Search results for: Systems and Synthetic Biology
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 10194

Search results for: Systems and Synthetic Biology

10104 Cash Flow Optimization on Synthetic CDOs

Authors: Timothée Bligny, Clément Codron, Antoine Estruch, Nicolas Girodet, Clément Ginet

Abstract:

Collateralized Debt Obligations are not as widely used nowadays as they were before 2007 Subprime crisis. Nonetheless there remains an enthralling challenge to optimize cash flows associated with synthetic CDOs. A Gaussian-based model is used here in which default correlation and unconditional probabilities of default are highlighted. Then numerous simulations are performed based on this model for different scenarios in order to evaluate the associated cash flows given a specific number of defaults at different periods of time. Cash flows are not solely calculated on a single bought or sold tranche but rather on a combination of bought and sold tranches. With some assumptions, the simplex algorithm gives a way to find the maximum cash flow according to correlation of defaults and maturities. The used Gaussian model is not realistic in crisis situations. Besides present system does not handle buying or selling a portion of a tranche but only the whole tranche. However the work provides the investor with relevant elements on how to know what and when to buy and sell.

Keywords: synthetic collateralized debt obligation (CDO), credit default swap (CDS), cash flow optimization, probability of default, default correlation, strategies, simulation, simplex

Procedia PDF Downloads 246
10103 ARABEX: Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder and Custom Convolutional Recurrent Neural Network

Authors: Hozaifa Zaki, Ghada Soliman

Abstract:

In this paper, we introduced an approach for Automated Dotted Arabic Expiration Date Extraction using Optimized Convolutional Autoencoder (ARABEX) with bidirectional LSTM. This approach is used for translating the Arabic dot-matrix expiration dates into their corresponding filled-in dates. A custom lightweight Convolutional Recurrent Neural Network (CRNN) model is then employed to extract the expiration dates. Due to the lack of available dataset images for the Arabic dot-matrix expiration date, we generated synthetic images by creating an Arabic dot-matrix True Type Font (TTF) matrix to address this limitation. Our model was trained on a realistic synthetic dataset of 3287 images, covering the period from 2019 to 2027, represented in the format of yyyy/mm/dd. We then trained our custom CRNN model using the generated synthetic images to assess the performance of our model (ARABEX) by extracting expiration dates from the translated images. Our proposed approach achieved an accuracy of 99.4% on the test dataset of 658 images, while also achieving a Structural Similarity Index (SSIM) of 0.46 for image translation on our dataset. The ARABEX approach demonstrates its ability to be applied to various downstream learning tasks, including image translation and reconstruction. Moreover, this pipeline (ARABEX+CRNN) can be seamlessly integrated into automated sorting systems to extract expiry dates and sort products accordingly during the manufacturing stage. By eliminating the need for manual entry of expiration dates, which can be time-consuming and inefficient for merchants, our approach offers significant results in terms of efficiency and accuracy for Arabic dot-matrix expiration date recognition.

Keywords: computer vision, deep learning, image processing, character recognition

Procedia PDF Downloads 52
10102 Redox-Mediated Supramolecular Radical Gel

Authors: Sonam Chorol, Sharvan Kumar, Pritam Mukhopadhyay

Abstract:

In biology, supramolecular systems require the use of chemical fuels to stay in sustained nonequilibrium steady states termed dissipative self-assembly in contrast to synthetic self-assembly. Biomimicking these natural dynamic systems, some studies have demonstrated artificial self-assembly under nonequilibrium utilizing various forms of energies (fuel) such as chemical, redox, and pH. Naphthalene diimides (NDIs) are well-known organic molecules in supramolecular architectures with high electron affinity and have applications in controlled electron transfer (ET) reactions, etc. Herein, we report the endergonic ET from tetraphenylborate to highly electron-deficient phosphonium NDI²+ dication to generate NDI•+ radical. The formation of radicals was confirmed by UV-Vis-NIR absorption spectroscopy. Electron-donor and electron-acceptor energy levels were calculated from experimental electrochemistry and theoretical DFT analysis. The HOMO of the electron donor locates below the LUMO of the electro-acceptor. This indicates that electron transfer is endergonic (ΔE°ET = negative). The endergonic ET from NaBPh₄ to NDI²+ dication was achieved thermodynamically by the formation of coupled biphenyl product confirmed by GC-MS analysis. NDI molecule bearing octyl phosphonium at the core and H-bond forming imide moieties at the axial position forms a gel. The rheological properties of purified radical ion NDI⦁+ gels were evaluated. The atomic force microscopy studies reveal the formation of large branching-type networks with a maximum height of 70-80 nm. The endergonic ET from NaBPh₄ to NDI²+ dication was used to design the assembly and disassembly redox reaction cycle using reducing (NaBPh₄) and oxidizing agents (Br₂) as chemical fuels. A part of NaBPh₄ is used to drive assembly, while a fraction of the NaBPh₄ is dissipated by forming a useful product. The system goes back to the disassembled NDI²+ dication state with the addition of Br₂. We think bioinspired dissipative self-assembly is the best approach to developing future lifelike materials with autonomous behavior.

