Search results for: multi-scale feature extraction

Authors: Harsh Agarwal, Pratikkumar Patel, Maharshi Pathak

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Recently the low prices of Crude oil and increase in strict environmental regulations limit limits the use of diesel based muds as these muds are relatively costlier and toxic, as a result disposal of cuttings into the eco-system is a major issue faced by the drilling industries. To overcome these issues faced by the Oil Industry, an attempt has been made to develop oil-in-water emulsion mud system using nettlespurge oil. Nettlespurge oil could be easily available and its cost is around ₹30/litre which is about half the price of diesel in India. Oil-based mud (OBM) was formulated with Nettlespurge oil extracted from Nettlespurge seeds using the Soxhlet extraction method. The formulated nettlespurge oil mud properties were analysed with diesel oil mud properties. The compared properties were rheological properties, yield point and gel strength, and mud density and filtration loss properties, fluid loss and filter cake. The mud density measurement showed that nettlespurge OBM was slightly higher than diesel OBM with mud density values of 9.175 lb/gal and 8.5 lb/gal, respectively, at barite content of 70 g. Thus it has a higher lubricating property. Additionally, the filtration loss test results showed that nettlespurge mud fluid loss volumes, oil was 11 ml, compared to diesel oil mud volume of 15 ml. The filtration loss test indicated that the nettlespurge oil mud with filter cake thickness of 2.2 mm had a cake characteristic of thin and squashy while the diesel oil mud resulted in filter cake thickness of 2.7 mm with cake characteristic of tenacious, rubbery and resilient. The filtration loss test results showed that nettlespurge oil mud fluid loss volumes was much less than the diesel based oil mud. The filtration loss test indicated that the nettlespurge oil mud filter cake thickness less than the diesel oil mud filter cake thickness. So Low formation damage and the emulsion stability effect was analysed with this experiment. The nettlespurge oil-in-water mud system had lower coefficient of friction than the diesel oil based mud system. All the rheological properties have shown better results relative to the diesel based oil mud. Therefore, with all the above mentioned factors and with the data of the conducted experiment we could conclude that the Nettlespurge oil based mud is economically and well as eco-logically much more feasible than the worn out and shabby diesel-based oil mud in the Drilling Industry.

Keywords: economical feasible, ecological feasible, emulsion stability, nettle spurge oil, rheological properties, soxhlet extraction method

Procedia PDF Downloads 188

Authors: Moshe Wasserblat, Jonathan Mamou, Oren Pereg

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Natural Language Processing (NLP) has made a major leap in the last few years, in practical integration into medical solutions; for example, extracting clinical concepts from medical texts such as medical condition, medication, treatment, and symptoms. However, training and deploying those models in real environments still demands a large amount of annotated data and NLP/Machine Learning (ML) expertise, which makes this process costly and time-consuming. We present a practical and efficient method for clinical concept extraction that does not require costly labeled data nor ML expertise. The method includes three steps: Step 1- the user injects a large in-domain text corpus (e.g., PubMed). Then, the system builds a contextual model containing vector representations of concepts in the corpus, in an unsupervised manner (e.g., Phrase2Vec). Step 2- the user provides a seed set of terms representing a specific medical concept (e.g., for the concept of the symptoms, the user may provide: ‘dry mouth,’ ‘itchy skin,’ and ‘blurred vision’). Then, the system matches the seed set against the contextual model and extracts the most semantically similar terms (e.g., additional symptoms). The result is a complete set of terms related to the medical concept. Step 3 –in production, there is a need to extract medical concepts from the unseen medical text. The system extracts key-phrases from the new text, then matches them against the complete set of terms from step 2, and the most semantically similar will be annotated with the same medical concept category. As an example, the seed symptom concepts would result in the following annotation: “The patient complaints on fatigue [symptom], dry skin [symptom], and Weight loss [symptom], which can be an early sign for Diabetes.” Our evaluations show promising results for extracting concepts from medical corpora. The method allows medical analysts to easily and efficiently build taxonomies (in step 2) representing their domain-specific concepts, and automatically annotate a large number of texts (in step 3) for classification/summarization of medical reports.

