Search results for: Global Accuracy Indicator (GAI)

Authors: Mohammad Hassan Ziraksaz

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Fuel atomizers are used in a wide range of IC engines, turbojets and a variety of liquid propellant rocket engines. As the fuel spray fully develops its characters approach their ultimate amounts. Fuel spray characters such as SMD, injection pressure, mass flow rate, droplet velocity and spray cone angle play important roles to atomize the liquid fuel to finely atomized fuel droplets and finally form the fine fuel spray. Well performed, fully developed, fine spray without any defections, brings the idea of finding an experimental relation between the main effective spray characters. Extracting an experimental relation between SMD and other fuel spray physical characters in swirl fuel atomizers is the main scope of this experimental work. Droplet velocity, fuel mass flow rate, SMD and spray cone angle are the parameters which are measured. A set of twelve reverse engineering atomizers without any spray defections and a set of eight original atomizers as referenced well-performed spray are contributed in this work. More than 350 tests, mostly repeated, were performed. This work shows that although spray cone angle plays a very effective role in spray formation, after formation, it smoothly approaches to an almost constant amount while the other characters are changed to create fine droplets. Therefore, the work to find the relation between the characters is focused on SMD, droplet velocity, fuel mass flow rate, and injection pressure. The process of fuel spray formation begins in 5 Psig injection pressures, where a tiny fuel onion attaches to the injector tip and ended in 250 Psig injection pressure, were fully developed fine fuel spray forms. Injection pressure is gradually increased to observe how the spray forms. In each step, all parameters are measured and recorded carefully to provide a data bank. Various diagrams have been drawn to study the behavior of the parameters in more detail. Experiments and graphs show that the power equation can best show changes in parameters. The SMD experimental relation with pressure P, fuel mass flow rate Q ̇ and droplet velocity V extracted individually in pairs. Therefore, the proportional relation of SMD with other parameters is founded. Now it is time to find an experimental relation including all the parameters. Using obtained proportional relation, replacing the parameters with experimentally measured ones and drawing the graphs of experimental SMD versus proportion SMD (〖SMD〗_P), a correctional equation and consequently the final experimental equation is obtained. This experimental equation is specified to use for swirl fuel atomizers and the use of this experimental equation in different conditions shows about 3% error, which is expected to achieve lower error and consequently higher accuracy by increasing the number of experiments and increasing the accuracy of data collection.

Keywords: droplet velocity, experimental relation, mass flow rate, SMD, swirl fuel atomizer

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Authors: Fatemeh Najafi

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The major stress factor limiting crop growth and development of sesame (Sesamum indicum L.) is drought stress in arid and semiarid regions of the world. For this study the effects of water stress on some qualitative and quantitative traits in sesame germplasm was conducted in the Research Farm of Seed and Plant Improvement Institute, Karaj, in the crop year. Genotypes in a randomized complete block design with three replications in two environments (moisture stress and normal) were studied in regard of the seed weight, capsule weight, grain yield, biomass, plant height, number of capsules per plant, etc. The characteristics were evaluated based on the combined analysis. Irrigation was based on first class evaporation basin. After flowering stage drought stress was applied. The water deficit reduced growth period. Days to reach full ripening decreased so that the reduction was significant at the five percent level. Drought stress reduces yield and plant biomass. Genotypes based on combined analysis of these two traits were significant at the one percent level. Genotypes differ in terms of yield stress in terms of density plots, grain yield, days to first flowering and days to the half of the cap on the confidence level of five percent and traits of days to emergence of the first capsule and days to reach full ripening at the one percent level were significant. Other traits were not significant. The correlation of traits in circumstances of stress the number of seeds per capsule has the greatest impact on performance. The sensitivity and stress tolerance index was calculated. Based on the indicators, (Fars variety) and variety Karaj were identified as the most tolerant genotypes among the studied genotypes to drought stress. The highest sensitivity indicator of stress was related to genotype (FARS).

Keywords: sesamum, drought, stress, germplasm, resistance

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Authors: Ahmed M. Mansour, Ali M. El-Sawy, Ahmed T. Sayed

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Logarithms reduce products to sums and powers to products; they play an important role in signal processing, communication and information theory. They are primarily used for hardware calculations, handling multiplications, divisions, powers, and roots effectively. There are three commonly used bases for logarithms; the logarithm with base-10 is called the common logarithm, the natural logarithm with base-e and the binary logarithm with base-2. This paper demonstrates different methods of calculation for log2 showing the complexity of each and finds out the most accurate and efficient besides giving in- sights to their hardware design. We present a new method called Floor Shift for fast calculation of log2, and then we combine this algorithm with Taylor series to improve the accuracy of the output, we illustrate that by using two examples. We finally compare the algorithms and conclude with our remarks.