Keywords: Ionic-gel, redox-cycle, self-assembly, useful product

Procedia PDF Downloads 54
10101 Growth Comparison and Intestinal Health in Broilers Fed Scent Leaf Meal (Ocimum gratissimum) and Synthetic Antibiotic

Authors: Adedoyin Akintunde Adedayo, Onilude Abiodun Anthony

Abstract:

The continuous usage of synthetic antibiotics in livestock production has led to the resistance of microbial pathogens. This has prompted research to find alternative sources. This study aims to compare the growth and intestinal health of broilers fed scent leaf meal (SLM) as an alternative to synthetic antibiotics. The study used a completely randomized design (CRD) with 300 one-week-old Arbor Acres broiler chicks. The chicks were divided into six treatments with five replicates of ten birds each. The feeding trial lasted 49 days, including a one-week acclimatization period. Commercial broiler diets were used. The diets included a negative control (no leaf meal or antibiotics), a positive control (0.10% oxy-tetracycline), and four diets with different levels of SLM (0.5%, 1.0%, 1.5%, and 2.0%). The supplementation of both oxy-tetracycline and SLM improved feed intake during the finisher phase. Birds fed SLM at a 1% inclusion level showed significantly (P<0.05) improved average body weight gain (ABWG), lowered feed-to-gain ratio, and cost per kilogram of weight gain compared to other diets. The mortality (2.0%) rate was significantly higher in the negative control group. White blood cell levels varied significantly (P<0.05) in birds fed SLM-supplemented diets, and the use of 2% SLM led to an increase in liver weight. However, welfare indices were not compromised.

Keywords: Arbor Acres, phyto-biotic, synthetic antibiotic, white blood cell, liver weight

Procedia PDF Downloads 44
10100 Dynamic Wind Effects in Tall Buildings: A Comparative Study of Synthetic Wind and Brazilian Wind Standard

Authors: Byl Farney Cunha Junior

Abstract:

In this work the dynamic three-dimensional analysis of a 47-story building located in Goiania city when subjected to wind loads generated using both the Wind Brazilian code, NBR6123 (ABNT, 1988) and the Synthetic-Wind method is realized. To model the frames three different methodologies are used: the shear building model and both bi and three-dimensional finite element models. To start the analysis, a plane frame is initially studied to validate the shear building model and, in order to compare the results of natural frequencies and displacements at the top of the structure the same plane frame was modeled using the finite element method through the SAP2000 V10 software. The same steps were applied to an idealized 20-story spacial frame that helps in the presentation of the stiffness correction process applied to columns. Based on these models the two methods used to generate the Wind loads are presented: a discrete model proposed in the Wind Brazilian code, NBR6123 (ABNT, 1988) and the Synthetic-Wind method. The method uses the Davenport spectrum which is divided into a variety of frequencies to generate the temporal series of loads. Finally, the 47- story building was analyzed using both the three-dimensional finite element method through the SAP2000 V10 software and the shear building model. The models were loaded with Wind load generated by the Wind code NBR6123 (ABNT, 1988) and by the Synthetic-Wind method considering different wind directions. The displacements and internal forces in columns and beams were compared and a comparative study considering a situation of a full elevated reservoir is realized. As can be observed the displacements obtained by the SAP2000 V10 model are greater when loaded with NBR6123 (ABNT, 1988) wind load related to the permanent phase of the structure’s response.

Keywords: finite element method, synthetic wind, tall buildings, shear building

Procedia PDF Downloads 255
10099 Attitudes of Secondary School Students towards Biology in Birnin Kebbi Metropolis, Kebbi State, Nigeria

Authors: I. A. Libata

Abstract:

The present study was carried out to determine the attitudes of Secondary School Students towards Biology in Birnin Kebbi metropolis. The population of the study is 2680 SS 2 Secondary School Students in Birnin Kebbi metropolis. Proportionate random sampling was used in selecting the samples. Oppinnionnaire was the only instrument used in the study. The instrument was subjected to test-retest reliability. The reliability index of the instrument was 0.69. Overall scores of the Students were analyzed and a mean score was determined, the mean score of students was 85. There were no significant differences between the attitudes of male and female students. The results also revealed that there was significant difference between the attitude of science and art students. The results also revealed that there was significant difference between the attitude of public and private school students. The study also reveals that majority of students in Birnin Kebbi Metropolis have positive attitudes towards biology. Based on the findings of this study, the researcher recommended that teachers should motivate students, which they can do through their teaching styles and by showing them the relevance of the learning topics to their everyday lives. Government and the school management should create the learning environment that helps motivate students not only to come to classes but also want to learn and enjoy learning Biology.

