Search results for: Bal Deep Sharma

Authors: Mehul Srivastava, Sharma Tushar Ravikant, Mridul Krishn Mishra

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In structural design, the evaluation of safety and probability failure of structure is of significant importance, mainly when the variables are random. On real structures, structural reliability can be evaluated obtaining an implicit limit state function. The structural reliability limit state function is obtained depending upon the statistically independent variables. In the analysis of reliability, we considered the statistically independent random variables to be the load intensity applied and the depth or height of the beam member considered. There are many approaches for structural reliability problems. In this paper Extreme Learning Machine technique and First Order Second Moment Method is used to determine the reliability indices for the same set of variables. The reliability index obtained using ELM is compared with the reliability index obtained using FOSM. Higher the reliability index, more feasible is the method to determine the reliability.

Keywords: reliability, reliability index, statistically independent, extreme learning machine

Procedia PDF Downloads 677

Authors: Nasrud Din, Fawad Saeed, Sajid Hussain, Rai Muhammad Dawood Sultan, Premkumar Sellan, Qasim Khan, Wei Lei

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The continuously increasing external quantum efficiencies of Perovskite light-emitting diodes (LEDs) have received significant interest in the scientific community. The need for monitoring and medical diagnostics has experienced a steady growth in recent years, primarily caused by older people and an increasing number of heart attacks, tumors, and cancer disorders among patients. The application of Perovskite near-infrared light-emitting diode (PeNIRLEDs) has exhibited considerable efficacy in bioimaging, particularly in the visualization and examination of blood arteries, blood clots, and tumors. PeNIRLEDs exhibit exciting potential in the field of blood vessel imaging because of their advantageous attributes, including improved depth penetration and less scattering in comparison to visible light. In this study, we synthesized FAPbI₃ Perovskite doped with different concentrations of 5-Aminovaleric acid (5-AVA) 1-6 mg. The incorporation of 5-AVA as a dopant during the FAPbI₃ Perovskite formation influences the FAPbI3 Perovskite’s structural and optical properties, improving its stability, photoluminescence efficiency, and charge transport characteristics. We found a resulting PL emission peak wavelength of 850 nm and bandwidth of 44 nm, along with a calculated quantum yield of 75%. The incorporation of 5-AVA-modified FAPbI₃ Perovskite into LEDs will show promising results, enhancing device efficiency, color purity, and stability. Making it suitable for various medical applications, including subcutaneous deep vein imaging, blood flow visualization, and tumor illumination.

Keywords: perovskite light-emitting diodes, deep vein imaging, blood flow visualization, tumor illumination

Procedia PDF Downloads 50

Authors: Soniya Takshak, R. K. Sharma

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Let q and n be positive integers, then Fᵩ denotes the finite field of q elements, and Fqn denotes the extension of Fᵩ of degree n. Also, Fᵩ* represents the multiplicative group of non-zero elements of Fᵩ, and the generators of Fᵩ* are called primitive elements. A normal element α of a finite field Fᵩⁿ is such that {α, αᵠ, . . . , αᵠⁿ⁻¹} forms a basis for Fᵩⁿ over Fᵩ. Primitive normal elements have several applications in coding theory and cryptography. So, establishing the existence of primitive normal elements under certain conditions is both theoretically important and a natural issue. In this article, we provide a sufficient condition for the existence of a primitive normal element α in Fᵩⁿ of a prescribed primitive norm and non-zero trace over Fᵩ such that f(α) is also primitive, where f(x) ∈ Fᵩⁿ(x) is a rational function of degree sum m. Particularly, we investigated the rational functions of degree sum 4 over Fᵩⁿ, where q = 11ᵏ and demonstrated that there are only 3 exceptional pairs (q, n), n ≥ 7 for which such kind of primitive normal elements may not exist. In general, we show that such elements always exist except for finitely many choices of (q, n). To arrive to our conclusion, we used additive and multiplicative character sums.

