Search results for: natural product based drug designs

Authors: Nor Jannah Mohd Sebri, Khairul Anuar Mat Amin

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Dication cross-linked gellan gum hydrogel loaded with Ibuprofen with excellent mechanical properties had been synthesized as potential candidate for non-toxic biocompatible polymer material in tissue engineering. The gellan gum hydrogel with 5% Ibuprofen had produced a slow release profile with total drug release time of 25 hours as a resulting low swelling value recorded at 22+0.5%. Its compressive strength, 200.13+21 kPa was highest of all other hydrogel ratio of 0.5% and 1.0% Ibuprofen incorporation. Young’s Modulus of the hydrogel with 5% Ibuprofen was recorded at 1.8+0.01 MPa, indicating good gel strength in which it is capable of withstanding a fair amount of subjected force during topical wound dressing application. Excellent mechanical properties, together with slow release profile, make the ibuprofen-loaded hydrogel a prospect candidate as biocompatible extracellular matrices in wound management.

Keywords: gellan gum, ibuprofen, slow drug release, hydrogel

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Authors: Ketevan Goletiani, Parmen Khvedelidze

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discusses the importance of the topic, which is reflected in the advanced and developed countries in the formation of a new innovative stage of the distinctive mark of the modern world development. This phase includes the construction of the economy, which generates stockpiling and use is based. Intensifying the production and use of the results of new scientific and technical innovation has led to a sharp reduction in the cycle and accelerate the pace of product and technology updates. The world's leading countries in the development of innovative management systems for the formation of long-term and stable development of the socio-economic order conditions. The last years of the 20th century, the social and economic relations, modification, accelerating economic reforms, and profound changes in the system of the time. At the same time, the country should own place in the world geopolitical and economic space. Accelerated economic development tasks, the World Trade Organization, the European Union deep and comprehensive trade agreement, the new system of economic management, technical and technological renewal of production potential, and scientific fields in the share of the total volume of GDP growth requires new approaches. XX - XXI centuries Georgia's socio-economic changes is one of the urgent tasks in the form of a rise to the need for change, involving the use of natural resource-based economy to the latest scientific and technical achievements of an innovative and dynamic economy based on an accelerated pace. But Georgia still remains unresolved in many methodological, theoretical, and practical nature of the problem relating to the management of the economy in various fields for the development of innovative systems for optimal implementation. Therefore, the development of an innovative system for the formation of a complex and multi-problem, which is reflected in the following: countries should have higher growth rates than the geopolitical space of the neighboring countries that its competitors are. Formation of such a system is possible only in a deep theoretical research and innovative processes in the multi-level (micro, meso- and macro-levels) management on the basis of creation.

Keywords: georgia, innovative, socio-economic, innovative manage

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Authors: Shazia Ali, Yves Mard, Éric Severin

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To the best of our knowledge, this is the first study to have analyzed the impact of a) firm-specific product-market power and b) industry competition on earnings management behavior of European firms in distress versus healthy years while controlling for firm-level characteristics. We predicted a significant relationship between firms’ product market power and earnings management tools and their trade-off under the moderation effect of financial distress. We found that the firm-level market power hereinafter referred to as MP (proxied by the industry-adjusted Lerner Index) is positively associated with both real and accrual earnings management. However, MP is associated with a higher level of real earnings management compared to accrual earnings management in distress years compared to healthy years. On the other hand, industry product market power (representing low competition and proxied by the inverse of the total number of firms in an industry hereinafter referred to as NUMB) and firms product market power (proxied by firm market share hereinafter referred to as MS) are associated with lower inflationary accruals and higher deflationary accruals respectively. On the other hand, they are found to be linked with higher real earnings management in distress versus healthy years. When we divided the sample into small and big firms based on their respective industry-year median total assets, we found that all three measures of industry competition (Industry Median Lerner Index (hereinafter referred to as IMLI), NUMB, and Herfindahl–Hirschman Index (hereinafter referred to as HHI) indicate that small firms in low-competitive industries in financial distress are more likely to inflate their earnings through discretionary accruals. While big firms in this situation are more likely to lower the use of both inflationary and deflationary discretionary accruals as indicated by IMLI and HHI and trade-off accruals earnings management for real earnings management as indicated by NUMB. Moreover, IMLI and HHI did not show any interesting results when we divided the sample based on the firm Lerner Index/Market Power. However, the distressed firms with high market power (MP>industry median) are found to engage in income-decreasing discretionary accruals in low-competitive industries (high NUMB). Whereas firms with low market power in the same industry use downward discretionary accruals but inflate income using real activities (abnCFO). Our findings are robust across alternate measures of discretionary accruals and financial distress, such as the Altman Z-Score. The finding of the study is valuable for accounting standard setters, competition authorities, policymakers, and investors alike to help in informed decision-making.