Keywords: attitudes, students, Birnin-Kebbi, metropolis

Procedia PDF Downloads 369
10098 Transformers in Gene Expression-Based Classification

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations of previous approaches, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with attention mechanism. In a previous work on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: transformers, generative ai, gene expression design, classification

Procedia PDF Downloads 32
10097 Synthetic Aperture Radar Remote Sensing Classification Using the Bag of Visual Words Model to Land Cover Studies

Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

Abstract:

Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

Procedia PDF Downloads 181
10096 On the Utility of Bidirectional Transformers in Gene Expression-Based Classification

Authors: Babak Forouraghi

Abstract:

A genetic circuit is a collection of interacting genes and proteins that enable individual cells to implement and perform vital biological functions such as cell division, growth, death, and signaling. In cell engineering, synthetic gene circuits are engineered networks of genes specifically designed to implement functionalities that are not evolved by nature. These engineered networks enable scientists to tackle complex problems such as engineering cells to produce therapeutics within the patient's body, altering T cells to target cancer-related antigens for treatment, improving antibody production using engineered cells, tissue engineering, and production of genetically modified plants and livestock. Construction of computational models to realize genetic circuits is an especially challenging task since it requires the discovery of the flow of genetic information in complex biological systems. Building synthetic biological models is also a time-consuming process with relatively low prediction accuracy for highly complex genetic circuits. The primary goal of this study was to investigate the utility of a pre-trained bidirectional encoder transformer that can accurately predict gene expressions in genetic circuit designs. The main reason behind using transformers is their innate ability (attention mechanism) to take account of the semantic context present in long DNA chains that are heavily dependent on the spatial representation of their constituent genes. Previous approaches to gene circuit design, such as CNN and RNN architectures, are unable to capture semantic dependencies in long contexts, as required in most real-world applications of synthetic biology. For instance, RNN models (LSTM, GRU), although able to learn long-term dependencies, greatly suffer from vanishing gradient and low-efficiency problem when they sequentially process past states and compresses contextual information into a bottleneck with long input sequences. In other words, these architectures are not equipped with the necessary attention mechanisms to follow a long chain of genes with thousands of tokens. To address the above-mentioned limitations, a transformer model was built in this work as a variation to the existing DNA Bidirectional Encoder Representations from Transformers (DNABERT) model. It is shown that the proposed transformer is capable of capturing contextual information from long input sequences with an attention mechanism. In previous works on genetic circuit design, the traditional approaches to classification and regression, such as Random Forrest, Support Vector Machine, and Artificial Neural Networks, were able to achieve reasonably high R2 accuracy levels of 0.95 to 0.97. However, the transformer model utilized in this work, with its attention-based mechanism, was able to achieve a perfect accuracy level of 100%. Further, it is demonstrated that the efficiency of the transformer-based gene expression classifier is not dependent on the presence of large amounts of training examples, which may be difficult to compile in many real-world gene circuit designs.

Keywords: machine learning, classification and regression, gene circuit design, bidirectional transformers

Procedia PDF Downloads 37
10095 Development, Characterization and Performance Evaluation of a Weak Cation Exchange Hydrogel Using Ultrasonic Technique

Authors: Mohamed H. Sorour, Hayam F. Shaalan, Heba A. Hani, Eman S. Sayed, Amany A. El-Mansoup

Abstract:

Heavy metals (HMs) present an increasing threat to aquatic and soil environment. Thus, techniques should be developed for the removal and/or recovery of those HMs from point sources in the generating industries. This paper reports our endeavors concerning the development of in-house developed weak cation exchange polyacrylate hydrogel kaolin composites for heavy metals removal. This type of composite enables desirable characteristics and functions including mechanical strength, bed porosity and cost advantages. This paper emphasizes the effect of varying crosslinker (methylenebis(acrylamide)) concentration. The prepared cation exchanger has been subjected to intensive characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray fluorescence (XRF) and Brunauer Emmett and Teller (BET) method. Moreover, the performance was investigated using synthetic and real wastewater for an industrial complex east of Cairo. Simulated and real wastewater compositions addressed; Cr, Co, Ni, and Pb are in the range of (92-115), (91-103), (86-88) and (99-125), respectively. Adsorption experiments have been conducted in both batch and column modes. In general, batch tests revealed enhanced cation exchange capacities of 70, 72, 78.2 and 99.9 mg/g from single synthetic wastes while, removal efficiencies of 82.2, 86.4, 44.4 and 96% were obtained for Cr, Co, Ni and Pb, respectively from mixed synthetic wastes. It is concluded that the mixed synthetic and real wastewaters have lower adsorption capacities than single solutions. It is worth mentioned that Pb attained higher adsorption capacities with comparable results in all tested concentrations of synthetic and real wastewaters. Pilot scale experiments were also conducted for mixed synthetic waste in a fluidized bed column for 48 hour cycle time which revealed 86.4%, 58.5%, 66.8% and 96.9% removal efficiency for Cr, Co, Ni, and Pb, respectively with maximum regeneration was also conducted using saline and acid regenerants. Maximum regeneration efficiencies for the column studies higher than the batch ones about by about 30% to 60%. Studies are currently under way to enhance the regeneration efficiency to enable successful scaling up of the adsorption column.