Keywords: logarithms, log2, floor, iterative, CORDIC, Taylor series

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Authors: A. Ramachandra Reddy, V. Murugan, Prema Kumari

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Residual solvents in Pharmaceutical samples are monitored using gas chromatography with headspace (GC-HS). Based on current regulatory and compendial requirements, measuring the residual solvents are mandatory for all release testing of active pharmaceutical ingredients (API). Generally, isopropyl alcohol is used as the residual solvent in proline and tryptophan; methanol in cysteine monohydrate hydrochloride, glycine, methionine and serine; ethanol in glycine and lysine monohydrate; acetic acid in methionine. In order to have a single method for determining these residual solvents (isopropyl alcohol, ethanol, methanol and acetic acid) in all these 7 amino acids a sensitive and simple method was developed by using gas chromatography headspace technique with flame ionization detection. During development, no reproducibility, retention time variation and bad peak shape of acetic acid peaks were identified due to the reaction of acetic acid with the stationary phase (cyanopropyl dimethyl polysiloxane phase) of column and dissociation of acetic acid with water (if diluent) while applying temperature gradient. Therefore, dimethyl sulfoxide was used as diluent to avoid these issues. But most the methods published for acetic acid quantification by GC-HS uses derivatisation technique to protect acetic acid. As per compendia, risk-based approach was selected as appropriate to determine the degree and extent of the validation process to assure the fitness of the procedure. Therefore, Total error concept was selected to validate the analytical procedure. An accuracy profile of ±40% was selected for lower level (quantitation limit level) and for other levels ±30% with 95% confidence interval (risk profile 5%). The method was developed using DB-Waxetr column manufactured by Agilent contains 530 µm internal diameter, thickness: 2.0 µm, and length: 30 m. A constant flow of 6.0 mL/min. with constant make up mode of Helium gas was selected as a carrier gas. The present method is simple, rapid, and accurate, which is suitable for rapid analysis of isopropyl alcohol, ethanol, methanol and acetic acid in amino acids. The range of the method for isopropyl alcohol is 50ppm to 200ppm, ethanol is 50ppm to 3000ppm, methanol is 50ppm to 400ppm and acetic acid 100ppm to 400ppm, which covers the specification limits provided in European pharmacopeia. The accuracy profile and risk profile generated as part of validation were found to be satisfactory. Therefore, this method can be used for testing of residual solvents in amino acids drug substances.

Keywords: amino acid, head space, gas chromatography, total error

Procedia PDF Downloads 144

Authors: Susan Scott, Dina Hanna, Bassel Zebian, Gary Ruiz, Sreena Das

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Although the number of cases of TB in England has fallen over the last 4 years, it remains an important public health burden with 1 in 20 cases dying annually. The vast majority of cases present in non-UK born individuals with social risk factors. We present a case of non-pulmonary TB presenting in a healthy child born in the UK to professional parents. We present a case of a healthy 10 year old boy who developed acute back pain during school PE. Over the next 5 months, he was seen by various health and allied professionals with worsening back pain and kyphosis. He became increasing unsteady and for the 10 days prior to admission to our hospital, he developed fevers. He was admitted to his local hospital for tonsillitis where he suffered two falls on account of his leg weakness. A spinal X-ray revealed a pathological fracture and gibbus formation. He was transferred to our unit for further management. On arrival, the patient had lower motor neurone signs of his left leg. He underwent spinal fixture, laminectomy and decompression. Microbiology samples taken intra-operatively confirmed Mycobacterium Tuberculosis. He had a positive Mantoux and T-spot and treatment were commenced. There was no evidence of immune compromise. The patient was born in the UK, had a BCG scar and his only travel history had been two years prior to presentation when he travelled to the Phillipines for a short holiday. The patient continues to have issues around neuropathic pain, mobility, pill burden and mild liver side effects from treatment. Discussion: There is a paucity of case reports on spinal TB in paediatrics and diagnosis is often difficult due to the non-specific symptomatology. Although prognosis on treatment is good, a delayed diagnosis can have devastating consequences. This case highlights the continued need for higher index of suspicion and diagnosis in a world with changing patterns of migration and increase global travel. Surgical intervention is limited to the most serious cases to minimise further neurological damage and improve prognosis. There remains the need for a multi-disciplinary approach to deal with challenges of treatment and rehabilitation.

Keywords: tuberculosis, non-pulmonary TB, public health burden, diagnostic challenge

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Authors: Abdelouaheb Rouabhi

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In order to study the plastic resource of structures, the elastic-plastic single degree of freedom model described by Prandtl diagram is widely used. The generalization of this model to tow degree of freedom beyond the scope of a simple rigid-plastic system allows investigating the plastic resource of structures under complex disproportionate by individual components of deformation (earthquake). This macro-model greatly increases the accuracy of the calculations carried out. At the same time, the implementation of the proposed macro-model calculations easier than the detailed dynamic elastic-plastic calculations existing software systems such as ANSYS.

