Search results for: machine and plant engineering

Authors: Chanida Tangjai Benchalak Muangmeesri, Dechrit Maneetham

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Collection of marine garbage in the modern world, where technology is vital to existence. Consequently, technology can assist in reducing the duplicate labor in the subject of collecting trash in the water that must be done the same way repeatedly owing to the consequence of suffering an emerging disease or COVID-19. This is due to the rapid advancement of technology. As a result, solid trash and plastic garbage are increasing. Agricultural gardens, canals, ponds, and water basins are all sources of water. Building boat-like instruments for rubbish collection in the water will be done this time. It has two control options, boat control via remote control and boat control via an Internet of Things system. A solar panel with a power output of 40 watts powers the system being able to store so accurate and precise waste collection, allowing for thorough water cleaning. The primary goals are to keep the water's surface clean and assess its quality to support the aquatic ecology.

Keywords: automatic boat, water treatment, cleaning machine, iot

Procedia PDF Downloads 91

Authors: Reda A. Elkhyat, Mahmoud Nasr, Amel A. Tammam, Mohamed A. Ghazy

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Pharmaceuticals and personal care products (PPCPs) are a unique group of emerging contaminants continuously introduced into the aquatic ecosystem at concentrations capable of inducing adverse effects on humans and aquatic organisms, even at trace levels ranging from ppt to ppm. Amongst the common pharmaceutical emerging pollutants detected in several aquatic environments, acetaminophen has been recognized for its high toxicity. Once released into the aquatic environment, acetaminophen could be degraded by the microbial community and adsorption/ uptake by the plants. Although many studies have investigated the hazard risks of acetaminophen pollutants on aquatic animals, the number of studies demonstrating its removal efficiency and effects on the aquatic plant still needs to be expanded. In this context, this study aims to apply the aquatic plant-based phytoremediation system to eliminate this emerging contaminant from domestic wastewater. The phytoremediation experiment was performed in a hydroponic system containing Eichhornia crassipes and operated under the natural environment at 25°C to 30°C. This system was subjected to synthetic domestic wastewater with the maximum initial chemical oxygen demand (COD) of 390 mg/L and three different acetaminophen concentrations of 25, 50, and 200 mg/L. After 17 d of operation, the phytoremediation system achieved removal efficiencies of about 100% and 85.6±4.2% for acetaminophen and COD, respectively.Moreover, the Eichhornia crassipes could withstand the toxicity associated with increasing the acetaminophen concentrations from 25 to 200 mg/L. This high treatment performance could be assigned to the well-adaptation of the water hyacinth to the phytoremediation factors. Moreover, it has been proposed that this phytoremediation system could be largely supported by phytodegradation and plant uptaking mechanisms; however, detecting the generated intermediates, metabolites, and degradation products are still under investigation. Applying this free-floating plant in wastewater treatment and reducing emerging contaminants would meet the targets of SDGs 3, 6, and. 14. The cost-benefit analysis was performed for the phytoremediation system. The phytoremediation system is financially viable as the net profit was 2921 US $/ y with a payback period of nine years.

Keywords: domestic wastewater, emerging pollutants, hydrophyte Eichhornia crassipes, paracetamol removal efficiency, sustainable development goals (SDGs)

Procedia PDF Downloads 115

Authors: Djebbar Atmani, Aghiles Karim Aissat, Nadjet Debbache-Benaida, Nassima Chaher-Bazizi, Dina Atmani-Kilani, Meriem Rahmani-Berboucha, Naima Saidene, Malika Benloukil, Lila Azib

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Medicinal plants are believed to be an important source for the discovery of potential antioxidant, anti-inflammatory and anti-diabetic substances. The present study was designed to investigate the neuroprotective, anti-inflammatory, anti-diabetic and anti-hyperuricemic potential of Pistacia lentiscus, as well as the identification of active compounds. The antioxidant potential of plant extracts against known radicals was measured using various standard in vitro methods. Anti-inflammatory activity was determined using the paw edema model in mice and by measuring the secretion of the pro-inflammatory cytokine, whereas the anti-diabetic effect was assessed in vivo on streptozotocin-induced diabetic rats and in vitro by inhibition of alpha-amylase. The anti-hyperuricemic activity was evaluated using the xanthine oxidase assay, whereas neuroprotective activity was investigated using an Aluminum-induced toxicity test. Pistacia lentiscus extracts and fractions exhibited high scavenging capacity against DPPH, NO. and ABTS+ radicals in a dose-dependent manner and restored blood glucose levels, in vivo, to normal values, in agreement with the in vitro anti-diabetic effect. Oral administration of plant extracts significantly decreased carrageenan-induced mice paw oedema, similar to the standard drug, diclofenac, was effective in reducing IL-1β levels in cell culture and induced a significant increase in urinary volume in mice, associated to a promising anti-hyperuricemic activity. Plant extracts showed good neuroprotection and restoration of cognitive functions in mice. HPLC-MS and NMR analyses allowed the identification of known and new phenolic compounds that could be responsible for the observed activities. Therefore, Pistacia lentiscus could be beneficial in the treatment of inflammatory conditions and diabetes complications and the enhancement of cognitive functions.

