Search results for: genetic algorithm

Authors: Ethan James

Abstract:

Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 177

Authors: Erifyli Tsagkari, Stephanie Connelly, Zhaowei Liu, Andrew McBride, William Sloan

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Biofilms play an essential role in treating water in biofiltration systems. The biofilm morphology and function are inextricably linked to the hydrodynamics of flow through a filter, and yet engineers rarely explicitly engineer this interaction. We develop a system that links computer simulation and 3-D printing to optimize and rapidly prototype filter media to optimize biofilm function with the hypothesis that biofilm function is intimately linked to the flow passing through the filter. A computational model that numerically solves the incompressible time-dependent Navier Stokes equations coupled to a model for biofilm growth and function is developed. The model is imbedded in an optimization algorithm that allows the model domain to adapt until criteria on biofilm functioning are met. This is applied to optimize the shape of filter media in a simple flow channel to promote biofilm formation. The computer code links directly to a 3-D printer, and this allows us to prototype the design rapidly. Its validity is tested in flow visualization experiments and by microscopy. As proof of concept, the code was constrained to explore a small range of potential filter media, where the medium acts as an obstacle in the flow that sheds a von Karman vortex street that was found to enhance the deposition of bacteria on surfaces downstream. The flow visualization and microscopy in the 3-D printed realization of the flow channel validated the predictions of the model and hence its potential as a design tool. Overall, it is shown that the combination of our computational model and the 3-D printing can be effectively used as a design tool to prototype filter media to optimize biofilm formation.

Keywords: biofilm, biofilter, computational model, von karman vortices, 3-D printing.

Procedia PDF Downloads 135

Authors: Lamees Nasser, Yago Diez, Robert Martí, Joan Martí, Ibrahim Sadek

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Automated 3D breast ultrasound (ABUS) screening is a novel modality in medical imaging because of its common characteristics shared with other ultrasound modalities in addition to the three orthogonal planes (i.e., axial, sagittal, and coronal) that are useful in analysis of tumors. In the literature, few automatic approaches exist for typical tasks such as segmentation or registration. In this work, we deal with two problems concerning ABUS images: nipple and rib detection. Nipple and ribs are the most visible and salient features in ABUS images. Determining the nipple position plays a key role in some applications for example evaluation of registration results or lesion follow-up. We present a nipple detection algorithm based on color and shape of the nipple, besides an automatic approach to detect the ribs. In point of fact, rib detection is considered as one of the main stages in chest wall segmentation. This approach consists of four steps. First, images are normalized in order to minimize the intensity variability for a given set of regions within the same image or a set of images. Second, the normalized images are smoothed by using anisotropic diffusion filter. Next, the ribs are detected in each slice by analyzing the eigenvalues of the 3D Hessian matrix. Finally, a breast mask and a probability map of regions detected as ribs are used to remove false positives (FP). Qualitative and quantitative evaluation obtained from a total of 22 cases is performed. For all cases, the average and standard deviation of the root mean square error (RMSE) between manually annotated points placed on the rib surface and detected points on rib borders are 15.1188 mm and 14.7184 mm respectively.

Keywords: Automated 3D Breast Ultrasound, Eigenvalues of Hessian matrix, Nipple detection, Rib detection

Procedia PDF Downloads 326

Authors: Flavio Cannavo

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Continuous evaluation of the status of potentially hazardous volcanos plays a key role for civil protection purposes. The importance of monitoring volcanic activity, especially for energetic paroxysms that usually come with tephra emissions, is crucial not only for exposures to the local population but also for airline traffic. Presently, real-time surveillance of most volcanoes worldwide is essentially delegated to one or more human experts in volcanology, who interpret data coming from different kind of monitoring networks. Unfavorably, the high nonlinearity of the complex and coupled volcanic dynamics leads to a large variety of different volcanic behaviors. Moreover, continuously measured parameters (e.g. seismic, deformation, infrasonic and geochemical signals) are often not able to fully explain the ongoing phenomenon, thus making the fast volcano state assessment a very puzzling task for the personnel on duty at the control rooms. With the aim of aiding the personnel on duty in volcano surveillance, here we introduce a system based on an ensemble of data-driven classifiers to infer automatically the ongoing volcano status from all the available different kind of measurements. The system consists of a heterogeneous set of independent classifiers, each one built with its own data and algorithm. Each classifier gives an output about the volcanic status. The ensemble technique allows weighting the single classifier output to combine all the classifications into a single status that maximizes the performance. We tested the model on the Mt. Etna (Italy) case study by considering a long record of multivariate data from 2011 to 2015 and cross-validated it. Results indicate that the proposed model is effective and of great power for decision-making purposes.

Keywords: Bayesian networks, expert system, mount Etna, volcano monitoring

Procedia PDF Downloads 240

Authors: Javier Carroquino, Nieves García-Casarejos, Pilar Gargallo, F. Javier García-Ramos

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The energy used in agriculture is a source of global emissions of greenhouse gases. The two main types of this energy are electricity for pumping and diesel for agricultural machinery. In order to reduce these emissions, the European project LIFE REWIND addresses the supply of this demand from renewable sources. First of all, comprehensive data on energy demand and available renewable resources have been obtained in several case studies. Secondly, a set of simulations and optimizations have been performed, in search of the best configuration and sizing, both from an economic and emission reduction point of view. For this purpose, it was used software based on genetic algorithms. Thirdly, a prototype has been designed and installed, that it is being used for the validation in a real case. Finally, throughout a year of operation, various technical and economic parameters are being measured for further analysis. The prototype is not connected to the utility grid, avoiding the cost and environmental impact of a grid extension. The system includes three kinds of photovoltaic fields. One is located on a fixed structure on the terrain. Another one is floating on an irrigation raft. The last one is mounted on a two axis solar tracker. Each has its own solar inverter. The total amount of nominal power is 44 kW. A lead acid battery with 120 kWh of capacity carries out the energy storage. Three isolated inverters support a three phase, 400 V 50 Hz micro-grid, the same characteristics of the utility grid. An advanced control subsystem has been constructed, using free hardware and software. The electricity produced feeds a set of seven pumps used for purification, elevation and pressurization of water in a drip irrigation system located in a vineyard. Since the irrigation season does not include the whole year, as well as a small oversize of the generator, there is an amount of surplus energy. With this surplus, a hydrolyser produces on site hydrogen by electrolysis of water. An off-road vehicle with fuel cell feeds on that hydrogen and carries people in the vineyard. The only emission of the process is high purity water. On the one hand, the results show the technical and economic feasibility of stand-alone renewable energy systems to feed seasonal pumping. In this way, the economic costs, the environmental impacts and the landscape impacts of grid extensions are avoided. The use of diesel gensets and their associated emissions are also avoided. On the other hand, it is shown that it is possible to replace diesel in agricultural machinery, substituting it for electricity or hydrogen of 100% renewable origin and produced on the farm itself, without any external energy input. In addition, it is expected to obtain positive effects on the rural economy and employment, which will be quantified through interviews.

