Search results for: augmentation

Authors: Kaleem Kashif, Tayyaba Anam, Yizhi Wu

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This paper introduces a methodology for advancing Italian speech vowels landmark detection within the distinctive feature-based speech recognition domain. Leveraging the legacy tool 'xkl' by integrating combined convolutional neural networks (CNNs) and recurrent neural networks (RNNs), the study presents a comprehensive enhancement to the 'xkl' legacy software. This integration incorporates re-assigned spectrogram methodologies, enabling meticulous acoustic analysis. Simultaneously, our proposed model, integrating combined CNNs and RNNs, demonstrates unprecedented precision and robustness in landmark detection. The augmentation of re-assigned spectrogram fusion within the 'xkl' software signifies a meticulous advancement, particularly enhancing precision related to vowel formant estimation. This augmentation catalyzes unparalleled accuracy in landmark detection, resulting in a substantial performance leap compared to conventional methods. The proposed model emerges as a state-of-the-art solution in the distinctive feature-based speech recognition systems domain. In the realm of deep learning, a synergistic integration of combined CNNs and RNNs is introduced, endowed with specialized temporal embeddings, harnessing self-attention mechanisms, and positional embeddings. The proposed model allows it to excel in capturing intricate dependencies within Italian speech vowels, rendering it highly adaptable and sophisticated in the distinctive feature domain. Furthermore, our advanced temporal modeling approach employs Bayesian temporal encoding, refining the measurement of inter-landmark intervals. Comparative analysis against state-of-the-art models reveals a substantial improvement in accuracy, highlighting the robustness and efficacy of the proposed methodology. Upon rigorous testing on a database (LaMIT) speech recorded in a silent room by four Italian native speakers, the landmark detector demonstrates exceptional performance, achieving a 95% true detection rate and a 10% false detection rate. A majority of missed landmarks were observed in proximity to reduced vowels. These promising results underscore the robust identifiability of landmarks within the speech waveform, establishing the feasibility of employing a landmark detector as a front end in a speech recognition system. The synergistic integration of re-assigned spectrogram fusion, CNNs, RNNs, and Bayesian temporal encoding not only signifies a significant advancement in Italian speech vowels landmark detection but also positions the proposed model as a leader in the field. The model offers distinct advantages, including unparalleled accuracy, adaptability, and sophistication, marking a milestone in the intersection of deep learning and distinctive feature-based speech recognition. This work contributes to the broader scientific community by presenting a methodologically rigorous framework for enhancing landmark detection accuracy in Italian speech vowels. The integration of cutting-edge techniques establishes a foundation for future advancements in speech signal processing, emphasizing the potential of the proposed model in practical applications across various domains requiring robust speech recognition systems.

Keywords: landmark detection, acoustic analysis, convolutional neural network, recurrent neural network

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Authors: Sajeeha Ansar, Asad Ali Safi, Sheikh Ziauddin, Ahmad R. Shahid, Faraz Ahsan

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Gliomas are most challenging and aggressive type of tumors which appear in different sizes, locations, and scattered boundaries. CNN is most efficient deep learning approach with outstanding capability of solving image analysis problems. A fully automatic deep learning based 2D-CNN model for brain tumor segmentation is presented in this paper. We used small convolution filters (3 x 3) to make architecture deeper. We increased convolutional layers for efficient learning of complex features from large dataset. We achieved better results by pushing convolutional layers up to 16 layers for HGG model. We achieved reliable and accurate results through fine-tuning among dataset and hyper-parameters. Pre-processing of this model includes generation of brain pipeline, intensity normalization, bias correction and data augmentation. We used the BRATS-2015, and Dice Similarity Coefficient (DSC) is used as performance measure for the evaluation of the proposed method. Our method achieved DSC score of 0.81 for complete, 0.79 for core, 0.80 for enhanced tumor regions. However, these results are comparable with methods already implemented 2D CNN architecture.

Keywords: brain tumor segmentation, convolutional neural networks, deep learning, HGG

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Louvill M. Ozarraga

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this study was designed to ascertain the concept of the revenue generation system of Bacolod, Lanao del Norte, through the completely enumerated elected officials and permanent employees sample respondents. The pertinent data were obtained through the use of a structured questionnaire and with the help of key informants. The study utilized a cross-sectional survey design to analyze and interpret the data using frequency count, percentage distribution, and weighted mean. For the major findings, the local revenue generation of the Municipality has increased by Php 4,465,394.21, roughly 73.52%, from the years 2018 to 2020. Administrative activities help the Municipality cope with development, namely, the issuance of ordinances, personnel augmentation, and collection strategies. Moreover, respondents were undecided about whether revenue generation contributed to infrastructures and purchases of assets. The majority of the respondents agreed that the municipality’s local revenue generation contributes to the social welfare of its constituents. Also, the respondents disagreed that locally generated revenue augments the 20% development fund. The study revealed that there is a big difference between the 2018 and 2020 Real Property Tax (RPT) collection. No committee was created to monitor and supervise the municipal revenue generation system. The Municipality, through a partnership with TESDA, provides skilled-job opportunity to its constituents and participants