Keywords: Pistacia lentiscus, anti-inflammatory, antidiabetic, flavanols, neuroprotective

Procedia PDF Downloads 136

Authors: Irena Maus, Katharina Gabriella Cibis, Andreas Bremges, Yvonne Stolze, Geizecler Tomazetto, Daniel Wibberg, Helmut König, Alfred Pühler, Andreas Schlüter

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Within the agricultural sector, the production of biogas from organic substrates represents an economically attractive technology to generate bioenergy. Complex consortia of microorganisms are responsible for biomass decomposition and biogas production. Recently, species belonging to the phylum Thermotogae were detected in thermophilic biogas-production plants utilizing renewable primary products for biomethanation. To analyze adaptive genome features of representative Thermotogae strains, Defluviitoga tunisiensis L3 was isolated from a rural thermophilic biogas plant (54°C) and completely sequenced on an Illumina MiSeq system. Sequencing and assembly of the D. tunisiensis L3 genome yielded a circular chromosome with a size of 2,053,097 bp and a mean GC content of 31.38%. Functional annotation of the complete genome sequence revealed that the thermophilic strain L3 encodes several genes predicted to facilitate growth of this microorganism on arabinose, galactose, maltose, mannose, fructose, raffinose, ribose, cellobiose, lactose, xylose, xylan, lactate and mannitol. Acetate, hydrogen (H2) and carbon dioxide (CO2) are supposed to be end products of the fermentation process. The latter gene products are metabolites for methanogenic archaea, the key players in the final step of the anaerobic digestion process. To determine the degree of relatedness of dominant biogas community members within selected digester systems to D. tunisiensis L3, metagenome sequences from corresponding communities were mapped on the L3 genome. These fragment recruitments revealed that metagenome reads originating from a thermophilic biogas plant covered 95% of D. tunisiensis L3 genome sequence. In conclusion, availability of the D. tunisiensis L3 genome sequence and insights into its metabolic capabilities provide the basis for biotechnological exploitation of genome features involved in thermophilic fermentation processes utilizing renewable primary products.

Keywords: genome sequence, thermophilic biogas plant, Thermotogae, Defluviitoga tunisiensis

Procedia PDF Downloads 499

Authors: Keziah Bazar

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The effect of Brassica rapa leaf extracts on the growth of upland Ipomoea aquatica was investigated. One hundred grams Brassica rapa leaf were blended using a heavy duty blender. These were diluted with water to have final concentrations of 75% (T1), 50% (T2) and 25% (T3) that served as treatments of the study. Pure water (T0) that served as control was also included Upland Ipomoea aquatic were grown in pots. A 3-4 in water level was maintained during the whole duration of the study. Plant height, leaf area, fruit size and shoot height, were taken after 6 months. Results showed that plant height and shoot height was highest in T1 while T0 was the lowest. On the other hand, T2 had the highest leaf area and fruit size. The study suggests that T1 and T2 can be a good fertilizer for Ipomoea aquatica.

Keywords: Ipomoea aquatica, leaf extracts, growth, Brassica rapa

Procedia PDF Downloads 226

Authors: David Ming Liu, Benjamin Hirsch, Bashir Aden

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Syndromic surveillance is to detect or predict disease outbreaks through the analysis of medical sources of data. Using social media data like tweets to do syndromic surveillance becomes more and more popular with the aid of open platform to collect data and the advantage of microblogging text and mobile geographic location features. In this paper, a Syndromic Surveillance Framework is presented with machine learning kernel using tweets data analytics. Influenza and the three cities Abu Dhabi, Al Ain and Dubai of United Arabic Emirates are used as the test disease and trial areas. Hospital cases data provided by the Health Authority of Abu Dhabi (HAAD) are used for the correlation purpose. In our model, Latent Dirichlet allocation (LDA) engine is adapted to do supervised learning classification and N-Fold cross validation confusion matrix are given as the simulation results with overall system recall 85.595% performance achieved.