Keywords: polyacrylate hydrogel kaolin, ultrasonic irradiation, heavy metals, adsorption and regeneration

Procedia PDF Downloads 100
10094 Efficient Synthesis of Highly Functionalized Biologically Important Spirocarbocyclic Oxindoles via Hauser Annulation

Authors: Kanduru Lokesh, Venkitasamy Kesavan

Abstract:

The unique structural features of spiro-oxindoles with diverse biological activities have made them privileged structures in new drug discovery. The key structural characteristic of these compounds is the spiro ring fused at the C-3 position of the oxindole core with varied heterocyclic motifs. Structural diversification of heterocyclic scaffolds to synthesize new chemical entities as pharmaceuticals and agrochemicals is one of the important goals of synthetic organic chemists. Nitrogen and oxygen containing heterocycles are by far the most widely occurring privileged structures in medicinal chemistry. The structural complexity and distinct three-dimensional arrangement of functional groups of these privileged structures are generally responsible for their specificity against biological targets. Structurally diverse compound libraries have proved to be valuable assets for drug discovery against challenging biological targets. Thus, identifying a new combination of substituents at C-3 position on oxindole moiety is of great importance in drug discovery to improve the efficiency and efficacy of the drugs. The development of suitable methodology for the synthesis of spiro-oxindole compounds has attracted much interest often in response to the significant biological activity displayed by the both natural and synthetic compounds. So creating structural diversity of oxindole scaffolds is need of the decade and formidable challenge. A general way to improve synthetic efficiency and also to access diversified molecules is through the annulation reactions. Annulation reactions allow the formation of complex compounds starting from simple substrates in a single transformation consisting of several steps in an ecologically and economically favorable way. These observations motivated us to develop the annulation reaction protocol to enable the synthesis of a new class of spiro-oxindole motifs which in turn would enable the enhancement of molecular diversity. As part of our enduring interest in the development of novel, efficient synthetic strategies to enable the synthesis of biologically important oxindole fused spirocarbocyclic systems, We have developed an efficient methodology for the construction of highly functionalized spirocarbocyclic oxindoles through [4+2] annulation of phthalides via Hauser annulation. functionalized spirocarbocyclic oxindoles was accomplished for the first time in the literature using Hauser annulation strategy. The reaction between methyleneindolinones and arylsulfonylphthalides catalyzed by cesium carbonate led to the access of new class of biologically important spiro[indoline-3,2'-naphthalene] derivatives in very good yields. The synthetic utility of the annulated product was further demonstrated by fluorination Using NFSI as a fluorinating agent to furnish corresponding fluorinated product.

Keywords: Hauser-Kraus annulation, spiro carbocyclic oxindoles, oxindole-ester, fluoridation

Procedia PDF Downloads 179
10093 Facilitating Written Biology Assessment in Large-Enrollment Courses Using Machine Learning

Authors: Luanna B. Prevost, Kelli Carter, Margaurete Romero, Kirsti Martinez

Abstract:

Writing is an essential scientific practice, yet, in several countries, the increasing university science class-size limits the use of written assessments. Written assessments allow students to demonstrate their learning in their own words and permit the faculty to evaluate students’ understanding. However, the time and resources required to grade written assessments prohibit their use in large-enrollment science courses. This study examined the use of machine learning algorithms to automatically analyze student writing and provide timely feedback to the faculty about students' writing in biology. Written responses to questions about matter and energy transformation were collected from large-enrollment undergraduate introductory biology classrooms. Responses were analyzed using the LightSide text mining and classification software. Cohen’s Kappa was used to measure agreement between the LightSide models and human raters. Predictive models achieved agreement with human coding of 0.7 Cohen’s Kappa or greater. Models captured that when writing about matter-energy transformation at the ecosystem level, students focused on primarily on the concepts of heat loss, recycling of matter, and conservation of matter and energy. Models were also produced to capture writing about processes such as decomposition and biochemical cycling. The models created in this study can be used to provide automatic feedback about students understanding of these concepts to biology faculty who desire to use formative written assessments in larger enrollment biology classes, but do not have the time or personnel for manual grading.

Keywords: machine learning, written assessment, biology education, text mining

Procedia PDF Downloads 251
10092 Flood Monitoring Using Active Microwave Remote Sensed Synthetic Aperture Radar Data

Authors: Bikramjit Goswami, Manoranjan Kalita

Abstract:

Active microwave remote sensing is useful in remote sensing applications in cloud-covered regions in the world. Because of high spatial resolution, the spatial variations of land cover can be monitored in greater detail using synthetic aperture radar (SAR). Inundation is studied using the SAR images obtained from Sentinel-1A in both VH and VV polarizations in the present experimental study. The temporal variation of the SAR scattering coefficient values for the area gives a good indication of flood and its boundary. The study area is the district of Morigaon in the state of Assam in India. The period of flood monitoring study is the monsoon season of the year 2017, during which high flood occurred in the state of Assam. The variation of microwave scattering value shows a distinctive indication of flood from the non-flooded period. Frequent monitoring of flood in a large area (10 km x 10 km) using passive microwave sensing and pin-pointing the actual flooded portions (5 m x 5 m) within the flooded area using active microwave sensing, can be a highly useful combination, as revealed by the present experimental results.