Keywords: elastic-plastic, single degree of freedom model, rigid-plastic system, plastic resource, complex plastic deformation, macro-model

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Authors: Ahmad S. Darwish, Tarek Lemaoui, Hanifa Taher, Inas M. AlNashef, Fawzi Banat

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This research reports a systematic top-down approach for designing neoteric hydrophobic solvents –particularly, deep eutectic solvents (DES) and ionic liquids (IL)– as furfural extractants from aqueous media for the application of sustainable biomass conversion. The first stage of the framework entailed screening 32 neoteric solvents to determine their efficacy against toluene as the application’s conventional benchmark for comparison. The selection criteria for the best solvents encompassed not only their efficiency in extracting furfural but also low viscosity and minimal toxicity levels. Additionally, for the DESs, their natural origins, availability, and biodegradability were also taken into account. From the screening pool, two neoteric solvents were selected: thymol:decanoic acid 1:1 (Thy:DecA) and trihexyltetradecyl phosphonium bis(trifluoromethylsulfonyl) imide [P₁₄,₆,₆,₆][NTf₂]. These solvents outperformed the toluene benchmark, achieving efficiencies of 94.1% and 97.1% respectively, compared to toluene’s 81.2%, while also possessing the desired properties. These solvents were then characterized thoroughly in terms of their physical properties, thermal properties, critical properties, and cross-contamination solubilities. The selected neoteric solvents were then extensively tested under various operating conditions, and an exceptional stable performance was exhibited, maintaining high efficiency across a broad range of temperatures (15–100 °C), pH levels (1–13), and furfural concentrations (0.1–2.0 wt%) with a remarkable equilibrium time of only 2 minutes, and most notably, demonstrated high efficiencies even at low solvent-to-feed ratios. The durability of the neoteric solvents was also validated to be stable over multiple extraction-regeneration cycles, with limited leachability to the aqueous phase (≈0.1%). Moreover, the extraction performance of the solvents was then modeled through machine learning, specifically multiple non-linear regression (MNLR) and artificial neural networks (ANN). The models demonstrated high accuracy, indicated by their low absolute average relative deviations with values of 2.74% and 2.28% for Thy:DecA and [P₁₄,₆,₆,₆][NTf₂], respectively, using MNLR, and 0.10% for Thy:DecA and 0.41% for [P₁₄,₆,₆,₆][NTf₂] using ANN, highlighting the significantly enhanced predictive accuracy of the ANN. The neoteric solvents presented herein offer noteworthy advantages over traditional organic solvents, including their high efficiency in both extraction and regeneration processes, their stability and minimal leachability, making them particularly suitable for applications involving aqueous media. Moreover, these solvents are more environmentally friendly, incorporating renewable and sustainable components like thymol and decanoic acid. This exceptional efficacy of the newly developed neoteric solvents signifies a significant advancement, providing a green and sustainable alternative for furfural production from biowaste.

Keywords: sustainable biomass conversion, furfural extraction, ionic liquids, deep eutectic solvents

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

Procedia PDF Downloads 183

Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

Procedia PDF Downloads 101

Authors: Hamidreza Fallah Lajimi , Elham Karami, Fatemeh Ali nasab, Mostafa Mahdavikia

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Being responsive is an increasingly important skill for firms in today’s global economy; thus firms must be agile. Naturally, it follows that an organization’s agility depends on its supply chain being agile. However, achieving supply chain agility is a function of other abilities within the organization. This paper analyses results from a survey of 71 Iran manufacturing companies in order to identify some of the factors for agile organizations in managing their supply chains. Then we classification this company in four cluster with fuzzy c-mean technique and with four validations functional determine automatically the optimal number of clusters.

