Search results for: machine and plant engineering

Authors: Mohammed A. Elhaj, Jamal S. Yassin

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In the present time, energy crises are considered a severe problem across the world. For the protection of global environment and maintain ecological balance, energy saving is considered one of the most vital issues from the view point of fuel consumption. As the industrial sectors everywhere continue efforts to improve their energy efficiency, recovering waste heat losses provides an attractive opportunity for an emission free and less costly energy resource. In the other hand the using of solar energy has become more insistent particularly after the high gross of prices and running off the conventional energy sources. Therefore, it is essential that we should endeavor for waste heat recovery as well as solar energy by making significant and concrete efforts. For these reasons this investigation is carried out to study and analyze the performance of a power plant working by a combined cycle in which Heat Recovery System Generator (HRSG) gets its energy from the waste heat of a gas turbine unit. Evaluation of the performance of the plant is based on different thermal efficiencies of the main components in addition to the second law analysis considering the exergy destructions for the whole components. The contribution factors including the solar as well as the wasted energy are considered in the calculations. The final results have shown that there is significant exergy destruction in solar concentrator and the combustion chamber of the gas turbine unit. Other components such as compressor, gas turbine, steam turbine and heat exchangers having insignificant exergy destruction. Also, solar energy can contribute by about 27% of the input energy to the plant while the energy lost with exhaust gases can contribute by about 64% at maximum cases.

Keywords: solar energy, environment, efficiency, waste heat, steam generator, performance, exergy destruction

Procedia PDF Downloads 298

Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

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Authors: T. D. Tran, H. Schlegel, R. Neugebauer

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Ball screws are an important component in machine tools. In mechatronic systems and machine tools, a ball screw has to work usually at a high speed. Otherwise the axial compliance of the ball screw, in combination with the inertia of the slide, the motor, the coupling and the screw, will cause an oscillation resonance, which limits the systems bandwidth and consequently influences performance of the motion controller. In this paper, the modal analysis method by measuring and analysing the vibrating parameters of the ball screw system to determine the dynamic characteristic of existing structures is used. On the one hand, the results of this study were obtained by the theoretical analysis and the modal testing of a ball screw system test station with the help of an impact hammer, respectively using excitation by motor. The experimental study showed oscillating forms of the ball screw for each frequency and obtained eigenfrequencies of the ball screw system. On the other hand, in this research a simulation with the help of the numerical modal analysis in order to analyse the oscillation and to find the eigenfrequencies of the ball screw system is used. Furthermore, the model order reduction by modal reduction and also according to Guyan is carried out. On the basis of these results a secure and also rapid commissioning of the control loops with regard to operating in their optimal function is targeted.

Keywords: modal analysis, ball screw, controller system, machine tools

Procedia PDF Downloads 460

Authors: Koppula Prawan, Kehinde D. Oyeyemi, Kushal P. Singh

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As people become more conscious of the detrimental consequences of conventional agricultural practices on the environment and human health, organic, sustainable agriculture and plant protection employing microorganisms have grown in importance. Although the use of microorganisms in agriculture is a centuries-old tradition, it has recently attracted renewed interest as a sustainable alternative to chemical-based plant protection and fertilization. Healthy soil is the cornerstone of sustainable agriculture, and microbes are essential to this process. Synthetic fertilizers and pesticides can destroy the beneficial microorganisms in the soil, upsetting the ecosystem's equilibrium. By utilizing organic farming's natural practices, such as the usage of microbes, it aims to maintain and improve the health of the soil. Microbes have several functions in agriculture, including nitrogen fixation, phosphorus solubilization, and disease suppression. Nitrogen fixation is the process by which certain microbes, such as rhizobia and Azotobacter, convert atmospheric nitrogen into a form that plants can use. Phosphorus solubilization involves the conversion of insoluble phosphorus into a soluble form that plants can absorb. Disease suppression involves the use of microbes to control plant diseases by competing with pathogenic organisms for resources or by producing antimicrobial compounds. Microbes can be applied to plants through seed coatings, foliar sprays, or soil inoculants. Seed coatings involve applying a mixture of microbes and nutrients to the surface of seeds before planting. Foliar sprays involve applying microbes and nutrients to the leaves of plants during the growing season. Soil inoculants involve adding microbes to the soil before planting. The use of microbes in plant protection and fertilization has several advantages over conventional methods. Firstly, microbes are natural and non-toxic, making them safe for human health and the environment. Secondly, microbes have the ability to adapt to changing environmental conditions, making them more resilient to drought and other stressors. Finally, the use of microbes can reduce the need for synthetic fertilizers and pesticides, reducing costs and minimizing environmental impact. In conclusion, organic, sustainable agriculture and plant protection using microbes are an effective and sustainable alternatives to conventional farming practices. The use of microbes can help to preserve and enhance soil health, increase plant productivity, and reduce the need for synthetic fertilizers and pesticides. As the demand for organic and sustainable agriculture continues to grow, the use of microbes is likely to become more widespread, providing a more environmentally friendly and sustainable future for agriculture.