Keywords: drip irrigation, greenhouse gases, hydrogen, renewable energy, vineyard

Procedia PDF Downloads 337

Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Keerti Kembhavi, M. R. Archana, V. Anjaneyappa

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Most asphalt pavement condition evaluation use rating frameworks in which asphalt pavement distress is estimated by type, extent, and severity. Rating is carried out by the pavement condition rating (PCR), which is tedious and expensive. This paper presents the development of a low-cost technique for image pavement distress analysis that permits the identification of pothole and cracks. The paper explores the application of image processing tools for the detection of potholes and cracks. Longitudinal cracking and pothole are detected using Fuzzy-C- Means (FCM) and proceeded with the Spectral Theory algorithm. The framework comprises three phases, including image acquisition, processing, and extraction of features. A digital camera (Gopro) with the holder is used to capture pavement distress images on a moving vehicle. FCM classifier and Spectral Theory algorithms are used to compute features and classify the longitudinal cracking and pothole. The Matlab2016Ra Image preparing tool kit utilizes performance analysis to identify the viability of pavement distress on selected urban stretches of Bengaluru city, India. The outcomes of image evaluation with the utilization semi-computerized image handling framework represented the features of longitudinal crack and pothole with an accuracy of about 80%. Further, the detected images are validated with the actual dimensions, and it is seen that dimension variability is about 0.46. The linear regression model y=1.171x-0.155 is obtained using the existing and experimental / image processing area. The R2 correlation square obtained from the best fit line is 0.807, which is considered in the linear regression model to be ‘large positive linear association’.

Keywords: crack detection, pothole detection, spectral clustering, fuzzy-c-means

Procedia PDF Downloads 175

Authors: Yakup Ulusu, Faruk Ozel, Numan Eczacioglu, Abdurrahman Ozen, Sabriye Acikgoz

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GFP discovered in the mid-1970s, has been used as a marker after replicated genetic study by scientists. In biotechnology, cell, molecular biology, the GFP gene is frequently used as a reporter of expression. In modified forms, it has been used to make biosensors. Many animals have been created that express GFP as an evidence that a gene can be expressed throughout a given organism. Proteins labeled with GFP identified locations are determined. And so, cell connections can be monitored, gene expression can be reported, protein-protein interactions can be observed and signals that create events can be detected. Additionally, monitoring GFP is noninvasive; it can be detected by under UV-light because of simply generating fluorescence. Moreover, GFP is a relatively small and inert molecule, that does not seem to treat any biological processes of interest. The synthesis of GFP has some steps like, to construct the plasmid system, transformation in E. coli, production and purification of protein. GFP carrying plasmid vector pBAD–GFPuv was digested using two different restriction endonuclease enzymes (NheI and Eco RI) and DNA fragment of GFP was gel purified before cloning. The GFP-encoding DNA fragment was ligated into pET28a plasmid using NheI and Eco RI restriction sites. The final plasmid was named pETGFP and DNA sequencing of this plasmid indicated that the hexa histidine-tagged GFP was correctly inserted. Histidine-tagged GFP was expressed in an Escherichia coli BL21 DE3 (pLysE) strain. The strain was transformed with pETGFP plasmid and grown on LuiraBertoni (LB) plates with kanamycin and chloramphenicol selection. E. coli cells were grown up to an optical density (OD 600) of 0.8 and induced by the addition of a final concentration of 1mM isopropyl-thiogalactopyranoside (IPTG) and then grown for additional 4 h. The amino-terminal hexa-histidine-tag facilitated purification of the GFP by using a His Bind affinity chromatography resin (Novagen). Purity of GFP protein was analyzed by a 12 % sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE). The concentration of protein was determined by UV absorption at 280 nm (Varian Cary 50 Scan UV/VIS spectrophotometer). Synthesis of GFP-Polymer composite nanofibers was produced by using GFP solution (10mg/mL) and polymer precursor Polyvinylpyrrolidone, (PVP, Mw=1300000) as starting materials and template, respectively. For the fabrication of nanofibers with the different fiber diameter; a sol–gel solution comprising of 0.40, 0.60 and 0.80 g PVP (depending upon the desired fiber diameter) and 100 mg GFP in 10 mL water: ethanol (3:2) mixtures were prepared and then the solution was covered on collecting plate via electro spinning at 10 kV with a feed-rate of 0.25 mL h-1 using Spellman electro spinning system. Results show that GFP-based nano-fiber can be used plenty of biomedical applications such as bio-imaging, bio-mechanic, bio-material and tissue engineering.

Keywords: biomaterial, GFP, nano-fibers, protein expression

Procedia PDF Downloads 316

Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka

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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.

Keywords: aggregation, consumption, data gathering, efficiency

Procedia PDF Downloads 487

Authors: David C. Ni

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Contemporary models for N-body systems are based on temporal, two-body, and mass point representation of Newtonian mechanics. Other mainstream models include 2D and 3D Ising models based on local neighborhood the lattice structures. In Quantum mechanics, the theories of collective modes are for superconductivity and for the long-range quantum entanglement. However, these models are still mainly for the specific phenomena with a set of designated parameters. We are therefore motivated to develop a new construction directly from the complex-variable N-body systems based on the extended Blaschke functions (EBF), which represent a non-temporal and nonlinear extension of Lorentz transformation on the complex plane – the normalized momentum spaces. A point on the complex plane represents a normalized state of particle momentums observed from a reference frame in the theory of special relativity. There are only two key parameters, normalized momentum and nonlinearity for modelling. An algorithm similar to Jenkins-Traub method is adopted for solving EBF iteratively. Through iteration, the solution sets show a form of σ + i [-t, t], where σ and t are the real numbers, and the [-t, t] shows various distributions, such as 1-peak, 2-peak, and 3-peak etc. distributions and some of them are analog to the canonical distributions. The results of the numerical analysis demonstrate continuum-to-discreteness transitions, evolutional invariance of distributions, phase transitions with conjugate symmetry, etc., which manifest the construction as a potential candidate for the unification of statistics. We hereby classify the observed distributions on the finite convergent domains. Continuous and discrete distributions both exist and are predictable for given partitions in different regions of parameter-pair. We further compare these distributions with canonical distributions and address the impacts on the existing applications.