Keywords: Syndromic surveillance, Tweets, Machine Learning, data mining, Latent Dirichlet allocation (LDA), Influenza

Procedia PDF Downloads 116

Authors: Ehsan Atefi, Michael Grigware

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Respiratory failure secondary to surfactant deficiency resulting from respiratory distress syndrome is considered one major cause of morbidity in preterm infants. Surfactant replacement treatment (SRT) is considered an effective treatment for this disease. Here, we introduce an AI-mediated approach for estimating the distribution of surfactant in the lung airway of a newborn infant during SRT. Our approach implements machine learning to precisely estimate the splitting ratio of a liquid drop during bifurcation at different injection velocities and patient orientations. This technique can be used to calculate the surfactant residue remaining on the airway wall during the surfactant injection process. Our model works by minimizing the pressure drop difference between the two airway branches at each generation, subject to mass and momentum conservation. Our platform can be used to generate feedback for immediately adjusting the velocity of injection and patient orientation during SRT.

Keywords: respiratory failure, surfactant deficiency, surfactant replacement, machine learning

Procedia PDF Downloads 126

Authors: H. Tavakoli, S. S. Mohtasebi, R. Alimardani, R. Gebbers

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Nitrogen (N) is one of the most important agricultural inputs affecting crop growth, yield and quality in rain-fed cereal production. N demand of crops varies spatially across fields due to spatial differences in soil conditions. In addition, the response of a crop to the fertilizer applications is heavily reliant on plant available water. Matching N supply to water availability is thus essential to achieve an optimal crop response. The objective of this study was to determine effect of drought stress on estimation of nitrogen status of winter wheat by some vegetation indices. During the 2012 growing season, a field experiment was conducted at the Bundessortenamt (German Plant Variety Office) Marquardt experimental station which is located in the village of Marquardt about 5 km northwest of Potsdam, Germany (52°27' N, 12°57' E). The experiment was designed as a randomized split block design with two replications. Treatments consisted of four N fertilization rates (0, 60, 120 and 240 kg N ha-1, in total) and two water regimes (irrigated (Irr) and non-irrigated (NIrr)) in total of 16 plots with dimension of 4.5 × 9.0 m. The indices were calculated using readings of a spectroradiometer made of tec5 components. The main parts were two “Zeiss MMS1 nir enh” diode-array sensors with a nominal rage of 300 to 1150 nm with less than 10 nm resolutions and an effective range of 400 to 1000 nm. The following vegetation indices were calculated: NDVI, GNDVI, SR, MSR, NDRE, RDVI, REIP, SAVI, OSAVI, MSAVI, and PRI. All the experiments were conducted during the growing season in different plant growth stages including: stem elongation (BBCH=32-41), booting stage (BBCH=43), inflorescence emergence, heading (BBCH=56-58), flowering (BBCH=65-69), and development of fruit (BBCH=71). According to the results obtained, among the indices, NDRE and REIP were less affected by drought stress and can provide reliable wheat nitrogen status information, regardless of water status of the plant. They also showed strong relations with nitrogen status of winter wheat.

Keywords: nitrogen status, drought stress, vegetation indices, precision agriculture

Procedia PDF Downloads 319

Authors: Rully D. Purwati, Tantri D.A. Anggraeni, Bambang Heliyanto, M. Machfud, Joko Hartono

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The availability of fuel in the world will be reduced in next few years, it is necessary to find alternative energy sources. Jatropha curcas L. is one of oil crops producing non-edible oil which is potential for bio-diesel. Jatropha cultivation and development program in Indonesia is facing several problems especially low seed yield resulting in inefficient crop cultivation cost. To cope with the problem, development of high yielding varieties is necessary. Development of new varieties to improve seed yield was conducted by hybridization and selection and resulted in fourteen potential genotypes. The yield potential of the fourteen genotypes were evaluated and compared with two check varieties. The objective of the evaluation was to find Jatropha hybrids with some characters i.e. their productivity was higher than check varieties, oil content > 40% and harvesting age ≤ 110 days. Hybridization and individual plant selection were carried out from 2010 to 2014. Evaluation of high yield was conducted in Asembagus experimental station, Situbondo, East Java in three years (2015-2017). The experimental designed was Randomized Complete Block Design with three replication, and plot size 10 m x 8 m. The characters observed were number of capsules per plant, dry seed yield (kg/ha) and seed oil content (%). The results of this experiment indicated that all the hybrids evaluated have higher productivity than check variety IP-3A. There were two superior hybrids i.e. HS-49xSP-65/32 and HS-49xSP-19/28 with highest seed yield per hectare and number of capsules per plant for three years.