Keywords: microbes, inoculants, fertilization, soil health, conventional.

Procedia PDF Downloads 83

Authors: J. Kaabi, Y. Harrath

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This paper studied the flow shop scheduling problem under machine availability constraints. The machines are subject to flexible preventive maintenance activities. The nonresumable scenario for the jobs was considered. That is, when a job is interrupted by an unavailability period of a machine it should be restarted from the beginning. The objective is to minimize the total tardiness time for the jobs and the advance/tardiness for the maintenance activities. To solve the problem, a genetic algorithm was developed and successfully tested and validated on many problem instances. The computational results showed that the new genetic algorithm outperforms another earlier proposed algorithm.

Keywords: flow shop scheduling, genetic algorithm, maintenance, priority rules

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Authors: Yetunde I. Bulu, Moses B. Adewole, Julius O. Faluyi

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This study investigates the effect of aggressive application of herbicide on weed succession in an urban environment in Ile-Ife, Osun State. An inspection of the communities was carried out to identify sites maintained by herbicides (test plots) and those without herbicide history (control plots). Four different experimental plots located at Olasode, Eleweran, Ife City and Parakin within Ile-Ife town were monitored during the study. Comprehensive enumeration and identification of plant populations to species level was carried out on each of the plots and at every visit to determine the direction of succession. Index of similarities was used to determine the relationship in plant species composition between plots treated with herbicide and the untreated plots. The trend of increasing plant species was observed in all the study plots. Low Similarity Index between the treated plots and the control vegetation was observed at all visitations. Low similarity was also observed between the above-ground vegetation and the seed bank in all the plots. The study concluded that the weed population observed from the experimental plots showed an increase in species richness and diversity when the plots were left to recover compared to the control plots.

Keywords: herbicide, index of similarity, population, soil seed bank, succession

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Authors: Kelli G. Thorup, Kristopher A. Blee

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Climate change enhances the occurrence of extreme weather: wildfires, drought, rising summer temperatures, all of which dramatically decline forest growth and increase tree mortality in the mixed-conifer forests of Sierra Nevada, California. However, microbiota living in mutualistic relations with plant rhizospheres have been found to mitigate the effects of suboptimal environmental conditions. The goal of this research is to isolate native beneficial bacteria, plant-growth promoting rhizobacteria (PGPR), that can alleviate heat stress in Pinus ponderosa seedlings. Bacteria were isolated from the rhizosphere of Pinus ponderosa juveniles located in mixed-conifer stand and further characterized for PGP potential based on their ability to produce key growth regulatory phytohormones including auxin, cytokinin, and gibberellic acid. Out of ten soil samples taken, sixteen colonies were isolated and qualitatively confirmed to produce indole-3-acetic acid (auxin) using Salkowski’s reagent. Future testing will be conducted to quantitatively assess phytohormone production in bacterial isolates. Furthermore, bioassays will be performed to determine isolates abilities to increase tolerance in heat-stressed Pinus ponderosa seedlings. Upon completion of this research, a PGPR could be utilized to support the growth and transplantation of conifer seedlings as summer temperatures continue to rise due to the effects of climate change.

