Search results for: machine and plant engineering

Authors: Abdulrahman Yarali

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The advancement of technology never stops because the demands for improved internet and communication connectivity are increasing. Just as 5G networks are rolling out, the world has begun to talk about the sixth-generation networks (6G). The semantics of 6G are more or less the same as 5G networks because they strive to boost speeds, machine-to-machine (M2M) communication, and latency reduction. However, some of the distinctive focuses of 6G include the optimization of networks of machines through super speeds and innovative features. This paper discusses many aspects of the technologies, architectures, challenges, and opportunities of 6G wireless communication systems.

Keywords: 6G, characteristics, infrastructures, technologies, AI, ML, IoT, applications

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: Walid Cherif

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Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.

Keywords: data mining, knowledge discovery, machine learning, similarity measurement, supervised classification

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Authors: K. Majumdar, S. Datta

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Electricity is recognized as fundamental to industrialization and improving the quality of life of the people. Harnessing the immense untapped hydropower potential in Tripura region opens avenues for growth and provides an opportunity to improve the well-being of the people of the region, while making substantial contribution to the national economy. Gumti hydro power plant generates power to mitigate the crisis of power in Tripura, India. The first unit of hydro power plant (5 MW) was commissioned in June 1976 & another two units of 5 MW was commissioned simultaneously. But out of 15 MW capacity at present only 8-9 MW power is produced from Gumti hydro power plant during rainy season. But during lean season the production reduces to 0.5 MW due to shortage of water. Now, it is essential to implement some mitigation measures so that the further atrocities can be prevented and originality will be possible to restore. The decision making ability of the Analytic Hierarchy Process (AHP) and Concordance Analysis Techniques (CAT) are utilized to identify the better decision or solution to the present problem. Some related attributes are identified by the method of surveying within the experts and the available reports and literatures. Similar criteria are removed and ultimately seven relevant ones are identified. All the attributes are compared with each other and rated accordingly to their importance over the other with the help of Pair wise Comparison Matrix. In the present investigation different mitigation measures are identified and compared to find the best suitable alternative which can solve the present uncertainties involving the existence of the Gumti Hydro Power Plant.

Keywords: concordance analysis techniques, analytic hierarchy process, hydro power

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Authors: Nouredine Benouzza, Ahmed Hamida Boudinar, Azeddine Bendiabdellah

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An algorithm for Eccentricity Fault Detection (EFD) applied to a squirrel cage induction machine is proposed in this paper. This algorithm employs the behavior of the stator current spectral analysis and the localization of the Rotor Slot Harmonic (RSH) frequency to detect eccentricity faults in three phase induction machine. The RHS frequency once obtained is used as a key parameter into a simple developed expression to directly compute the eccentricity fault frequencies in the induction machine. Experimental tests performed for both a healthy motor and a faulty motor with different eccentricity fault severities illustrate the effectiveness and merits of the proposed EFD algorithm.

Keywords: squirrel cage motor, diagnosis, eccentricity faults, current spectral analysis, rotor slot harmonic

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Authors: Rasheed Gbenga Jimoh, Akinbowale Nathaniel Babatunde

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The use of Automated Teller Machine (ATM) has become an important tool among commercial banks, customers of banks have come to depend on and trust the ATM conveniently meet their banking needs. Although the overwhelming advantages of ATM cannot be over-emphasized, its alarming fraud rate has become a bottleneck in it’s full adoption in Nigeria. This study examined the menace of ATM in the society another cost of running ATM services by banks in the country. The researcher developed a prototype of an enhanced Automated Teller Machine Authentication using Short Message Service (SMS) Verification. The developed prototype was tested by Ten (10) respondents who are users of ATM cards in the country and the data collected was analyzed using Statistical Package for Social Science (SPSS). Based on the results of the analysis, it is being envisaged that the developed prototype will go a long way in reducing the alarming rate of ATM fraud in Nigeria.