Keywords: Local Revenue Generation: Its Contribution To The Development Of The Municipality Of Bacolod, Lanao Del Sur

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Authors: Aakash Kumar R. G., Anees K. Ahamed, Raj M. Mohan

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Effective engine cooling can improve the engine’s life and efficacy. The design of the fin of the cylinder head and block determines the cooling mechanism of air cooled engine. The heat conduction takes place through the engine parts and convection of heat from the surface of the fins takes place with air as the heat transferring medium. The air surrounding the cooling fins helps in removal of heat built up by the air cooled engine. If the heat removal rate is inadequate, it will result in lower engine efficiency and high thermal stresses in the engine. The main drawback of the air cooled engine is the low heat transfer rate of the cooling fins .This work is based on scrutiny of previous researches that involves enhancing of heat transfer rate of cooling fins. The current research is about augmentation of heat transfer rate of longitudinal rectangular fin profiles by varying the length of the fin and diameter of holes on the fins. Thermal and flow analysis is done for two different models of fins. One is simple fin without holes and the other is perforated (consist of holes). It can be inferred from the research that the fins with holes have a higher fin efficiency than the fins without holes. The geometry of the fin is done in CREO. The heat transfer analysis is done using ANSYS software.

Keywords: fins, heat transfer, perforated fins, thermal analysis, thermal flux

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Authors: Khries Sawsan

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the phenomenon of urbanization is considered as one of the most important tourism resources that differ from one country to another and from one region to another in the same country. Our concern in tourism accommodation is explained by the fact that their location is directly related to the movement to tourist sites .Besides, these constructions comport security considered as the most important motivation for tourists in their choice of any destination. Hotels are the most representative expression of tourism. This is due to their physical prominence in the landscape and being the sole urban component totally unique to tourism. This study sheds light on the impact of tourism development on the spatial pattern in Jordan. It describes the linkages between existing tourism development policies and the spatial development patterns that have occurred as a result throughout Jordan, particularly looking at the impact that tourism has had on the physical environment of major tourism destinations. It puts an illustrative plan of the impact of the augmentation of tourism accommodations in Jordan in the past 40 years ago. The findings of this study help us to understand better the operation of Jordan’ dynamic changes in the location An intensive analysis is then applied on a representative case study in three regions: Amman, Petra and Aqaba. The study proceeds from an historical perspective to, show the evolution of the current development patterns an increase of tourism’s impact on spatial, in the presence of factors as political and economic stability, is expected.

Keywords: spatial patterns, urbanisation, spatial transformations, tourism planning, Jordan

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Authors: Daniel C. Bonzo

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Practical considerations lead to the use of unit of analysis within subjects, e.g., bleeding episodes or treatment-related adverse events, in rare disease settings. This is coupled with data augmentation techniques such as extrapolation to enlarge the subject base. In general, one can think about extrapolation of data as extending information and conclusions from one estimand to another estimand. This approach induces hierarchichal clustered data with varying cluster sizes. Extrapolation of clinical trial data is being accepted increasingly by regulatory agencies as a means of generating data in diverse situations during drug development process. Under certain circumstances, data can be extrapolated to a different population, a different but related indication, and different but similar product. We consider here the problem of estimation (point and interval) using a mixed-models approach under an extrapolation. It is proposed that estimators (point and interval) be constructed using weighting schemes for the clusters, e.g., equally weighted and with weights proportional to cluster size. Simulated data generated under varying scenarios are then used to evaluate the performance of this approach. In conclusion, the evaluation result showed that the approach is a useful means for improving statistical inference in rare disease settings and thus aids not only signal detection but risk-benefit evaluation as well.

Keywords: clustered data, estimand, extrapolation, mixed model

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Authors: Louvill Manangan Ozarraga

Abstract:

this study was designed to ascertain the concept of revenue generation system of Bacolod, Lanao del Norte, through the completely enumerated elected officials and permanent employees sample respondents. The pertinent data were obtained through the use of structured questionnaire and with the help of key informants. The study utilized a cross-sectional survey design to analyze and interpret the data using frequency count, percentage distribution, and weighted mean. For the major findings, the local revenue generation of the Municipality has increased by Php 4,465,394.21 roughly 73.52% from years 2018 to 2020. Administrative activities help the Municipality cope up with development namely, issuance of ordinance, personnel augmentation and collection strategies. Moreover, respondents were undecided whether revenue generation contributed to infrastructures and purchases of assets. Majority of the respondents agreed that the municipality’s local revenue generation contributes to the social welfare of its constituents. Also, the respondents disagreed that locally generated revenue augments the 20% development fund. The study revealed that there is a big difference on the 2018 and 2020 Real Property Tax (RPT) collection. No committee was created to monitor and supervise the municipal revenue generation system. The Municipality, through partnership with TESDA, provides skilled-job opportunity to its constituents and participants.