Keywords: blaschke, lorentz transformation, complex variables, continuous, discrete, canonical, classification

Procedia PDF Downloads 306

Authors: Geetha Baskaran, Andrzej Barjiela, Rong Qu

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Motivation: Nurse scheduling is a complex combinatorial optimization problem. It is also known as NP-hard. It needs an efficient re-scheduling to minimize some trade-off of the measures of violation by reducing selected constraints to soft constraints with measurements of their violations. Problem Statement: In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. Approach: This approach satisfies the rules of a typical hospital environment based on a standard benchmark problem. Generating good work schedules has a great influence on nurses' working conditions which are strongly related to the level of a quality health care. Domain transformation that combines the strengths of operation research and artificial intelligence was proposed for the solution of the problem. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) package is used to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave for Windows to solve this problem. Results: The scheduling problem has been solved in the proposed formalism resulting in a high quality schedule. Conclusion: Domain transformation represents departure from a conventional one-shift-at-a-time scheduling approach. It offers an advantage of efficient and easily understandable solutions as well as offering deterministic reproducibility of the results. We note, however, that it does not guarantee the global optimum.

Keywords: domain transformation, nurse scheduling, information granulation, artificial intelligence, simulation

Procedia PDF Downloads 387

Authors: Muhammad Arslan Usman, Muhammad Rehan Usman, Soo Young Shin

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Live video streaming is one of the most widely used service among end users, yet it is a big challenge for the network operators in terms of quality. The only way to provide excellent Quality of Experience (QoE) to the end users is continuous monitoring of live video streaming. For this purpose, there are several objective algorithms available that monitor the quality of the video in a live stream. Subjective tests play a very important role in fine tuning the results of objective algorithms. As human perception is considered to be the most reliable source for assessing the quality of a video stream, subjective tests are conducted in order to develop more reliable objective algorithms. Temporal impairments in a live video stream can have a negative impact on the end users. In this paper we have conducted subjective evaluation tests on a set of video sequences containing temporal impairment known as frame freezing. Frame Freezing is considered as a transmission error as well as a hardware error which can result in loss of video frames on the reception side of a transmission system. In our subjective tests, we have performed tests on videos that contain a single freezing event and also for videos that contain multiple freezing events. We have recorded our subjective test results for all the videos in order to give a comparison on the available No Reference (NR) objective algorithms. Finally, we have shown the performance of no reference algorithms used for objective evaluation of videos and suggested the algorithm that works better. The outcome of this study shows the importance of QoE and its effect on human perception. The results for the subjective evaluation can serve the purpose for validating objective algorithms.

Keywords: objective evaluation, subjective evaluation, quality of experience (QoE), video quality assessment (VQA)

Procedia PDF Downloads 594

Authors: Safia Hasan Varachhia, Neela Badrie, Marsha Singh

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Genetic modification of food is an innovative technology that offers a host of benefits and advantages to consumers. Consumer attitudes towards GM food and GM technologies can be identified a major determinant in conditioning market force and encouraging policy makers and regulators to recognize the significance of consumer influence on the market. This study aimed to investigate and evaluate the extent of consumer awareness, knowledge, perception and acceptance of GM foods and its associated health risks and benefit in Trinidad and Tobago, West Indies. The specific objectives of this study were to (determine consumer awareness to GM foods, ascertain their perspectives on health and safety risks and ethical issues associated with GM foods and determine whether labeling of GM foods and ingredients will influence consumers’ willingness to purchase GM foods. A survey comprising of a questionnaire consisting of 40 questions, both open-ended and close-ended was administered to 240 shoppers in small, medium and large-scale supermarkets throughout Trinidad between April-May, 2015 using convenience sampling. This survey investigated consumer awareness, knowledge, perception and acceptance of GM foods and its associated health risks/benefits. The data was analyzed using SPSS 19.0 and Minitab 16.0. One-way ANOVA investigated the effects categories of supermarkets and knowledge scores on shoppers’ awareness, knowledge, perception and acceptance of GM foods. Linear Regression tested whether demographic variables (category of supermarket, age of consumer, level of were useful predictors of consumer’s knowledge of GM foods). More than half of respondents (64.3%) were aware of GM foods and GM technologies, 28.3% of consumers indicated the presence of GM foods in local supermarkets and 47.1% claimed to be knowledgeable of GM foods. Furthermore, significant associations (P < 0.05) were observed between demographic variables (age, income, and education), and consumer knowledge of GM foods. Also, significant differences (P < 0.05) were observed between demographic variables (education, gender, and income) and consumer knowledge of GM foods. In addition, age, education, gender and income (P < 0.05) were useful predictors of consumer knowledge of GM foods. There was a contradiction as whilst 35% of consumers considered GM foods safe for consumption, 70% of consumers were wary of the unknown health risks of GM foods. About two-thirds of respondents (67.5%) considered the creation of GM foods morally wrong and unethical. Regarding GM food labeling preferences, 88% of consumers preferred mandatory labeling of GM foods and 67% of consumers specified that any food product containing a trace of GM food ingredients required mandatory GM labeling. Also, despite the declaration of GM food ingredients on food labels and the reassurance of its safety for consumption by food safety and regulatory institutions, the majority of consumers (76.1%) still preferred conventionally produced foods over GM foods. The study revealed the need to inform shoppers of the presence of GM foods and technologies, present the scientific evidence as to the benefits and risks and the need for a policy on labeling so that informed choices could be taken.

Keywords: genetically modified foods, income, labeling consumer awareness, ingredients, morality and ethics, policy

Procedia PDF Downloads 325

Authors: Marina Benito-Parejo, Raul Perez-Lopez, Miguel Herraiz, Carolina Guardiola-Albert, Cesar Martinez

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Persistency, long-term memory and randomness are intrinsic properties of time-series of earthquakes. The Rescaled Range Analysis (RS-Analysis) was introduced by Hurst in 1956 and modified by Mandelbrot and Wallis in 1964. This method represents a simple and elegant analysis which determines the range of variation of one natural property (the seismic energy released in this case) in a time interval. Despite the simplicity, there is complexity inherent in the property measured. The cumulative curve of the energy released in time is the well-known fractal geometry of a devil’s staircase. This geometry is used for determining the maximum and minimum value of the range, which is normalized by the standard deviation. The rescaled range obtained obeys a power-law with the time, and the exponent is the Hurst value. Depending on this value, time-series can be classified in long-term or short-term memory. Hence, an algorithm has been developed for compiling the RS-Analysis for time series of earthquakes by days. Completeness time distribution and locally stationarity of the time series are required. The interest of this analysis is their application for a complex seismic crisis where different earthquakes take place in clusters in a short period. Therefore, the Hurst exponent has been obtained for the seismic crisis of Alhoceima (Mediterranean Sea) of January-March, 2016, where at least five medium-sized earthquakes were triggered. According to the values obtained from the Hurst exponent for each cluster, a different mechanical origin can be detected, corroborated by the focal mechanisms calculated by the official institutions. Therefore, this type of analysis not only allows an approach to a greater understanding of a seismic series but also makes possible to discern different types of seismic origins.