Keywords: ATM, ATM fraud, e-banking, prototyping

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Authors: Eleni Stefanidou, Nikolaos Katsenios, Ioanna Karamichali, Aspasia Efthimiadou, Panagiotis Madesis

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The excessive use of pesticides and fertilizers has significant environmental and human health-related negative effects. In the frame of the development of sustainable agriculture practices, especially in the context of extreme environmental changes (climate change), it is important to develop alternative practices to increase productivity and biotic and abiotic stress tolerance. Beneficial bacteria, such as symbiotic bacteria in legumes (rhizobia) and symbiotic or free-living Plant Growth Promoting Rhizobacteria (PGPR), which could act as "plant probiotics", can promote plant growth and significantly increase the resistance of crops under adverse environmental conditions. In this study, we explored the symbiotic relationships between Faba bean (Vicia faba L.) cultivars with different PGPR bacteria, aiming to identify the possible influence on yield and biotic-abiotic phytoprotection benefits. Transcriptomic analysis of root and whole plant samples was executed for two Vicia faba L. cultivars (Polikarpi and Solon) treated with selected PGPR bacteria (6 treatments: B. subtilis + Rhizobium-mixture, A. chroococcum + Rhizobium-mixture, B. subtilis, A. chroococcum and Rhizobium-mixture). Preliminary results indicate a significant yield (Seed weight and Total number of pods) increase in both varieties, ranging around 25%, in comparison to the control, especially for the Solon cultivar. The increase was observed for all treatments, with the B. subtilis + Rhizobium-mixture treatment being the highest performing. The correlation of the physiological and morphological data with the transcriptome analysis revealed molecular mechanisms and molecular targets underlying the observed yield increase, opening perspectives for the use of nitrogen-fixing bacteria as a natural, more ecological enhancer of legume crop productivity.

Keywords: plant probiotics, PGPR, legumes, sustainable agriculture

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Authors: Samad Sajjadi

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Machine translation is a new subject increasingly being used by academic writers, especially students and researchers whose native language is not English. There are numerous studies conducted on machine translation, but few investigations have assessed the accuracy of machine translation from Persian to English at lexical, semantic, and syntactic levels. Using Groves and Mundt’s (2015) Model of error taxonomy, the current study evaluated Persian-to-English translations produced by two famous online translators, Google Translate and Bing Translator. A total of 240 texts were randomly selected from different academic fields (law, literature, medicine, and mass media), and 60 texts were considered for each domain. All texts were rendered by the two translation systems and then by four human translators. All statistical analyses were applied using SPSS. The results indicated that Google translations were more accurate than the translations produced by the Bing Translator, especially in the domains of medicine (lexis: 186 vs. 225; semantic: 44 vs. 48; syntactic: 148 vs. 264 errors) and mass media (lexis: 118 vs. 149; semantic: 25 vs. 32; syntactic: 110 vs. 220 errors), respectively. Nonetheless, both machines are reasonably accurate in Persian-to-English translation of lexicons and syntactic structures, particularly from mass media and medical texts.

Keywords: machine translations, accuracy, human translation, efficiency

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Authors: Akshay S. Dalvi, Hazim El-Mounayri

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The internal and the external interactions between the complex structural and behavioral characteristics of the complex energy system result in unpredictable emergent behaviors. These emergent behaviors are not well understood, especially when modeled using the traditional top-down systems engineering approach. The intrinsic nature of current complex energy systems has called for an elegant solution that provides an integrated framework in Model-Based Systems Engineering (MBSE). This paper mainly presents a MBSE driven approach to define and handle the complexity that arises due to emergent behaviors. The approach provides guidelines for developing system architecture that leverages in predicting the complexity index of the system at different levels of abstraction. A framework that integrates indefinite and definite modeling aspects is developed to determine the complexity that arises during the development phase of the system. This framework provides a workflow for modeling complex systems using Systems Modeling Language (SysML) that captures the system’s requirements, behavior, structure, and analytical aspects at both problem definition and solution levels. A system architecture for a district cooling plant is presented, which demonstrates the ability to predict the complexity index. The result suggests that complex energy systems like district cooling plant can be defined in an elegant manner using the unconventional modified MBSE driven approach that helps in estimating development time and cost.