Keywords: contribution, development, Bacolod Lanao del Norte, revenue generation system

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Authors: Wafa Baccouch, Adel Ghith, Xavier Legrand, Faten Fayala

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Textile waste recycling has become a necessity considering the augmentation of the amount of waste generated each year and the ecological problems that landfilling and burning can cause. Textile waste can be recycled into many different forms according to its composition and its final utilization. Using this waste as reinforcement to composite panels is a new recycling area that is being studied. Compared to virgin fabrics, recycled ones present the disadvantage of having lower structural characteristics, when they are eco-friendly and with low cost. The objective of this work is transforming textile waste into composite material with good characteristic and low price. In this study, we used sewing as a method to improve the characteristics of the recycled textile waste in order to use it as reinforcement to composite material. Textile non-woven waste was afforded by a local textile recycling industry. Performances tests were evaluated using tensile testing machine and based on the testing direction for both reinforcements and composite panels; machine and transverse direction. Tensile tests were conducted on sewed and non sewed fabrics, and then they were used as reinforcements to composite panels via epoxy resin infusion method. Rule of mixtures is used to predict composite characteristics and then compared to experimental ones.

Keywords: composite material, epoxy resin, non woven waste, recycling, sewing, textile

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Authors: F. A. Hamad, S. He

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In this paper, the average heat transfer characteristics for a cross flow cylinder of 16 mm diameter in a vertical pipe has been studied for single-phase flow (water/oil) and multicomponent (non-boiling) flow (water-air, water-oil, oil-air and water-oil-air). The cylinder is uniformly heated by electrical heater placed at the centre of the element. The results show that the values of average heat transfer coefficients for water are around four times the values for oil flow. Introducing air as a second phase with water has very little effect on heat transfer rate, while the heat transfer increased by 70% in case of oil. For water–oil flow, the heat transfer coefficient values are reflecting the percentage of water up to 50%, but increasing the water more than 50% leads to a sharp increase in the heat transfer coefficients to become close to the values of pure water. The enhancement of heat transfer by mixing two phases may be attributed to the changes in flow structure near to cylinder surface which lead to thinner boundary layer and higher turbulence. For three-phase flow, the heat transfer coefficients for all cases fall within the limit of single-phase flow of water and oil and are very close to pure water values. The net effect of the turbulence augmentation due to the introduction of air and the attenuation due to the introduction of oil leads to a thinner boundary layer of oil over the cylinder surface covered by a mixture of water and air bubbles.

Keywords: circular cylinder, cross flow, hear transfer, multicomponent multiphase flow

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Theodoros Tziolas, Konstantinos Papageorgiou, Theodosios Theodosiou, Sebastian Pantoja, Nikos Dimitriou Dimosthenis, Elpiniki Papageorgiou

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Assembly processes involve disparate materials that possess dissimilar resiliencies and, therefore, are prone to generating defective products. Manually performed quality inspection of such products is a time-consuming and susceptible to error process. The emerging computer vision techniques in smart manufacturing can alleviate the need for thorough, manually performed quality control. Object detection techniques provide crucial localization abilities, thus helping the operators further validate the identified defect with ease. In this work, several state-of-the-art object detection models are assessed in a real industrial imagery dataset and with the use of transfer learning. EfficientDet D2 is proposed for the identification and localization of antenna defects that are generated during the assembly process. To further enhance the dataset, heavy on-the-fly data augmentation was employed, along with synthetic samples generated with the use of image processing software. The proposed approach utilizing EfficientDet D2 can increase the Average Precision from 0.90 (at IoU 0.5) to 0.97 (at IoU 0.3). The overall performance is further evaluated by applying the F1-Score at each confidence score. For conducting the experiments, the TensorFlow object detection API is employed.