Keywords: finite field, primitive element, normal element, norm, trace, character

Procedia PDF Downloads 97

Authors: Nandini Sharma

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Monitoring the characteristics of a nanostructured material comprises measurements of structural, morphological, mechanical, optical and electronic properties of the synthesized nanopowder and different layers and coatings of nanomaterials coated on transparent conducting oxides (TCOs) substrates like fluorine doped tin oxide (FTO) or Indium doped tin oxide (ITO). This article focuses on structural and optical characterization with emphasis on measurements of the photocatalytic efficiency as a photocatalyst and their interpretation to extract relevant information about various TCOs and materials, their emitter regions, and surface passivation. It also covers a brief description of techniques based on photoluminescence that can portray high resolution pictorial graphs for application as solar energy devices. With the advancement in the scientific techniques, detailed information about the structural, morphological, and optical properties can be investigated, which is further useful for engineering and designing of an efficient device. The common principles involved in the prevalent characterization techniques aid to illustrate the range of options that can be broadened in near future for acurate device characterization and diagnosis.

Keywords: characterization, structural, optical, nanomaterial

Procedia PDF Downloads 141

Authors: Mahmoud S. Zaghloul, Mohammed M. Khalaf, Wael N. Thabet, Haidy N. Asham

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Background. Hypertrophic scarring is a difficult problem for burn patients, and scar management is an essential aspect of outpatient burn therapy. Post-burn pathologic scars involve functional and aesthetic limitations that have a dramatic influence on the patient’s quality of life. The aim was to investigate the use of extracorporeal shock wave therapy (ESWT), which targets the fibroblasts in scar tissue, as an effective modality for scar treatment in burn patients. Subjects and methods: forty patients with post-burn scars were assigned randomly into two equal groups; their ages ranged from 20-45 years. The study group received ESWT and traditional physical therapy program (deep friction massage, stretching exercises). The control group received traditional physical therapy program (deep friction massage, stretching exercises). All groups received two sessions per week for six successful weeks. The data were collected before and after the same period of treatment for both groups. Evaluation procedures were carried out to measure scar thickness using ultrasonography and Vancouver Scar Scale (VSS) was completed before and after treatment. Results: Post-treatment results showed that there was a significant improvement difference in scar thickness in both groups in favor of the study group. Percentage of improvement in scar thickness in the study group was 42.55%, while it was 12.15% in the control group. There was also a significant improvement difference between results obtained using VSS in both groups in favor of the study group. Conclusion: ESWT is effective in management of pathologic post burn scars.

Keywords: extracorporeal shock wave therapy, post-burn scars, ultrasonography, Vancouver scar scale

Procedia PDF Downloads 254

Authors: Trapti Sharma, Devesh Kumar Srivastava

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This paper is basically a analysis study of above MapReduce implementation and also to verify and validate the MapReduce solution model for Parallel K-Mediod algorithm on Hadoop Cluster. MapReduce is a programming model which authorize the managing of huge amounts of data in parallel, on a large number of devices. It is specially well suited to constant or moderate changing set of data since the implementation point of a position is usually high. MapReduce has slowly become the framework of choice for “big data”. The MapReduce model authorizes for systematic and instant organizing of large scale data with a cluster of evaluate nodes. One of the primary affect in Hadoop is how to minimize the completion length (i.e. makespan) of a set of MapReduce duty. In this paper, we have verified and validated various MapReduce applications like wordcount, grep, terasort and parallel K-Mediod clustering algorithm. We have found that as the amount of nodes increases the completion time decreases.