Keywords: conifer, heat-stressed, phytohormones, Pinus ponderosa, plant-growth promoting rhizobacteria

Procedia PDF Downloads 118

Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Jung–Min Yang

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In this paper, fault recovery for parallel interconnected asynchronous sequential machines is studied. An adversarial input can infiltrate into one of two submachines comprising parallel composition of the considered asynchronous sequential machine, causing an unauthorized state transition. The control objective is to elucidate the condition for the existence of a corrective controller that makes the closed-loop system immune against any occurrence of adversarial inputs. In particular, an efficient existence condition is presented that does not need the complete modeling of the interconnected asynchronous sequential machine.

Keywords: asynchronous sequential machines, parallel composi-tion, corrective control, fault tolerance

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

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Authors: M. Yakoubi, N. Belboukhari, A. Cheriti, K. Sekoum

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Launaea nudicaulis (L.) Hook.f. is a desert, spontaneous plant and endemic to northem Sahara, which belongs to the Asteraceae family. This species exists in the region of Bechar (Local name; El-Rghamma). In our knowledge, no work has been founded, except studies showing the antimicrobial and antifungal activity of methalonic extract of this plant. The present paper describes the chemical composition of the essential oil from Launaea nudicaulis and qualification of isolation and identification of some pure products by column chromatography. The essential oil from the aerial parts of Launaea nudicaulis (Asteraceae) was obtained by hydroditillation in 0.4% yield, led to isolation of four several new products. The isolation is made by column chromatography and followed by GC-IK and GC-MS analysis.

Keywords: Launaea nudicaulis, asteraceae, essential oil, column chromatography, GC-FID, GC-MS

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Sushil Pradhan

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Biotechnology contributes to sustainable development in several ways such as biofertilizer production, biopesticide production and management of environmental pollution, tissue culture and biodiversity conservation in vitro, in vivo and in situ, Insectivorous medicinal plant Drosera burmannii Vahl belongs to the Family-Droseraceae under Order-Caryophyllales, Dicotyledoneae, Angiospermeae which has 31 (thirty one) living genera and 194 species besides 7 (seven) extinct (fossil) genera. Locally it is known as “Patkanduri” in Odia. Its Hindi name is “Mukhajali” and its English name is “Sundew”. The earliest species of Drosera was first reported in 1753 by Carolous Linnaeus called Drosera indica L (Indian Sundew). The latest species of Drosera reported by Fleisch A, Robinson, AS, McPherson S, Heinrich V, Gironella E and Madulida D.A. (2011) is Drosera ultramafica from Malaysia. More than 50 % species of Drosera have been reported from Australia and next to Australia is South Africa. India harbours only 3 species such as D. indica L, Drosera burmannii Vahl and D. peltata L. From our Odisha only D. burmannii Vahl is being reported for the first time from the district of Subarnapur near Sonepur (Arjunpur Reserve Forest Area). Drosera plant is autotrophic but to supplement its Nitrogen (N2) requirement it adopts heterotrophic mode of nutrition (insectivorous/carnivorous) as well. The colour of plant in mostly red and about 20-30cm in height with beautiful pink or white pentamerous flowers. Plants grow luxuriantly during November to February in shady and moist places near small water bodies of running water stream. Medicinally it is a popular herb in the locality for the treatment of cold and cough in children in rainy season by the local Doctors (Kabiraj and Baidya). In the present field investigation an attempt has been made to understand the unique reproductive phase and life cycle of the plant thereby planning for its conservation and propagation through various techniques of tissue culture and biotechnology. More importantly besides morphological and anatomical studies, cytological investigation is being carried out to find out the number of chromosomes in the cell and its genomics as there is no such report as yet for Drosera burmannii Vahl. The ecological significance and biodiversity conservation of Drosera with special reference to energy, environmental and chemical engineering has been discussed in the research paper presentation.