Keywords: district cooling plant, energy systems, framework, MBSE

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Authors: Jin-Feng Hu

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Over the past decades, the global biodiversity has continued to decline. The threats to the terrestrial plant species have increased under anthropogenic activities and other massive ecological change impacts. The situation is much more serious in China, the third richest countries regarding plant biodiversity in the world. It was not until 1992 that the first volume of the China Plant Red Data Book was published. Nowadays, a significant number of Chinese endemic plants have been threatened (The IUCN Red List). Nevertheless, plant-originated natural products (NPs) have continued to play a crucial role in the drug discovery and development process. The opportunity for identifying new chemical entities for emerging and malignant diseases depends on a diversity of drug-producing species. Several statistical surveys unveiled that the rare and endangered plants (REPs) have proven to be better sources for drug discovery than other botanic sources. The identification of bioactive NPs from REPs reveals the importance of conservation efforts in preventing species diversity loss and addressing human diseases at the same time. Thus, there is an urgent need to investigate these fragile REPs. Since 2013, our group has initially launched a special program to systematically identify bioactive/novel NPs from REPs native to China. The selected plant species were generally collected from the remote Mountain areas, and have never been chemically or pharmacologically investigated. Due to the difficult collection of the mass-limited samples of REPs, studies on the secondary metabolites of REPs-associated endophytes would provide a promising alternative potential solution. This presentation details the achievements that related to a series of “Phytochemical and biological studies on rare and endangered plants endemic to China”.

Keywords: bioactive naturally-occrring compounds, rare and endengered plants (REPs), plant endophytes, drug discovery

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Authors: Shohreh Ghasemi

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Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

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Authors: Yashasvi Thakur, Vikas K. Sharma

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The fragile Himalayan ecosystems are sensitive to environmental stresses, including direct and indirect impacts of climate stresses. A total of 297 naturalized alien plant species belonging to 65 families in the IHR have already been reported. Of the total 297 naturalized alien plant species in IHR, the maximum species occur in Himachal Pradesh (232; 78.1%), followed by Jammu & Kashmir (192; 64.6%) and Uttarakhand (181; 60.90%). The present study reports the spread of some invasive and existing weed species like Ageratum conyzoides, Bidens pilosa, Chromolaena odorata, Lantana camara, Brossnetia papyrifera, Oxalis corniculata, Galinsoga parviflora, Panicum maximum at an extent that they are not only invading the agricultural fields but are also replacing the native plant species and degrading the existing grassland quality. Moreover, the degradation of grassland has led to the dry fodder shortage for livestock in the lower Shivalik ranges of the state of Himachal Pradesh and has also encouraged the use of herbicides at an extensive scale. This article provides a mapping of the current spread of some of these species at the block level to allow the development of appropriate management strategies and policy planning for addressing issues pertaining to plant invasion, agricultural fields, and grasslands across the IHR states.

Keywords: climate change, invasive alien species, agriculture, grassland, IHR

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Authors: Samira-Sadat JamaliDinan, Haidar Almohri, Mohammad-Reza Nazem-Zadeh

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Lateralization of temporal lobe epilepsy (TLE) is very important for positive surgical outcomes. We propose a machine learning framework to ultimately identify the epileptogenic hemisphere for temporal lobe epilepsy (TLE) cases using magnetoencephalography (MEG) coherence source imaging (CSI) and diffusion tensor imaging (DTI). Unlike most studies that use classification algorithms, we propose an effective clustering approach to distinguish between normal and TLE cases. We apply the famous Minkowski weighted K-Means (MWK-Means) technique as the clustering framework. To overcome the problem of poor initialization of K-Means, we use particle swarm optimization (PSO) to effectively select the initial centroids of clusters prior to applying MWK-Means. We demonstrate that compared to K-means and MWK-means independently, this approach is able to improve the result of a benchmark data set.

Keywords: temporal lobe epilepsy, machine learning, clustering, magnetoencephalography

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Authors: Gheorghe Ionita, Sebastian Brad, Ioan Stefanescu

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In any fusion device, tritium plays a key role both as a fuel component and, due to its radioactivity and easy incorporation, as tritiated water (HTO). As for the ITER project, to reduce the constant potential of tritium emission, there will be implemented a Water Detritiation System (WDS) and an Isotopic Separation System (ISS). In the same time, during operation of fission CANDU reactors, the tritium content increases in the heavy water used as moderator and cooling agent (due to neutron activation) and it has to be reduced, too. In Romania, at the National Institute for Cryogenics and Isotopic Technologies (ICIT Rm-Valcea), there is an Experimental Pilot Plant for Tritium Removal (Exp. TRF), with the aim of providing technical data on the design and operation of an industrial plant for heavy water depreciation of CANDU reactors from Cernavoda NPP. The selected technology is based on the catalyzed isotopic exchange process between deuterium and liquid water (LPCE) combined with the cryogenic distillation process (CD). This paper presents an updated review of activities in the field carried out in Romania after the year 2000 and in particular those related to the development and operation of Tritium Removal Experimental Pilot Plant. It is also presented a comparison between the experimental pilot plant and industrial plant to be implemented at Cernavoda NPP. The similarities between the experimental pilot plant from ICIT Rm-Valcea and water depreciation and isotopic separation systems from ITER are also presented and discussed. Many aspects or 'opened issues' relating to WDS and ISS could be checked and clarified by a special research program, developed within ExpTRF. By these achievements and results, ICIT Rm - Valcea has proved its expertise and capability concerning tritium management therefore its competence may be used within ITER project.