Keywords: hadoop, mapreduce, k-mediod, validation, verification

Procedia PDF Downloads 363

Authors: Manaranjan Pradhan, Shailaja Grover, U. Dinesh Kumar

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Farming community in India, as well as other parts of the world, is one of the highly stressed communities due to reasons such as increasing input costs (cost of seeds, fertilizers, pesticide), droughts, reduced revenue leading to farmer suicides. Lack of integrated farm advisory system in India adds to the farmers problems. Farmers need right information during the early stages of crop’s lifecycle to prevent damage and loss in revenue. In this paper, we use deep learning techniques to develop an early warning system for detection of crop diseases using images taken by farmers using their smart phone. The research work leads to building a smart assistant using analytics and big data which could help the farmers with early diagnosis of the crop diseases and corrective actions. The classical approach for crop disease management has been to identify diseases at crop level. Recently, ImageNet Classification using the convolutional neural network (CNN) has been successfully used to identify diseases at individual plant level. Our model uses convolution filters, max pooling, dense layers and dropouts (to avoid overfitting). The models are built for binary classification (healthy or not healthy) and multi class classification (identifying which disease). Transfer learning is used to modify the weights of parameters learnt through ImageNet dataset and apply them on crop diseases, which reduces number of epochs to learn. One shot learning is used to learn from very few images, while data augmentation techniques are used to improve accuracy with images taken from farms by using techniques such as rotation, zoom, shift and blurred images. Models built using combination of these techniques are more robust for deploying in the real world. Our model is validated using tomato crop. In India, tomato is affected by 10 different diseases. Our model achieves an accuracy of more than 95% in correctly classifying the diseases. The main contribution of our research is to create a personal assistant for farmers for managing plant disease, although the model was validated using tomato crop, it can be easily extended to other crops. The advancement of technology in computing and availability of large data has made possible the success of deep learning applications in computer vision, natural language processing, image recognition, etc. With these robust models and huge smartphone penetration, feasibility of implementation of these models is high resulting in timely advise to the farmers and thus increasing the farmers' income and reducing the input costs.

Keywords: analytics in agriculture, CNN, crop disease detection, data augmentation, image recognition, one shot learning, transfer learning

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Authors: Ramanpreet Kaur, Upasana Sharma

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The purpose of this paper is to address the challenges facing the water supply for the Machine Learning Database (MLDB) system at the piston foundry plant. In the MLDB system, one main unit, i.e., robotic, is connected by two sub-units. The functioning of the system depends on the robotic and water supply. Lack of water supply causes system failure. The system operates at full capacity with the help of two sub-units. If one sub-unit fails, the system runs at a low capacity. Reliability modeling is performed using semi-Markov processes and regenerative point techniques. Several system effects such as mean time to system failure, availability at full capacity, availability at reduced capacity, busy period for repair and expected number of visits have been achieved. Benefits have been analyzed. The graphical study is designed for a specific case using programming in C++ and MS Excel.

Keywords: MLDB system, robotic, semi-Markov process, regenerative point technique

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Authors: Monica Sharma, Harjit Pal Singh, Jasbinder Singh, Manju Bala

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In Voice over Internet Protocol (VoIP) system, the speech signal is degraded when passed through the network layers. The speech signal is processed through the best effort policy based IP network, which leads to the network degradations including delay, packet loss and jitter. The packet loss is the major issue of the degradation in the VoIP signal quality; even a single lost packet may generate audible distortion in the decoded speech signal. In addition to these network degradations, the quality of the speech signal is also affected by the environmental noises and coder distortions. The signal quality of the VoIP system is improved through the interleaving technique. The performance of the system is evaluated for various types of noises at different network conditions. The performance of the enhanced VoIP signal is evaluated using perceptual evaluation of speech quality (PESQ) measurement for narrow band signal.

Keywords: VoIP, interleaving, packet loss, packet size, background noise

Procedia PDF Downloads 475

Authors: S. P. Sharma, Som Nath Saha

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This study investigates the effect of Global Solar Radiation (GSR) on the performance of double flow corrugated absorber solar air heater. A mathematical model of a double flow solar air heater, in which air is flowing simultaneously over and under the absorbing plate is presented and solved by developing a computer program in C++ language. The performance evaluation is studied in terms of air temperature rise, energy, effective and exergy efficiencies. The performance of double flow corrugated absorber is compared with double flow flat plate and conventional solar air heaters. It is found that double flow effectively increases the air temperature rise and efficiencies in comparison to a conventional collector. However, corrugated absorber is more superior to that of flat plate double flow solar air heater. The results show that increasing the solar radiation leads to achieve higher air temperature rise and efficiencies.