Keywords: financial distress, earnings management, market competition

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Authors: V. V. Lakshmi, Y. V. S. Annapurna

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Urinary tract infection (UTI) is one of the most common infectious diseases seen in the community. Susceptible individuals experience multiple episodes, and progress to acute pyelonephritis or uro-sepsis or develop asymptomatic bacteriuria (ABU). Ability to cause extraintestinal infections depends on several virulence factors required for survival at extraintestinal sites. Presence of virulence phenotypes enhances the pathogenicity of these otherwise commensal organisms and thus augments its ability to cause extraintestinal infections, the most frequent in urinary tract infections(UTI). The present study focuses on detection of the virulence characters exhibited by the uropathogenic organism and most common factors exhibited in the local pathogens. A total of 700 isolates of E.coli and Klebsiella spp were included in the study.These were isolated from patients from local hospitals reported to be suffering with UTI over a period of three years. Isolation and identification was done based on Gram character and IMVIC reactions. Antibiotic sensitivity profile was carried out by disc diffusion method and multi drug resistant strains with MAR index of 0.7 were further selected. Virulence features examined included their ability to produce exopolysaccharides, protease- gelatinase production, hemolysin production, haemagglutination and hydrophobicity test. Exopolysaccharide production was most predominant virulence feature among the isolates when checked by congo red method. The biofilms production examined by microtitre plates using ELISA reader confirmed that this is the major factor contributing to virulencity of the pathogens followed by hemolysin production.

Keywords: Escherichia coli, Klebsiella spp, Uropathogens, virulence features

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Authors: H. Hruschka

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This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

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Authors: Jamboor K. Vishwanatha

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Among the potent anti-cancer agents, curcumin has been found to be very efficacious against various cancer cells. Despite multiple medicinal benefits of curcumin, poor water solubility, poor physiochemical properties and low bioavailability continue to pose major challenges in developing a formulation for clinical efficacy. To improve its potential application in the clinical area, we formulated poly lactic-co-glycolic acid (PLGA) nanoparticles. The PLGA nanoparticles were formulated using solid-oil/water emulsion solvent evaporation method and then characterized for percent yield, encapsulation efficiency, surface morphology, particle size, drug distribution within nanoparticles and drug polymer interaction. Our studies showed the successful formation of smooth and spherical curcumin loaded PLGA nanoparticles with a high percent yield of about 92.01±0.13% and an encapsulation efficiency of 90.88±0.14%. The mean particle size of the nanoparticles was found to be 145nm. The in vitro drug release profile showed 55-60% drug release from the nanoparticles over a period of 24 hours with continued sustained release over a period of 8 days. Exposure to curcumin loaded nanoparticles resulted in reduced cell viability of cancer cells compared to normal cells. We used a novel non-covalent insertion of a homo-bifunctional spacer for targeted delivery of curcumin to various cancer cells. Functionalized nanoparticles for antibody/targeting agent conjugation was prepared using a cross-linking ligand, bis(sulfosuccinimidyl) suberate (BS3), which has reactive carboxyl group to conjugate efficiently to the primary amino groups of the targeting agents. In our studies, we demonstrated successful conjugation of antibodies, Annexin A2 or prostate specific membrane antigen (PSMA), to curcumin loaded PLGA nanoparticles for targeting to prostate and breast cancer cells. The percent antibody attachment to PLGA nanoparticles was found to be 92.8%. Efficient intra-cellular uptake of the targeted nanoparticles was observed in the cancer cells. These results have emphasized the potential of our multifunctional curcumin nanoparticles to improve the clinical efficacy of curcumin therapy in patients with cancer.

Keywords: polymeric nanoparticles, cancer therapy, sustained release, curcumin

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Authors: Klazina T. Havinga, Hilde R. H. de Geus

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Introduction: Pre-operative coagulation management of Apixaban prescribed patients, a new oral anticoagulant (a factor Xa inhibitor), is difficult, especially when chronic kidney disease (CKD) causes drug overdose. Apixaban is not dialyzable due to its high level of protein binding. An antidote, Andexanet α, is available but expensive and has an unfavorable short half-life. We report the successful extracorporeal removal of Apixaban prior to emergency surgery with the CytoSorb® Hemoadsorption device. Methods: A 89-year-old woman with CKD, with an Apixaban prescription for atrial fibrillation, was presented at the ER with traumatic rib fractures, a flail chest, and an unstable spinal fracture (T12) for which emergency surgery was indicated. However, due to very high Apixaban levels, this surgery had to be postponed. Based on the Apixaban-specific anti-factor Xa activity (AFXaA) measurements at admission and 10 hours later, complete clearance was expected after 48 hours. In order to enhance the Apixaban removal and reduce the time to operation, and therefore reduce pulmonary complications, CRRT with CytoSorb® cartridge was initiated. Apixaban-specific anti-factor Xa activity (AFXaA) was measured frequently as a substitute for Apixaban drug concentrations, pre- and post adsorber, in order to calculate the adsorber-related clearance. Results: The admission AFXaA concentration, as a substitute for Apixaban drug levels, was 218 ng/ml, which decreased to 157 ng/ml after ten hours. Due to sustained anticoagulation effects, surgery was again postponed. However, the AFXaA levels decreased quickly to sub-therapeutic levels after CRRT (Multifiltrate Pro, Fresenius Medical Care, Blood flow 200 ml/min, Dialysate Flow 4000 ml/h, Prescribed renal dose 51 ml-kg-h) with Cytosorb® connected in series into the circuit was initiated (within 5 hours). The adsorber-related (indirect) Apixaban clearance was calculated every half hour (Cl=Qe * (AFXaA pre- AFXaA post/ AFXaA pre) with Qe=plasma flow rate calculated with Ht=0.38 and system blood flow rate 200 ml-min): 100 ml/min, 72 ml/min and 57 ml/min. Although, as expected, the adsorber-related clearance decreased quickly due to saturation of the beads, still the reduction rate achieved resulted in a very rapid decrease in AFXaA levels. Surgery was ordered and possible within 5 hours after Cytosorb initiation. Conclusion: The CytoSorb® Hemoadsorption device enabled rapid correction of Apixaban associated anticoagulation.