Keywords: ITER project, heavy water detritiation, tritium removal, isotopic exchange

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Authors: Brian Marsh

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Nitrogen fertilizer is the most used and often the most mismanaged nutrient input. Nitrogen management has tremendous implications on crop productivity, quality and environmental stewardship. Sufficient nitrogen is needed to optimum yield and quality. Soil and in-season plant tissue testing for nitrogen status are a time consuming and expensive process. Real time sensing of plant nitrogen status can be a useful tool in managing nitrogen inputs. The objectives of this project were to assess the reliability of remotely sensed non-destructive plant nitrogen measurements compared to wet chemistry data from sampled plant tissue, develop in-season nitrogen recommendations based on remotely sensed data for improved nitrogen use efficiency and assess the potential for determining yield and quality from remotely sensed data. Very good correlations were observed between early-season remotely sensed crop nitrogen status and plant nitrogen concentrations and subsequent in-season fertilizer recommendations. The transmittance/absorbance type meters gave the most accurate readings. Early in-season fertilizer recommendation would be to apply 40 kg nitrogen per hectare plus 16 kg nitrogen per hectare for each unit difference measured with the SPAD meter between the crop and reference area or 25 kg plus 13 kg per hectare for each unit difference measured with the CCM 200. Once the crop was sufficiently fertilized meter readings became inconclusive and were of no benefit for determining nitrogen status, silage yield and quality and grain yield and protein.

Keywords: wheat, nitrogen fertilization, chlorophyll meter

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Authors: G. Ultanbekova, Zh. Suleimenova, Zh. Rakhmetova, G. Mombekova, S. Mantieva

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Plant Growth Promoting Rhizobacteria (PGPR) are a group of free-living bacteria that colonize the rhizosphere, enhance plant growth of many cereals and other important agricultural crops and protect plants from disease and abiotic stresses through a wide variety of mechanisms. The use of PGPR has been proven to be an environmentally sound way of increasing crop yields by facilitating plant growth. In the present study, strain improvement of PGPR isolates were carried out by chemical mutagenesis for the improvement of growth and yield of lima bean. Induced mutagenesis is widely used for the selection of microorganisms producing biologically active substances and further improving their activities. Strain improvement is usually done by classical mutagenesis which involves exposing the microbes to chemical or physical mutagens. The strains of Pseudomonas putida 4/1, Azotobacter chroococcum Р-29 and Bacillus subtilis were subjected to mutation process for strain improvement by treatment with a chemical agent (sodium nitrite) to cause mutation and were observed for its consequent action on the seeds germination and plant growth of lima bean (Phaseolus lunatus). Bacterial mutant strains of Pseudomonas putida M-1, Azotobacter chroococcum M-1 and Bacillus subtilis M-1, treated with sodium nitrite in the concentration of 5 mg/ml for 120 min, were found effective to enhance the germination of lima bean seeds compared to parent strains. Moreover, treatment of the lima bean seeds with a mutant strain of Bacillus subtilis M-1 had a significant stimulation effect on plant growth. The length of the stems and roots of lima bean treated with Bacillus subtilis M-1 increased significantly in comparison with parent strain in 1.6 and 1.3 times, respectively.