Keywords: active remote sensing, flood monitoring, microwave remote sensing, synthetic aperture radar

Procedia PDF Downloads 123
10091 Synthetic Dermal Template Use in the Reconstruction of a Chronic Scalp Wound

Authors: Stephanie Cornish

Abstract:

The use of synthetic dermal templates, also known as dermal matrices, such as PolyNovo® Biodegradable Temporising Matrix (BTM), has been well established in the reconstruction of acute wounds with a full thickness defect of the skin. Its use has become common place in the treatment of full thickness burns and is not unfamiliar in the realm of necrotising fasciitis, free flap donor site reconstruction, and the management of acute traumatic wounds. However, the use of dermal templates for more chronic wounds is rare. The authors present the successful use of BTM in the reconstruction of a chronic scalp wound following the excision of a malignancy and multiple previous failed attempts at repair, thus demonstrating the potential for an increased scope of use.

Keywords: dermal template, BTM, chronic, scalp wound, reconstruction

Procedia PDF Downloads 65
10090 Toughness Factor of Polypropylene Fiber Reinforced Concrete in Aggressive Environment

Authors: R. E. Vasconcelos, K. R. M. da Silva, J. M. B. Pinto

Abstract:

This study aims to determine and to present the results of an experimental study of Synthetic (polypropylene) Fibers Reinforced Concrete (SFRC), in levels of 0.33% - 3kg/m3, 0.50% - 4.5kg/m3, and 0.66% - 6kg/m3, using cement CP V – ARI, at ages 28 and 88 days after specimens molding. The specimens were exposed for 60 days in aggressive environment (in solution of water and 3% of sodium chloride), after 28 days. The bending toughness tests were performed in prismatic specimens of 150 x 150 x 500 mm. The toughness factor values of the specimens in aggressive environment were the same to those obtained in normal environment (in air).

Keywords: concrete reinforced with polypropylene fibers, toughness in bending, synthetic fibers, concrete reinforced

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10089 Copper Removal from Synthetic Wastewater by a Novel Fluidized-bed Homogeneous Crystallization (FBHC) Technology

Authors: Cheng-Yen Huang, Yu-Jen Shih, Ming-Chun Yen, Yao-Hui Huang

Abstract:

This research developed a fluidized-bed homogeneous crystallization (FBHC) process to remove copper from synthetic wastewater in terms of recovery of highly pure malachite (Cu2(OH)2CO3) pellets. The experimental parameters of FBHC which included pH, molar ratio of copper to carbonate, copper loading, upper flowrate and bed height were tested in the absence of seed particles. Under optimized conditions, both the total copper removal (TR) and crystallization ratio (CR) reached 99%. The malachite crystals were characterized by XRD and SEM. FBHC was capable of treating concentrated copper (1600 ppm) wastewater and minimizing the sludge production.

Keywords: copper, carbonate, fluidized-bed, crystallization, malachite

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10088 Green Natural Rubber Composites Reinforced with Synthetic Graphite: Effects of Reinforcing Agent on Film’s Mechanical Properties and Electrical Conductivity

Authors: Veerapat Kitsawat, Muenduen Phisalaphong

Abstract:

Green natural rubber (NR) composites reinforced with synthetic graphite, using alginate as thickening and dispersing agent, were developed to improve mechanical properties and electrical conductivity. The film fabrication was performed using a latex aqueous microdispersion process. The research found that up to 60 parts per hundred rubbers (phr) of graphite could be successfully integrated into the NR matrix without causing agglomeration and phase separation. Accordingly, the mechanical properties, in terms of tensile strength and Young’s modulus of the composite films, were significantly increased, while the elongation at break decreased with higher graphite loading. The reinforcement strongly improved the hydrophilicity of the composite films, resulting in a higher water absorption rate compared to the neat NR film. Moreover, the incorporation of synthetic graphite significantly improved the chemical resistance of the composite films when exposed to toluene. It is demonstrated that the electrical conductivity of the composite films was considerably enhanced with graphite loading. According to the obtained properties, the developed composites offer potential for further development as conductive substrate for electronic applications.

Keywords: alginate, composite, graphite, natural rubber

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10087 The Impact of Intelligent Control Systems on Biomedical Engineering and Research

Authors: Melkamu Tadesse Getachew

Abstract:

Intelligent control systems have revolutionized biomedical engineering, advancing research and enhancing medical practice. This review paper examines the impact of intelligent control on various aspects of biomedical engineering. It analyzes how these systems enhance precision and accuracy in biomedical instrumentation, improving diagnostics, monitoring, and treatment. Integration challenges are addressed, and potential solutions are proposed. The paper also investigates the optimization of drug delivery systems through intelligent control. It explores how intelligent systems contribute to precise dosing, targeted drug release, and personalized medicine. Challenges related to controlled drug release and patient variability are discussed, along with potential avenues for overcoming them. The comparison of algorithms used in intelligent control systems in biomedical control is also reviewed. The implications of intelligent control in computational and systems biology are explored, showcasing how these systems enable enhanced analysis and prediction of complex biological processes. Challenges such as interpretability, human-machine interaction, and machine reliability are examined, along with potential solutions. Intelligent control in biomedical engineering also plays a crucial role in risk management during surgical operations. This section demonstrates how intelligent systems improve patient safety and surgical outcomes when integrated into surgical robots, augmented reality, and preoperative planning. The challenges associated with these implementations and potential solutions are discussed in detail. In summary, this review paper comprehensively explores the widespread impact of intelligent control on biomedical engineering, showing the future of human health issues promising. It discusses application areas, challenges, and potential solutions, highlighting the transformative potential of these systems in advancing research and improving medical practice.