Keywords: Apixaban, CytoSorb, emergency surgery, Hemoadsorption

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Authors: Tushar Vasant Salunkhe, Sariput M. Nawghare, Maheboobsab B. Nadaf, Sushovan Dutta, J. N. Mandal

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The model tests were conducted in the laboratory without and with plastic recycled polymer in fly ash steep slopes overlaying soft foundation soils like fly ash and power soil in order to check the stability of steep slope. In this experiment, fly ash is used as a filling material, and Plastic Recycled Polymers of diameter = 3mm and length = 4mm were made from the waste plastic product (lower grade plastic product). The properties of fly ash and plastic recycled polymers are determined. From the experiments, load and settlement have measured. From these data, load–settlement curves have been reported. It has been observed from test results that the load carrying capacity of mixture fly ash with Plastic Recycled Polymers slope is more than that of fly ash slope. The deformation of Plastic Recycled Polymers slope is slightly more than that of fly ash slope. A Finite Element Method (F.E.M.) was also evaluated using PLAXIS 3D version. The failure pattern, deformations and factor of safety are reported based on analytical programme. The results from experimental data and analytical programme are compared and reported.

Keywords: factor of safety, finite element method (FEM), fly ash, plastic recycled polymer

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Authors: M. F. Wu, F. T. Cheng, C. S. Wu, M. C. Tan

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It is a critical time to upgrade technology and increase value added with manufacturing skills developing and management strategies that will highly satisfy the customers need in the precision machinery global market. In recent years, the supply side, each precision machinery manufacturers in each country are facing the pressures of price reducing from the demand side voices that pushes the high-end precision machinery manufacturers adopts low-cost and high-quality strategy to retrieve the market. Because of the trend of the global market, the manufacturers must take price reducing strategies and upgrade technology of low-end machinery for differentiations to consolidate the market. By using six key success factors (KSFs), customer perceived value, customer satisfaction, customer service, product design, product effectiveness and machine structure quality are causal conditions to explore the impact of competitive advantage of the enterprise, such as overall profitability and product pricing power. This research uses key success paths (KSPs) approach and f/s QCA software to explore various combinations of causal relationships, so as to fully understand the performance level of KSFs and business objectives in order to achieve competitive advantage. In this study, the combination of a causal relationships, are called Key Success Paths (KSPs). The key success paths guide the enterprise to achieve the specific outcomes of business. The findings of this study indicate that there are thirteen KSPs to achieve the overall profitability, sixteen KSPs to achieve the product pricing power and seventeen KSPs to achieve both overall profitability and pricing power of the enterprise. The KSPs provide the directions of resources integration and allocation, improve utilization efficiency of limited resources to realize the continuous vision of the enterprise.

Keywords: precision machinery industry, key success factors (KSFs), key success paths (KSPs), overall profitability, product pricing power, competitive advantages

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Authors: Huiqiang Yang, Domingo Santana

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This paper proposes a novel configuration which introduces a natural draft dry cooling tower system in a molten salt power tower. A three-dimensional numerical modeling was developed based on the novel configuration. A plan of building 20 new concentrating solar power plants has been announced by Chinese government in September 2016, and among these 20 new plants, most of them are located in regions with long winter and harsh winter climate. The innovative configuration proposed includes an external receiver concrete tower at the center, a natural draft dry cooling tower which is surrounding the external receiver concrete tower and whose shell is fixed on the external receiver concrete tower, and a power block (including a steam generation system, a steam turbine system and hot/cold molten salt tanks, and water treatment systems) is covered by the roof of the natural draft dry cooling tower. Heat exchanger bundles are vertically installed at the furthest edge of the power block. In such a way, all power block equipment operates under suitable environmental conditions through whole year operation. The monthly performance of the novel configuration is simulated as compared to a standard one. The results show that the novel configuration is much more efficient in each separate month in a typical meteorological year. Moreover, all systems inside the power block have less thermal losses at low ambient temperatures, especially in harsh winter climate. It is also worthwhile mentioning that a photovoltaic power plant can be installed on the roof of the cooling tower to reduce the parasites of the molten salt power tower.