Keywords: Alhoceima crisis, earthquake time series, Hurst exponent, rescaled range analysis

Procedia PDF Downloads 316

Authors: Sibgha Noreen, Muhammad Salim Akhter, Seema Mahmood

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Salt stress is an abiotic stress that causes a heavy toll on growth and development and also reduces the productivity of arable and horticultural crops. Globally, a quarter of total arable land has fallen prey to this menace, and more is being encroached because of the usage of brackish water for irrigation purposes. Though barley is categorized as salt-tolerant crop, but cultivars show a wide genetic variability in response to it. In addressing salt stress, silicon nutrition would be a facile tool for enhancing salt tolerant to sustain crop production. A greenhouse study was conducted to evaluate the response of barley (Hordeum vulgare L.) cultivars to silicon nutrition under salt stress. The treatments included [(a) four barley cultivars (Jou-87, B-14002, B-14011, B-10008); (b) two salt levels (0, 200 mM, NaCl); and (c) two silicon levels (0, 200ppm, K2SiO3. nH2O), arranged in a factorial experiment in a completely randomized design with 16 treatments and repeated 4 times. Plants were harvested at 15 days after exposure to different experimental salinity and silicon foliar conditions. Results revealed that various physiological and biochemical attributes differed significantly (p<0.05) in response to different treatments and their interactive effects. Cultivar “B-10008” excelled in biological yield, chlorophyll constituents, antioxidant enzymes, and grain yield compared to other cultivars. The biological yield of shoot and root organs was reduced by 27.3 and 26.5 percent under salt stress, while it was increased by 14.5 and 18.5 percent by exogenous application of silicon over untreated check, respectively. The imposition of salt stress at 200 mM caused a reduction in total chlorophyll content, chl ‘a’ , ‘b’ and ratio a/b by 10.6,16.8,17.1 and 7.1, while spray of 200 ppm silicon improved the quantum of the constituents by 10.4,12.1,10.2,10.3 over untreated check, respectively. The quantum of free amino acids and protein content was enhanced in response to salt stress and the spray of silicon nutrients. The amounts of superoxide dismutase, catalases, peroxidases, hydrogen peroxide, and malondialdehyde contents rose to 18.1, 25.7, 28.1, 29.5, and 17.6 percent over non-saline conditions under salt stress. However, the values of these antioxidants were reduced in proportion to salt stress by 200 ppm silicon applied as rooting medium on barley crops. The salt stress caused a reduction in the number of tillers, number of grains per spike, and 100-grain weight to the amount of 29.4, 8.6, and 15.8 percent; however, these parameters were improved by 7.1, 10.3, and 9.6 percent by foliar spray of silicon over untreated crop, respectively. It is concluded that the barley cultivar “B-10008” showed greater tolerance and adaptability to saline conditions. The yield of barley crops could be potentiated by a foliar spray of 200 ppm silicon at the vegetative growth stage under salt stress.

Keywords: salt stress, silicon nutrition, chlorophyll constituents, antioxidant enzymes, barley crop

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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Recently, user’s interests for location-based social network service increases according to the advances of social web and location-based technologies. It may be easy to recommend preferred items if we can use user’s preference, context and social network information simultaneously. In this study, we propose context-aware POI (point-of-interests) recommender systems using location-based network analysis and sentiment analysis which consider context, social network information and implicit user’s preference score. We propose a context-aware POI recommendation system consisting of three sub-modules and an integrated recommendation system of them. First, we will develop a recommendation module based on network analysis. This module combines social network analysis and cluster-indexing collaboration filtering. Next, this study develops a recommendation module using social singular value decomposition (SVD) and implicit SVD. In this research, we will develop a recommendation module that can recommend preference scores based on the frequency of POI visits of user in POI recommendation process by using social and implicit SVD which can reflect implicit feedback in collaborative filtering. We also develop a recommendation module using them that can estimate preference scores based on the recommendation. Finally, this study will propose a recommendation module using opinion mining and emotional analysis using data such as reviews of POIs extracted from location-based social networks. Finally, we will develop an integration algorithm that combines the results of the three recommendation modules proposed in this research. Experimental results show the usefulness of the proposed model in relation to the recommended performance.

Keywords: sentiment analysis, network analysis, recommender systems, point-of-interests, business analytics

Procedia PDF Downloads 244

Authors: Manav Sabharwal, Ruda Rai Md, Candace Parker, James Ridley

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Wheat is one of the most commonly consumed grains worldwide, which contains gluten. Nowadays, gluten intake is considered to be a trigger for GRDs, including Celiac disease (CD), a common genetic disease affecting 1% of the US population, non-celiac gluten sensitivity (NCGS) and wheat allergy. NCGS is being recognized as an acquired gluten-sensitive enteropathy that is prevalent across age, ethnic and geographic groups. The cause of this entity is not fully understood, and recent studies suggest that it is more common in participants with irritable bowel syndrome (IBS), with iron deficiency anemia, symptoms of fatigue, and has considerable overlap in symptoms with IBS and Crohn’s disease. However, these studies were lacking in availability of complete serologies, imaging tests and/or pan-endoscopy. We performed a prospective study of 745 adult patients who presented to an outpatient clinic for evaluation of chronic upper gastro-intestinal symptoms and subsequently underwent an upper endoscopic (EGD) examination as standard of care. Evaluation comprised of comprehensive celiac antibody panel, inflammatory bowel disease (IBD) serologic markers, duodenal biopsies and Small Bowel Video Capsule Endoscopy (VCE), when available. At least 6 biopsy specimens were obtained from the duodenum and proximal jejunum during EGD, and CD3+ Intraepithelial lymphocytes (IELs) and villous architecture were evaluated by a single experienced pathologist, and VCE was performed by a single experienced gastroenterologist. Of the 745 patients undergoing EGD, 12% (93/745) patients showed elevated CD3+ IELs in the duodenal biopsies. 52% (387/745) completed a comprehensive CD panel and 7.2% (28/387) were positive for at least 1 CD antibody (Tissue transglutaminase (tTG), being the most common antibody in 65% (18/28)). Of these patients, 18% (5/28) showed increased duodenal CD3+ IELs, but 0% showed villous blunting or distortion to meet criteria for CD. Surprisingly, 43% (12/28) were positive for at 1 IBD serology (ASCA, ANCA or expanded IBD panel (LabCorp)). Of these 28 patients, 29% (8/28) underwent a SB VCE, of which 100 % (8/8) showed significant jejuno-ileal mucosal lesions diagnostic for IBD. Findings of abnormal CD antibodies (7.2%, 28/387) and increased CD3+ IELs on duodenal biopsy (12%, 93/745) were observed frequently in patients with UGI symptoms undergoing EGD in an outpatient clinic. None met criteria for CD, and a high proportion (43%, 12/28) showed evidence of overlap with IBD. This suggests a potential causal link of acquired GRDs to underlying inflammation and gut mucosal barrier disruption. Further studies to investigate a role for abnormal antigen presentation of dietary gluten to gut associated lymphoid tissue as a cause are justified. This may explain a high prevalence of GRDs in the population and correlation with IBS, IBD and other gut inflammatory disorders.