Keywords: defect detection, EfficientDet, deep learning, smart manufacturing, classification

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Authors: Azadeh Golshan, Craig Evans, Phillip Geary, Abigail Morrow, Zoe Rogers, Marcel Maeder

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22 physicochemical variables have been determined in water samples collected weekly from January to December in 2013 from three sampling stations located within a major drinking water reservoir. Classical Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) analysis was used to investigate the environmental factors associated with the physico-chemical variability of the water samples at each of the sampling stations. Matrix augmentation MCR-ALS (MA-MCR-ALS) was also applied, and the two sets of results were compared for interpretative clarity. Links between these factors, reservoir inflows and catchment land-uses were investigated and interpreted in relation to chemical composition of the water and their resolved geographical distribution profiles. The results suggested that the major factors affecting reservoir water quality were those associated with agricultural runoff, with evidence of influence on algal photosynthesis within the water column. Water quality variability within the reservoir was also found to be strongly linked to physical parameters such as water temperature and the occurrence of thermal stratification. The two methods applied (MCR-ALS and MA-MCR-ALS) led to similar conclusions; however, MA-MCR-ALS appeared to provide results more amenable to interpretation of temporal and geological variation than those obtained through classical MCR-ALS.

Keywords: drinking water reservoir, multivariate analysis, physico-chemical parameters, water quality

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Authors: Pranav Gunhal

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This paper presents an analysis of sentiment on Twitter towards the Karnataka elections held in 2023, utilizing transformer-based models specifically designed for sentiment analysis in Indic languages. Through an innovative data collection approach involving a combination of novel methods of data augmentation, online data preceding the election was analyzed. The study focuses on sentiment classification, effectively distinguishing between positive, negative, and neutral posts while specifically targeting the sentiment regarding the loss of the Bharatiya Janata Party (BJP) or the win of the Indian National Congress (INC). Leveraging high-performing transformer architectures, specifically IndicBERT, coupled with specifically fine-tuned hyperparameters, the AI models employed in this study achieved remarkable accuracy in predicting the INC’s victory in the election. The findings shed new light on the potential of cutting-edge transformer-based models in capturing and analyzing sentiment dynamics within the Indian political landscape. The implications of this research are far-reaching, providing invaluable insights to political parties for informed decision-making and strategic planning in preparation for the forthcoming 2024 Lok Sabha elections in the nation.

Keywords: sentiment analysis, twitter, Karnataka elections, congress, BJP, transformers, Indic languages, AI, novel architectures, IndicBERT, lok sabha elections

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: Tarandeep Kaur

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Generation Y and Z nursing students have a much higher need for technology-based stimulation than previous generations, as they may find traditional methods of education boring and disinterested. These generations prefer experiential learning and the use of advanced technology for enhanced learning. Therefore, nursing educators must acquire knowledge to make better use of technology and technological tools for instruction. Millennials and generation are digital natives, optimistic, assertive, want engagement, instant feedback, and collaborative approach. The integration of technology and the efficacy of its use can be challenging for nursing educators. The SAMR (substitution, augmentation, modification, and redefinition) model designed and developed by Dr. Ruben Puentedura can help nursing educators to engage their students in different levels of technology integration for effective learning. Nursing educators should understand that technology use in the classroom must be purposeful. The influx of technology in nursing education is ever-changing; therefore, nursing educators have to constantly enhance and develop technical skills to keep up with the emerging technology in the schools as well as hospitals. In the Saskatchewan Collaborative Bachelor of Nursing (SCBSCN) program at Saskatchewan polytechnic, we use technology at various levels using the SAMR model in our program, including low and high-fidelity simulation labs. We are also exploring futuristic options of using virtual reality and gaming in our classrooms as an innovative way to motivate, increase critical thinking, create active learning, provide immediate feedback, improve student retention and create collaboration.

Keywords: generations, nursing, SAMR, technology

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Authors: Abhishikta Ghosh Roy

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Breast cancer is the most common malignancy among women globally, including India. Human breast cancer results from the genetic and environmental interaction. The present study attempts to understand the molecular heterogeneity of BRCA1 and BRCA2 genes, as well as to understand the association of various lifestyle and reproductive variables for the Breast Cancer risk. The study was conducted amongst 110 patients and 128 controls with total DNA sequencing of flanking and coding regions of BRCA1 BRCA2 genes that revealed ten Single Nucleotide Polymorphisms (SNPs) (6 novels). The controls selected for the study were age, sex and ethnic group matched. After written and informed consent biological samples were collected from the subjects. After detailed molecular analysis, significant (p < 0.005) molecular heterogeneity is revealed in terms of SNPs in BRCA1 (4 Exonic & 1 Intronic) and BRCA2 (2exonic and 3 Intronic) genes. The augmentation study investigated significant (p < 0.05) association with positive family history, early age at menarche, irregular menstrual periods, menopause, prolong contraceptive use, nulliparity, history of abortions, consumption of alcohol and smoking for breast cancer risk. To the best of authors knowledge, this study is the first of its kind, envisaged that the identification of the SNPs and modification of the lifestyle factors might aid to minimize the risk among the Bengalee Hindu females.