Keywords: chemical mutagenesis, germination, kidney bean, plant growth promoting rhizobacteria (PGPR)

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Authors: Carol Anne Hargreaves

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A key issue in stock investment is how to select representative features for stock selection. The objective of this paper is to firstly determine whether an automated stock investment system, using machine learning techniques, may be used to identify a portfolio of growth stocks that are highly likely to provide returns better than the stock market index. The second objective is to identify the technical features that best characterize whether a stock’s price is likely to go up and to identify the most important factors and their contribution to predicting the likelihood of the stock price going up. Unsupervised machine learning techniques, such as cluster analysis, were applied to the stock data to identify a cluster of stocks that was likely to go up in price – portfolio 1. Next, the principal component analysis technique was used to select stocks that were rated high on component one and component two – portfolio 2. Thirdly, a supervised machine learning technique, the logistic regression method, was used to select stocks with a high probability of their price going up – portfolio 3. The predictive models were validated with metrics such as, sensitivity (recall), specificity and overall accuracy for all models. All accuracy measures were above 70%. All portfolios outperformed the market by more than eight times. The top three stocks were selected for each of the three stock portfolios and traded in the market for one month. After one month the return for each stock portfolio was computed and compared with the stock market index returns. The returns for all three stock portfolios was 23.87% for the principal component analysis stock portfolio, 11.65% for the logistic regression portfolio and 8.88% for the K-means cluster portfolio while the stock market performance was 0.38%. This study confirms that an automated stock investment system using machine learning techniques can identify top performing stock portfolios that outperform the stock market.

Keywords: machine learning, stock market trading, logistic regression, cluster analysis, factor analysis, decision trees, neural networks, automated stock investment system

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Authors: J. K. D. M. P. Madara, R. B. L. Dharmawickreme, Linu John, Ivee Boiss

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Flemingia vestita, commonly known as ‘Sohphlang’ is an important medicinal plant found in the North-Eastern region of India, which is traditionally recognized for its anthelmintic properties. This study was aimed to evaluate the phytochemical constituents and antibacterial activity of the tuber skin extracts of the plant species. Methanol, acetone, and water were used to obtain the solvent extractions of the skin peel extracts. Concentrated extracts of skin peel were tested using previously established qualitative phytochemical assays. The antibacterial efficacy of methanol tuber skin extract was tested against Gram-negative and positive microorganisms, namely, Klebsiella pneumonia, Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Mycobacterium tuberculosis strains. Agar well diffusion method was employed to determine the zone of inhibition of the plant extracts. Obtained data were statistically analyzed. Methanol extracts of Flemingia vestita were found to be effective against Bacillus subtilis and Mycobacterium tuberculosis at concentrations of 0.5 mg/ml. The reported zone of inhibition for the two strains was 13.3mm ± 0.57 and 16.3mm ± 4.9, respectively. However Klebsiella pneumoniae, Pseudomonas aeruginosa and Escherichia coli were resistant to the plant extracts with no zone of inhibition. Alkaloids, glycosides, and phenols were found to be present in aqueous, methanol, and acetone extracts of the plant in qualitative phytochemical analysis.

Keywords: flemingia vestita, antibacterial activity, phytochemical screening, well diffusion method

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Authors: F. Lazzeri, I. Reiter

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Energy production optimization has been traditionally very important for utilities in order to improve resource consumption. However, load forecasting is a challenging task, as there are a large number of relevant variables that must be considered, and several strategies have been used to deal with this complex problem. This is especially true also in microgrids where many elements have to adjust their performance depending on the future generation and consumption conditions. The goal of this paper is to present a solution for short-term load forecasting in microgrids, based on three machine learning experiments developed in R and web services built and deployed with different components of Cortana Intelligence Suite: Azure Machine Learning, a fully managed cloud service that enables to easily build, deploy, and share predictive analytics solutions; SQL database, a Microsoft database service for app developers; and PowerBI, a suite of business analytics tools to analyze data and share insights. Our results show that Boosted Decision Tree and Fast Forest Quantile regression methods can be very useful to predict hourly short-term consumption in microgrids; moreover, we found that for these types of forecasting models, weather data (temperature, wind, humidity and dew point) can play a crucial role in improving the accuracy of the forecasting solution. Data cleaning and feature engineering methods performed in R and different types of machine learning algorithms (Boosted Decision Tree, Fast Forest Quantile and ARIMA) will be presented, and results and performance metrics discussed.