Keywords: molten salt power tower, natural draft dry cooling, commercial scale, power block, harsh winter climate

Procedia PDF Downloads 341

Authors: Isaac K. E. Ampomah, Seong-Bae Park, Sang-Jo Lee

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Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

Keywords: deep neural models, natural language inference, recognizing textual entailment (RTE), sentence-to-sentence relation

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Authors: Kholil Kholil, Aniwidayati

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MDS (Multi-Dimensional Scaling) is one method that has been widely used to evaluate the use of natural resources. By using Raffish software tool, we will able to analyze sustainability level of the natural resources use. This paper will discuss the level of sustainability of the reservoir using MDS (Multi-Dimensional Scaling) based on five dimensions: (1) Ecology & Layout, (2) Economics, (3) Social & Culture, (4) Regulations & Institutional, and (5) Infrastructure and Technology. MDS analysis results show that the dimension of ecological and layout, institutional and the regulation are lack of sustainability due to the low index score of 45.76 and 42.24. While for the economic, social and culture, and infrastructure and technology dimension reach each score of 63.12, 64.42, and 68.64 (only the sufficient sustainability category). It means that the sustainability performance of Cirata Reservoir seriously threatened.

Keywords: MDS, cirata reservoir, carrying capacity, water quality, sustainable development, sedimentation, sustainability index

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Authors: Nerella Srinivas

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This study explores the significance of patent protection for artificial intelligence (AI) innovations in the pharmaceutical sector, emphasizing applications in drug discovery, personalized medicine, and clinical trial optimization. The challenges of patenting AI-driven inventions are outlined, focusing on the classification of algorithms as abstract ideas, meeting the non-obviousness standard, and issues around defining inventorship. The methodology includes examining case studies and existing patents, with an emphasis on how companies like Benevolent AI and Insilico Medicine have successfully secured patent rights. Findings demonstrate that a strategic approach to patent protection is essential, with particular attention to showcasing AI’s technical contributions to pharmaceutical advancements. Conclusively, the study underscores the critical role of understanding patent law and innovation strategies in leveraging intellectual property rights in the rapidly advancing field of AI-driven pharmaceuticals.

Keywords: artificial intelligence, pharmaceutical industry, patent protection, drug discovery, personalized medicine, clinical trials, intellectual property, non-obviousness

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Authors: Reenu Yadav, Vidhi Guha, Udit N. Soni, Jay Ram Patel

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Chewing gums are mobile novel drug delivery systems, with a potential for administering drugs either for local action or for systemic absorption via the buccal route. An antimicrobial chewing gum delivery system of the methanolic extracts of Beatea monosperma (barks and twigs), Cordia obliqua (leaves and seeds) and Cuminun cyminum (seeds) against periodontal diseases caused by some oral pathogens, was designed and characterized on various parameters.The results of the study support the traditional application of the plants and suggest, plant extracts possess compounds with antimicrobial properties that can be used as potential antimicrobial agents and gums can be a good carrier of herbal extracts. Developed formulation will cure/protect from various periodontal diseases. Further development and evaluations chewing gums including the isolated compounds on the commercial scale and their clinical and toxicological studies are the future challenges.

Keywords: periodontal diseases, herbal chewing gum, herbal extracts, novel drug delivery systems

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Authors: Agnes Simone

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A 52-year-old male with unknown psychiatric and medical history was brought to the Psychiatric Emergency Room by ambulance directly from jail. He had been detained for three weeks for possession of a firearm while intoxicated. On initial evaluation, the patient was unable to provide a reliable history. He presented with odd jerking movements of his extremities and catatonic features, including mutism and stupor. His vital signs were stable. Patient was transferred to the medical emergency department for work-up of altered mental status. Due to suspicion for opioid overdose, the patient was given naloxone (Narcan) with no improvement. Laboratory work-up included complete blood count, comprehensive metabolic panel, thyroid stimulating hormone, vitamin B12, folate, magnesium, rapid plasma reagin, HIV, blood alcohol level, aspirin, and Tylenol blood levels, urine drug screen, and urinalysis, which were all negative. CT head and chest X-Ray were also negative. With this negative work-up, the medical team concluded there was no organic etiology and requested inpatient psychiatric admission. Upon re-evaluation by psychiatry, it was evident that the patient continued to have an altered mental status. Of note, the medical team did not include substance withdrawal in the differential diagnosis due to stable vital signs and a negative urine drug screen. The psychiatry team decided to check California's prescription drug monitoring program (CURES) and discovered that the patient was prescribed benzodiazepine alprazolam (Xanax) 2mg BID, a sedative-hypnotic, and hydrocodone/acetaminophen 10mg/325mg (Norco) QID, an opioid. After a thorough chart review, his daughter's contact information was found, and she confirmed his benzodiazepine and opioid use, with recent escalation and misuse. It was determined that the patient was experiencing alprazolam withdrawal, given this collateral information, his current symptoms, negative urine drug screen, and recent abrupt discontinuation of medications while incarcerated. After admission to the medical unit and two doses of alprazolam 2mg, the patient's mental status, alertness, and orientation improved, but he had no memory of the events that led to his hospitalization. He was discharged with a limited supply of alprazolam and a close follow-up to arrange a taper. Accompanying this case report, a qualitative review of presentations with alprazolam withdrawal was completed. This case and the review highlights: (1) Alprazolam withdrawal can occur at low doses and within just one week of use. (2) Alprazolam withdrawal can present without any vital sign instability. (3) Alprazolam withdrawal does not respond to short-acting benzodiazepines but does respond to certain long-acting benzodiazepines due to its unique chemical structure. (4) Alprazolam withdrawal is distinct from and more severe than other benzodiazepine withdrawals. This case highlights (1) the importance of physician utilization of drug-monitoring programs. This case, in particular, relied on California's drug monitoring program. (2) The importance of obtaining collateral information, especially in cases in which the patient is unable to provide a reliable history. (3) The importance of including substance intoxication and withdrawal in the differential diagnosis even when there is a negative urine drug screen. Toxidrome of withdrawal can be delayed. (4) The importance of discussing addiction and withdrawal risks of medications with patients.