Keywords: breast cancer, BRCA, lifestyle, India

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Authors: Ashish Kumar, Munesh Raghuraj Varma, Nisarg Joshi, Gujjula Vishwa Teja, Srikanth Sambi, Arpit Awasthi

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Autonomous driving vehicles are revolutionizing the transportation system of the 21st century. This has been possible due to intensive research put into making a robust, reliable, and intelligent program that can perceive and understand its environment and make decisions based on the understanding. It is a very data-intensive task with data coming from multiple sensors and the amount of data directly reflects on the performance of the system. Researchers have to design the preprocessing pipeline for different datasets with different sensor orientations and alignments before the dataset can be fed to the model. This paper proposes a solution that provides a method to unify all the data from different sources into a uniform format using the intrinsic and extrinsic parameters of the sensor used to capture the data allowing the same pipeline to use data from multiple sources at a time. This also means easy adoption of new datasets or In-house generated datasets. The solution also automates the complete deep learning pipeline from preprocessing to post-processing for various tasks allowing researchers to design multiple custom end-to-end pipelines. Thus, the solution takes care of the input and output data handling, saving the time and effort spent on it and allowing more time for model improvement.

Keywords: augmentation, autonomous driving, camera, custom end-to-end pipeline, data unification, lidar, post-processing, preprocessing

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Authors: Savvas Rogotis, Nikolaos Kalatzis, Stergios Dimou-Sakellariou, Nikolaos Marianos

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As precision holds the key for the introduction of distinct benefits in agriculture (e.g., energy savings, reduced labor costs, optimal application of inputs, improved products, and yields), it steadily becomes evident that new initiatives should focus on rendering Precision Agriculture (PA) more accessible to the average farmer. PA leverages on technologies such as the Internet of Things (IoT), earth observation, robotics and positioning systems (e.g., the Global Navigation Satellite System – GNSS - as well as individual positioning systems like GPS, Glonass, Galileo) that allow: from simple data georeferencing to optimal navigation of agricultural machinery to even more complex tasks like Variable Rate Applications. An identified customer pain point is that, from one hand, typical triangulation-based positioning systems are not accurate enough (with errors up to several meters), while on the other hand, high precision positioning systems reaching centimeter-level accuracy, are very costly (up to thousands of euros). Within this paper, a Ground-Based Augmentation System (GBAS) is introduced, that can be adapted to any existing IoT field-sensing station infrastructure. The latter should cover a minimum set of requirements, and in particular, each station should operate as a fixed, obstruction-free towards the sky, energy supplying unit. Station augmentation will allow them to function in pairs with GNSS rovers following the differential GNSS base-rover paradigm. This constitutes a key innovation element for the proposed solution that encompasses differential GNSS capabilities into an IoT field-sensing infrastructure. Integrating this kind of information supports the provision of several additional PA beneficial services such as spatial mapping, route planning, and automatic field navigation of unmanned vehicles (UVs). Right at the heart of the designed system, there is a high-end GNSS toolkit with base-rover variants and Real-Time Kinematic (RTK) capabilities. The GNSS toolkit had to tackle all availability, performance, interfacing, and energy-related challenges that are faced for a real-time, low-power, and reliable in the field operation. Specifically, in terms of performance, preliminary findings exhibit a high rover positioning precision that can even reach less than 10-centimeters. As this precision is propagated to the full dataset collection, it enables tractors, UVs, Android-powered devices, and measuring units to deal with challenging real-world scenarios. The system is validated with the help of Gaiatrons, a mature network of agro-climatic telemetry stations with presence all over Greece and beyond ( > 60.000ha of agricultural land covered) that constitutes part of “gaiasense” (www.gaiasense.gr) smart farming (SF) solution. Gaiatrons constantly monitor atmospheric and soil parameters, thus, providing exact fit to operational requirements asked from modern SF infrastructures. Gaiatrons are ultra-low-cost, compact, and energy-autonomous stations with a modular design that enables the integration of advanced GNSS base station capabilities on top of them. A set of demanding pilot demonstrations has been initiated in Stimagka, Greece, an area with a diverse geomorphological landscape where grape cultivation is particularly popular. Pilot demonstrations are in the course of validating the preliminary system findings in its intended environment, tackle all technical challenges, and effectively highlight the added-value offered by the system in action.