Keywords: insectivorous, medicinal, drosera, biotechnology, chromosome, genome

Procedia PDF Downloads 383

Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

Procedia PDF Downloads 262

Authors: Nesengani Elelwani Clinton

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This paper presents reflective learning as an opportunity commonly available and used for food waste learning in a food processing company in the transition to sustainable and just food systems. In addressing how employees learn about food waste during food processing, the opportunities available for food waste learning were investigated. Reflective learning appeared to be the most used approach to learning about food waste. In the case of food waste learning, reflective learning was a response after employees wasted a substantial amount of food, where process controllers and team leaders would highlight the issue to employees who wasted food and explain how food waste could be reduced. This showed that learning about food waste is not proactive, and there continues to be a lack of structured learning around food waste. Several challenges were highlighted around reflective learning about food waste. Some of the challenges included understanding the language, lack of interest from employees, set times to reach production targets, and working pressures. These challenges were reported to be hindering factors in understanding food waste learning, which is not structured. A need was identified for proactive learning through structured methods. This is because it was discovered that in the plant, where food processing activities happen, the signage and posters that are there are directly related to other sustainability issues such as food safety and health. This indicated that there are low levels of awareness about food waste. Therefore, this paper argues that food waste learning should be proactive. The proactive learning approach should include structured learning materials around food waste during food processing. In the structuring of the learning materials, individual trainers should be multilingual. This will make it possible for those who do not understand English to understand in their own language. And lastly, there should be signage and posters in the food processing plant around food waste. This will bring more awareness around food waste, and employees' behaviour can be influenced by the posters and signage in the food processing plant. Thus, will enable a transition to a just and sustainable food system.

Keywords: sustainable and just food systems, food waste, food waste learning, reflective learning approach

Procedia PDF Downloads 130

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

Procedia PDF Downloads 128

Authors: A. Bahieldin, A. Atef, S. Edris, N. O. Gadalla, S. M. Hassan, M. A. Al-Kordy, A. M. Ramadan, A. S. M. Al- Hajar, F. M. El-Domyati

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The idea of this work is based on a natural exciting phenomenon suggesting that suppression of genes related to the program cell death (or PCD) mechanism might help the plant cells to efficiently tolerate abiotic stresses. The scope of this work was the detection of PCD-related transcription factors (TFs) that might also be related to salt stress tolerance in plant. Two model plants, e.g., tobacco and Arabidopsis, were utilized in order to investigate this phenomenon. Occurrence of PCD was first proven by Evans blue staining and DNA laddering after tobacco leaf discs were treated with oxalic acid (OA) treatment (20 mM) for 24 h. A number of 31 TFs up regulated after 2 h and co-expressed with genes harboring PCD-related domains were detected via RNA-Seq analysis and annotation. These TFs were knocked down via virus induced gene silencing (VIGS), an RNA interference (RNAi) approach, and tested for their influence on triggering PCD machinery. Then, Arabidopsis SALK knocked out T-DNA insertion mutants in selected TFs analogs to those in tobacco were tested under salt stress (up to 250 mM NaCl) in order to detect the influence of different TFs on conferring salt tolerance in Arabidopsis. Involvement of a number of candidate abiotic-stress related TFs was investigated.

Keywords: VIGS, PCD, RNA-Seq, transcription factors

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

Procedia PDF Downloads 87

Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

Procedia PDF Downloads 119

Authors: Hong Zhang

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The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

Procedia PDF Downloads 144

Authors: Ja’afar U., A. M. Gumi, Salisu N., Obadiah C. D.