Keywords: celiac, gluten sensitive enteropathy, lymphocitic enteritis, IBS, IBD

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Authors: Azadeh Jafari, Robert G. Owens

Abstract:

In this study, a geometrical multiscale approach which means coupling together the 2-D Navier-Stokes equations, constitutive equations and 0-D lumped parameter models is investigated. A multiscale approach, suggest a natural way of coupling detailed local models (in the flow domain) with coarser models able to describe the dynamics over a large part or even the whole cardiovascular system at acceptable computational cost. In this study we introduce a new velocity correction scheme to decouple the velocity computation from the pressure one. To evaluate the capability of our new scheme, a comparison between the results obtained with Neumann outflow boundary conditions on the velocity and Dirichlet outflow boundary conditions on the pressure and those obtained using coupling with the lumped parameter model has been performed. Comprehensive studies have been done based on the sensitivity of numerical scheme to the initial conditions, elasticity and number of spectral modes. Improvement of the computational algorithm with stable convergence has been demonstrated for at least moderate Weissenberg number. We comment on mathematical properties of the reduced model, its limitations in yielding realistic and accurate numerical simulations, and its contribution to a better understanding of microvascular blood flow. We discuss the sophistication and reliability of multiscale models for computing correct boundary conditions at the outflow boundaries of a section of the cardiovascular system of interest. In this respect the geometrical multiscale approach can be regarded as a new method for solving a class of biofluids problems, whose application goes significantly beyond the one addressed in this work.

Keywords: geometrical multiscale models, haemorheology model, coupled 2-D navier-stokes 0-D lumped parameter modeling, computational fluid dynamics

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Authors: Mahesh K. Jat, Ankan Jana, Mahender Choudhary

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Rapid population and economic growth resulted in changes in large-scale land use land cover (LULC) changes. Changes in the biophysical properties of the Earth's surface and its impact on climate are of primary concern nowadays. Different approaches, ranging from location-based relationships or modelling earth surface - atmospheric interaction through modelling techniques like surface energy balance (SEB) are used in the recent past to examine the relationship between changes in Earth surface land cover and climatic characteristics like temperature and precipitation. A remote sensing-based model i.e., Surface Energy Balance Algorithm for Land (SEBAL), has been used to estimate the surface heat fluxes over Mahi Bajaj Sagar catchment (India) from 2001 to 2020. Landsat ETM and OLI satellite data are used to model the SEB of the area. Changes in observed precipitation and temperature, obtained from India Meteorological Department (IMD) have been correlated with changes in surface heat fluxes to understand the relative contributions of LULC change in changing these climatic variables. Results indicate a noticeable impact of LULC changes on climatic variables, which are aligned with respective changes in SEB components. Results suggest that precipitation increases at a rate of 20 mm/year. The maximum and minimum temperature decreases and increases at 0.007 ℃ /year and 0.02 ℃ /year, respectively. The average temperature increases at 0.009 ℃ /year. Changes in latent heat flux and sensible heat flux positively correlate with precipitation and temperature, respectively. Variation in surface heat fluxes influences the climate parameters and is an adequate reason for climate change. So, SEB modelling is helpful to understand the LULC change and its impact on climate.

Keywords: LULC, sensible heat flux, latent heat flux, SEBAL, landsat, precipitation, temperature

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Authors: Chettyparambil Lalchand Thejalakshmi, Sonal Sabu Edattukaran

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Hair loss is one of the main issues that contemporary society is dealing with. It can be attributable to a wide range of factors, listing from one's genetic composition and the anxiety we experience on a daily basis. Telogen effluvium [TE] is a condition that causes temporary hair loss after a stressor that might shock the body and cause the hair follicles to temporarily rest, leading to hair loss. Most frequently, women are the ones who bring up these difficulties. Extreme illness or trauma, an emotional or important life event, rapid weight loss and crash dieting, a severe scalp skin problem, a new medication, or ceasing hormone therapy are examples of potential causes. Men frequently do not notice hair thinning with time, but women with long hair may be easily identified when shedding, which can occasionally result in bias because women tend to be more concerned with aesthetics and beauty standards of the society, and approach frequently with the concerns .The woman, who formerly possessed a full head of hair, is worried about the hair loss from her scalp . There are several cases of hair loss reported every day, and Telogen effluvium is said to be the most prevalent one of them all without any hereditary risk factors. While the patient has loss in hair volume, baldness is not the result of this problem . The exponentially growing Dermatology and Aesthetic medical division has discovered that this problem is the most common and also the easiest to cure since it is feasible for these people to regrow their hair, unlike those who have scarring alopecia, in which the follicle itself is damaged and non-viable. Telogen effluvium comes in two different forms: acute and chronic. Acute TE occurs in all the age groups with a hair loss of less than three months, while chronic TE is more common in those between the ages of 30 and 60 with a hair loss of more than six months . Both kinds are prevalent throughout all age groups, regardless of the predominance. It takes between three and six months for the lost hair to come back, although this condition is readily reversed by eliminating stresses. After shedding their hair, patients frequently describe having noticeable fringes on their forehead. The current medical treatments for this condition include topical corticosteroids, systemic corticosteroids, minoxidil and finasteride, CNDPA (caffeine, niacinamide, panthenol, dimethicone, and an acrylate polymer) .Individual terminal hair growth was increased by 10% as a result of the innovative intervention CNDPA. Botulinum Toxin A, Scalp Micro Needling, Platelet Rich Plasma Therapy [PRP], and sessions with Multivitamin Mesotherapy Injections are some recently enhanced techniques with partially or completely reversible hair loss. Also, it has been shown that supplements like Nutrafol and Biotin are producing effective outcomes. There is virtually little evidence to support the claim that applying sulfur-rich ingredients to the scalp, such as onion juice, can help TE patients' hair regenerate.