Keywords: clinical concepts, concept expansion, medical records annotation, medical records summarization

Procedia PDF Downloads 119

Authors: Daniela Nedeltcheva-Antonova, Kamelia Getchovska, Vera Deneva, Stanislav Bozhanov, Liudmil Antonov

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Rosa damascena Mill. (Damask rose) is one of the most important plants belonging to the Rosaceae family, with a long historical use in traditional medicine and as a valuable oil-bearing plant. Many pharmacological effects have been reported from this plant, including anti-inflammatory, hypnotic, analgesic, anticonvulsant, anti-depressant, antianxiety, antitussive, antidiabetic, relaxant effects on tracheal chains, laxative, prokinetic and hepatoprotective activities. Pharmacological studies have shown that the various health effects of R. damascena flowers can mainly be attributed to its large amount of polyphenolic components. Phenolics possess a wide range of pharmacological activities, such as antioxidants, free-radical scavengers, anticancer, anti-inflammatory, antimutagenic, and antidepressant, with flavonoids being the most numerous group of natural polyphenolic compounds. According to the technological process in the production of rose concrete (solvent extraction with non-polar solvents of fresh rose flowers), it can be assumed that the resulting plant residue would be as rich of polyphenolics, as the plant itself, and could be used for the development of novel products with promising health-promoting effect. Therefore, an optimisation of the extraction procedure of the by-product from the rose concrete production was carried out. An assay of the extracts in respect of their total polyphenols and total flavonoids content was performed. HPLC analysis of quercetin and kaempferol, the two main flavonoids found in R. damascena, was also carried out. The preliminary results have shown that the flavonoid content in the rose extracts is comparable to that of the green tea or Gingko biloba, and they could be used for the development of various products (food supplements, natural cosmetics and phyto-pharmaceutical formulation, etc.). The fact that they are derived from the by-product of industrial plant processing could add the marketing value of the final products in addition to the well-known reputation of the products obtained from Bulgarian roses (R. damascena Mill.).

Keywords: gas chromatography-mass-spectromrtry, dry extract, flavonoids, Rosa damascena Mill

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Authors: Shamila Ahmed

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The paper examines how the fluidity between the local level and the global level is an intrinsic feature of new terrorism. Through using cosmopolitanism, the narratives of the two opposing sides of ISIS and the ‘war on terrorism’ response are explored. It is demonstrated how the fluidity between these levels facilitates the radicalisation process through exploring how groups such as ISIS highlight the perceived injustices against Muslims locally and globally and therefore exploit the globalisation process which has reduced the space between these levels. Similarly, it is argued that the ‘war on terror’ involves the intersection of fear, security, threat, risk and social control as features of both the international ‘war on terror’ and intra state policies.

Keywords: terrorism, war on terror, cosmopolitanism, global level terrorism

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Authors: Ahmed Machmoum, Malika El Kyal

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We consider iterative parallel multi-splitting method for differential algebraic equations. The main feature of the proposed idea is to use the asynchronous form. We prove that the multi-splitting technique can effectively accelerate the convergent performance of the iterative process. The main characteristic of an asynchronous mode is that the local algorithm not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays tobe substantial and unpredictable. Note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: computer, multi-splitting methods, asynchronous mode, differential algebraic systems

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Authors: Seyed Abolhasan Naeini, Mohammad Mahdi Shojaedin

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Oil leakage from the pipelines and the tanks carrying them, or during oil extraction, could lead to the changes in the characteristics and properties of the soil. In this paper, conducting a series of experimental cyclic triaxial tests, the effects of oil contamination on the liquefaction potential of sandy soils is investigated. The studied specimens are prepared by mixing the Firoozkuh sand with crude oil in 4, 8 and 12 percent by soil dry weight. The results show that the oil contamination up to 8% causes an increase in the soil liquefaction resistance and then with increase in the contamination, the liquefaction resistance decreases.

Keywords: cyclic triaxial test, liquefaction resistance, oil contamination, sandy soil

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Authors: Nitin Rajan, Kashif Shakeel, Shashank Tiwari, Shachan Sagar