Keywords: GNSS, GBAS, precision agriculture, RTK, smart farming

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Authors: Divya P. Soman, S. Karthika, P. Kalaichelvi, T. K. Radhakrishnan

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Heat transfer characteristics of ionic liquid solution as cold fluid in plate heat exchanger with dimple plate geometry was studied. The ionic liquid solution used in this study was 1-butyl-3-methylimidazolium bromide in water. The present experimental study is to understand the heat transfer behavior of different 1-butyl-3-methylimidazolium bromide concentrations (0.1 and 0.2% w/w) in water. In addition, the heat transfer activity of ionanofluid as cold fluid was investigated. The ionanofluid was prepared by dispersing 0.3% w/w Al2O3 in the ionic liquid solution as base fluid. Experiments were also conducted to determine thermophysical properties of ionanofluid. The empirical correlations as a function of temperature were developed to predict the thermophysical properties. Finally, the heat transfer performance of ionic liquid solution, ionanofluid, nanofluid and water were compared. The impact of hot fluid’s (water) Reynolds number on overall heat transfer coefficient and Nusselt number of cold fluids were analyzed. The nanofluid and ionanofluid were found to possess better heat transfer behavior than water and ionic liquid solution. Heat transfer augmentation was observed for ionanofluid when compared with the base fluid (0.1% w/w ionic liquid solution).

Keywords: ionic liquid, nanofluid, ionanofluid, dimple plate heat exchanger, Nusselt number, overall heat transfer coefficient

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Authors: John Paul Q. Tomas, Mark Wilson L. de los Reyes, Kirsten Joyce P. Vasquez

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The most frequent type of fracture is a wrist fracture, which often makes it difficult for medical professionals to find and locate. In this study, fractures in wrist x-ray pictures were located and identified using deep learning and computer vision. The researchers used image filtering, masking, morphological operations, and data augmentation for the image preprocessing and trained the RetinaNet and Faster R-CNN models with ResNet50 backbones and Adam optimizers separately for each image filtering technique and projection. The RetinaNet model with Anisotropic Diffusion Smoothing filter trained with 50 epochs has obtained the greatest accuracy of 99.14%, precision of 100%, sensitivity/recall of 98.41%, specificity of 100%, and an IoU score of 56.44% for the Posteroanterior projection utilizing augmented data. For the Lateral projection using augmented data, the RetinaNet model with an Anisotropic Diffusion filter trained with 50 epochs has produced the highest accuracy of 98.40%, precision of 98.36%, sensitivity/recall of 98.36%, specificity of 98.43%, and an IoU score of 58.69%. When comparing the test results of the different individual projections, models, and image filtering techniques, the Anisotropic Diffusion filter trained with 50 epochs has produced the best classification and regression scores for both projections.

Keywords: Artificial Intelligence, Computer Vision, Wrist Fracture, Deep Learning

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Authors: Imen Hamed, Burcu Ak, Oya Işık, Leyla Uslu, Kubilay Kazım Vursavuş

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In this study, 3 species of Dunaliella (Dunaliella sp. Salt Lake isoalte (Tuz Gölü), Dunaliella salina CCAP19/18, and Dunaliella bardawil LB 2538) and their optical density, dry matter, chlorophyll a, total carotenoids, and β-carotene production were investigated in a batch system. The aim of this research was to compare carotenoids, and β-carotene production were investigated in a batch those 3 species. Therefore 2 stress factors were used: 2 different temperatures (20°C and 30°C) and 2 different salinities (30‰, and 60‰) were tested over a 17-day study. The highest growth and chlorophyll a was reported for Dunaliella sp. under 20°C/30‰ and 20°C/60‰ conditions respectively followed by D. bardawil and D. salina. Significant differences were noticed (p<0.05) for the other 3 species. The growth decreased as temperature and salinity increased since the lowest growth was noticed for the 30°C/60‰ group. The chlorophyll a content decreased also as temperature increased however when the NaCl concentration increased an augmentation of the content was noticed . In the 17^th day of experiment the highest carotenoids concentration was reported for D. bardawil 20°C/30‰ (65,639±0,400 μg.mL⁻¹) and the most important β carotene concentration was for D. salina 20°C/60‰ (8,98E-07±0,013 mol/L).

Keywords: Dunaliella sp., Dunaliella salina, Dunaliella bardawil, growth, pigments, stress factors

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Authors: M. A. Akhavan-Behabadi, M. Najafi, A. Abbasi

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An empirical investigation was performed in order to study the heat transfer characteristics of R600a flow boiling inside horizontal flattened tubes and the simultaneous effect of nanoparticles on boiling heat transfer in flattened channel. Round copper tubes of 8.7 mm I.D. were deformed into flattened shapes with different inside heights of 6.9, 5.5, and 3.4 mm as test areas. The effect of different parameters such as mass flux, vapor quality and inside height on heat transfer coefficient was studied. Flattening the tube caused a significant enhancement in heat transfer performance, so that the maximum augmentation ratio of 163% was obtained in flattened channel with lowest internal height. A new correlation was developed based on the present experimental data to predict the heat transfer coefficient in flattened tubes. This correlation estimated 90% of the entire database within ±20%. The best flat channel with the point of view of heat transfer performance was selected to study the effect of nanoparticle on heat transfer enhancement. Four homogenized mixtures containing 1% weight fraction of R600a/oil with different CuO nanoparticles concentration including 0.5%, 1% and 1.5% mass fraction of R600a/oil/CuO were studied. Observations show that heat transfer was improved by adding nanoparticles, which lead to maximum enhancement of 79% compare to the pure refrigerant at the same test condition.