Keywords: corrugated absorber, double flow, flat plate, solar air heater

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Authors: Hermalina Sinay, Estri L. Arumingtyas

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The research objective was to determine the expression level of dehydration responsive element binding/DREB gene of local corn cultivars from Kisar Island Maluku. The study design was a randomized block design with single factor consist of six local corn cultivars obtained from farmers in Kisar Island and one reference varieties wich has been released by the government as a drought-tolerant varieties and obtained from Cereal Crops Research Institute (ICERI) Maros South Sulawesi. Leaf samples were taken is the second leaf after the flag leaf at the 65 days after planting. Isolation of total RNA from leaf samples was carried out according to the protocols of the R & A-BlueTM Total RNA Extraction Kit and was used as a template for cDNA synthesis. The making of cDNA from total RNA was carried out according to the protocol of One-Step Reverse Transcriptase PCR Premix Kit. Real Time-PCR was performed on cDNA from reverse transcription followed the procedures of Real MODTM Green Real-Time PCR Master Mix Kit. Data obtained from the real time-PCR results were analyzed using relative quantification method based on the critical point / Cycle Threshold (CP / CT). The results of gene expression analysis of DREB gene showed that the expression level of the gene was highest obtained at Deep Yellow local corn cultivar, and the lowest one was obtained at the Rubby Brown Cob cultivar. It can be concluded that the expression level of DREB gene of Deep Yellow local corn cultivar was highest than other local corn cultivars and Srikandi variety as a reference variety.

Keywords: expression, level, DREB gene, local corn cultivars, Kisar Island, Maluku

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Authors: Hussein Bizimana, Osman Sönmez

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Constructed wetlands are current the most widely recognized waste water treatment option, especially in developing countries where they have the potential for improving water quality and creating valuable wildlife habitat in ecosystem with treatment requirement relatively simple for operation and maintenance cost. Lack of grey waste water treatment facilities in Kigali İnstitute of Science and Technology in Rwanda, causes pollution in the surrounding localities of Rugunga sector, where already a problem of poor sanitation is found. In order to treat grey water produced at Kigali İnstitute of Science and Technology, with high BOD concentration, high nutrients concentration and high alkalinity; a Horizontal Sub-surface Flow pilot-scale constructed wetland was designed and can operate in Kigali İnstitute of Science and Technology. The study was carried out in a sedimentation tank of 5.5 m x 1.42 m x 1.2 m deep and a Horizontal Sub-surface constructed wetland of 4.5 m x 2.5 m x 1.42 m deep. The grey waste water flow rate of 2.5 m3/d flew through vegetated wetland and sandy pilot plant. The filter media consisted of 0.6 to 2 mm of coarse sand, 0.00003472 m/s of hydraulic conductivity and cattails (Typha latifolia spp) were used as plants species. The effluent flow rate of the plant is designed to be 1.5 m3/ day and the retention time will be 24 hrs. 72% to 79% of BOD, COD, and TSS removals are estimated to be achieved, while the nutrients (Nitrogen and Phosphate) removal is estimated to be in the range of 34% to 53%. Every effluent characteristic will meet exactly the Rwanda Utility Regulatory Agency guidelines primarily because the retention time allowed is enough to make the reduction of contaminants within effluent raw waste water. Treated water reuse system was developed where water will be used in the campus irrigation system again.

Keywords: constructed wetlands, hydraulic conductivity, grey waste water, cattails

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Authors: Jhuma Debnath, Hrishikesh Sharma

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The work done here in this paper is dedicated to studying the effect of high blast explosives over the seismically designed buildings. Buildings are seismically designed in SAP 2000 software to simulate seismic designs of buildings using response spectrum method. Later these buildings have been studied applying blast loads with the same amount of the blast explosives. This involved varying the standoff distances of the buildings from the blast explosion. The study found out that, for a seismically designed building, the minimum standoff distance is to be at least 120m from the place of explosion for an average blast explosive weight of 20kg TNT. This has shown that the building does not fail due to this huge explosive weight of TNT but resists immediate collapse of the building. The results also show that the adverse effect of the column failure due to blasting is reduced to 73.75% from 22.5% due to the increase of the standoff distance from the blast loads. The maximum affected locations due to the blast loads are also detected in this study.