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Background: - Aegle Marmelos (Bael) leaf extract is taken twice daily to treat ophthalmia, ulcers, and intestinal worms, among other ailments. Poultice made from bael leaf is used in the treatment of eye conditions. The leaf juice has a variety of therapeutic applications, with the most notable being the treatment of diabetes. Fenugreek is used to cure red spots around the eyes, as well as to soften the throat and chest and to give relief from coughing. The use of this plant in the form of infusion, powder, pomade, and decoction has been extremely popular in Iranian traditional medicine. The plant may be used to wash one's vaginal linings. This plant is used as an emollient in the lack of appetite, treatment of pellagra, and gastrointestinal problems, as well as a general tonic. Calendula officinalis leaves are used to treat varicose veins on the outside of the body by infusing them. In Europe, the leaves are diaphoretic and resolvent in nature, while the blooms are employed as an emmenagogue and antispasmodic stimulant in Canada and the United States. The flowers were decocted and served as a posset drink when smallpox and measles were common in England, and the fresh juice was used to treat jaundice. Objective: - This study is done to compare the physicochemical parameter of the alcoholic extract of the leaves of Aegle Marmelos, Calendula Officinalis, and Fenugreek. Materials and Methods: Extraction and Isolation of Aegle Marmelos, Calendula Officinalis, Fenugreek, were done. Preliminary phytochemical study for alkaloids, cardiac glycosides, flavonoids, glycosides, phenols, resins, saponins, steroids, tannins, terpenoids of the extract was done individual by using the standard procedure. Result: - The phytochemical screening of Aegle Marmelos, Calendula Officinalis, and Fenugreek shows the presence of alkaloids, carbohydrates, total phenolics, total flavonoids, tannins, saponins gum. Conclusion: - In this study, we have found that crude aqueous and organic solvent extracts of Aegle Marmelos, Calendula Officinalis, and Fenugreek leaves contain some important bioactive compounds and it justifies their use in the traditional medicines for the treatment of different diseases.

Keywords: Aegle Marmelos, Calendula Officinalis, Fenugreek, physiochemical parameter

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Authors: El Mahdi Barrah, Said Safi, Abdessamad Malaoui

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In this paper, we present an algorithm for reconstruction from incomplete 2D scans for tifinagh characters. This algorithm is based on using correlation between the lost block and its neighbors. This system proposed contains three main parts: pre-processing, features extraction and recognition. In the first step, we construct a database of tifinagh characters. In the second step, we will apply “shape analysis algorithm”. In classification part, we will use Neural Network. The simulation results demonstrate that the proposed method give good results.

Keywords: Tifinagh character recognition, neural networks, local cost computation, ANN

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Authors: V. Girard, I. Ziegler-Devin, H. Chapuis, N. Canilho, L. Marchal-Heussler, N. Brosse

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Lignocellulosic biomass is made up of the three polymeric fractions that are cellulose, hemicellulose, and lignin, which are highly entangled. In this project, we are particularly interested in the under-valued lignin polymer, which is mainly used for thermal valorization. Lignin from Macro to Nanosize (LIMINA) project will first focus on the extraction of macro lignin from forestry waste (hardwood and softwood) by the mean of eco-friendly processes (organosolv and steam explosion) and then the valorization of nano lignins produced by using anti-solvent precipitation (UV-blocker, cosmetic, food products).

Keywords: nanolignin, nanoparticles, organosolv, steam explosion

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Authors: Lin Huo, Xianwei Liu, Jingxiong Zhou

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Recently, due to the existence of semantic gap between image visual features and human concepts, the semantic of image auto-annotation has become an important topic. Firstly, by extract low-level visual features of the image, and the corresponding Hash method, mapping the feature into the corresponding Hash coding, eventually, transformed that into a group of binary string and store it, image auto-annotation by search is a popular method, we can use it to design and implement a method of image semantic auto-annotation. Finally, Through the test based on the Corel image set, and the results show that, this method is effective.

Keywords: image auto-annotation, color correlograms, Hash code, image retrieval

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Authors: Nassima Behidj-Benyounes, Thoraya Dahmene

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Essential oils have a significant antimicrobial activity. These oils can successfully replace the antibiotics. So, the microorganisms show their inefficiencies resistant for the antibiotics. For this reason, we study the physic-chemical analysis and antimicrobial activity of the essential oil of Daucus carota. The extraction is done by steam distillation of water which brought us a very significant return of 4.65%. The analysis of the essential oil is performed by GC/MS and has allowed us to identify 32 compounds in the oil of D. carota flowering tops of Bouira. Three of which are in the majority are the α-pinene (22.3%), the carotol (21.7%) and the limonene (15.8%).

Keywords: daucus carota, essential oil, α-pinene, carotol, limonene

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Authors: K. Eryilmaz, G. Mercanoglu

