 7 (d.f=7) urea and uric acid contain P>0.01 and P>0.05 for creatine. 1. Aerobic exercise can effect on the concentration of urea of blood as well as uric acid and creatine in blood serum and increase the amount of them. 2. Urea of blood serum increases from 26.75±2.59 to 28.9±2.67 (25%) with 40 minutes running and 75% heart beat. 3. Aerobic exercise causes uric acid increase 12.5% from 5.7±0.52 (before exercise) to 6.1±0.71 (after exercise). Creatine of blood serum increases from 1.36±0.27 (before exercise) to 1.85±0.49 (after exercise). We came to this result that during aerobic exercise catabolism of protein substrate increases. Moreover, augmentation of urea, uric acid and creatine in blood serum as metabolic poisons causes disorder in kidney. Also, tendons and joints are affected by these poisons. Appropriate diet and exercise can prevent production of these poisons resulted from heavy exercise.

Keywords: aerobic exercise, urea, uric acid, creatine, blood, soccer national players

Procedia PDF Downloads 534

Authors: Evelyn Asante Yeboah, Kwabena O. Asubonteng, Justice Camillus Mensah, Christine Furst

Abstract:

Smallholder-dominated mosaic landscapes in rural Africa are relevant for food production, biodiversity conservation, and climate regulation. Land-use transitions threaten the multifunctionality of such landscapes, especially the production capacity of arable lands resulting in food security challenges. Using land-cover maps derived from maximum likelihood classification of Landsat satellite images for the years 2002, 2015, and 2020, post-classification change detection, landscape metrics, and key informant interviews, the study assessed the implications of rubber plantation expansion and oil business development on the food production capacity of Ahanta West District, Ghana. The analysis reveals that settlement and rubber areas expanded by 5.82% and 10.33% of the landscape area, respectively, between 2002 and 2020. This increase translates into over twice their initial sizes (144% in settlement change and 101% in rubber change). Rubber plantation spread dominates the north and southwestern areas, whereas settlement is widespread in the eastern parts of the landscape. Rubber and settlement expanded at the expense of cropland, palm, and shrublands. Land-use transitions between cropland, palm, and shrubland were targeting each other, but the net loss in shrubland was higher (-17.27%). Isolation, subdivision, connectedness, and patch adjacency indices showed patch consolidation in the landscape configuration from 2002 to 2015 and patch fragmentation from 2015 to 2020. The study also found patches with consistent increasing connectivity in settlement areas indicating the influence of oil discovery developments and fragmentation tendencies in rubber, shrubland, cropland, and palm, indicating springing up of smaller rubber farms, the disappearance of shrubland, and splitting up of cropland and palm areas respectively. The results revealed a trend in land-use transitions in favor of smallholder rubber plantation expansion and oil discovery developments, which suggest serious implications on food production systems and poses a risk for food security and landscape multifunctional characteristics. To ensure sustainability in land uses, this paper recommends the enforcement of legislative instruments governing spatial planning and land use in Ghana as embedded in the 2016 land-use and spatial planning act.

Keywords: food production systems, food security, Ghana’s west coast, land-use transitions, multifunctional rural landscapes

Procedia PDF Downloads 144

Authors: Maria De Albuquerque Bercot, Caio Gustavo Mesquita Angelo, Daniela Maria Moreira Siqueira, Augusto Assucena De Vasconcellos, Rodrigo Studart Correa

Abstract:

Mining is an important socioeconomic activity in Brazil although it negatively impacts the environment. Mineral operations cause irreversible changes in topography, removal of vegetation and topsoil, habitat destruction, displacement of fauna, loss of biodiversity, soil erosion, siltation of watercourses and have potential to enhance climate change. Due to the impacts and its pollution potential, mining activity in Brazil is legally subjected to environmental licensing. Unlicensed mining operations or operations that not abide to the terms of an obtained license are taken as environmental crimes in the country. This work reports a case analyzed in the Forensic Institute of the Brazilian Federal District Civil Police. The case consisted of detecting illegal aspects of sand exploitation from a licensed mine in Federal District, nearby Brasilia city. The fieldwork covered an area of roughly 6 ha, which was surveyed with an unmanned aerial vehicle (UAV) (PHANTOM 3 ADVANCED). The overflight with UAV took about 20 min, with maximum flight height of 100 m. 592 UAV georeferenced images were obtained and processed in a photogrammetric software (AGISOFT PHOTOSCAN 1.1.4), which generated a mosaic of geo-referenced images and a 3D model in less than six working hours. The 3D model was analyzed in a forensic software for accurate modeling and volumetric analysis. (MAPTEK I-SITE FORENSIC 2.2). To ensure the 3D model was a true representation of the mine site, coordinates of ten control points and reference measures were taken during fieldwork and compared to respective spatial data in the model. Finally, these spatial data were used for measuring mining area, excavation depth and volume of exploited sand. Results showed that mine holder had not complied with some terms and conditions stated in the granted license, such as sand exploration beyond authorized extension, depth and volume. Easiness, the accuracy and expedition of procedures used in this case highlight the employment of UAV imagery and computational photogrammetry as efficient tools for outdoor forensic exams, especially on environmental issues.

Keywords: computational photogrammetry, environmental monitoring, mining, UAV

Procedia PDF Downloads 318

Authors: M. P. Dandekar, A. P. Bharne, P. T. Borkar, D. M. Kokare, N. K. Subhedar

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Ethanol ingestion by the mother ensue adverse consequences for her offspring. Herein, we examine the behavioral phenotype and neural substrate of the offspring of the mother on ethanol. Female rats were fed with ethanol-containing liquid diet from 8 days prior of conception and continued till 25 days post-parturition to coincide with weaning. Behavioral changes associated with anxiety, depression and learning and memory were assessed in the offspring, after they attained adulthood (day 85), using elevated plus maze (EPM), forced swim (FST) and novel object recognition tests (NORT), respectively. The offspring of the alcoholic mother, compared to those of the pair-fed mother, spent significantly more time in closed arms of EPM and showed more immobility time in FST. Offspring at the age of 25 and 85 days failed to discriminate between novel versus familiar object in NORT, thus reflecting anxiogenic, depressive and amnesic phenotypes. Neuropeptide cocaine- and amphetamine-regulated transcript peptide (CART) is known to be involved in central effects of ethanol and hence selected for the current study. Twenty-five days old pups of the alcoholic mother showed significant augmentation in CART-immunoreactivity in the cells of Edinger-Westphal (EW) nucleus and lateral hypothalamus. However, a significant decrease in CART-immunoreactivity was seen in nucleus accumbens shell (AcbSh), lateral part of bed nucleus of the stria terminalis (BNSTl), locus coeruleus (LC), hippocampus (CA1, CA2 and CA3), and arcuate nucleus (ARC) of the pups and/or adults offspring. While no change in the CART-immunoreactive fibers of AcbSh and BNSTl, CA2 and CA3 was noticed in the 25 days old pups, the CART-immunoreactive cells in EW and paraventricular nucleus (PVN), and fibers in the central nucleus of amygdala of 85 days old offspring remained unaffected. We suggest that the endogenous CART system in these discrete areas, among other factors, may be a causal to the abnormalities in the next generation of an alcoholic mother.

Keywords: anxiety, depression, CART, ethanol, immunocytochemistry

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