Keywords: addiction risk of benzodiazepines, alprazolam withdrawal, altered mental status, benzodiazepines, drug monitoring programs, sedative-hypnotics, substance use disorder

Procedia PDF Downloads 138

Authors: Zeineb Ben Azzouz, Wahiba Ben Abdessalem Karaa

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The automated extraction of UML class diagram from natural language requirements is a highly challenging task. Many approaches, frameworks and tools have been presented in this field. Nonetheless, the experiments of these tools have shown that there is no approach that can work best all the time. In this context, we propose a new accurate approach to facilitate the automatic mapping from textual requirements to UML class diagram. Our new approach integrates the best properties of statistical Natural Language Processing (NLP) techniques to reduce ambiguity when analysing natural language requirements text. In addition, our approach follows the best practices defined by conceptual modelling experts to determine some patterns indispensable for the extraction of basic elements and concepts of the class diagram. Once the relevant information of class diagram is captured, a XMI document is generated and imported with a CASE tool to build the corresponding UML class diagram.

Keywords: class diagram, user’s requirements, XMI, software engineering

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Authors: Sushish Baral, Pratibha Joshi, Yaman Maharjan

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Glaucoma is a chronic eye condition that causes vision loss that is irreversible. Early detection and treatment are critical to prevent vision loss because it can be asymptomatic. For the identification of glaucoma, multiple deep learning algorithms are used. Transformer-based architectures, which use the self-attention mechanism to encode long-range dependencies and acquire extremely expressive representations, have recently become popular. Convolutional architectures, on the other hand, lack knowledge of long-range dependencies in the image due to their intrinsic inductive biases. The aforementioned statements inspire this thesis to look at transformer-based solutions and investigate the viability of adopting transformer-based network designs for glaucoma detection. Using retinal fundus images of the optic nerve head to develop a viable algorithm to assess the severity of glaucoma necessitates a large number of well-curated images. Initially, data is generated by augmenting ocular pictures. After that, the ocular images are pre-processed to make them ready for further processing. The system is trained using pre-processed images, and it classifies the input images as normal or glaucoma based on the features retrieved during training. The Vision Transformer (ViT) architecture is well suited to this situation, as it allows the self-attention mechanism to utilise structural modeling. Extensive experiments are run on the common dataset, and the results are thoroughly validated and visualized.

Keywords: glaucoma, vision transformer, convolutional architectures, retinal fundus images, self-attention, deep learning

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Authors: C. S. Caires, A. Barbosa, W. Hanyou

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In this paper, we present the main results achieved during a five-week international workshop on Interactive Furniture for the Classroom, with 22 Chinese design students, in Jiangmen city (Guangdong province, China), and five teachers from Portugal, France, Iran, Macao SAR, and China. The main goal was to engage design students from China with new skills and practice methodologies towards interactive design research for furniture and product design for the classroom. The final results demonstrate students' concerns on improving Chinese furniture design for the classrooms, including solutions related to collaborative learning and human-interaction design for interactive furniture products. The findings of the research led students to the fabrication of five original prototypes: two for kindergartens ('Candy' and 'Tilt-tilt'), two for primary schools ('Closer' and 'Eks(x)'), and one for art/creative schools ('Wave'). From the findings, it was also clear that collaboration, personalization, and project-based teaching are still neglected when designing furniture products for the classroom in China. Students focused on these issues and came up with creative solutions that could transform this educational field in China.

Keywords: product design, collaborative education, interactive design, design research and prototyping

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Authors: Ilma Robo, Saimir Heta, Greta Plaka, Vera Ostreni

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Purpose: Drug gingival hyperplasia is an uncommon pathology encountered during routine work in dental units. The purpose of this paper is to present the clinical appearance of gingival hyperplasia caused by medications. There are already three classes of medications that cause hyperplasia and based on data from the literature, the clinical cases encountered and included in this study have been compared. Materials and Methods: The study was conducted in a total of 311 patients, out of which 182 patients were included in our study, meeting the inclusion criteria. After each patient's history was recorded and it was found that patients were in their knowledge of chronic illness, undergoing treatment of gingivitis hypertrophic drugs was performed with a clinical examination of oral cavity and assessment by vertical and horizontal evaluation according to the periodontal indexes. Results: Of the data collected during the study, it was observed that 97% of patients with gingival hyperplasia are treated with nifedipine. 84% of patients treated with selected medicines and gingival hyperplasia in the oral cavity has been exposed at time period for more than 1 year and 1 month. According to the GOI, in the first rank of this index are about 21% of patients, in the second rank are 52%, in the third rank are 24% and in the fourth grade are 3%. According to the horizontal growth index of gingival hyperplasia, grade 1 included about 61% of patients and grade 2 included about 39% of patients with gingival hyperplasia. Bacterial index divides patients by degrees: grading 0 - 8.2%, grading 1 - 32.4%, grading 2 - 14% and grading 3 - 45.1%. Conclusions: The highest percentage of gingival hyperplasia caused by drugs is due to dosing of nifedipine for a duration of dosing and application for systemic healing for more than 1 year.