Keywords: Intelligent control systems, biomedical instrumentation, drug delivery systems, robotic surgical instruments, Computational monitoring and modeling

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10086 ISMARA: Completely Automated Inference of Gene Regulatory Networks from High-Throughput Data

Authors: Piotr J. Balwierz, Mikhail Pachkov, Phil Arnold, Andreas J. Gruber, Mihaela Zavolan, Erik van Nimwegen

Abstract:

Understanding the key players and interactions in the regulatory networks that control gene expression and chromatin state across different cell types and tissues in metazoans remains one of the central challenges in systems biology. Our laboratory has pioneered a number of methods for automatically inferring core gene regulatory networks directly from high-throughput data by modeling gene expression (RNA-seq) and chromatin state (ChIP-seq) measurements in terms of genome-wide computational predictions of regulatory sites for hundreds of transcription factors and micro-RNAs. These methods have now been completely automated in an integrated webserver called ISMARA that allows researchers to analyze their own data by simply uploading RNA-seq or ChIP-seq data sets and provides results in an integrated web interface as well as in downloadable flat form. For any data set, ISMARA infers the key regulators in the system, their activities across the input samples, the genes and pathways they target, and the core interactions between the regulators. We believe that by empowering experimental researchers to apply cutting-edge computational systems biology tools to their data in a completely automated manner, ISMARA can play an important role in developing our understanding of regulatory networks across metazoans.

Keywords: gene expression analysis, high-throughput sequencing analysis, transcription factor activity, transcription regulation

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10085 Lab Bench for Synthetic Aperture Radar Imaging System

Authors: Karthiyayini Nagarajan, P. V. Ramakrishna

Abstract:

Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar (SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System (Lab Bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench, imaging engineering

Procedia PDF Downloads 466
10084 Design and Implementation of a Lab Bench for Synthetic Aperture Radar Imaging System

Authors: Karthiyayini Nagarajan, P. V. RamaKrishna

Abstract:

Radar Imaging techniques provides extensive applications in the field of remote sensing, majorly Synthetic Aperture Radar(SAR) that provide high resolution target images. This paper work puts forward the effective and realizable signal generation and processing for SAR images. The major units in the system include camera, signal generation unit, signal processing unit and display screen. The real radio channel is replaced by its mathematical model based on optical image to calculate a reflected signal model in real time. Signal generation realizes the algorithm and forms the radar reflection model. Signal processing unit provides range and azimuth resolution through matched filtering and spectrum analysis procedure to form radar image on the display screen. The restored image has the same quality as that of the optical image. This SAR imaging system has been designed and implemented using MATLAB and Quartus II tools on Stratix III device as a System(lab bench) that works in real time to study/investigate on radar imaging rudiments and signal processing scheme for educational and research purposes.

Keywords: synthetic aperture radar, radio reflection model, lab bench

Procedia PDF Downloads 443
10083 Analysis of Airborne Data Using Range Migration Algorithm for the Spotlight Mode of Synthetic Aperture Radar

Authors: Peter Joseph Basil Morris, Chhabi Nigam, S. Ramakrishnan, P. Radhakrishna

Abstract:

This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data using the Range Migration Algorithm (RMA) for the spotlight mode of operation. Unlike in polar format algorithm (PFA), space-variant defocusing and geometric distortion effects are mitigated in RMA since it does not assume that the illuminating wave-fronts are planar. This facilitates the use of RMA for imaging scenarios involving severe differential range curvatures enabling the imaging of larger scenes at fine resolution and at shorter ranges with low center frequencies. The RMA algorithm for the spotlight mode of SAR is analyzed in this paper using the airborne data. Pre-processing operations viz: - range de-skew and motion compensation to a line are performed on the raw data before being fed to the RMA component. Various stages of the RMA viz:- 2D Matched Filtering, Along Track Fourier Transform and Slot Interpolation are analyzed to find the performance limits and the dependence of the imaging geometry on the resolution of the final image. The ability of RMA to compensate for severe differential range curvatures in the two-dimensional spatial frequency domain are also illustrated in this paper.

Keywords: range migration algorithm, spotlight SAR, synthetic aperture radar, matched filtering, slot interpolation

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10082 Optimizing Pediatric Pneumonia Diagnosis with Lightweight MobileNetV2 and VAE-GAN Techniques in Chest X-Ray Analysis

Authors: Shriya Shukla, Lachin Fernando

Abstract:

Pneumonia, a leading cause of mortality in young children globally, presents significant diagnostic challenges, particularly in resource-limited settings. This study presents an approach to diagnosing pediatric pneumonia using Chest X-Ray (CXR) images, employing a lightweight MobileNetV2 model enhanced with synthetic data augmentation. Addressing the challenge of dataset scarcity and imbalance, the study used a Variational Autoencoder-Generative Adversarial Network (VAE-GAN) to generate synthetic CXR images, improving the representation of normal cases in the pediatric dataset. This approach not only addresses the issues of data imbalance and scarcity prevalent in medical imaging but also provides a more accessible and reliable diagnostic tool for early pneumonia detection. The augmented data improved the model’s accuracy and generalization, achieving an overall accuracy of 95% in pneumonia detection. These findings highlight the efficacy of the MobileNetV2 model, offering a computationally efficient yet robust solution well-suited for resource-constrained environments such as mobile health applications. This study demonstrates the potential of synthetic data augmentation in enhancing medical image analysis for critical conditions like pediatric pneumonia.