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Rice is susceptible to flooding due to its semi-aquatic characteristics, which enable it to thrive in wet or submerged environments. The development of aerenchyma allows for oxygen transfer, enabling faster lengthening of submerged shoot organs. Rice's germination and early seedling growth phases are highly intolerant of submersion, resulting in survival in low-oxygen environments. The research involved a study on rice plants treated with salicylic acid at different concentrations. Hypo was used for washing, while a reagent was used for submergence treatment. The plants were waterlogged for 11 days and submerged for 7 days, with control plants receiving distilled water. The study found a significant difference between Jirani Zawara's control and treated plants, with plants treated with 2 g/L of S.A. showing a 6.00 node increase per plant and Faro cultivars having more nodes. The study found significant differences between the control and treated plants, with the Jirani Zawara plant showing longer internode lengths and the Faro cultivar having longer internode lengths, while the B.G. cultivar had the longest. The research found that the Jirani Zawara cultivar treated with 3 g/L of S.A. produced tallest plants, with heights increasing from 14.43 cm to 15.50 cm in Faro cultivar S.A., and the highest height was 16.30 cm. The study reveals that salicylic acid significantly enhances the number of nodes, internode length, plant height, and root length in rice cultivars, thereby improving submerged stress recovery and promoting plant development.

Keywords: rice, submergence, stress, salicylic acid

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Authors: Johanna Bapela, Heino Heyman, Marion Meyer

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Regardless of the significant advances accomplished in reducing the burden of malaria and other tropical diseases in recent years, malaria remains a major cause of mortality in endemic countries. This is especially the case in sub-Saharan Africa where 99% of the estimated global malaria deaths occurs on an annual basis. The emergence of resistant Plasmodium species and the lack of diversified chemotherapeutic agents provide the rationale for bioprospecting for antiplasmodial scaffolds. Crude extracts from twenty indigenous antimalarial plant species were screened for antimalarial activity and then subjected to 1H NMR-based metabolomic analysis. Ten plant extracts exhibited significant in vitro antiplasmodial activity (IC50 ≤ 5 µg/ml). The Principal Component Analysis (PCA) of the acquired 1H NMR spectra could not separate the analyzed plant extracts according to the detected antiplasmodial bioactivity. Application of supervised Orthogonal Projections to Latent Structures–Discriminant Analysis (OPLS-DA) to the 1H NMR profiles resulted in a discrimination pattern that could be correlated to bioactivity. A contribution plot generated from the OPLS-DA scoring plot illustrated the classes of compounds responsible for the observed grouping. Given the preliminary in vitro results, Tabernaemontana elegans Stapf. (Apocynaceae) and Vangueria infausta Burch. subsp. infausta (Rubiaceae) were subjected to further phytochemical investigations. Two indole alkaloids, dregamine and tabernaemontanine possessing antiplasmodial activity were isolated from T. elegans. Two compounds were isolated from V. infausta subsp. infausta and identified as friedelin (IC50 = 3.01 µg/ml) and morindolide (IC50 = 18.5 µg/ml). While these compounds have been previously identified, this is the first account of their occurrence in the genus Vangueria and their antiplasmodial activity. Based on the results of the study, metabolomics can be used to globally identify classes of plant secondary metabolites that are responsible for antiplasmodial activity.

Keywords: ethnopharmacology, Malaria, medicinal plants, metabolomics

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Authors: Indu Barwal, Shloka Thakur, Subhash C. Yadav

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The plant virus capsid protein based nanoparticles are extensively studied for their application in biomedical research for development of nanomedicines and drug delivery systems. We have developed a chimeric drug delivery system by controlled in vitro assembly of separately bioconjugated fluorescent dye (as reporting molecule), folic acid (as receptor binding biomolecule for targeted delivery) and doxorubicin (as anticancer drug) using modified EDC NHS chemistry on heterologously overexpressed (E. coli) capsid proteins of cowpea chlorotic mottle virus (CCMV). This chimeric vehicle was further encapsidated on gold nanoparticles (20nm) coated with 5≠ thiolated DNA probe to neutralize the positive charge of capsid proteins. This facilitates the in vitro assembly of modified capsid subunits on the gold nanoparticles to develop chimeric GNPs encapsidated targeted drug delivery system. The bioconjugation of functionalities, number of functionality on capsid subunits as well as virus like nanoparticles, structural stability and in vitro assembly were confirmed by SDS PAGE, relative absorbance, MALDI TOF, ESI-MS, Circular dichroism, intrinsic tryptophan fluorescence, zeta particle size analyzer and TEM imaging. This vehicle was stable at pH 4.0 to 8.0 suitable for many organelles targeting. This in vitro assembled chimeric plant virus like particles could be suitable for ideal drug delivery vehicles for subcutaneous cancer treatment and could be further modified for other type of cancer treatment by conjugating other functionalities (targeting, drug) on capsids.