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Aim: Purification of the final product, which is the last step in the synthesis of lanthanide-labeled peptide compounds, can be accomplished by different methods. Among these methods, the two most commonly used methods are C18 solid phase extraction (SPE) and weak cation exchanger cartridge elution. SPE C18 solid phase extraction method yields high purity final product, while elution from the weak cation exchanger cartridge is pH dependent and ineffective in removing colloidal impurities. The aim of this work is to develop an additional purification method for the lanthanide-labeled peptide compound in cases where the desired radionuclidic and radiochemical purity of the final product can not be achieved because of pH problem or colloidal impurity. Material and Methods: For colloidal impurity formation, 3 mL of water for injection (WFI) was added to 30 mCi of 177LuCl3 solution and allowed to stand for 1 day. 177Lu-DOTATATE was synthesized using EZAG ML-EAZY module (10 mCi/mL). After synthesis, the final product was mixed with the colloidal impurity solution (total volume:13 mL, total activity: 40 mCi). The resulting mixture was trapped in SPE-C18 cartridge. The cartridge was washed with 10 ml saline to remove impurities to the waste vial. The product trapped in the cartridge was eluted with 2 ml of 50% ethanol and collected to the final product vial via passing through a 0.22μm filter. The final product was diluted with 10 mL of saline. Radiochemical purity before and after purification was analysed by HPLC method. (column: ACE C18-100A. 3µm. 150 x 3.0mm, mobile phase: Water-Acetonitrile-Trifluoro acetic acid (75:25:1), flow rate: 0.6 mL/min). Results: UV and radioactivity detector results in HPLC analysis showed that colloidal impurities were completely removed from the 177Lu-DOTATATE/ colloidal impurity mixture by purification method. Conclusion: The improved purification method can be used as an additional method to remove impurities that may result from the lanthanide-peptide synthesis in which the weak cation exchange purification technique is used as the last step. The purification of the final product and the GMP compliance (the final aseptic filtration and the sterile disposable system components) are two major advantages.

Keywords: lanthanide, peptide, labeling, purification, radionuclide, radiopharmaceutical, synthesis

Procedia PDF Downloads 146

Authors: Kam Wen Hang, Tan Kee Teng, Huang Poh Ching, Chia Kai Xiang, H. V. Annegowda, H. S. Naveen Kumar

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Gynura procumbens (G. procumbens) leaves, commonly known as ‘sambung nyawa’ in Malaysia is a well-known medicinal plant commonly used as folk medicines in controlling blood glucose, cholesterol level as well as treating cancer. These medicinal properties were believed to be related to the polyphenolic content present in G. procumbens extract, therefore optimization of its extraction process is vital to obtain highest possible antioxidant activities. The current study was conducted to investigate the effect of different concentrations of extracting solvent (ethanol) on the amount of polyphenolic content and antioxidant activities of G. procumbens leaf extract. The concentrations of ethanol used were 30-70%, with the temperature and time kept constant at 50°C and 30 minutes, respectively using ultrasound-assisted extraction. The polyphenolic content of these extracts were quantified by Folin-Ciocalteu colorimetric method and results were expressed as milligram gallic acid equivalent (mg GAE)/g. Phosphomolybdenum method and 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging assays were used to investigate the antioxidant properties of the extract and the results were expressed as milligram ascorbic acid equivalent (mg AAE)/g and effective concentration (EC50) respectively. Among the three different (30%, 50% and 70%) concentrations of ethanol studied, the 50% ethanolic extract showed total phenolic content of 31.565 ± 0.344 mg GAE/g and total antioxidant activity of 78.839 ± 0.199 mg AAE/g while 30% ethanolic extract showed 29.214 ± 0.645 mg GAE/g and 70.701 ± 1.394 mg AAE/g, respectively. With respect to DPPH radical scavenging assay, 50% ethanolic extract had exhibited slightly lower EC50 (314.3 ± 4.0 μg/ml) values compared to 30% ethanol extract (340.4 ± 5.3 μg/ml). Out of all the tested extracts, 70% ethanolic extract exhibited significantly (p< 0.05) highest total phenolic content (38.000 ± 1.009 mg GAE/g), total antioxidant capacity (95.874 ± 2.422 mg AAE/g) and demonstrated the lowest EC50 in DPPH assay (244.2 ± 5.9 μg/ml). An excellent correlations were drawn between total phenolic content, total antioxidant capacity and DPPH radical scavenging activity (R2 = 0.949 and R2 = 0.978, respectively). It was concluded from this study that, 70% ethanol should be used as the optimal polarity solvent to obtain G. procumbens leaf extract with maximum polyphenolic content with antioxidant properties.