Keywords: drug gingival hyperplasia, horizontal growth index, vertical growth index

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Authors: Mohammed Abaoli Abafogi, Sinem Satiroglu, M. Misir

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The current study has addressed the effect of Hazelnut (Corylus avellana) agroforestry on carbon sequestration. Eight sample plots were collected from Hazelnut (Corylus avellana) agroforestry using random sampling method. The diameter of all trees in each plot with ≥ 2cm at 1.3m DBH was measured by using a calliper. Average diameter, aboveground biomass, and carbon stock were calculated for each plot. Comparative data for natural forestland was used for C was taken from KTU, and the soil C was converted from the biomass conversion equation. Biomass carbon was significantly higher in the Natural forest (68.02Mgha⁻¹) than in the Hazelnut agroforestry (16.89Mgha⁻¹). SOC in Hazelnut agroforestry, Natural forest, and arable agricultural land were 7.70, 385.85, and 0.00 Mgha⁻¹ respectively. Biomass C, on average accounts for only 0.00% of the total C in arable agriculture, and 11.02% for the Hazelnut agroforestry while 88.05% for Natural forest. The result shows that the conversion of arable crop field to Hazelnut agroforestry can sequester a large amount of C in the soil as well as in the biomass than Arable agricultural lands.

Keywords: arable agriculture, biomass carbon, carbon sequestration, hazelnut (Corylus avellana) agroforestry, soil organic carbon

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Authors: Ali Reza Tahavvor, Saeed Hosseini, Nazli Jowkar, Behnam Amiri

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Heat transfer of leaves is a crucial factor in optimal operation of metabolic functions in plants. In order to quantify this phenomenon in different leaves and investigate the influence of leaf shape on heat transfer, natural convection for pine, orange and olive leaves was simulated as representatives of different groups of leaf shapes. CFD techniques were used in this simulation with the purpose to calculate heat transfer of leaves in similar environmental conditions. The problem was simulated for steady state and three-dimensional conditions. From obtained results, it was concluded that heat fluxes of all three different leaves are almost identical, however, total rate of heat transfer have highest and lowest values for orange leaves and pine leaves, respectively.

Keywords: computational fluid dynamic, heat flux, heat transfer, natural convection

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Authors: Johannes Gantner, Michael Held, Matthias Fischer

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The refurbishment of the building stock in terms of energy supply and efficiency is one of the major challenges of the German turnaround in energy policy. As the building sector accounts for 40% of Germany’s total energy demand, additional insulation is key for energy efficient refurbished buildings. Nevertheless the energetic benefits often the environmental and economic performances of insulation materials are questioned. The methods Life Cycle Assessment (LCA) as well as Life Cycle Costing (LCC) can form the standardized basis for answering this doubts and more and more become important for material producers due efforts such as Product Environmental Footprint (PEF) or Environmental Product Declarations (EPD). Due to increasing use of LCA and LCC information for decision support the robustness and resilience of the results become crucial especially for support of decision and policy makers. LCA and LCC results are based on respective models which depend on technical parameters like efficiencies, material and energy demand, product output, etc.. Nevertheless, the influence of parameter uncertainties on lifecycle results are usually not considered or just studied superficially. Anyhow the effect of parameter uncertainties cannot be neglected. Based on the example of an exterior wall the overall lifecycle results are varying by a magnitude of more than three. As a result simple best case worst case analyses used in practice are not sufficient. These analyses allow for a first rude view on the results but are not taking effects into account such as error propagation. Thereby LCA practitioners cannot provide further guidance for decision makers. Probabilistic analyses enable LCA practitioners to gain deeper understanding of the LCA and LCC results and provide a better decision support. Within this study, the environmental and economic impacts of an exterior wall system over its whole lifecycle are illustrated, and the effect of different uncertainty analysis on the interpretation in terms of resilience and robustness are shown. Hereby the approaches of error propagation and Monte Carlo Simulations are applied and combined with statistical methods in order to allow for a deeper understanding and interpretation. All in all this study emphasis the need for a deeper and more detailed probabilistic evaluation based on statistical methods. Just by this, misleading interpretations can be avoided, and the results can be used for resilient and robust decisions.