Keywords: Jatropha, bio energy, hybrid, high seed yield

Procedia PDF Downloads 145

Authors: Fikri Suwaileh

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In the reverse osmosis (RO) water treatment plant, operation was suffering from several leaks on the acid injection point spool and downstream spools, due to insufficient injection monitoring and the coating failure leading to pin holes. The paper will go over the background of the leaks in the acid injection point, the process in the RO plant, the material, and coating used in the existing spools, the impact of these repeated leaks, the type of damage mechanism that occurred in the system due to the manner of acid injection and the heat in the spools, which lead to coating failure, leaks and water release. This paper will also look at the analysis, both the short- and long-term recommendations, and the utilization of Teflon internal lining to stop the leaks. Sharing this case study will enhance the knowledge of the importance of taking all factors that will lead to leaks in the acid injection points, along with the importance of utilizing the appropriate coating material lining to enhance the full system.

Keywords: corrosion, coating, raw water, lining

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Authors: Juliana Janet R. Martin-Puzon, Demetrio L. Valle, Windell L. Rivera

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This study aimed to determine the presence of bioactive phytochemical constituents and evaluate the in vitro antibacterial activities of Glinus oppositifolius or carpet weed, a plant valued for its use in traditional medicine and as a vegetable. The leaves, stems, and roots were extracted using chloroform, ethanol, and methanol. Phytochemical screening revealed that the entire G. oppositifolius plant, i.e. roots, stems, and leaves, is a rich source of alkaloids, flavonoids, glycosides, saponins, sterols, tannins, and triterpenes. The antibacterial activity of the leaf and stem extracts were evaluated through disc diffusion, minimum inhibitory concentration, and bactericidal concentration assays against methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), extended spectrum β-lactamase-producing (ESβL+), carbapenem-resistant Enterobacteriaceae (CRE), and metallo-β-lactamase-producing (MβL+) Pseudomonas aeruginosa and Acinetobacter baumannii. The leaf extracts revealed antibacterial activities, inhibiting the growth of non-resistant and multidrug-resistant (MDR) strains of the Gram-negative bacteria E. coli, P. aeruginosa, and A. baumanii. In conclusion, the various biological activities of G. oppositifolius, including its antibacterial activity, are due to the presence of diverse bioactive secondary metabolites. The presence of phytochemical compounds in G. oppositifolius is scientific evidence on its use for treatment of many ailments. Thus, the results demonstrate the great potential of the plant as a new, alternative source of antimicrobials and other components with therapeutic value.

Keywords: antibacterial, Glinus oppositifolius, multidrug-resistant, secondary metabolites

Procedia PDF Downloads 576

Authors: R. R. Govind

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In the digital age, artificial intelligence (AI) is having a significant impact on the rapid changes that cities are experiencing. This study explores the profound impact of AI on urban morphology, especially with regard to promoting friendly design choices. It addresses a significant research gap by examining the real-world effects of integrating AI into urban design and management. The main objective is to outline a framework for integrating AI to transform urban settings. The study employs an urban design framework to effectively navigate complicated urban environments, emphasize the need for urban management, and provide efficient planning and design strategies. Taking Gangtok's informal settlements as a focal point, the study employs AI methodologies such as machine learning, predictive analytics, and generative AI to tackle issues of 'urban informality'. The insights garnered not only offer valuable perspectives but also unveil AI's transformative potential in addressing contemporary urban challenges.

Keywords: urban design, artificial intelligence, urban challenges, machine learning, urban informality

Procedia PDF Downloads 61

Authors: Beulah Rani Inbanathan

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In the present era, it is vivid from the numerous outcomes that data privacy is being compromised in various ways. Machine learning is one technology that uses the centralized server, and then data is given as input which is being analyzed by the algorithms present on this mentioned server, and hence outputs are predicted. However, each time the data must be sent by the user as the algorithm will analyze the input data in order to predict the output, which is prone to threats. The solution to overcome this issue is federated learning, where the models alone get updated while the data resides on the local machine and does not get exchanged with the other local models. Nevertheless, even on these local models, there are chances of data poisoning, and it is crystal clear from various experiments done by many people. This paper delves into many ways where data poisoning occurs and the many methods through which it is prevalent that data poisoning still exists. It includes the poisoning attacks on IoT devices, Edge devices, Autoregressive model, and also, on Industrial IoT systems and also, few points on how these could be evadible in order to protect our data which is personal, or sensitive, or harmful when exposed.