Keywords: dermatology, telogen effluvium, hair loss, modern hair loass treatments

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Authors: Maryam Zarabian, Hector Guzman, Pedro Pereira-Almao, Abraham Fapojuwo

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Dry reforming of methane (DRM) has sparked significant industrial and scientific interest not only as a viable alternative for addressing the environmental concerns of two main contributors of the greenhouse effect, i.e., carbon dioxide (CO₂) and methane (CH₄), but also produces syngas, i.e., a mixture of hydrogen (H₂) and carbon monoxide (CO) utilized by a wide range of downstream processes as a feedstock for other chemical productions. In this study, we develop an AI-enable syngas production model to tackle the problem of achieving an equivalent H₂/CO ratio [1:1] with respect to the most efficient conversion. Firstly, the unsupervised density-based spatial clustering of applications with noise (DBSAN) algorithm removes outlier data points from the original experimental dataset. Then, random forest (RF) and deep neural network (DNN) models employ the error-free dataset to predict the DRM results. DNN models inherently would not be able to obtain accurate predictions without a huge dataset. To cope with this limitation, we employ reusing pre-trained layers’ approaches such as transfer learning and greedy layer-wise pretraining. Compared to the other deep models (i.e., pure deep model and transferred deep model), the greedy layer-wise pre-trained deep model provides the most accurate prediction as well as similar accuracy to the RF model with R² values 1.00, 0.999, 0.999, 0.999, 0.999, and 0.999 for the total outlet flow, H₂/CO ratio, H₂ yield, CO yield, CH₄ conversion, and CO₂ conversion outputs, respectively.

Keywords: artificial intelligence, dry reforming of methane, artificial neural network, deep learning, machine learning, transfer learning, greedy layer-wise pretraining

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Authors: An Chengrui, Yin Zi, Wu Bingbing, Ma Yuanzhu, Jin Kaixiu, Chen Xiao, Ouyang Hongwei

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Single cell RNA sequence (scRNA-seq) is one of the effective tools to study transcriptomics of biological processes. Recently, similarity measurement of cells is Euclidian distance or its derivatives. However, the process of scRNA-seq is a multi-variate Bernoulli event model, thus we hypothesize that it would be more efficient when the divergence between cells is valued with relative entropy than Euclidian distance. In this study, we compared the performances of Euclidian distance, Spearman correlation distance and Relative Entropy using scRNA-seq data of the early, medial and late stage of limb development generated in our lab. Relative Entropy is better than other methods according to cluster potential test. Furthermore, we developed KL-SNE, an algorithm modifying t-SNE whose definition of divergence between cells Euclidian distance to Kullback–Leibler divergence. Results showed that KL-SNE was more effective to dissect cell heterogeneity than t-SNE, indicating the better performance of relative entropy than Euclidian distance. Specifically, the chondrocyte expressing Comp was clustered together with KL-SNE but not with t-SNE. Surprisingly, cells in early stage were surrounded by cells in medial stage in the processing of KL-SNE while medial cells neighbored to late stage with the process of t-SNE. This results parallel to Heatmap which showed cells in medial stage were more heterogenic than cells in other stages. In addition, we also found that results of KL-SNE tend to follow Gaussian distribution compared with those of the t-SNE, which could also be verified with the analysis of scRNA-seq data from another study on human embryo development. Therefore, it is also an effective way to convert non-Gaussian distribution to Gaussian distribution and facilitate the subsequent statistic possesses. Thus, relative entropy is potentially a better way to determine the divergence of cells in scRNA-seq data analysis.

Keywords: Single cell RNA sequence, Similarity measurement, Relative Entropy, KL-SNE, t-SNE

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Authors: Jeet Chakraborty, Sanjay Dutta

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Antibiotic resistance by bacteria has proved to be a severe threat to mankind in recent times, and this fortifies an urgency to design and develop potent antibacterial small molecules/compounds with nonconventional mechanisms than the conventional ones. DNA carries the genetic signature of any organism, and bacteria maintain their genomic DNA inside the cell in a well-regulated compact form with the help of various nucleoid associated proteins like HU, HNS, etc. These proteins control various fundamental processes like gene expression, replication, etc., inside the cell. Alteration of the native DNA structure of bacteria can lead to severe consequences in cellular processes inside the bacterial cell that ultimately result in the death of the organism. The change in the global DNA structure by small molecules initiates a plethora of cellular responses that have not been very well investigated. Echinomycin and Triostin-A are biologically active Quinoxaline small molecules that typically consist of a quinoxaline chromophore attached with an octadepsipeptide ring. They bind to double-stranded DNA in a sequence-specific way and have high activity against a wide variety of bacteria, mainly against Gram-positive ones. To date, few synthetic quinoxaline scaffolds were synthesized, displaying antibacterial potential against a broad scale of pathogenic bacteria. QNOs (Quinoxaline N-oxides) are known to target DNA and instigate reactive oxygen species (ROS) production in bacteria, thereby exhibiting antibacterial properties. The divergent role of Quinoxaline small molecules in medicinal research qualifies them for the evaluation of their antimicrobial properties as a potential candidate. The previous study from our lab has given new insights on a 6-nitroquinoxaline derivative 1d as an intercalator of DNA, which induces conformational changes in DNA upon binding.7 The binding event observed was dependent on the presence of a crucial benzyl substituent on the quinoxaline moiety. This was associated with a large induced CD (ICD) appearing in a sigmoidal pattern upon the interaction of 1d with dsDNA. The induction of DNA superstructures by 1d at high Drug:DNA ratios was observed that ultimately led to DNA condensation. Eviction of invitro-assembled nucleosome upon treatment with a high dose of 1d was also observed. In this work, monoquinoxaline derivatives of 1d were synthesized by various modifications of the 1d scaffold. The set of synthesized 6-nitroquinoxaline derivatives along with 1d were all subjected to antibacterial evaluation across five different bacteria species. Among the compound set, 3a displayed potent antibacterial activity against Staphylococcus aureus bacteria. 3a was further subjected to various biophysical studies to check whether the DNA structural alteration potential was still intact. The biological response of S. aureus cells upon treatment with 3a was studied using various cell biology processes, which led to the conclusion that 3d can initiate DNA damage in the S. aureus cells. Finally, the potential of 3a in disrupting preformed S.aureus and S.epidermidis biofilms was also studied.