Keywords: nano fluids, heat transfer, flattend tube, transport phenomena

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Authors: Hiba Akrout, Khaoula Hidouri, Béchir Chaouachi, Romdhane Ben Slama

Abstract:

A simple solar distiller has been constructed in order to desalt water via the solar distillation process. An experimental study has been conducted in June. The aim of this work is to study the effect of the distance between the cold condensing surface and the hot steam generation surface in order to optimize the geometric characteristics of a simple solar still. To do this, we have developed a mathematical model based on thermal and mass equations system. Subsequently, the equations system resolution has been made through a program developed on MATLAB software, which allowed us to evaluate the production of this system as a function of the distance separating the two surfaces. In addition, this model allowed us to determine the evolution of the humid air temperature inside the solar still as well as the humidity ratio profile all over the day. Simulations results show that the solar distiller production, as well as the humid air temperature, are proportional to the global solar radiation. It was also found that the air humidity ratio inside the solar still has a similar evolution of that of solar radiation. Moreover, the solar distiller average height augmentation, for constant water depth, induces the diminution of the production. However, increasing the water depth for a fixed average height of solar distiller reduces the production.

Keywords: distillation, solar energy, heat transfer, mass transfer, average height

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Authors: Hye Kyung Kim, Myung-Gyou Kim, Kang-Hyun Leem

Abstract:

It has been reported that deer antler extract has bone-strengthening activity and effectively used in bone diseases therapy. However, little is known about the cellular and molecular mechanism of this effect. The upper section, mid section, and base of the antler has been known to exhibit different biological properties. Present study investigated the effects of these three parts of deer antler extracts on bone formation and resorption. The effects of deer antler extracts (DH) on bone formation were determined by cell proliferation, alkaline phosphatase (ALP) activity, collagen synthesis, and mineralization in human osteoblastic MG-63 cells. The effect on bone resorption was determined by osteoclastogenesis from bone marrow-derived precursor cells driven by RANKL. Ethanol extracts of DH (50 ~ 100 µg/ml) dose-dependently increased cell proliferation, and upper part increased the cell proliferation by 118.4% while mid and base parts increased proliferation by 107.8% and 102.3%, respectively. ALP activity was significantly increased by upper part of the DH treatment. After enhancement of ALP activity, significant augmentation of collagen synthesis and calcification assessed by Sirus red and Alzarin red staining, respectively, was observed in upper part of the DH treatment. The effect of DH on bone resorption was not observed in all three parts of the DH. These results could provide a mechanistic explanation for the bone-strengthening effects of DH.

Keywords: alkaline phosphatase, collagen synthesis, deer antler, osteoblastic MG-63 cells

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Authors: Nadia Allouache

Abstract:

Energy conservation is one of the major concerns in the modern high tech era due to the limited amount of energy resources and the increasing cost of energy. Predicting an efficient use of energy in thermal systems like heat exchangers can only be achieved if the second law of thermodynamics is accounted for. The performance of heat exchangers can be substantially improved by many passive heat transfer augmentation techniques. These letters permit to improve heat transfer rate and to increase exchange surface, but on the other side, they also increase the friction factor associated with the flow. This raises the question of how to employ these passive techniques in order to minimize the useful energy. The objective of this present study is to use a porous substrate attached to the walls as a passive enhancement technique in heat exchangers and to find the compromise between the hydrodynamic and thermal performances under turbulent flow conditions, by using a second law approach. A modified k- ε model is used to simulating the turbulent flow in the porous medium and the turbulent shear flow is accounted for in the entropy generation equation. A numerical modeling, based on the finite volume method is employed for discretizing the governing equations. Effects of several parameters are investigated such as the porous substrate properties and the flow conditions. Results show that under certain conditions of the porous layer thickness, its permeability, and its effective thermal conductivity the minimum rate of entropy production is obtained.