Keywords: uncertainty, life cycle assessment, life cycle costing, Monte Carlo simulation

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Authors: Loreta Serniene, Dalia Sekmokiene, Justina Tomkeviciute, Lina Lauciene, Vaida Andruleviciute, Ingrida Sinkeviciene, Kristina Kondrotiene, Neringa Kasetiene, Mindaugas Malakauskas

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One of the most important areas of product development and research in the dairy industry is the product enrichment with active ingredients as well as leading to increased product safety and sustainability. The most expanding field of the active ingredients is the various plants' CO₂ extracts with aromatic, antioxidant and antimicrobial properties. In this study, 15 plant extracts were evaluated based on their antioxidant, antimicrobial properties as well as sensory acceptance indicators for the development of new dairy products. In order to increase the total antioxidant capacity of the milk products, it was important to determine the content of phenolic compounds and antioxidant activity of CO₂ extract. The total phenolic content of fifteen different commercial CO₂ extracts was determined by the Folin-Ciocalteu reagent and expressed as milligrams of the Gallic acid equivalents (GAE) in gram of extract. The antioxidant activities were determined by 2.2′-azinobis-(3-ethylbenzthiazoline)-6-sulfonate (ABTS) methods. The study revealed that the antioxidant activities of investigated CO₂ extract vary from 4.478-62.035 µmole Trolox/g, while the total phenolic content was in the range of 2.021-38.906 mg GAE/g of extract. For the example, the estimated antioxidant activity of Chinese cinnamon (Cinammonum aromaticum) CO₂ extract was 62.023 ± 0.15 µmole Trolox/g and the total flavonoid content reached 17.962 ± 0.35 mg GAE/g. These two parameters suggest that cinnamon could be a promising supplement for the development of new cheese. The inhibitory effects of these essential oils were tested by using agar disc diffusion method against pathogenic bacteria, most commonly found in dairy products. The obtained results showed that essential oil of lemon myrtle (Backhousia citriodora) and cinnamon (Cinnamomum cassia) has antimicrobial activity against E. coli, S. aureus, B. cereus, P. florescens, L. monocytogenes, Br. thermosphacta, P. aeruginosa and S. typhimurium with the diameter of inhibition zones variation from 10 to 52 mm. The sensory taste acceptability of plant extracts in combination with a dairy product was evaluated by a group of sensory evaluation experts (31 individuals) by the criteria of overall taste acceptability in the scale of 0 (not acceptable) to 10 (very acceptable). Each of the tested samples included 200g grams of natural unsweetened greek yogurt without additives and 1 drop of single plant extract (essential oil). The highest average of overall taste acceptability was defined for the samples with essential oils of orange (Citrus sinensis) - average score 6.67, lemon myrtle (Backhousia citriodora) – 6.62, elderberry flower (Sambucus nigra flos.) – 6.61, lemon (Citrus limon) – 5.75 and cinnamon (Cinnamomum cassia) – 5.41, respectively. The results of this study indicate plant extracts of Cinnamomum cassia and Backhousia citriodora as a promising additive not only to increase the total antioxidant capacity of the milk products and as alternative antibacterial agent to combat pathogenic bacteria commonly found in dairy products but also as a desirable flavour for the taste pallet of the consumers with expressed need for safe, sustainable and innovative dairy products. Acknowledgment: This research was funded by the European Regional Development Fund according to the supported activity 'Research Projects Implemented by World-class Researcher Groups' under Measure No. 01.2.2-LMT-K-718.

Keywords: antioxidant properties, antimicrobial properties, cinnamon, CO₂ plant extracts, dairy products, essential oils, lemon myrtle

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Authors: Phutthiwat Waiyawuththanapoom, Pimploi Tirastittam, Manop Tirastittam

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Thailand is the agriculture country which mainly exports the agriculture product to the other countries in so many ways which are fresh vegetable, chilled vegetable or frozen vegetable. The gross export for Thailand’s vegetable is 30-40 billion baht per year, and the growth rate is about 15-20 percent per year. Ginger is one of the main vegetable product that Thailand export to Japan because Thailand’s Ginger has a good quality and be able to supply Japan’s demand with a reasonable price. This research paper is aimed to study the factors which affect the efficiency of the supply chain process of Thailand’s ginger to Japan. There are 5 factors which related to the exporting Thailand’s ginger to Japan which are quality, price, equipment and supply standard, custom process and distribution pattern. The result of the research showed that the factor which reached the 'very good' significant level is quality of Thailand’s ginger with the score of 4.86. The other 5 factors are in the 'good' significant level. So the most important factor for Thai ginger farmer to concern is the quality of the product.

Keywords: critical success factor, export, ginger, supply chain

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Authors: Jatinder Kumar, Ajay Bansal

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Simulation of a photocatalytic reactor helps in understanding the complex behavior of the photocatalytic degradation. Simulation also aids the designing and optimization of the photocatalytic reactor. Lack of simulation strategies is a huge hindrance in the commercialization of the photocatalytic technology. With the increased performance of computational resources, and development of simulation software, computational fluid dynamics (CFD) is becoming an affordable engineering tool to simulate and optimize reactor designs. In the present paper, a CFD (Computational fluid dynamics) model for simulating the performance of an immobilized-titanium dioxide based annular photocatalytic reactor was developed. The computational model integrates hydrodynamics, species mass transport, and chemical reaction kinetics using a commercial CFD code Fluent 6.3.26. The CFD model was based on the intrinsic kinetic parameters determined experimentally in a perfectly mixed batch reactor. Rhodamine B, a complex organic compound, was selected as a test pollutant for photocatalytic degradation. It was observed that CFD could become a valuable tool to understand and improve the photocatalytic systems.