Keywords: agile supply chain, clustering, fuzzy clustering

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

Procedia PDF Downloads 239

Authors: Sigal Gat-Lazer, Ronny Geva, Dan Ramon, Eitan Gur, Daniel Stein

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Introduction: Eating disorders (ED) are common psychopathologies which involve distorted body image and eating disturbances. Binge-purge eating disorders (B/P ED) are characterized by repetitive events of binge eating followed by purges. Patients with B/P ED behavior may be seen as impulsive especially when relate to food stimulation and affective conditions. The current study included innovative modification of the day-night task targeted to assess inhibitory control among patients with B/P ED. Methods: This prospective study included 50 patients with B/P ED during acute phase of illness (T1) upon their admission to specialized ED department in tertiary center. 34 patients repeated the study towards discharge to ambulatory care (T2). Treatment effect was evaluated by BMI and emotional questionnaires regarding depression and anxiety by the Beck Depression Inventory and State Trait Anxiety Inventory questionnaires. Control group included 36 healthy controls with matched demographic parameters who performed both T1 and T2 assessments. The current modification is based on the emotional day-night task (EDNT) which involves five emotional stimulation added to the sun and moon pictures presented to participants. In the current study, we designed the food-emotional modification day night task (F-EDNT) food stimulations of egg and banana which resemble the sun and moon, respectively, in five emotional states (angry, sad, happy, scrambled and neutral). During this computerized task, participants were instructed to push on “day” bottom in response to moon and banana stimulations and on “night” bottom when sun and egg were presented. Accuracy (A) and reaction time (RT) were evaluated and compared between EDNT and F-EDNT as a reflection of participants’ inhibitory control. Results: Patients with B/P ED had significantly improved BMI, depression and anxiety scores on T2 compared to T1 (all p<0.001). Task performance was similar among patients and controls in the EDNT without significant A or RT differences in both T1 and T2. On F-EDNT during T1, B/P ED patients had significantly reduced accuracy in 4/5 emotional stimulation compared to controls: angry (73±25% vs. 84±15%, respectively), sad (69±25% vs. 80±18%, respectively), happy (73±24% vs. 82±18%, respectively) and scrambled (74±24% vs. 84±13%, respectively, all p<0.05). Additionally, patients’ RT to food stimuli was significantly faster compared to neutral ones, in both cry and neutral emotional stimulations (356±146 vs. 400±141 and 378±124 vs. 412±116 msec, respectively, p<0.05). These significant differences between groups as a function of stimulus type were diminished on T2. Conclusion: Having to process food related content, in particular in emotional context seems to be impaired in patients with B/P ED during the acute phase of their illness and elicits greater impulsivity. Innovative modification using such procedures seem to be sensitive to patients’ illness phase and thus may be implemented during screening and follow up through the clinical management of these patients.

Keywords: binge purge eating disorders, day night task modification, eating disorders, food related stimulations

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Authors: Prateep Wajeetongratana

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This research aims to study the audit quality that affected to the reputation of the certified public accountants in Thailand. The researcher defined the population for this research as a group of the certified public accountants in Thailand who are the member of the federation of accounting professions under the royal patronage of his majesty the king also disclose their information .The total sampling size is 325. The results showed the audit quality factor has influence to the reputation of the certified public accountants in Thailand by accuracy auditing, objectiveness auditing and clearness auditing .These factors show by y1 = 1.381 + .372x1.1 + .309x1.2 + .305x1.3 can be describe as professional standard strictly factor (Y.1.1) and the new clients raised from word of mount of old clients regularly factor (Y.1.2) by regression coefficient (R2) as.242, this shows that such variables could predict the audit quality variable as 24.2 percent.

Keywords: audit quality, certified public accountants in Thailand, reputation

Procedia PDF Downloads 253

Authors: Gracious Tendai Matayaya

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The world is facing problems in finding alternative resources to offset the decline in global petroleum reserves. The use of fossil fuels has prompted biofuel development, particularly in the transportation sector. In these circumstances, looking for alternative renewable energy sources makes sense. Petroleum-based fuels also result in a lot of carbon dioxide being released into the environment causing global warming. Replacing petroleum and fossil fuel-based fuels with biofuels has the advantage of reducing undesirable aspects of these fuels, which are mostly the production of greenhouse gas and dependence on unstable foreign suppliers. Algae refer to a group of aquatic microorganisms that produce a lot of lipids up to 60% of their total weight. This project aims to exploit the large amounts of oil produced by these microorganisms in the Soxhlet extraction to make biodiesel. Experiments were conducted to establish the cultivability of algae, harvesting methods, the oil extraction process, and the transesterification process. Although there are various methods for producing algal oil, the Soxhlet extraction method was employed for this particular research. After extraction, the oil was characterized before being used in the transesterification process that used methanol and hydrochloric acid as the process reactants. The properties of the resulting biodiesel were then determined. Because there is a requirement to dry wet algae, the experimental findings showed that Soxhlet extraction was the optimum way to produce a higher yield of microalgal oil. Upon cultivating algae, Compound D fertilizer was added as a source of nutrients (Phosphorous and Nitrogen), and the highest growth of algae was observed at 6 days (using 2 g of fertilizer), after which it started to decrease. Butanol, hexane, heptane and acetone have been experimented with as solvents, and heptane gave the highest amount of oil (89ml of oil) when 300 ml of solvent was used. This was compared to 73.21ml produced by butanol, 81.90 produced by hexane and 69.57ml produced by acetone, and as a result, heptane was used for the rest of the experiments, which included a variation of the mass of dried algae and time of extraction. This meant that the oil composition of algae was higher than other oil sources like peanuts, soybean etc. Algal oil was heated at 150℃ for 150 minutes in the presence of methanol (reactant) and hydrochloric acid (HCl), which was used as a catalyst. A temperature of 200℃ produced 93.64%, and a temperature of 250℃ produced 92.13 of biodiesel at 150 minutes.

Keywords: microalgae, algal oil, biodiesel, soxhlet extraction

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Authors: Rafik Djemili, Hocine Bourouba, M. C. Amara Korba

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In this paper, we present a method in order to classify EEG signals for Brain-Computer Interfaces (BCI). EEG signals are first processed by means of spectral estimation methods to derive reliable features before classification step. Spectral estimation methods used are standard periodogram and the periodogram calculated by the Welch method; both methods are compared with Logarithm of Band Power (logBP) features. In the method proposed, we apply Linear Discriminant Analysis (LDA) followed by Support Vector Machine (SVM). Classification accuracy reached could be as high as 85%, which proves the effectiveness of classification of EEG signals based BCI using spectral methods.