Keywords: chimeric drug delivery vehicles, bioconjugated plant, virus, capsid

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Authors: Homa Ghave, Parmis Shahmaleki

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This paper presents a new multi-criteria stochastic mathematical model for a single machine scheduling with outsourcing allowed. There are multiple jobs processing in batch. For each batch, all of job or a quantity of it can be outsourced. The jobs have stochastic processing time and lead time and deterministic due dates arrive randomly. Because of the stochastic inherent of processing time and lead time, we use the chance constrained programming for modeling the problem. First, the problem is formulated in form of stochastic programming and then prepared in a form of deterministic mixed integer linear programming. The objectives are considered in the model to minimize the maximum tardiness and outsourcing cost simultaneously. Several procedures have been developed to deal with the multi-criteria problem. In this paper, we utilize the concept of satisfaction functions to increases the manager’s preference. The proposed approach is tested on instances where the random variables are normally distributed.

Keywords: single machine scheduling, multi-criteria mathematical model, outsourcing strategy, uncertain lead times and processing times, chance constrained programming, satisfaction function

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Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

Procedia PDF Downloads 502

Authors: Samuel Abebe Ebebo

Abstract:

Scale precipitation causes a major issue for geothermal power plants because it reduces the production rate of geothermal energy. Each geothermal power plant's different chemical and physical conditions can cause the scale to precipitate under a particular set of fluid-rock interactions. Depending on the mineral, it is possible to have scale in the production well, steam separators, heat exchangers, reinjection wells, and everywhere in between. The scale consists mainly of smectite and trace amounts of chlorite, magnetite, quartz, hematite, dolomite, aragonite, and amorphous silica. The smectite scale is one of the difficult scales at injection wells in geothermal power plants. X-ray diffraction and chemical composition identify this smectite as Stevensite. The characteristics and the scale of each injection well line are different depending on the fluid chemistry. The smectite scale has been widely distributed in pipelines and surface plants. Mineral water equilibrium showed that the main factors controlling the saturation indices of smectite increased pH and dissolved Mg concentration due to the precipitate on the equipment surface. This study aims to characterize the scales and geothermal fluids collected from the Onuma geothermal power plant in Akita Prefecture, Japan. Field tests were conducted on October 30–November 3, 2021, at Onuma to determine the pH control methods for preventing magnesium silicate scaling, and as exemplified, the formation of magnesium silicate hydrates (M-S-H) with MgO to SiO2 ratios of 1.0 and pH values of 10 for one day has been studied at 25 °C. As a result, M-S-H scale formation could be suppressed, and stevensite formation could also be suppressed when we can decrease the pH of the fluid by less than 8.1, 7.4, and 8 (at 97 °C) in the fluid from O-3Rb and O-6Rb, O-10Rg, and O-12R, respectively. In this context, the scales and fluids collected from injection wells at a geothermal power plant in Japan were analyzed and characterized to understand the formation conditions of Mg-silicate scales with on-site synthesis experiments. From the results of the characterizations and on-site synthesis experiments, the inhibition method of their scale formation is discussed based on geochemical modeling in this study.