Keywords: time-series, features engineering methods for forecasting, energy demand forecasting, Azure Machine Learning

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Authors: Akwu A. Nneka, Naidoo, Yougasphree

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A significantly high number of microbes exist in higher plants; these microbes include bacteria, fungi, and actinomycetes. There are reports on the benefits of endophytic fungi and their products of metabolism to the host plant and man, consequently, it is expedient to explore the changes that could arise as a result of manipulating their growth media. Grewia lasiocarpa E. Mey. ex Harv. (Malvaceae) is an indigenous Southern African plant, that belongs to a genus with known medicinal properties. Three media were used to culture the endophytic fungi viz., Potato Dextrose Agar (PDA), Malt Extract Agar (MEA), and Bacteriological Agar (BA) were used singly, and supplemented with three dilutions of the leaves and stem bark extracts. The manipulated growth media composition had a significant effect on the diversity of the isolated fungal populations. Several endophytic fungi were isolated; their distribution and diversity revealed a significant relatedness with the manipulated media. The media supplemented with the plant extracts was observed to give a significant increase in the growth rate and yield of the endophytes. To the best of our knowledge, this is the first study describing the endophytic fungi present in the leaves and stem bark of G. lasiocarpa E. Mey. ex Harv.

Keywords: Grewia lasiocarpa, plant-based extracts, endophytic fungi, Malvaceae

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Authors: Sekkal Nawel, Mahammed Nadir

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The proliferation of online activities on Online Social Networks (OSNs) has captured significant user attention. However, this growth has been hindered by the emergence of fraudulent accounts that do not represent real individuals and violate privacy regulations within social network communities. Consequently, it is imperative to identify and remove these profiles to enhance the security of OSN users. In recent years, researchers have turned to machine learning (ML) to develop strategies and methods to tackle this issue. Numerous studies have been conducted in this field to compare various ML-based techniques. However, the existing literature still lacks a comprehensive examination, especially considering different OSN platforms. Additionally, the utilization of bio-inspired algorithms has been largely overlooked. Our study conducts an extensive comparison analysis of various fake profile detection techniques in online social networks. The results of our study indicate that supervised models, along with other machine learning techniques, as well as unsupervised models, are effective for detecting false profiles in social media. To achieve optimal results, we have incorporated six bio-inspired algorithms to enhance the performance of fake profile identification results.

Keywords: machine learning, bio-inspired algorithm, detection, fake profile, system, social network

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Authors: Muazu A.

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Haphazard and inappropriate plant spacing and poor soilfertility management practice are among the major factorsconstraining onion production in Gashua, Bade Locale Government Yobe State.Field experiments were conducted in 2023 dry season ar Federal University, Gashua university farm assess the influence of intra-row spacing (2.5, 5, 7.5, 10 and 12.5 cm) and nitrogen fertilizerrate (0, 41, 82 and 123kg Nha-1) growth, bulb yield and quality of onion. The experiment was laid out in a randomized complete block design (RCBD) with three replications. The main effects of nitrogen rate and intra-row spacing influenced only the plant height stand count significantly obtained from 7.5cm and 82kg Nha-1 intra-row spacing and nitrogen fertilizer respectively. The highest yield was obtained from the application of 82kg Nha-1 and plant spacing of 5.0cm and 7.5cm respectively.

Keywords: onion, intra-row spacing, nitrogen fertilizer, yield

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Authors: Barenten Suciu, Akio Miyamura

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In this paper, following the study-case of an inclined plane gravitational machine, efficiency of a double-cone gravitational motor and generator is evaluated. Two types of efficiency ratios, called translational efficiency and rotational efficiency, are defined relative to the intended duty of the gravitational machine, which can be either the production of translational kinetic energy, or rotational kinetic energy. One proved that, for pure rolling movement of the double- cone, in the absence of rolling friction, the total mechanical energy is conserved. In such circumstances, as the motion of the double-cone progresses along rails, the translational efficiency decreases and the rotational efficiency increases, in such way that sum of the rotational and translational efficiencies remains unchanged and equal to 1. Results obtained allow a comparison of the gravitational machine with other types of motor-generators, in terms of the achievable efficiency.

Keywords: efficiency, friction, gravitational motor and generator, rolling and sliding, truncated double-cone

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Authors: Ferydon Salehifar, Hassan Safarikia, Hossein Boromandfar

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Karkheh plant in Iran's Khuzestan province and is located in the city Andimeshk. The plant has a production capacity of 400 MW units with water and three hours. One of the important parameters of each country's power grid stability is the stability of the power grid is affected by the voltage and frequency In plants, the amount of active power frequency control is done so that when the unit is placed in the frequency control their productivity is a function of frequency and output power varies with frequency. Produced by hydroelectric power plants with the water level behind the dam has a direct relationship And to decrease and increase the water level behind the dam in order to reduce the power output increases But these changes have a different interval is due to some mechanical problems such as turbine cavitation and vibration are limited. In this study, the range of the frequency control can be Karkheh manufacturing plants have been identified and their effectiveness has been determined.