Keywords: blast loads, seismically designed buildings, standoff distance, reinforced concrete buildings

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Authors: Shahul Hamid Khan, S. Santhosh, Abhinav Kumar Sharma

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Environmental performance along with social performance is becoming vital factors for industries to achieve global standards. With a good environmental policy global industries are differentiating them from their competitors. This paper concentrates on multi stage, multi product and multi period manufacturing network. Bi-objective mathematical models for total cost and total emission for the entire forward supply chain are considered. Here five different problems are considered by varying the number of suppliers, manufacturers, and environmental levels, for illustrating the taken mathematical model. GA, and Random search are used for finding the optimal solution. The input parameters of the optimal solution are used to find the tradeoff between the initial investment by the industry and the long term benefit of the environment.

Keywords: closed loop supply chain, genetic algorithm, random search, green supply chain

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Authors: Deepak Mohan, Sushma Sharma

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Diclofenac sodium, a member of the acetic acid family of non-steroidal anti-inflammatory drugs, is used to retard inflammation, arthritis pain and ankylosing spondylitis. The drug is known to cause severe injury in different tissues due to formation of reactive oxygen species. The present study is focused on the effect of different doses of diclofenac (4 mg/kg/body weight and 14 mg/kg/body weight on histoarchitecture of the liver from 7-28 days of the investigation. Diclofenac administration resulted in distorted hepatic degeneration and formation of wide areas in the form of sinusoidal gaps. Hepatic fibrosis noticed in different stages of investigation could be attributed to chronic inflammation and reactive oxygen species which results in deposition of extracellular matrix proteins. The abrupt degenerative changes observed during later stages of the experiment showed maximum damage to the liver, and there was enlargement of sinusoidal gaps accompanied by maximum necrosis in the tissues.

Keywords: arthritis, diclofenac, histoarchitecture, sinusoidal

Procedia PDF Downloads 267

Authors: Bikis Muhammed, Sehra Sedigh Sarvestani, Ali R. Hurson, Lasanthi Gamage

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Vehicular traffic events have overly complex spatial correlations and temporal interdependencies and are also influenced by environmental events such as weather conditions. To capture these spatial and temporal interdependencies and make more realistic vehicular traffic predictions, graph neural networks (GNN) based traffic prediction models have been extensively utilized due to their capability of capturing non-Euclidean spatial correlation very effectively. However, most of the already existing GNN-based traffic prediction models have some limitations during learning complex and dynamic spatial and temporal patterns due to the following missing factors. First, most GNN-based traffic prediction models have used static distance or sometimes haversine distance mechanisms between spatially separated traffic observations to estimate spatial correlation. Secondly, most GNN-based traffic prediction models have not incorporated environmental events that have a major impact on the normal traffic states. Finally, most of the GNN-based models did not use an attention mechanism to focus on only important traffic observations. The objective of this paper is to study and make real-time vehicular traffic predictions while incorporating the effect of weather conditions. To fill the previously mentioned gaps, our prediction model uses a real-time driving distance between sensors to build a distance matrix or spatial adjacency matrix and capture spatial correlation. In addition, our prediction model considers the effect of six types of weather conditions and has an attention mechanism in both spatial and temporal data aggregation. Our prediction model efficiently captures the spatial and temporal correlation between traffic events, and it relies on the graph attention network (GAT) and Bidirectional bidirectional long short-term memory (Bi-LSTM) plus attention layers and is called GAT-BILSTMA.

Keywords: deep learning, real time prediction, GAT, Bi-LSTM, attention

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Authors: Sankara Rao Gollu, Ramakant Sharma, G. Srinivas, Souvik Kundu, Dipti Gupta

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In recent years, bulk heterojunction organic solar cells (BHJ OSCs) based on polymer–fullerene attracted a large research attention due to their numerous advantages such as light weight, easy processability, eco-friendly, low-cost, and capability for large area roll-to-roll manufacturing. BHJ OSCs usually suffer from insufficient light absorption due to restriction on keeping thin ( < 150 nm) photoactive layer because of small exciton diffusion length ( ~ 10 nm) and low charge carrier mobilities. It is thus highly desirable that light absorption as well as charge transport properties are enhanced by alternative methods so as to improve the device efficiency. In this work, therefore, we have focused on the strategy of incorporating metallic nanostructures in the active layer or charge transport layer to enhance the absorption and improve the charge transport.