Keywords: pneumonia, MobileNetV2, image classification, GAN, VAE, deep learning

Procedia PDF Downloads 43
10081 A Basic Understanding of Viral Disease and Education Level Influences Disease Risk Perception, Disease Severity Perception, and Mask Wearing Behavior During the COVID-19 Pandemic

Authors: Ilse Kreme

Abstract:

To the best of this author’s knowledge, no studies have been identified on the connection between a refusal to engage in health-protective behaviors and a basic understanding of viral biology among community college students, faculty, and staff during the COVID-19 pandemic. Lack of scientific knowledge could prevent understanding of why these behaviors are important to prevent the community spread of COVID-19, even when they are not shown to offer much individual protection. In this study, a possible correlation was examined between a basic knowledge level of viral disease that comes from having taken a college biology course and disease perceptions of COVID-19. In particular, disease risk perception, disease severity percept and mask-wearing behaviors were examined as they correlated with having taken an undergraduate biology course. The effect of covariates of age, gender, and education level were investigated along with the main dependent variables. A representative sample of the population included students, faculty, and staff at Paradise Valley Community College (PVCC) in Phoenix, Arizona. Participants were recruited by an email sent to all students, faculty, and staff at PVCC using an all-college email distribution. Disease risk and severity perception were assessed with the Brief Illness Perception Questionnaire 5 (BIP-Q5), which was modified to include questions measuring participant age, education level, and whether they took or ever took a college biology course. Two additional questions measured compliance of willingness to wear a face mask. The results showed an effect of gender on mask-wearing behavior and a correlation between having taken a biology course and disease severity perception. No differences were seen in mask-wearing behavior and disease risk perception as a result of having taken a biology course. These findings suggest that taking an undergraduate biology course leads to a greater awareness of COVID-19 disease severity through an understanding of the basic biological principles of viral disease transmission. The results can be used to modify existing health education strategies. Further research is needed on how to best reach target audiences in all education brackets.

Keywords: COVID-19, education, gender, mask wearing, disease risk perception, disease severity perception

Procedia PDF Downloads 81
10080 The Prediction of Evolutionary Process of Coloured Vision in Mammals: A System Biology Approach

Authors: Shivani Sharma, Prashant Saxena, Inamul Hasan Madar

Abstract:

Since the time of Darwin, it has been considered that genetic change is the direct indicator of variation in phenotype. But a few studies in system biology in the past years have proposed that epigenetic developmental processes also affect the phenotype thus shifting the focus from a linear genotype-phenotype map to a non-linear G-P map. In this paper, we attempt at explaining the evolution of colour vision in mammals by taking LWS/ Long-wave sensitive gene under consideration.

Keywords: evolution, phenotypes, epigenetics, LWS gene, G-P map

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10079 Mean Field Model Interaction for Computer and Communication Systems: Modeling and Analysis of Wireless Sensor Networks

Authors: Irina A. Gudkova, Yousra Demigha

Abstract:

Scientific research is moving more and more towards the study of complex systems in several areas of economics, biology physics, and computer science. In this paper, we will work on complex systems in communication networks, Wireless Sensor Networks (WSN) that are considered as stochastic systems composed of interacting entities. The current advancements of the sensing in computing and communication systems is an investment ground for research in several tracks. A detailed presentation was made for the WSN, their use, modeling, different problems that can occur in their application and some solutions. The main goal of this work reintroduces the idea of mean field method since it is a powerful technique to solve this type of models especially systems that evolve according to a Continuous Time Markov Chain (CTMC). Modeling of a CTMC has been focused; we obtained a large system of interacting Continuous Time Markov Chain with population entities. The main idea was to work on one entity and replace the others with an average or effective interaction. In this context to make the solution easier, we consider a wireless sensor network as a multi-body problem and we reduce it to one body problem. The method was applied to a system of WSN modeled as a Markovian queue showing the results of the used technique.

Keywords: Continuous-Time Markov Chain, Hidden Markov Chain, mean field method, Wireless sensor networks

Procedia PDF Downloads 139
10078 Robust Barcode Detection with Synthetic-to-Real Data Augmentation

Authors: Xiaoyan Dai, Hsieh Yisan

Abstract:

Barcode processing of captured images is a huge challenge, as different shooting conditions can result in different barcode appearances. This paper proposes a deep learning-based barcode detection using synthetic-to-real data augmentation. We first augment barcodes themselves; we then augment images containing the barcodes to generate a large variety of data that is close to the actual shooting environments. Comparisons with previous works and evaluations with our original data show that this approach achieves state-of-the-art performance in various real images. In addition, the system uses hybrid resolution for barcode “scan” and is applicable to real-time applications.