Keywords: brain-computer interface, motor imagery, electroencephalogram, linear discriminant analysis, support vector machine

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Authors: Wenjing Ding, Weiwei Shan, Zijuan, Wang, Chao He

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Liquid spray heat exchanger is the critical equipment of temperature regulating system by gaseous nitrogen which realizes the environment temperature in the range of -180 ℃~+180 ℃. Liquid nitrogen is atomized into smaller liquid drops through liquid nitrogen sprayer and then contacts with gaseous nitrogen to be cooled. By adjusting the pressure of liquid nitrogen and gaseous nitrogen, the flowrate of liquid nitrogen is changed to realize the required outlet temperature of heat exchanger. The temperature accuracy of shrouds is ±1 ℃. Liquid nitrogen spray heat exchanger is simulated by CATIA, and the numerical simulation is performed by FLUENT. The comparison between the tests and numerical simulation is conducted. Moreover, the results help to improve the design of liquid nitrogen spray heat exchanger.

Keywords: liquid nitrogen spray, temperature regulating system, heat exchanger, numerical simulation

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Authors: Razi Khalafi

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Myocardial Infarction (MI) or acute Myocardial Infarction (AMI), commonly known as a heart attack, occurs when blood flow stops to part of the heart causing damage to the heart muscle. An ECG can often show evidence of a previous heart attack or one that's in progress. The patterns on the ECG may indicate which part of your heart has been damaged, as well as the extent of the damage. In chaos theory, the correlation dimension is a measure of the dimensionality of the space occupied by a set of random points, often referred to as a type of fractal dimension. In this research by considering ECG signal as a random walk we work on forecasting the oncoming heart attack by analysing the ECG signals using the correlation dimension. In order to test the model a set of ECG signals for patients before and after heart attack was used and the strength of model for forecasting the behaviour of these signals were checked. Results show this methodology can forecast the ECG and accordingly heart attack with high accuracy.

Keywords: heart attack, ECG, random walk, correlation dimension, forecasting

Procedia PDF Downloads 502

Authors: Ana Paula Almeida Castaldelli Maciel, Gabriela Medeiros, Amanda de Souza Machado, Maria Clara Pilatti, Ralpho Rinaldo dos Reis, Silvio Cesar Sampaio

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Sustainable agricultural systems are crucial to ensuring global food security and the long-term production of nutritious food. Comprehensive soil and water management practices, including nutrient management, balanced fertilizer use, and appropriate waste management, are essential for sustainable agriculture. Swine wastewater (SWW) treatment has become a significant focus due to environmental concerns related to heavy metals, antibiotics, resistant pathogens, and nutrients. In South America, small farms use soil to dispose of animal waste, a practice that is expected to increase with global pork production. The potential of SWW as a nutrient source is promising, contributing to global food security, nutrient cycling, and mineral fertilizer reduction. Short- and long-term studies evaluated the effects of SWW on soil and plant parameters, such as nutrients, heavy metals, organic matter (OM), cation exchange capacity (CEC), and pH. Although promising results have been observed in short- and medium-term applications, long-term applications require more attention due to heavy metal concentrations. Organic soil amendment strategies, due to their economic and ecological benefits, are commonly used to reduce the bioavailability of heavy metals. However, the rate of degradation and initial levels of OM must be monitored to avoid changes in soil pH and release of metals. The study aimed to evaluate the long-term effects of SWW application on soil fertility parameters, focusing on calcium (Ca), magnesium (Mg), and potassium (K), in addition to CEC and OM. Experiments were conducted at the Universidade Estadual do Oeste do Paraná, Brazil, using 24 drainage lysimeters for nine years, with different application rates of SWW and mineral fertilization. Principal Component Analysis (PCA) was then conducted to summarize the composite variables, known as principal components (PC), and limit the dimensionality to be evaluated. The retained PCs were then correlated with the original variables to identify the level of association between each variable and each PC. Data were interpreted using Analysis of Variance - ANOVA for general linear models (GLM). As OM was not measured in the 2007 soybean experiment, it was assessed separately from PCA to avoid loss of information. PCA and ANOVA indicated that crop type, SWW, and mineral fertilization significantly influenced soil nutrient levels. Soybeans presented higher concentrations of Ca, Mg, and CEC. The application of SWW influenced K levels, with higher concentrations observed in SWW from biodigesters and higher doses of swine manure. Variability in nutrient concentrations in SWW due to factors such as animal age and feed composition makes standard recommendations challenging. OM levels increased in SWW-treated soils, improving soil fertility and structure. In conclusion, the application of SWW can increase soil fertility and crop productivity, reducing environmental risks. However, careful management and long-term monitoring are essential to optimize benefits and minimize adverse effects.