Keywords: magnesium silicate, scaling, inhibitor, geothermal power plant

Procedia PDF Downloads 66

Authors: C. Bay, A. Mahr, H. Groneberg, F. Döpper

Abstract:

Additive Manufacturing processes are becoming increasingly established in the industry for the economic production of complex prototypes and functional components. Laser beam melting (LBM), the most frequently used Additive Manufacturing technology for metal parts, has been gaining in industrial importance for several years. The LBM process chain – from material storage to machine set-up and component post-processing – requires many manual operations. These steps often depend on the manufactured component and are therefore not standardized. These operations are often not performed in a standardized manner, but depend on the experience of the machine operator, e.g., levelling of the build plate and adjusting the first powder layer in the LBM machine. This lack of standardization limits the reproducibility of the component quality. When processing metal powders with inhalable and alveolar particle fractions, the machine operator is at high risk due to the high reactivity and the toxic (e.g., carcinogenic) effect of the various metal powders. Faulty execution of the operation or unintentional omission of safety-relevant steps can impair the health of the machine operator. In this paper, all the steps of the LBM process chain are first analysed in terms of their influence on the two aforementioned challenges: reproducibility and work safety. Standardization to avoid errors increases the reproducibility of component quality as well as the adherence to and correct execution of safety-relevant operations. The corresponding lean method 5S will therefore be applied, in order to develop approaches in the form of recommended actions that standardize the work processes. These approaches will then be evaluated in terms of ease of implementation and their potential for improving reproducibility and work safety. The analysis and evaluation showed that sorting tools and spare parts as well as standardizing the workflow are likely to increase reproducibility. Organizing the operational steps and production environment decreases the hazards of material handling and consequently improves work safety.

Keywords: additive manufacturing, lean production, reproducibility, work safety

Procedia PDF Downloads 184

Authors: Thaís T. Valério Caetano, Lívia de Carvalho Ferreira, João Máximo De Siqueira, Carlos Alexandre Carollo, Arthur Ladeira Macedo, Vanessa C. Stein

Abstract:

Nicotiana glauca, commonly known as wild tobacco or tobacco bush, belongs to the Solanaceae family. It is native to South America but has become naturalized in various regions, including Australia, California, Africa, and the Mediterranean. N. glauca is listed in the Global Invasive Species Database (GISD) and the Invasive Species Compendium (CABI). It is known for producing pyridine alkaloids, including anabasine, which is highly toxic. Anabasine is predominantly found in the leaves and can cause severe health issues such as neuromuscular blockade, respiratory arrest, and cardiovascular problems when ingested. Mistaken identity with edible plants like spinach has resulted in food poisoning cases in Israel and Brazil. Anabasine, a minor alkaloid constituent of tobacco, may contribute to tobacco addiction by mimicking or enhancing the effects of nicotine. Therefore, it is essential to investigate the production pattern of anabasine and its relationship to the developmental stages of the plant. This study aimed to establish the relationship between the phenological plant age, cultivation place, and the increase in anabasine concentration, which can lead to human intoxication cases. In this study, N. glauca plants were collected from three different rural areas in Brazil during a year to examine leaves at various stages of development. Samples were also obtained from cultivated plants in Marilândia, Minas Gerais, Brazil, as well as from Divinópolis, Minas Gerais, Brazil, and Arraial do Cabo, Rio de Janeiro, Brazil. In vitro cultivated plants on MS medium were included in the study. The collected leaves were dried, powdered, and stored. Alkaloid extraction was performed using a methanol and water mixture, followed by liquid-liquid extraction with chloroform. The anabasine content was determined using HPLC-DAD analysis with nicotine as a standard. The results indicated that anabasine production increases with the plant's development, peaking in adult leaves during the reproduction phase and declining afterward. In vitro, plants showed similar anabasine production to young leaves. The successful adaptation of N. glauca in new environments poses a global problem, and the correlation between anabasine production and the plant's developmental stages has been understudied. The presence of substances produced by the plant can pose a risk to other species, especially when mistaken for edible plants. The findings from this study shed light on the pattern of anabasine production and its association with plant development, contributing to a better understanding of the potential risks associated with N. glauca and the importance of accurate identification.

Keywords: alkaloid production, invasive species, nicotiana glauca, plant phenology

Procedia PDF Downloads 83

Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

Abstract:

Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

Procedia PDF Downloads 93

Authors: Soheila Sadeghi

Abstract:

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

Procedia PDF Downloads 49