Keywords: antioxidant activity, DPPH assay, Gynura procumbens, phenolic compounds

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Authors: Noury Bakrim

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Formalization is indeed a foundational Mathematical Linguistics as demonstrated by the pioneering works. While dialoguing with this frame, we nonetheless propone, in our approach of language as a real object, a mathematical linguistics/biosemiotics defined as a dialectical synthesis between induction and computational deduction. Therefore, relying on the parametric interaction of cycles, rules, and features giving way to a sub-hypothetic biological point of view, we first hypothesize a factorial equation as an explanatory principle within Category Mathematics of the Ergobrain: our computation proposal of Universal Grammar rules per cycle or a scalar determination (multiplying right/left columns of the determinant matrix and right/left columns of the logarithmic matrix) of the transformable matrix for rule addition/deletion and cycles within representational mapping/cycle heredity basing on the factorial example, being the logarithmic exponent or power of rule deletion/addition. It enables us to propone an extension of minimalist merge/label notions to a Language Merge (as a computing principle) within cycle recursion relying on combinatorial mapping of rules hierarchies on external Entax of the Event Sequence. Therefore, to define combinatorial maps as language merge of features and combinatorial hierarchical restrictions (governing, commanding, and other rules), we secondly hypothesize from our results feature/hierarchy exponentiation on graph representation deriving from Gromov's Symbolic Dynamics where combinatorial vertices from Fe are set to combinatorial vertices of Hie and edges from Fe to Hie such as for all combinatorial group, there are restriction maps representing different derivational levels that are subgraphs: the intersection on I defines pullbacks and deletion rules (under restriction maps) then under disjunction edges H such that for the combinatorial map P belonging to Hie exponentiation by intersection there are pullbacks and projections that are equal to restriction maps RM₁ and RM₂. The model will draw on experimental biomathematics as well as structural frames with focus on Amazigh and English (cases from phonology/micro-semantics, Syntax) shift from Structure to event (especially Amazigh formant principle resolving its morphological heterogeneity).

Keywords: rule/cycle addition/deletion, bio-mathematical methodology, general merge calculation, feature exponentiation, combinatorial maps, event sequence

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Authors: Rituraj Deka, Nomi Baruah

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The development of information technology during the past few years resulted in designing of more and more complex software. The outsourcing of software development makes a higher requirement for the management of software development project. Various software enterprises follow various paths in their pursuit of excellence, applying various principles, methods and techniques along the way. The new research is proving that CMMI and Agile methodologies can benefit from using both methods within organizations with the potential to dramatically improve business performance. The paper describes a mapping between CMMI key process areas (KPAs) and Feature-Driven Development (FDD) communication perspective, so as to increase the understanding of how improvements can be made in the software development process.

Keywords: Agile, CMMI, FDD, KPAs

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Authors: Matheus Nunes, Rafael Mendes, Taygoara Felamingo Oliveira, Antonio Brasil Junior

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Wind tunnel experiments of horizontal axis propeller hydrokinetic turbines model were carried out, in order to determine the performance behavior for different configurations and operational range. The present experiments introduce the use of two different geometries of rear diffusers to enhance the performance of the free flow machine. The present paper reports an increase of the power coefficient about 50%-80%. It represents an important feature that has to be taken into account in the design of this kind of machine.

Keywords: diffuser-enhanced turbines, hydrokinetic turbine, wind tunnel experiments, micro hydro

Procedia PDF Downloads 254

Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Mohamad Saab, Florent Real, Francois Virot, Laurent Cantrel, Valerie Vallet

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All spent nuclear fuel reprocessing plants use the PUREX process (Plutonium Uranium Refining by Extraction), which is a liquid-liquid extraction method. The organic extracting solvent is a mixture of tri-n-butyl phosphate (TBP) and hydrocarbon solvent such as hydrogenated tetra-propylene (TPH). By chemical complexation, uranium and plutonium (from spent fuel dissolved in nitric acid solution), are separated from fission products and minor actinides. During a normal extraction operation, uranium is extracted in the organic phase as the UO₂(NO₃)₂(TBP)₂ complex. The TBP solvent can form an explosive mixture called red oil when it comes in contact with nitric acid. The formation of this unstable organic phase originates from the reaction between TBP and its degradation products on the one hand, and nitric acid, its derivatives and heavy metal nitrate complexes on the other hand. The decomposition of the red oil can lead to violent explosive thermal runaway. These hazards are at the origin of several accidents such as the two in the United States in 1953 and 1975 (Savannah River) and, more recently, the one in Russia in 1993 (Tomsk). This raises the question of the exothermicity of reactions that involve TBP and all other degradation products, and calls for a better knowledge of the underlying chemical phenomena. A simulation tool (Alambic) is currently being developed at IRSN that integrates thermal and kinetic functions related to the deterioration of uranyl nitrates in organic and aqueous phases, but not of the n-butyl phosphate. To include them in the modeling scheme, there is an urgent need to obtain the thermodynamic and kinetic functions governing the deterioration processes in liquid phase. However, little is known about the thermodynamic properties, like standard enthalpies of formation, of the n-butyl phosphate molecules and of the UO₂(NO₃)₂(TBP)₂ UO₂(NO₃)₂(HDBP)(TBP) and UO₂(NO₃)₂(HDBP)₂ complexes. In this work, we propose to estimate the thermodynamic properties with Quantum Methods (QM). Thus, in the first part of our project, we focused on the mono, di, and tri-butyl complexes. Quantum chemical calculations have been performed to study several reactions leading to the formation of mono-(H₂MBP), di-(HDBP), and TBP in gas and liquid phases. In the gas phase, the optimal structures of all species were optimized using the B3LYP density functional. Triple-ζ def2-TZVP basis sets were used for all atoms. All geometries were optimized in the gas-phase, and the corresponding harmonic frequencies were used without scaling to compute the vibrational partition functions at 298.15 K and 0.1 Mpa. Accurate single point energies were calculated using the efficient localized LCCSD(T) method to the complete basis set limit. Whenever species in the liquid phase are considered, solvent effects are included with the COSMO-RS continuum model. The standard enthalpies of formation of TBP, HDBP, and H2MBP are finally predicted with an uncertainty of about 15 kJ mol⁻¹. In the second part of this project, we have investigated the fundamental properties of three organic species that mostly contribute to the thermal runaway: UO₂(NO₃)₂(TBP)₂, UO₂(NO₃)₂(HDBP)(TBP), and UO₂(NO₃)₂(HDBP)₂ using the same quantum chemical methods that were used for TBP and its derivatives in both the gas and the liquid phase. We will discuss the structures and thermodynamic properties of all these species.