Keywords: contamination, water research, biodigester, nutrients

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Authors: Cherry Dhiman, Ayushi Paliwal, Mohd. Shahid Khan, M. N. Reddy, Vinay Gupta, Monika Tomar

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The laser based high resolution spectroscopic experimental techniques such as Laser Induced Breakdown Spectroscopy (LIBS), Rotating Disk Electrode Optical Emission spectroscopy (RDE-OES) and Surface Plasmon Resonance (SPR) have been used for the study of composition and degradation analysis of used engine oils. Engine oils are mainly composed of aliphatic and aromatics compounds and its soot contains hazardous components in the form of fine, coarse and ultrafine particles consisting of wear metal elements. Such coarse particulates matter (PM) and toxic elements are extremely dangerous for human health that can cause respiratory and genetic disorder in humans. The combustible soot from thermal power plants, industry, aircrafts, ships and vehicles can lead to the environmental and climate destabilization. It contributes towards global pollution for land, water, air and global warming for environment. The detection of such toxicants in the form of elemental analysis is a very serious issue for the waste material management of various organic, inorganic hydrocarbons and radioactive waste elements. In view of such important points, the current study on used engine oils was performed. The fundamental characterization of engine oils was conducted by measuring water content and kinematic viscosity test that proves the crude analysis of the degradation of used engine oils samples. The microscopic quantitative and qualitative analysis was presented by RDE-OES technique which confirms the presence of elemental impurities of Pb, Al, Cu, Si, Fe, Cr, Na and Ba lines for used waste engine oil samples in few ppm. The presence of such elemental impurities was confirmed by LIBS spectral analysis at various transition levels of atomic line. The recorded transition line of Pb confirms the maximum degradation which was found in used engine oil sample no. 3 and 4. Apart from the basic tests, the calculations for dielectric constants and refractive index of the engine oils were performed via SPR analysis.

Keywords: surface plasmon resonance, laser-induced breakdown spectroscopy, ICCD spectrometer, engine oil

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Authors: Dolores Sucozhañay, Gustavo Pacheco, Paul Vanegas

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In the context of sustainable development and the transition towards a sustainable circular economy (CE), evaluating the social dimension remains a challenge. Therefore, developing a respective methodology is highly important. First, the change of the economic model may cause significant social effects, which today remain unaddressed. Second, following the current level of globalization, CE implementation requires targeting global material cycles and causes social impacts on potentially vulnerable social groups. A promising methodology is the Social Life Cycle Assessment (SLCA), which embraces the philosophy of life cycle thinking and provides complementary information to environmental and economic assessments. In this context, the present work uses the updated Social Life Cycle Assessment (SLCA) Guidelines 2020 to assess the social performance of the recycling system of Cuenca, Ecuador, to exemplify a social assessment method. Like many other developing countries, Ecuador heavily depends on the work of informal waste pickers (recyclers), who, even contributing to a CE, face harsh socio-economic circumstances, including inappropriate working conditions, social exclusion, exploitation, etc. Under a Reference Scale approach (Type 1), 12 impact subcategories were assessed through 73 site-specific inventory indicators, using an ascending reference scale ranging from -2 to +2. Findings reveal a social performance below compliance levels with local and international laws, basic societal expectations, and practices in the recycling sector; only eight and five indicators present a positive score. In addition, a social hotspot analysis depicts collection as the most time-consuming lifecycle stage and the one with the most hotspots, mainly related to working hours and health and safety aspects. This study provides an integrated view of the recyclers’ contributions, challenges, and opportunities within the recycling system while highlighting the relevance of assessing the social dimension of CE practices. It also fosters an understanding of the social impact of CE operations in developing countries, highlights the need for a close north-south relationship in CE, and enables the connection among the environmental, economic, and social dimensions.

Keywords: SLCA, circular economy, recycling, social impact assessment

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Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

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Authors: Yinwei Lin, Cha'o-Kuang Chen

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In this article, the Laplace Adomian transform method (LADM) and double decomposition method (DDM) are used to solve the annular hyperbolic profile fins with variable thermal conductivity. As the thermal conductivity parameter ε is relatively large, the numerical solution using DDM become incorrect. Moreover, when the terms of DDM are more than seven, the numerical solution using DDM is very complicated. However, the present method can be easily calculated as terms are over seven and has more precisely numerical solutions. As the thermal conductivity parameter ε is relatively large, LADM also has better accuracy than DDM.

Keywords: fins, thermal conductivity, Laplace transform, Adomian, nonlinear

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Authors: Piyush Samant, Ravinder Agarwal

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Complementary and Alternative Medicine (CAM) techniques are quite popular and effective for chronic diseases. Iridology is more than 150 years old CAM technique which analyzes the patterns, tissue weakness, color, shape, structure, etc. for disease diagnosis. The objective of this paper is to validate the use of iridology for the diagnosis of the diabetes. The suggested model was applied in a systemic disease with ocular effects. 200 subject data of 100 each diabetic and non-diabetic were evaluated. Complete procedure was kept very simple and free from the involvement of any iridologist. From the normalized iris, the region of interest was cropped. All 63 features were extracted using statistical, texture analysis, and two-dimensional discrete wavelet transformation. A comparison of accuracies of six different classifiers has been presented. The result shows 89.66% accuracy by the random forest classifier.