Keywords: simulation, computational fluid dynamics (CFD), annular photocatalytic reactor, titanium dioxide

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Authors: Shryas Bhurat, Aryan Vashistha, Sampreet Dinakar Nayak, Ayush Gupta

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As the technology is advancing more towards high computational resources, there is a paradigm shift in the usage of these resources to optimize the design process. This paper discusses the usage of ‘Generative Design using Artificial Intelligence’ to build better models that adapt the operations like selection, mutation, and crossover to generate results. The human mind thinks of the simplest approach while designing an object, but the intelligence learns from the past & designs the complex optimized CAD Models. Generative Design takes the boundary conditions and comes up with multiple solutions with iterations to come up with a sturdy design with the most optimal parameter that is given, saving huge amounts of time & resources. The new production techniques that are at our disposal allow us to use additive manufacturing, 3D printing, and other innovative manufacturing techniques to save resources and design artistically engineered CAD Models. Also, this paper discusses the Genetic Algorithm, the Non-Domination technique to choose the right results using biomimicry that has evolved for current habitation for millions of years. The computer uses parametric models to generate newer models using an iterative approach & uses cloud computing to store these iterative designs. The later part of the paper compares the topology optimization technology with Generative Design that is previously being used to generate CAD Models. Finally, this paper shows the performance of algorithms and how these algorithms help in designing resource-efficient models.

Keywords: genetic algorithm, bio mimicry, generative modeling, non-dominant techniques

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Authors: A. M. S. S. Amarasiri, A. P. Attanayake, K. A. P. W. Jayatilaka, L. K. B. Mudduwa

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Adriamycin (ADR) is an effective anthracyclin antitumor drug, but its clinical use is limited due to renal toxicity. The leaves of Asparagus falcatus (Family: Liliaceae) have been used in the management of renal diseases since antiquity. In the present investigation, the aqueous leaf extract of A. falcatus was evaluated for acute nephroprotective activity in ADR induced nephrotoxic rats. Nephrotoxicity was induced in healthy male Wistar rats by intraperitoneal administration of ADR 20 mg/kg. The lyophilized powder of the aqueous refluxed (4h) leaf extract of A. falcatus was administered orally at three selected doses; 200, 400 and 600 mg/kg for three consecutive days. Fosinopril sodium (0.09 mg/kg) was used as the standard drug. Administration of the plant extract and the standard drug was commenced 24 hours after the induction of nephrotoxicity to rats. The nephroprotective effect was determined by selected biochemical parameters and by the assessment of histopathology on H and E stained kidney sections. The results were compared to a group of control rats with ADR induced nephrotoxicity. A group of rats administered with the equivalent volume of normal saline served as the healthy control. Administration of ADR 20 mg/kg produced a significant increase in the concentrations of serum creatinine (61%) and urine protein (73%) followed by a significant decrease in serum total protein (21%) and albumin (44%) of the plant extract treated animals compared to the healthy control group (p < 0.05). The aqueous extract of Asparagus falcatus at the three doses; 200, 400 and 600 mg/kg and the standard drug were found to decrease the elevation of concentrations of serum creatinine (33%, 51%, 54% and 42%) and urine protein (8%, 63%, 80% and 86%) respectively. The serum concentrations of total protein (12%, 17%, 29% and 12%) and albumin (3%, 17%, 17% and 16%) were significantly increased compared to the nephrotoxic control group respectively. Assessment of histopathology on H and E stained kidney sections demonstrated that ADR induced renal injury, as evidenced by loss of brush border, cytoplasmic vacuolization, pyknosis in renal tubular epithelial cells, haemorrhages, glomerular congestion and presence of hyaline casts. Treatment with the plant extract and the standard drug resulted in attenuation of the morphological destruction in rats. The results of the present study revealed that the aqueous leaf extract of A. falcatus possesses significant nephroprotective activity against adriamycin induced acute nephrotoxicity. The improved kidney functions were supported with the results of selected biochemical parameters and histological changes observed on H and E stained sections of the kidney tissues in Wistar rats.

Keywords: adriamycin induced nephrotoxicity, asparagus falcatus, biochemical assessment, histopathological assessment, nephroprotective activity

Procedia PDF Downloads 164

Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: capacity-booking, SPA, monthly production planning, linear programming

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Authors: Hadi Rezghimaleki, Masatoshi Kubouchi, Yoshihiko Arao

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The use of natural fiber composites (NFCs) is growing at a fast rate regarding industrial applications and principle researches due to their eco-friendly, renewable nature, and low density/costs. Drilling is one of the most important machining operations that are carried out on natural fiber composites. Delamination is a major concern in the drilling process of NFCs that affects the structural integrity and long-term reliability of the machined components. Flax fiber reinforced epoxy composite laminates were prepared by hot press technique. In this research, we evaluated drilling-induced delamination of flax/epoxy composites by X-ray computed tomography (CT), ultrasonic testing (UT), and optical methods and compared the results.

Keywords: natural fiber composites, flax/epoxy, X-ray CT, ultrasonic testing

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

Procedia PDF Downloads 346