Keywords: organic solar cell, efficiency, bulk heterojunction, polymer-fullerene

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Authors: Abbas Raza Ali

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Big Data technology is gradually becoming a dire need of large enterprises. These enterprises are generating massively large amount of off-line and streaming data in both structured and unstructured formats on daily basis. It is a challenging task to effectively extract useful insights from the large scale datasets, even though sometimes it becomes a technology constraint to manage transactional data history of more than a few months. This paper presents a framework to efficiently manage massively large and complex datasets. The framework has been tested on a communication service provider producing massively large complex streaming data in binary format. The communication industry is bound by the regulators to manage history of their subscribers’ call records where every action of a subscriber generates a record. Also, managing and analyzing transactional data allows service providers to better understand their customers’ behavior, for example, deep packet inspection requires transactional internet usage data to explain internet usage behaviour of the subscribers. However, current relational database systems limit service providers to only maintain history at semantic level which is aggregated at subscriber level. The framework addresses these challenges by leveraging Big Data technology which optimally manages and allows deep analysis of complex datasets. The framework has been applied to offload existing Intelligent Network Mediation and relational Data Warehouse of the service provider on Big Data. The service provider has 50+ million subscriber-base with yearly growth of 7-10%. The end-to-end process takes not more than 10 minutes which involves binary to ASCII decoding of call detail records, stitching of all the interrogations against a call (transformations) and aggregations of all the call records of a subscriber.

Keywords: big data, communication service providers, enterprise data warehouse, stream computing, Telco IN Mediation

Procedia PDF Downloads 173

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Mohammed Abu Shquier

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This paper introduces a word segmentation method using the novel BP-LSTM-CRF architecture for processing semantic output training. The objective of web morphological analysis tools is to link a formal morpho-syntactic description to a lemma, along with morpho-syntactic information, a vocalized form, a vocalized analysis with morpho-syntactic information, and a list of paradigms. A key objective is to continuously enhance the proposed system through an inductive learning approach that considers semantic influences. The system is currently under construction and development based on data-driven learning. To evaluate the tool, an experiment on homograph analysis was conducted. The tool also encompasses the assumption of deep binary segmentation hypotheses, the arbitrary choice of trigram or n-gram continuation probabilities, language limitations, and morphology for both Modern Standard Arabic (MSA) and Dialectal Arabic (DA), which provide justification for updating this system. Most Arabic word analysis systems are based on the phonotactic morpho-syntactic analysis of a word transmitted using lexical rules, which are mainly used in MENA language technology tools, without taking into account contextual or semantic morphological implications. Therefore, it is necessary to have an automatic analysis tool taking into account the word sense and not only the morpho-syntactic category. Moreover, they are also based on statistical/stochastic models. These stochastic models, such as HMMs, have shown their effectiveness in different NLP applications: part-of-speech tagging, machine translation, speech recognition, etc. As an extension, we focus on language modeling using Recurrent Neural Network (RNN); given that morphological analysis coverage was very low in dialectal Arabic, it is significantly important to investigate deeply how the dialect data influence the accuracy of these approaches by developing dialectal morphological processing tools to show that dialectal variability can support to improve analysis.

Keywords: NLP, DL, ML, analyser, MSA, RNN, CNN

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Authors: Harish Kumar Sharma

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The influence of different levels of inulin as a fat replacer on the quality of ice cream was investigated. The physicochemical, rheological and textural properties of control ice cream and ice cream prepared with inulin in different proportions were determined and correlated to the different parameters using Pearson correlation and Principle Component Analysis (PCA). Based on the overall acepectability, ice cream with 4% inulin was found best and was selected for preparation of ice cream with inulin:SPI in different proportions. Compared with control ice cream, Inulin:SPI showed different rheological properties, resulting in significantly higher apparent viscosities, consistency coefficient and greater deviations from Newtonian flow. In addition, both hardness and melting resistance significantly increased with increase in the SPI content in ice cream prepared with inulin: SPI. Also hardness value increased for inulin based ice cream compared to control ice cream but it melted significantly faster than the latter. Colour value significantly decreased in both the cases compared to the control sample. The deliberation shall focus on the effect of incorporation of inulin on the quality of ice-cream.