Keywords: Karkheh power, frequency control system, active power, efficiency

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Authors: Nermin Gozukirmizi, Buket Cakmak, Sevgi Marakli

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Sireviruses are genera of copia LTR retrotransposons with a unique genome structure among retrotransposons. Barley (Hordeum vulgare L.) is an economically important plant and has been studied as a model plant regarding its short annual life cycle and seven chromosome pairs. In this study, we used mature barley embryos, 10-day-old roots and 10-day-old leaves derived from the same barley plant to investigate SIRE1 retrotransposon movements by Inter-Retrotransposon Amplified Polymorphism (IRAP) technique. We found polymorphism rates between 0-64% among embryos, roots and leaves. Polymorphism rates were detected to be 0-27% among embryos, 8-60% among roots, and 11-50% among leaves. Polymorphisms were observed not only among the parts of different individuals, but also on the parts of the same plant (23-64%). The internal domains of SIRE1 (gag, env and rt) were also analyzed in the embryos, roots and leaves. Analysis of band profiles showed no polymorphism for gag, however, different band patterns were observed among samples for rt and env. The sequencing of SIRE1 gag, env and rt domains revealed 79% similarity for gag, 95% for env and 84% for rt to Ty1-copia retrotransposons. SIRE1 retrotransposon was identified in the soybean genome and has been studied on other plants (maize, rice, tomatoe etc.). This study is the first detailed investigation of SIRE1 in barley genome. The obtained findings are expected to contribute to the comprehension of SIRE1 retrotransposon and its role in barley genome.

Keywords: barley, polymorphism, retrotransposon, SIRE1 virus

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Authors: G. Raviraja Shetty, K. G. Poojitha, Pranay Kumar

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Biodiversity refers to the numbers, variety and variability of living organisms and ecosystem. The climatic and altitudinal variations, coupled with varied ecological habitats of this country, have contributed to the development of immensely rich vegetation with a unique diversity in medicinal plants which provides an important source of medicinal raw materials for traditional medicine systems as well as for pharmaceutical industries in the country and abroad. World Health Organization has listed over 21000 plant species used around the world for medicinal purpose. In India, about 2500 plant species are being used in indigenous system of medicine. The red data book lists 427 Indian Medicinal plant entries on endangered species, of which 28 are considered extinct, 124 endangered, 81 rare, and 34 insufficiently known. It is abundantly clear from the experience of all govt agencies that on their own they cannot efficiently conserve the biodiversity. Participatory Approach with the involvement of local people in conservation is found to be more effective these days. Involvement of local people reduces the cost involved in conservation. Local communities have long tradition of resource use in particular area, hold in depth knowledge and experience of plant which can be invaluable for conservation efforts.Medicinal plants occupy a vital sector of health care system in India and represent a major national resource.There is an immense need for conservation of diversity of medicinal plant wealth for the present and fore coming generations, by adapting the suitable strategy with most appropriate method of conservation.

Keywords: conservation, biodiversity, participatory, medicinal plants

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Authors: Szilvia Várallyay, Szilvia Veres, Éva Bódi, Farzaneh Garousi, Béla Kovács

Abstract:

The agricultural environment is contaminated with heavy metals and other toxic elements, which means more and more threats. One of the most important toxic element is the arsenic. Consequences of arsenic toxicity in the plant organism is decreases the weight of the roots, and causes discoloration and necrosis of leaves. The toxicity of arsenic depends on the quality and quantity of the arsenic specialization. The arsenic in the soil and in the plant presents as a most hazardous specialization. A dicotyledon plant were chosen for the experiment, namely sunflower. The sunflower plants were grown in nutrient solution in different As(III) levels. The content of As, P, Fe were measured from experimental plants, using by ICP-MS.Negative correlation was observed between the higher concentration of As(V) and As(III) in the nutrition solution and the content of P in the sunflower tissue. The amount of Fe was decreasing if we used a higher concentration of arsenic (30 mg kg-1). We can tell the conclusion that the arsenic had a negative effect on the sunflower tissue P and Fe content.