Keywords: DNA structural change, antibacterial, intercalator, DNA superstructures, biofilms

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Authors: Abolghasem Yousefi-Babadi, Ali Bozorgi-Amiri, Aida Kazempour, Reza Tavakkoli-Moghaddam, Maryam Irani

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All over the world, natural disasters (e.g., earthquakes, floods, volcanoes and hurricanes) causes a lot of deaths. Earthquakes are introduced as catastrophic events, which is accident by unusual phenomena leading to much loss around the world. Such could be replaced by disasters or any other synonyms strongly demand great long-term help and relief, which can be hard to be managed. Supplies and facilities are very important challenges after any earthquake which should be prepared for the disaster regions to satisfy the people's demands who are suffering from earthquake. This paper proposed disaster response facility allocation problem for disaster relief operations as a mathematical programming model. Not only damaged people in the earthquake victims, need the consumable commodities (e.g., food and water), but also they need non-consumable commodities (e.g., clothes) to protect themselves. Therefore, it is concluded that paying attention to disaster points and people's demands are very necessary. To deal with this objective, both commodities including consumable and need non-consumable commodities are considered in the presented model. This paper presented the multi-objective multi-period mathematical programming model regarding the minimizing the average of the weighted response times and minimizing the total operational cost and penalty costs of unmet demand and unused commodities simultaneously. Furthermore, a Chebycheff multi-objective solution procedure as a powerful solution algorithm is applied to solve the proposed model. Finally, to illustrate the model applicability, a case study of the Tehran earthquake is studied, also to show model validation a sensitivity analysis is carried out.

Keywords: facility location, multi-objective model, disaster response, commodity

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Authors: Anthony D. Rhodes, Manan Goel

Abstract:

We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

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Authors: Abida Mariam, Anwaar Ahmed, Asif Ahmad, Muhammad Sheeraz Ahmad, Muhammad Akram Khan, Muhammad Mazahir

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The plant olive (Olea europaea L.) is known for its commercial significance due to nutritional and health benefits. Pakistan is ranked 4th among countries who import olive oil whereas, 70% of edible oil is imported to fulfil the needs of the country. There exists great potential for Olea europaea cultivation in Pakistan. The popularity and cultivation of olive fruit has increased in recent past due to its high socio-economic and health significance. There exist almost negligible data on the chemical composition of extra virgin olive oil extracted from cultivars grown in Pothwar, an area with arid climate conducive for growth of olive trees. Keeping in view these factors a study has been conducted to characterize the olive oil extracted from olive cultivars collected from Pothwar regions of Pakistan for their nutritional potential and value addition. Ten olive cultivars (Gemlik, Coratina, Sevillano, Manzanilla, Leccino, Koroneiki, Frantoio, Arbiquina, Earlik and Ottobratica) were collected from Barani Agriculture Research Institute, Chakwal. Extra Virgin Olive Oil (EVOO) was extracted by cold pressing and centrifuging of olive fruits. The highest amount of oil was yielded in Coratina (23.9%) followed by Frantoio (23.7%), Koroneiki (22.8%), Sevillano (22%), Ottobratica (22%), Leccino (20.5%), Arbiquina (19.2%), Manzanilla (17.2%), Earlik (14.4%) and Gemllik (13.1%). The extracted virgin olive oil was studied for various physico- chemical properties and fatty acid profile. The Physical and chemical properties i.e., characteristic odor and taste, light yellow color with no foreign matter, insoluble impurities (≤0.08), fee fatty acid (0.1 to 0.8), acidity (0.5 to 1.6 mg/g acid), peroxide value (1.5 to 5.2 meqO2/kg), Iodine value (82 to 90), saponification value (186 to 192 mg/g) and unsaponifiable matter (4 to 8g/kg), ultraviolet spectrophotometric analysis (k232 and k270), showed values in the acceptable range, established by PSQCA and IOOC set for extra virgin olive oil. Olive oil was analyzed by Near Infra-Red spectrophotometry (NIR) for fatty acids sin olive oils which were found as: palmitic, palmitoleic, stearic, oleic, linoleic and alpha-linolenic. Major fatty acid was Oleic acid in the highest percentage ranging from (55 to 66.1%), followed by linoleic (10.4 to 20.4%), palmitic (13.8 to 19.5%), stearic (3.9 to 4.4%), palmitoleic (0.3 to 1.7%) and alpha-linolenic (0.9 to 1.7%). The results were significant with differences in parameters analyzed for all ten cultivars which confirm that genetic factors are important contributors in the physico-chemical characteristics of oil. The olive oil showed superior physical and chemical properties and recommended as one of the healthiest forms of edible oil. This study will help consumers to be more aware of and make better choices of healthy oils available locally thus contributing towards their better health.

Keywords: characterization, extra virgin olive oil, oil yield, fatty acids

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Authors: Elisavet Kechagia

Abstract:

Due to the interplay of different genetic and environmental risk factors and its heterogeneous nature, the concept of attention deficit hyperactivity disorder (ADHD) has shaped controversy and conflicts, which have been, in turn, reflected in the controversial arguments about its treatment. Taking into account recent well evidence-based researches suggesting that ADHD is a condition, in which biopsychosocial factors are all weaved together, the current paper explores the multiple risk-factors that are likely to influence ADHD, with a particular focus on adolescents with ADHD who might experience comorbid social, emotional and behavioural disorders (SEBD). In the first section of this paper, the primary objective was to investigate the conflicting ideas regarding the definition, diagnosis and treatment of ADHD at an international level as well as to critically examine and identify the limitations of the two most prevailing sets of diagnostic criteria that inform current diagnosis, the American Psychiatric Association’s (APA) diagnostic scheme, DSM-V, and the World Health Organisation’s (WHO) classification of diseases, ICD-10. Taking into consideration the findings of current longitudinal studies on ADHD association with high rates of comorbid conditions and social dysfunction, in the second section the author moves towards an investigation of the transitional points −physical, psychological and social ones− that students with ADHD might experience during early adolescence, as informed by neuroscience and developmental contextualism theory. The third section is an exploration of the different perspectives of ADHD as reflected in individuals’ with ADHD self-reports and the KENT project’s findings on school staff’s attitudes and practices. In the last section, given the high rates of SEBDs in adolescents with ADHD, it is examined how cognitive behavioural therapy (CBT), coupled with other interventions, could be effective in ameliorating anti-social behaviours and/or other emotional and behavioral difficulties of students with ADHD. The findings of a range of randomised control studies indicate that CBT might have positive outcomes in adolescents with multiple behavioural problems, hence it is suggested to be considered both in schools and other community settings. Finally, taking into account the heterogeneous nature of ADHD, the different biopsychosocial and environmental risk factors that take place during adolescence and the discourse and practices concerning ADHD and SEBD, it is suggested how it might be possible to make sense of and meaningful improvements to the education of adolescents with ADHD within a multi-modal and multi-disciplinary whole-school approach that addresses the multiple problems that not only students with ADHD but also their peers might experience. Further research that would be based on more large-scale controls and would investigate the effectiveness of various interventions, as well as the profiles of those students who have benefited from particular approaches and those who have not, will generate further evidence concerning the psychoeducation of adolescents with ADHD allowing for generalised conclusions to be drawn.