Keywords: fat replacer, inulin, ice cream, viscosity, principal component analysis

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Authors: Govind Sharma, Bahni Ray

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Ubiquitous nature of particle settling is governed by the presence of the surrounding fluid medium. Thermally stratified fluid alters the settling phenomenon of particles as well as their interactions. Direct numerical simulation (DNS) is carried out with an open-source library Immersed Boundary Adaptive Mesh Refinement (IBAMR) to quantify the fundamental mechanism based on Distributed Lagrangian Multiplier (DLM). The presence of background density gradient due to thermal stratification replaces the drafting-kissing-tumbling in a homogeneous fluid to drafting-kissing-separation behavior. Simulations are performed with a varying range of particle-fluid density ratios, and it is shown that the stratification effect on particle interactions varies with density ratio. It is observed that the combined role of buoyancy and inertia govern the physical mechanism of particle-particle interaction.

Keywords: direct numerical simulation, distributed lagrangian multiplier, rigidity constraint, sedimentation, stratification

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Authors: Geeta Negi, Pankaj, Anjana Srivastava, Anita Sharma

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In the present study, the presence of endosulfan, imidacloprid, carbendazim, in the soil /vegetables/cereals and water samples was observed in agriculture fields of Uttarakhand. In view of biodegradation of these pesticides, nine bacterial isolates were recovered from the soil samples of the fields which tolerated endosulfan, imidacloprid, carbendazim from 100 to 200 µg/ml. Three bacterial consortia used for in vitro bioremediation experiments were three bacterial isolates for carbendazim, imidacloprid and endosulfan, respectively. Maximum degradation (87 and 83%) of α and β endosulfan respectively was observed in soil slurry by consortium. Degradation of Imidacloprid and carbendazim under similar conditions was 88.4 and 77.5% respectively. FT-IR analysis of biodegraded samples of pesticides in liquid media showed stretching of various bonds. GC-MS of biodegraded endosulfan sample in soil slurry showed the presence of non-toxic intermediates. A pot trial with Bacterial treatments lowered down the uptake of pesticides in onion plants.

Keywords: biodegradation, carbendazim, consortium, endosulfan

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Authors: Mehrnaz Mostafavi

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The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Dheeraj Sharma, Santosh Kumar Vishvakarma

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We present a detailed analysis of analog and radiofrequency (RF) performance with different gate lengths for nanowire cylindrical gate (CylG) gate-all-around (GAA) MOSFET. CylG GAA MOSFET not only suppresses the short channel effects (SCEs), it is also a good candidate for analog/RF device due to its high transconductance (gm) and high cutoff frequency (fT ). The presented work would be beneficial for a new generation of RF circuits and systems in a broad range of applications and operating frequency covering the RF spectrum. For this purpose, the analog/RF figures of merit for CylG GAA MOSFET is analyzed in terms of gate to source capacitance (Cgs), gate to drain capacitance (Cgd), transconductance generation factor gm = Id (where Id represents drain current), intrinsic gain, output resistance, fT, maximum frequency of oscillation (fmax) and gain bandwidth (GBW) product.

Keywords: Gate-All-Around MOSFET, GAA, output resistance, transconductance generation factor, intrinsic gain, cutoff frequency, fT

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

Abstract:

Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Clotilde Guyon, Nada Jmari, Yen-Chin Li, Jean Denoyel, Noriyuki Fujikado, Christophe Blanchet, David Root, Matthieu Giraud

Abstract:

Aire induces ectopic expression of a large repertoire of tissue-specific antigen (TSA) genes in thymic medullary epithelial cells (MECs), driving immunological self-tolerance in maturing T cells. Although important mechanisms of Aire-induced transcription have recently been disclosed through the identification and the study of Aire’s partners, the fine transcriptional functions underlied by a number of them and conferred to Aire are still unknown. Alternative cleavage and polyadenylation (APA) is an essential mRNA processing step regulated by the termination complex consisting of 85 proteins, 10 of them have been related to Aire. We evaluated APA in MECs in vivo by microarray analysis with mRNA-spanning probes and RNA deep sequencing. We uncovered the preference of Aire-dependent transcripts for short-3’UTR isoforms and for proximal poly(A) site selection marked by the increased binding of the cleavage factor Cstf-64. RNA interference of the 10 Aire-related proteins revealed that Clp1, a member of the core termination complex, exerts a profound effect on short 3’UTR isoform preference. Clp1 is also significantly upregulated in the MECs compared to 25 mouse tissues in which we found that TSA expression is associated with longer 3’UTR isoforms. Aire-dependent transcripts escape a global 3’UTR lengthening associated with MEC differentiation, thereby potentiating the repressive effect of microRNAs that are globally upregulated in mature MECs. Consistent with these findings, RNA deep sequencing of actinomycinD-treated MECs revealed the increased stability of short 3’UTR Aire-induced transcripts, resulting in TSA transcripts accumulation and contributing for their enrichment in the MECs.

Keywords: Aire, central tolerance, miRNAs, transcription termination

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Authors: Hardisiswo Soedjana, Inne Caroline

Abstract:

Deep dermal or full thickness burns are inevitably lead to post-burn contractures. These contractures remain to be one of the most concerning late complications of burn injuries. Surgical management includes releasing the contracture followed by resurfacing the defect accompanied by post-operative rehabilitation. Optimal treatment of post-burn contractures depends on the characteristics of the contractures. This study is aimed to describe clinical characteristics, problems, and management of post-burn contractures in adults and children. A retrospective analysis was conducted from medical records of patients suffered from contractures after burn injuries admitted to Hasan Sadikin general hospital between January 2016 and January 2018. A total of 50 patients with post burn contractures were included in the study. There were 17 adults and 33 children. Most patients were male, whose age range within 15-59 years old and 5-9 years old. Educational background was mostly senior high school among adults, while there was only one third of children who have entered school. Etiology of burns was predominantly flame in adults (82.3%); whereas flame and scald were the leading cause of burn injury in children (11%). Based on anatomical regions, hands were the most common affected both in adults (35.2%) and children (48.5%). Contractures were identified in 6-12 months since the initial burns. Most post-burn hand contractures were resurfaced with full-thickness skin graft (FTSG) both in adults and children. There were 11 patients who presented with recurrent contracture after previous history of contracture release. Post-operative rehabilitation was conducted for all patients; however, it is important to highlight that it is still challenging to control splinting and exercise when patients are discharged and especially the compliance in children. In order to improve quality of life in patients with history of deep burn injuries, prevention of contractures should begin right after acute care has been established. Education for the importance of splinting and exercise should be administered as comprehensible as possible for adult patients and parents of pediatric patients.

Keywords: burn, contracture, education, exercise, splinting

Procedia PDF Downloads 126

Authors: Khet Raj Dahal, Dhruva Dhital, Chhatra Mani Sharma

Abstract:

A study was conducted during 2012 to 2013 in the selected reach of Tinau River, Nepal. The main objective of the study was to quantify employment and income generation from the extraction of construction materials from the river. A 10 km stretch of the river was selected for the study. Sample survey with a semi-structured questionnaire and field observation were the main tools used during field investigation. Extraction of riverbed materials from the banks, beds and floodplain areas of the river has provided many kinds of job opportunities for the people living in the vicinity of the river. It has also generated an adequate amount of revenues. The collected revenue has been invested for many kinds of social and infrastructures development for years. Though extraction of riverbed materials is beneficial for income and employment generation, it has also negative environmental impacts in and around the river. Furthermore, the study concluded that river bed extraction should be continued with special monitoring and evaluation in the areas where there is still room for extraction.

Keywords: extraction, crusher plants, economic activities, Tinau River

Procedia PDF Downloads 689

Authors: Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Abstract:

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new framework model which can be trained and adapt itself to new data and make accurate predictions. This work also throws some light on use of SVM’s methodology for text classifiers from the obtained traffic data. Finally, it emphasizes the uniqueness and adaptability of SVMs methodology appropriate for this kind of research work.

Keywords: support vector mechanism (SVM), machine learning (ML), support vector machines (SVM), department of transportation (DFT)

Procedia PDF Downloads 267