Keywords: PUREX process, red oils, quantum chemical methods, hydrolysis

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Authors: Dalila Chouder, Djaafer Benachour

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This work concerns the study of the adsorption of metal ions Ag +, Mg +, and Ni2+ in aqueous medium by polydibenzoether-ROWN based on three factors: Temperature, time and concentration. The polydibenzoether crown was synthesized by two means: Chemical and electrochemical. The behavior of the two polymers has been different, and turns out very interesting for chemical polydibenzoether crown has identified conditions. Chemical and électronique polydibenzoether crown have different extraction screw vi property of adsoption of ions fifférents, this study also shows that plyméres doped may have an advantageous electrical conductivity.

Keywords: polymerization, electrochemical, conductivity, complexing metal ions

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Authors: H. Yousefnia, S. Zolghadri, S. Shanesazzadeh, A.Lahooti, A. R. Jalilian

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In this study, 68Ga-ECC and 67Ga-ECC were both prepared with the radiochemical purity of higher than 97% in less than 30 min. The biodistribution data for 68Ga-ECC showed the extraction of the most of the activity from the urinary tract. The absorbed dose was estimated based on biodistribution data in mice by the medical internal radiation dose (MIRD) method. Comparison between human absorbed dose estimation for these two agents indicated the values of approximately ten-fold higher after injection of 67Ga-ECC than 68Ga-ECC in the most organs. The results showed that 68Ga-ECC can be considered as a more potential agent for renal imaging compared to 67Ga-ECC.

Keywords: effective absorbed dose, ethylenecysteamine cysteine, Ga-67, Ga-68

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Authors: Ravinder Bahl, Jamini Sharma

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The paper demonstrates analysis of the cervical cancer based on a probabilistic model. It involves technique for classification and prediction by recognizing typical and diagnostically most important test features relating to cervical cancer. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases. The combination of the conventional statistical and machine learning tools is applied for the analysis. Experimental study with real data demonstrates the feasibility and potential of the proposed approach for the said cause.

Keywords: cervical cancer, recurrence, no recurrence, probabilistic, classification, prediction, machine learning

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Authors: Ting Lu, Ya-Ru Diao, Fei Fan, Ye Xue, Lei Shi, Xian-e Tang, Meng-jun Zhan, Zhen-hua Deng