Keywords: second law approach, annular heat exchanger, turbulent flow, porous medium, modified model, numerical analysis

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

Abstract:

Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Jan M. Mari

Abstract:

Safflower is a renowned drought tolerant oil seed crop. Previously its flowers were used for cooking and herbal medicines in China and it was cultivated by small growers for his personal needs of oil. A field study was conducted at experimental field, faculty of crop protection, Sindh Agricultural University Tandojam, during winter, 2012-13, to observe ecological effect on aphid population in safflower crop. Aphid population gradually increased with the growth of safflower. It developed with maximum aphid per leaf on 3rd week of February and it decreased in March as crop matured. A non-significant interaction was found with temperature of aphid, zigzag and hoverfly, respectively and a highly significant interaction with temperature was found with 7-spotted, lacewing, 9-spotted, and Brumus, respectively. The data revealed the overall mean population of zigzag was highest, followed by 9-spotted, 7-spotted, lace wing, hover fly and Brumus, respectively. In initial time the predator and prey ratio indicated that there was not a big difference between predator and prey ratio. After January 1st, the population of aphid increased suddenly until 18th February and it established a significant difference between predator prey ratios. After that aphid population started decreasing and it affected ratio between pest and predators. It is concluded that biotic factors, 7-spotted, zigzag, 9-spotted Brumus and lacewing exhibited a strong and positive correlation with aphid population. It is suggested that aphid pest should be monitored regularly and before reaching economic threshold level augmentation of natural enemies may be managed.

Keywords: aphid, ecology, population, safflower

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Authors: Ana-Maria Gheldiu, Bianca M. Abrudan, Maria A. Neag, Laurian Vlase, Dana M. Muntean

Abstract:

Background information: The objective of this study was to investigate the effect of multiple-dose fluvoxamine on the pharmacokinetic profile of single-dose carvedilol in rats, in order to evaluate this possible drug-drug pharmacokinetic interaction. Methods: A preclinical study, in 28 white male Wistar rats, was conducted. Each rat was cannulated on the femoral vein, prior to being connected to BASi Culex ABC®. Carvedilol was orally administrated in rats (3.57 mg/kg body mass (b.m.)) in the absence of fluvoxamine or after a pre-treatment with multiple oral doses of fluvoxamine (14.28 mg/kg b.m.). The plasma concentrations of carvedilol were estimated by high performance liquid chromatography-tandem mass spectrometry. The pharmacokinetic parameters of carvedilol were analyzed by non-compartmental method. Results: After carvediol co-administration with fluvoxamine, an approximately 2-fold increase in the exposure of carvedilol was observed, considering the significantly elevated value of the total area under the concentration versus time curve (AUC₀₋∞). Moreover, an increase by approximately 145% of the peak plasma concentration was found, as well as an augmentation by approximately 230% of the half life time of carvedilol was observed. Conclusion: Fluvoxamine co-administration led to a significant alteration of carvedilol’s pharmacokinetic profile in rats, these effects could be explained by the existence of a drug-drug interaction mediated by CYP2D6 inhibition. Acknowledgement: This work was supported by CNCS Romania – project PNII-RU-TE-2014-4-0242.

Keywords: carvedilol, fluvoxamine, drug-drug pharmacokinetic interaction, rats

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Authors: Yu-Shan Wu, Po-Liang Lai, I-Ming Chu

Abstract:

Poly(methylmethacrylate)(PMMA) is the most frequently used bone void filler for vertebral augmentation in osteoporotic fracture. PMMA bone cement not only exhibits strong mechanical properties but also can fabricate according to the shape of bone defect. However, the adhesion between the PMMA-based cement and the adjacent bone is usually weak and as PMMA bone cement is inherently bioinert. The combination of bioceramics and polymers as composites may increase cell adhesion and improve biocompatibility. The nano-hydroxyapatite(HAP) not only plays a significant role in maintaining the properties of the natural bone but also offers a favorable environment for osteoconduction, protein adhesion, and osteoblast proliferation. However, defects and cracks can form at the polymer/ceramics interface, resulting in uneven distribution of stress and subsequent inferior mechanical strength. Surface-modified HAP nano-crystals were prepared by chemically grafting poly(ε-caprolactone)(PCL) on surface-modified nano-HAP surface to increase the affinity of polymer/ceramic phases .Thus, incorporation of surface-modified nano-hydroxyapatite (EC-HAP) may not only improve the interfacial adhesion between cement and bone and between nanoparticles and cement, but also increase biocompatibility. In this research, PMMA mixing with 0, 5, 10, 15, 20, 25 and 30 wt% EC-HAP were examined. MC3T3-E1 cells were used for the biological evaluation of the response to the cements in vitro. Morphology was observed using scanning electron microscopy (SEM). Mechanical properties of HAP/PMMA and EC-HAP/PMMA cement were investigated by compression test. Surface wettability of the cements was measured by contact angles.

Keywords: bone cement, biocompatibility, nano-hydroxyapatite, polycaprolactone, PMMA, surface grafting

Procedia PDF Downloads 370