Keywords: complementary and alternative medicine, classification, iridology, iris, feature extraction, disease prediction

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Authors: Xiao Chen, Xiaoying Kong, Min Xu

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This paper presents a road vehicle detection approach for the intelligent transportation system. This approach mainly uses low-cost magnetic sensor and associated data collection system to collect magnetic signals. This system can measure the magnetic field changing, and it also can detect and count vehicles. We extend Mel Frequency Cepstral Coefficients to analyze vehicle magnetic signals. Vehicle type features are extracted using representation of cepstrum, frame energy, and gap cepstrum of magnetic signals. We design a 2-dimensional map algorithm using Vector Quantization to classify vehicle magnetic features to four typical types of vehicles in Australian suburbs: sedan, VAN, truck, and bus. Experiments results show that our approach achieves a high level of accuracy for vehicle detection and classification.

Keywords: vehicle classification, signal processing, road traffic model, magnetic sensing

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Authors: Tharini N. de Silva, Xiao Zhibo, Zhao Rui, Mao Kezhi

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Causal relation identification is a crucial task in information extraction and knowledge discovery. In this work, we present two approaches to causal relation identification. The first is a classification model trained on a set of knowledge-based features. The second is a deep learning based approach training a model using convolutional neural networks to classify causal relations. We experiment with several different convolutional neural networks (CNN) models based on previous work on relation extraction as well as our own research. Our models are able to identify both explicit and implicit causal relations as well as the direction of the causal relation. The results of our experiments show a higher accuracy than previously achieved for causal relation identification tasks.

Keywords: causal realtion extraction, relation extracton, convolutional neural network, text representation

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Authors: Ayobami Kehinde

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The literary representations of the family, tradition and change in African literature offer an immense, and as yet little theorised area of literary scholarship. Therefore, this paper explores the nexus among the family, tradition and change in five purposively selected post-colonial African fiction: Chimamanda Adichie’s Purple Hibiscus, Wale Okediran’s Tenants of the House, J. M. Coetzee’s In the Heart of the Country, Tsitsi Dangrembga’s Nervous Condition and Meja Mwangi’s Striving for the Wind. The methodology centres on analysing, questioning, undermining and celebrating the family and its contemporary vicissitudes as depicted in the texts. This is with a view to exploring the postcolonial novel with references to concepts developed by major theorists in the field of postcolonial studies, including Frantz Fanon, Edward Said, Gayatri Spivak, Homi Bhabha, Kwame Appiah and Achille Mbembe. It is revealed that in spite of the fact that the family is a vital institution, the primary social unit in any community, an agent of acculturation and the first focus of development, independence and growth, the texts reflect a diversity of problems confronting the family unit in Africa. These include the multiple problems of disrupted family lives, enforced family separation, political and personal violence with the domestic environment. It is concluded that the post-colonial African novel is a quintessential weapon to analyse the continent, opening up to the reader the specific expressions and experiences of human lives and their wider contexts. Therefore, the post-colonial African novel is a primary socio-cultural indicator representing an immense variety of lived realities in the continent. The study, therefore, suggests a concerted concern with the preservation of traditional family structures and other related aspects, such as cultural values, spirituality, gender roles and mutual trust.

Keywords: family, African fiction, postcolonialism, African tradition, domestic dissonance

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Authors: Javier Comes Fayos, Isabel Rodríguez Moreno, Sara Bressanutti, Marisol Lila, Angel Romero Martínez, Luis Moya Albiol

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Empathy is a cognitive-affective capacity whose deterioration is associated with aggressive behaviour. Deficient affective processing is one of the predominant risk factors in men convicted of intimate partner violence (IPV perpetrators), since it makes their capacity to empathize very difficult. The objective of this study is to compare the response of electrodermal activity (EDA), as an indicator of emotionality, to an empathic induction task, between IPV perpetrators and men without a history of violence. The sample was composed of 51 men who attended the CONTEXTO program, with penalties for gender violence under two years, and 47 men with no history of violence. Empathic induction was achieved through the visualization of 4 negative emotional-eliciting videos taken from an emotional induction battery of videos validated for the Spanish population. The participants were asked to actively empathize with the video characters (previously pointed out). The psychophysiological recording of the EDA was accomplished by the "Vrije Universiteit Ambulatory Monitoring System (VU-AMS)." An analysis of repeated measurements was carried out with 10 intra-subject measurements (time) and "group" (IPV perpetrators and non-violent perpetrators) as the inter-subject factor. First, there were no significant differences between groups in the baseline AED levels. Yet, a significant interaction between the “time” and “group” was found with IPV perpetrators exhibiting lower EDA response than controls after the empathic induction task. These findings provide evidence of a subdued EDA response after an empathic induction task in IPV perpetrators with respect to men without a history of violence. Therefore, the lower psychophysiological activation would be indicative of difficulties in the emotional processing and response, functions that are necessary for the empathic function. Consequently, the importance of addressing possible empathic difficulties in IPV perpetrator psycho-educational programs is reinforced, putting special emphasis on the affective dimension that could hinder the empathic function.