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Accurate adult age estimation (AAE) is a significant and challenging task in forensic and archeology fields. Attempts have been made to explore optimal adult age metrics, and the rib is considered a potential age marker. The traditional way is to extract age-related features designed by experts from macroscopic or radiological images followed by classification or regression analysis. Those results still have not met the high-level requirements for practice, and the limitation of using feature design and manual extraction methods is loss of information since the features are likely not designed explicitly for extracting information relevant to age. Deep learning (DL) has recently garnered much interest in imaging learning and computer vision. It enables learning features that are important without a prior bias or hypothesis and could be supportive of AAE. This study aimed to develop DL models for AAE based on CT images and compare their performance to the manual visual scoring method. Chest CT data were reconstructed using volume rendering (VR). Retrospective data of 2500 patients aged 20.00-69.99 years were obtained between December 2019 and September 2021. Five-fold cross-validation was performed, and datasets were randomly split into training and validation sets in a 4:1 ratio for each fold. Before feeding the inputs into networks, all images were augmented with random rotation and vertical flip, normalized, and resized to 224×224 pixels. ResNeXt was chosen as the DL baseline due to its advantages of higher efficiency and accuracy in image classification. Mean absolute error (MAE) was the primary parameter. Independent data from 100 patients acquired between March and April 2022 were used as a test set. The manual method completely followed the prior study, which reported the lowest MAEs (5.31 in males and 6.72 in females) among similar studies. CT data and VR images were used. The radiation density of the first costal cartilage was recorded using CT data on the workstation. The osseous and calcified projections of the 1 to 7 costal cartilages were scored based on VR images using an eight-stage staging technique. According to the results of the prior study, the optimal models were the decision tree regression model in males and the stepwise multiple linear regression equation in females. Predicted ages of the test set were calculated separately using different models by sex. A total of 2600 patients (training and validation sets, mean age=45.19 years±14.20 [SD]; test set, mean age=46.57±9.66) were evaluated in this study. Of ResNeXt model training, MAEs were obtained with 3.95 in males and 3.65 in females. Based on the test set, DL achieved MAEs of 4.05 in males and 4.54 in females, which were far better than the MAEs of 8.90 and 6.42 respectively, for the manual method. Those results showed that the DL of the ResNeXt model outperformed the manual method in AAE based on CT reconstruction of the costal cartilage and the developed system may be a supportive tool for AAE.

Keywords: forensic anthropology, age determination by the skeleton, costal cartilage, CT, deep learning

Procedia PDF Downloads 58

Authors: Rajkumar Ghosh

Abstract:

Water scarcity is a pressing issue in urban areas, even in smart cities where efficient resource management is a priority. This scarcity is mainly caused by factors such as lifestyle changes, excessive groundwater extraction, over-usage of water, rapid urbanization, and uncontrolled population growth. In the specific case of Gomti Nagar, Lucknow, Uttar Pradesh, India, the depletion of groundwater resources is particularly severe, leading to a water imbalance and posing a significant challenge for the region's sustainable development. The aim of this study is to address the water shortage in the Gomti Nagar region by focusing on the implementation of artificial groundwater recharge methods. Specifically, the research aims to investigate the effectiveness of rainwater collection through rooftop rainwater harvesting systems (RTRWHs) as a sustainable approach to reduce aquifer depletion and bridge the gap between groundwater recharge and extraction. The research methodology for this study involves the utilization of RTRWHs as the main method for collecting rainwater. This approach is considered effective in managing and conserving water resources in a sustainable manner. The focus is on implementing RTRWHs in residential and commercial buildings to maximize the collection of rainwater and its subsequent utilization for various purposes in the Gomti Nagar region. The study reveals that the installation of RTRWHs in the Gomti Nagar region has a positive impact on addressing the water scarcity issue. Currently, RTRWHs cover only a small percentage (0.04%) of the total rainfall collected in the region. However, when RTRWHs are installed in all buildings, their influence on increasing water availability and reducing aquifer depletion will be significantly greater. The study also highlights the significant water imbalance of 24519 ML/yr in the region, emphasizing the urgent need for sustainable water management practices. This research contributes to the theoretical understanding of sustainable water management systems in smart cities. By highlighting the effectiveness of RTRWHs in reducing aquifer depletion, it emphasizes the importance of implementing such systems in urban areas. The findings of this study can serve as a basis for policymakers, urban planners, and developers to prioritize and incentivize the installation of RTRWHs as a potential solution to the water shortage crisis. The data for this study were collected through various sources such as government reports, surveys, and existing groundwater abstraction patterns. The collected data were then analysed to assess the current water situation, groundwater depletion rate, and the potential impact of implementing RTRWHs. Statistical analysis and modelling techniques were employed to quantify the water imbalance and evaluate the effectiveness of RTRWHs. The findings of this study demonstrate that the implementation of RTRWHs can effectively mitigate the water scarcity crisis in Gomti Nagar. By reducing aquifer depletion and bridging the gap between groundwater recharge and extraction, RTRWHs offer a sustainable solution to the region's water scarcity challenges. The study highlights the need for widespread adoption of RTRWHs in all buildings and emphasizes the importance of integrating such systems into the urban planning and development process. By doing so, smart cities like Gomti Nagar can achieve efficient water management, ensuring a better future with improved water availability for its residents.