Keywords: arsenic, sunflower, ICP-MS, toxicity

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Authors: Ali Albadri

Abstract:

The logistics equation has never been used or studied in scientific fields outside the field of ecology. It has never been used to understand the behavior of a dynamic system of mechanical machines, like an escalator. We have studied the compatibility of the logistic map against real measurements from an escalator. This study has proven that there is good compatibility between the logistics equation and the experimental measurements. It has discovered the potential of a relationship between the fractal dimension and the non-linearity parameter, R, in the logistics equation. The fractal dimension increases as the R parameter (non-linear parameter) increases. It implies that the fractal dimension increases as the phase of the life span of the machine move from the steady/stable phase to the periodic double phase to a chaotic phase. The fractal dimension and the parameter R can be used as a tool to verify and check the health of machines. We have come up with a theory that there are three areas of behaviors, which they can be classified during the life span of a machine, a steady/stable stage, a periodic double stage, and a chaotic stage. The level of attention to the machine differs depending on the stage that the machine is in. The rate of faults in a machine increases as the machine moves through these three stages. During the double period and the chaotic stages, the number of faults starts to increase and become less predictable. The rate of predictability improves as our monitoring of the changes in the fractal dimension and the parameter R improves. The principles and foundations of our theory in this work have and will have a profound impact on the design of systems, on the way of operation of systems, and on the maintenance schedules of the systems. The systems can be mechanical, electrical, or electronic. The discussed methodology in this paper will give businesses the chance to be more careful at the design stage and planning for maintenance to control costs. The findings in this paper can be implied and used to correlate the three stages of a mechanical system to more in-depth mechanical parameters like wear and fatigue life.

Keywords: logistcs map, bifurcation map, fractal dimension, logistics equation

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Authors: Khoshnaw Khalid Hama Saleh, Ergun Ercelebi

Abstract:

Increasingly complex modern power systems require stability, especially for transient and small disturbances. Transient stability plays a major role in stability during fault and large disturbance. This paper compares a power system stabilizer (PSS) and static Var compensator (SVC) to improve damping oscillation and enhance transient stability. The effectiveness of a PSS connected to the exciter and/or governor in damping electromechanical oscillations of isolated synchronous generator was tested. The SVC device is a member of the shunt FACTS (flexible alternating current transmission system) family, utilized in power transmission systems. The designed model was tested with a multi-machine system consisting of four machines six bus, using MATLAB/SIMULINK software. The results obtained indicate that SVC solutions are better than PSS.

Keywords: FACTS, MATLAB/SIMULINK, multi-machine system, PSS, SVC, transient stability

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Authors: Chin-Hsien Yeh, Shao-Wen Chen, Wen-Shu Huang, Chun-Fu Huang, Jong-Rong Wang, Jung-Hua Yang, Yuh-Ming Ferng, Chunkuan Shih

Abstract:

In this research, a Fukushima-like conditions is simulated with TRACE and RELAP5. Fukushima Daiichi Nuclear Power Plant (NPP) occurred the disaster which caused by the earthquake and tsunami. This disaster caused extended loss of all AC power (ELAP). Hence, loss of ultimate heat sink (LUHS) happened finally. In order to handle Fukushima-like conditions, Taiwan Atomic Energy Council (AEC) commanded that Taiwan Power Company should propose strategies to ensure the nuclear power plant safety. One of the diverse and flexible coping strategies (FLEX) is a different water injection strategy. It can execute core injection at 20 Kg/cm2 without depressurization. In this study, TRACE and RELAP5 were used to simulate Maanshan nuclear power plant, which is a three loops PWR in Taiwan, under Fukushima-like conditions and make sure the success criteria of FLEX. Reducing core cooling ability is due to failure of emergency core cooling system (ECCS) in extended loss of all AC power situation. The core water level continues to decline because of the seal leakage, and then FLEX is used to save the core water level and make fuel rods covered by water. The result shows that this mitigation strategy can cool the reactor pressure vessel (RPV) as soon as possible under Fukushima-like conditions, and keep the core water level higher than Top of Active Fuel (TAF). The FLEX can ensure the peak cladding temperature (PCT) below than the criteria 1088.7 K. Finally, the FLEX can provide protection for nuclear power plant and make plant safety.

Keywords: TRACE, RELAP5/MOD3.3, ELAP, FLEX

Procedia PDF Downloads 250