Keywords: electrodermal activity, emotional induction, empathy, intimate partner violence

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Authors: Marcin Sowa

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The paper discusses the subinterval-based numerical method for fractional derivative computations. It is now referred to by its acronym – SubIval. The basis of the method is briefly recalled. The ability of the method to be applied in time stepping solvers is discussed. The possibility of implementing a time step size adaptive solver is also mentioned. The solver is tested on a transient circuit example. In order to display the accuracy of the solver – the results have been compared with those obtained by means of a semi-analytical method called gcdAlpha. The time step size adaptive solver applying SubIval has been proven to be very accurate as the results are very close to the referential solution. The solver is currently able to solve FDE (fractional differential equations) with various derivative orders for each equation and any type of source time functions.

Keywords: numerical method, SubIval, fractional calculus, numerical solver, circuit analysis

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Authors: Tahisa M. Pedroso, Hérida R. N. Salgado

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The microbiological turbidimetric assay allows the determination of potency of the drug, by measuring the turbidity (absorbance), caused by inhibition of microorganisms by ertapenem sodium. Ertapenem sodium (ERTM), a synthetic antimicrobial agent of the class of carbapenems, shows action against Gram-negative, Gram-positive, aerobic and anaerobic microorganisms. Turbidimetric assays are described in the literature for some antibiotics, but this method is not described for ertapenem. The objective of the present study was to develop and validate a simple, sensitive, precise and accurate microbiological assay by turbidimetry to quantify ertapenem sodium injectable as an alternative to the physicochemical methods described in the literature. Several preliminary tests were performed to choose the following parameters: Staphylococcus aureus ATCC 25923, IAL 1851, 8 % of inoculum, BHI culture medium, and aqueous solution of ertapenem sodium. 10.0 mL of sterile BHI culture medium were distributed in 20 tubes. 0.2 mL of solutions (standard and test), were added in tube, respectively S1, S2 and S3, and T1, T2 and T3, 0.8 mL of culture medium inoculated were transferred to each tube, according parallel lines 3 x 3 test. The tubes were incubated in shaker Marconi MA 420 at a temperature of 35.0 °C ± 2.0 °C for 4 hours. After this period, the growth of microorganisms was inhibited by addition of 0.5 mL of 12% formaldehyde solution in each tube. The absorbance was determined in Quimis Q-798DRM spectrophotometer at a wavelength of 530 nm. An analytical curve was constructed to obtain the equation of the line by the least-squares method and the linearity and parallelism was detected by ANOVA. The specificity of the method was proven by comparing the response obtained for the standard and the finished product. The precision was checked by testing the determination of ertapenem sodium in three days. The accuracy was determined by recovery test. The robustness was determined by comparing the results obtained by varying wavelength, brand of culture medium and volume of culture medium in the tubes. Statistical analysis showed that there is no deviation from linearity in the analytical curves of standard and test samples. The correlation coefficients were 0.9996 and 0.9998 for the standard and test samples, respectively. The specificity was confirmed by comparing the absorbance of the reference substance and test samples. The values obtained for intraday, interday and between analyst precision were 1.25%; 0.26%, 0.15% respectively. The amount of ertapenem sodium present in the samples analyzed, 99.87%, is consistent. The accuracy was proven by the recovery test, with value of 98.20%. The parameters varied did not affect the analysis of ertapenem sodium, confirming the robustness of this method. The turbidimetric assay is more versatile, faster and easier to apply than agar diffusion assay. The method is simple, rapid and accurate and can be used in routine analysis of quality control of formulations containing ertapenem sodium.

Keywords: ertapenem sodium, turbidimetric assay, quality control, validation

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Authors: Kholod Hajo

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In this study COMSOL Multiphysics was used to design lateral flow biosensors (LFBs) which provide advantages in low cost, simplicity, rapidity, stability and portability thus making LFBs popular in biomedical, agriculture, food and environmental sciences. This study was focused on simulation model of biosensor based on gold nanoparticles (GNPs) designed using software package (COMSOL Multiphysics), the magnitude of the laminar velocity field in the flow cell, concentration distribution in the analyte stream and surface coverage of adsorbed species and average fractional surface coverage of adsorbed analyte were discussed from the model and couples of suggestion was given in order to functionalize GNPs and to increase the accuracy of the biosensor design, all above were obtained acceptable results.

Keywords: model, gold nanoparticles, biosensor, COMSOL Multiphysics

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