Keywords: rooftop rainwater harvesting, rainwater, water management, aquifer

Procedia PDF Downloads 77

Authors: Malika Elkyal

Abstract:

We consider an iterative parallel multi-splitting method for differential algebraic equations. The main feature of the proposed idea is to use the asynchronous form. We prove that the multi-splitting technique can effectively accelerate the convergent performance of the iterative process. The main characteristic of an asynchronous mode is that the local algorithm does not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays to be substantial and unpredictable. Accordingly, we note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: parallel methods, asynchronous mode, multisplitting, differential algebraic equations

Procedia PDF Downloads 543

Authors: Dina Alnefaie

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This poster presents a study of a Discord private server with thirteen multilingual gamers, aiming to explore the elements that construct a multilingual online gaming community. The study focuses on the communication practices of four Saudi female and male gamers, using various data collection methods, including online observations through recorded videos and screenshots, interviews, and informal conversations for one year. The primary findings show that translanguaging was a prominent feature of their verbal and textual communication practices. Besides, these practices that mostly accompany cultural ones were used to facilitate communication and express their identities in an intercultural context.

Keywords: online community construction, perceptions, multilingualism, digital identity

Procedia PDF Downloads 74

Authors: Erin Lynne Plettenberg, Jeremy Vickery

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In an access-control situation, the role of a user determines whether a data request is appropriate. This paper combines vetted web mining and logic modeling to build a lightweight system for determining the role of a health care provider based only on their prior authorized requests. The model identifies provider roles with 100% recall from very little data. This shows the value of vetted web mining in AI systems, and suggests the impact of the ICD classification on medical practice.

Keywords: electronic medical records, information extraction, logic modeling, ontology, vetted web mining

Procedia PDF Downloads 160

Authors: Le Kang

Abstract:

According to the nature of the university, as a free and responsible academic community, USR is based on a different foundation —academic responsibility, so the Pyramid and the IC Model of CSR could not fully explain the most distinguished feature of USR. This paper sought to put forward a new model— Ferris Wheel Model, to illustrate the nature of USR and the process of achievement. The Ferris Wheel Model of USR shows the university creates a balanced, fairness and neutrality systemic structure to afford social responsibilities; that makes the organization could obtain a synergistic effect to achieve more extensive interests of stakeholders and wider social responsibilities.

Keywords: USR, achievement model, ferris wheel model, social responsibilities

Procedia PDF Downloads 709

Authors: Qiong Li

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This study investigated in which condition computer-mediated communication (CMC) could promote pragmatic development. The focal feature included four Chinese sentence final particles (SFPs), a, ya, ba, and ne. They occur frequently in Chinese, and function as mitigators to soften the tone of speech. However, L2 acquisition of SFPs is difficult, suggesting the necessity of additional exposure to or explicit instruction on Chinese SFPs. This study follows this line and aims to explore two research questions: (1) Is CMC combined with data-driven instruction more effective than CMC alone in promoting L2 Chinese learners’ SFP use? (2) How does L2 Chinese learners’ SFP use change over time, as compared to the production of native Chinese speakers? The study involved 19 intermediate-level learners of Chinese enrolled at a private American university. They were randomly assigned to two groups: (1) the control group (N = 10), which was exposed to SFPs through CMC alone, (2) the treatment group (N = 9), which was exposed to SFPs via CMC and data-driven instruction. Learners interacted with native speakers on given topics through text-based CMC over Skype. Both groups went through six 30-minute CMC sessions on a weekly basis, with a one-week interval after the first two CMC sessions and a two-week interval after the second two CMC sessions (nine weeks in total). The treatment group additionally received a data-driven instruction after the first two sessions. Data analysis focused on three indices: token frequency, type frequency, and acceptability of SFP use. Token frequency was operationalized as the raw occurrence of SFPs per clause. Type frequency was the range of SFPs. Acceptability was rated by two native speakers using a rating rubric. The results showed that the treatment group made noticeable progress over time on the three indices. The production of SFPs approximated the native-like level. In contrast, the control group only slightly improved on token frequency. Only certain SFPs (a and ya) reached the native-like use. Potential explanations for the group differences were discussed in two aspects: the property of Chinese SFPs and the role of CMC and data-driven instruction. Though CMC provided the learners with opportunities to notice and observe SFP use, as a feature with low saliency, SFPs were not easily noticed in input. Data-driven instruction in the treatment group directed the learners’ attention to these particles, which facilitated the development.

Keywords: computer-mediated communication, data-driven instruction, pragmatic development, second language Chinese, sentence final particles

Procedia PDF Downloads 404

Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

Abstract:

Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming and resource intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine (SVM), pattern recognition algorithms, ethanol treatment

Procedia PDF Downloads 4