Keywords: adolescence, attention deficit hyperctivity disorder, cognitive behavioural theory, comorbid social emotional behavioural disorders, treatment

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Authors: Biki Sarmah, Priyanko Raj Mudiar

Abstract:

In the modern world of remotely interactive virtual reality-based learning and teaching, including professional skill-building training and acquisition practices, as well as data acquisition and robotic systems, the revolutionary application or implementation of field-programmable neurostimulator aids and first-hand interactive sensitisation techniques into 3D holographic audio-visual platforms have been a coveted dream of many scholars, professionals, scientists, and students. Integration of 'kinaesthetic vibrotactile haptic perception' along with an actuated step magnitude contact profiloscopy in augmented reality-based learning platforms and professional training can be implemented by using an extremely calculated and well-coordinated image telemetry including remote data mining and control technique. A real-time, computer-aided (PLC-SCADA) field calibration based algorithm must be designed for the purpose. But most importantly, in order to actually realise, as well as to 'interact' with some 3D holographic models displayed over a remote screen using remote laser image telemetry and control, all spatio-physical parameters like cardinal alignment, gyroscopic compensation, as well as surface profile and thermal compositions, must be implemented using zero-order type 1 actuators (or transducers) because they provide zero hystereses, zero backlashes, low deadtime as well as providing a linear, absolutely controllable, intrinsically observable and smooth performance with the least amount of error compensation while ensuring the best ergonomic comfort ever possible for the users.

Keywords: haptic feedback, kinaesthetic vibrotactile 3D design, medical simulation training, piezo diaphragm based actuator

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Authors: Somya R. Patro, Arnab Banerjee, G. V. Ramana

Abstract:

A flexural beam carrying elastically mounted concentrated masses, such as engines, motors, oscillators, or vibration absorbers, is often encountered in mechanical, civil, and aeronautical engineering domains. To prevent resonance conditions, the designers must predict the natural frequencies of such a constrained beam system. This paper investigates experimental and analytical studies on vibration suppression in a cantilever beam with a tip mass with the help of spring-mass to achieve local resonance conditions. The system consists of a 3D printed polylactic acid (PLA) beam screwed at the base plate of the shaker system. The top of the free end is connected by an accelerometer which also acts as a tip mass. A spring and a mass are attached at the bottom to replicate the mechanism of the spring-mass resonator. The Fast Fourier Transform (FFT) algorithm converts time acceleration plots into frequency amplitude plots from which transmittance is calculated as a function of the excitation frequency. The mathematical formulation is based on the transfer matrix method, and the governing differential equations are based on Euler Bernoulli's beam theory. The experimental results are successfully validated with the analytical results, providing us essential confidence in our proposed methodology. The beam spring-mass system is then converted to an equivalent two-degree of freedom system, from which frequency response function is obtained. The H2 optimization technique is also used to obtain the closed-form expression of optimum spring stiffness, which shows the influence of spring stiffness on the system's natural frequency and vibration response.

Keywords: euler bernoulli beam theory, fast fourier transform, natural frequencies, polylactic acid, transmittance, vibration absorbers

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Authors: Supriya Ambawat, Subaran Singh, C. Tara Satyavathi, B. S. Rajpurohit, Ummed Singh, Balraj Singh

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Pearl millet [Pennisetum glaucum (L.) R. Br.] is an important staple food of the arid and semiarid tropical regions of Asia, Africa, and Latin America. It is rightly termed as nutricereal as it has high nutrition value and a good source of carbohydrate, protein, fat, ash, dietary fiber, potassium, magnesium, iron, zinc, etc. Pearl millet has low prolamine fraction and is gluten free which is useful for people having a gluten allergy. It has several health benefits like reduction in blood pressure, thyroid, diabe¬tes, cardiovascular and celiac diseases but its direct consumption as food has significantly declined due to several reasons. Keeping this in view, it is important to reorient the ef¬forts to generate demand through value-addition and quality improvement and create awareness on the nutritional merits of pearl millet. In India, through Indian Council of Agricultural Research-All India Coordinated Research Project on Pearl millet, multilocational coordinated trials for developed hybrids were conducted at various centers. The gene banks of pearl millet contain varieties with high levels of iron and zinc which were used to produce new pearl millet varieties with elevated iron levels bred with the high‐yielding varieties. Thus, using breeding approaches and biochemical analysis, a total of 167 hybrids and 61 varieties were identified and released for cultivation in different agro-ecological zones of the country which also includes some biofortified hybrids rich in Fe and Zn. Further, using several biotechnological interventions such as molecular markers, next-generation sequencing (NGS), association mapping, nested association mapping (NAM), MAGIC populations, genome editing, genotyping by sequencing (GBS), genome wide association studies (GWAS) advancement in millet improvement has become possible by identifying and tagging of genes underlying a trait in the genome. Using DArT markers very high density linkage maps were constructed for pearl millet. Improved HHB67 has been released using marker assisted selection (MAS) strategies, and genomic tools were used to identify Fe-Zn Quantitative Trait Loci (QTL). The draft genome sequence of millet has also opened various ways to explore pearl millet. Further, genomic positions of significantly associated simple sequence repeat (SSR) markers with iron and zinc content in the consensus map is being identified and research is in progress towards mapping QTLs for flour rancidity. The sequence information is being used to explore genes and enzymatic pathways responsible for rancidity of flour. Thus, development and application of several biotechnological approaches along with biofortification can accelerate the genetic gain targets for pearl millet improvement and help improve its quality.

Keywords: Biotechnological approaches, genomic tools, malnutrition, MAS, nutricereal, pearl millet, sequencing.

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