Keywords: barcode detection, data augmentation, deep learning, image-based processing

Procedia PDF Downloads 134
10077 Induced Breeding of Neolissochilus hexagonolepis Using Pituitary and Synthetic Hormone under the Agro-Climatic Condition of Meghalaya, India

Authors: Lydia Booney Jyrwa, Rabindra Nath Bhuyan

Abstract:

Chocolate Mahseer (Neolissochilus hexagonolepis) is one of the Mahseer species inhabiting the North-eastern region of India and is a native species to the state of Meghalaya since it can adapt and grow well under the agro climatic conditions of the region. The natural population of this fish has been declining over the years from this part of the country. The natural population of this fish has been declining over the years from this part of the country. The fish is considered as one of the endangered species of the Mahseer group, which is having tremendous scope for culture, sports and tourism. But non-availability of quality seed is a hindrance for the culture of this fish. Thus induced breeding of the fish by hormonal administration with pituitary gland and synthetic hormones is the only reliable method to procure the pure seed of the fish. Chocolate Mahseer was successfully bred at the Hatchery Complex, St. Anthony’s College, Shillong, Meghalaya by using pituitary extract and synthetic hormone viz. ovaprim, ovatide and gonopro-FH. The dose standardized is @ 4mg/kg body weight to both male and female as 1st dose and @ 7.9 mg/kg body weight only to female as 2nd dose for pituitary extract and single dose @ 0.8 ml/kg body weight to female and @ 0.3 ml/kg body weight to male of both ovaprim and ovatide, while a single dose @ 0.9 ml/kg body weight to female and @ 0.3 ml/kg body weight to male of gonopro-FH. The doses are standardized after a series of trial and error experiment performed. The fecundity of the fish was 3500 eggs/ kg body weight. The final hatching percentage achieved was 60%. The survival rate of hatchling was 50% up to fry stage. The use of synthetic hormone and positive response of the fish to the hormone will go in long way for production of quality seed of the fish which in turn help in culture of the species as the fish can be a very good candidate species for the culture in the state. This study will also help in the ranching of the fish in the natural habitat leading to conservation of the species. However, the study should be continued further for the large scale production of seeds.

Keywords: chocolate mahseer, induced breeding, pituitary extract, synthetic hormone

Procedia PDF Downloads 215
10076 Growth of Struvite Crystals in Synthetic Urine Using Magnesium Nitrate

Authors: Reneiloe Seodigeng, John Kabuba, Hilary Rutto, Tumisang Seodigeng

Abstract:

Urine diversion toilets have become popular as a means of solving the challenges in sanitation. As a result, the source-separated urine must be adequately treated so that it can be disposed of safely and valuable struvite can be extracted for use as fertilizer. In this study, synthetic urine was prepared, and struvite crystallisation experiments carried out using magnesium nitrate. The effect of residence time on crystal growth was studied. At residence time of 10, 30 and 60 minutes, mean particle sizes were 17, 34 and 53 µm showing that with higher residence times, larger crystal sizes can be achieved. SEM analysis of the crystal showed that the resultant crystals had the typical morphology of struvite crystals.

Keywords: struvite, magnesium nitrate, crystallisation, urine treatment

Procedia PDF Downloads 135
10075 Oxidative Stability of Methyl and Ethyl Microalgae Biodiesel with Synthetic Antioxidants

Authors: Willian L. G. Silva, Fabio R. M. Batista, Matthieu Tubino

Abstract:

Microalgae can be considered a potential source of oil for biodiesel synthesis since this microorganism can grow rapidly in either fresh or salty water, not competing with food production. There are several favorable conditions in Brazil for this type of culture due to the country’s great amount of water. Another very positive aspect of this type of culture is its ability to fix atmospheric CO2, contributing to the reduction of greenhouse gases and their effects on global warming. Despite this biodiesel environmental advantages it degrades resulting in changes in its physical and chemical properties. In this work, the methyl and ethyl microalgae biodiesel oxidative stability was studied in the absence and presence of a synthetic antioxidant. The synthetic antioxidants used were propyl gallate (PG) and tert-butylhydroquinone (TBHQ), at a 0,12% (w/w) concentration. The biodiesel mixture was kept in a sealed glass flask, sheltered from light, and at room temperature (about 25 ºC) for 180 days. During this period, aliquots from this biodiesel were subjected to induced degradation by the Rancimat method, which determines an important quality parameter, provided in the current methods, and is used to monitor the degradation processes that occur in the biodiesel over time. The induction period (IP) expresses the biodiesel oxidative stability. It was stablished that the minimum accepted IP value for biodiesel is 8 hours. The results show that ethylic biodiesel increased its IP value from 7,6 hours to 31 hours when using PG, and to 67 hours when using TBHQ, exceeding the minimum accepted IP value. When the antioxidants were added to the methylic biodiesel samples, the IP was raised to 28 hours when using PG, and to 62 hours when using TBHQ. These values were maintained throughout the entire period of study (180 days). On the other hand, the biodiesel samples without additives maintained an IP above the allowed value for only 30 days. Therefore, in order to preserve microalgae biodiesel for longer periods of time, it is necessary to add antioxidants to both derivatives, i.e., the ethylic and methylic.

Keywords: biodiesel, microalgae, oxidative stability, storage, synthetic antioxidants

Procedia PDF Downloads 437