Keywords: data poisoning, federated learning, Internet of Things, edge computing

Procedia PDF Downloads 87

Authors: Jazim Sohail, Filipe Teixeira-Dias

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Mild traumatic brain injury (mTBI) within non-helmeted contact sports is a growing concern due to the serious risk of potential injury. Extensive research is being conducted looking into head kinematics in non-helmeted contact sports utilizing instrumented mouthguards that allow researchers to record accelerations and velocities of the head during and after an impact. This does not, however, allow the location of the impact on the head, and its magnitude and orientation, to be determined. This research proposes and validates two methods to quantify impact locations from instrumented mouthguard kinematic data, one using rigid body dynamics, the other utilizing machine learning. The rigid body dynamics technique focuses on establishing and matching moments from Euler’s and torque equations in order to find the impact location on the head. The methodology is validated with impact data collected from a lab test with the dummy head fitted with an instrumented mouthguard. Additionally, a Hybrid III Dummy head finite element model was utilized to create synthetic kinematic data sets for impacts from varying locations to validate the impact location algorithm. The algorithm calculates accurate impact locations; however, it will require preprocessing of live data, which is currently being done by cross-referencing data timestamps to video footage. The machine learning technique focuses on eliminating the preprocessing aspect by establishing trends within time-series signals from instrumented mouthguards to determine the impact location on the head. An unsupervised learning technique is used to cluster together impacts within similar regions from an entire time-series signal. The kinematic signals established from mouthguards are converted to the frequency domain before using a clustering algorithm to cluster together similar signals within a time series that may span the length of a game. Impacts are clustered within predetermined location bins. The same Hybrid III Dummy finite element model is used to create impacts that closely replicate on-field impacts in order to create synthetic time-series datasets consisting of impacts in varying locations. These time-series data sets are used to validate the machine learning technique. The rigid body dynamics technique provides a good method to establish accurate impact location of impact signals that have already been labeled as true impacts and filtered out of the entire time series. However, the machine learning technique provides a method that can be implemented with long time series signal data but will provide impact location within predetermined regions on the head. Additionally, the machine learning technique can be used to eliminate false impacts captured by sensors saving additional time for data scientists using instrumented mouthguard kinematic data as validating true impacts with video footage would not be required.

Keywords: head impacts, impact location, instrumented mouthguard, machine learning, mTBI

Procedia PDF Downloads 217

Authors: Katharina Lapin, Debashis Sanyal

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Worldwide urban areas are growing. Urbanization has a great impact on social and economic development and ecosystem services. This global trend of urbanization also has significant impact on habitat and biodiversity. The impact of urbanization on the biodiversity of cities in Europe and North America is well studied, while there is a lack of data from cities in currently fast growing urban areas. Indian cities are expanding. The scientific community and the governmental authorities are facing the ongoing urbanization process as an opportunity for the environment. This case study supports the evaluation of urban biodiversity of the city Raipur in the North-West of India. The aim of this study is to assess the overview of the environmental and ecological implications of urbanization. The collected data and analysis was used to discuss the challenges for the sustainable city development. Vascular plants were chosen as an appropriate indicator for the assessment of local biodiversity changes. On the one hand, the vegetation cover is sensible to anthropogenic influence, and in the other hand, the local species composition is comparable to changes at the regional and national scale, using the plant index of India. Further information of abiotic situation can be gathered with the determination of indicator species. In order to calculate the influence of urbanization on the native plant diversity, the Shannon diversity index H´ was chosen. The Pielou`s pooled quadrate method was used for estimating diversity when a random sample is not expected. It was used to calculate the Pilou´s index of evenness. The estimated species coverage was used for calculating the H´ and J. Pearson correlation was performed to test the relationship between urbanization pattern and plant diversity. Further, a SWOT analysis was used in for analyzing internal and external factors impinging on a decision making process. The city of Raipur (21.25°N 81.63°E) has a population of 1,010,087 inhabitants living in an urban area of 226km², in the district of the Indian state of Chhattisgarh. Within the last decade, the urban area of Raipur increased. The results show that various novel ecosystems exist in the urban area of Raipur. The high amount of native flora is mainly to find at the shore of urban lakes and along the river Karun. These areas of high Biodiversity Index are to protect as urban biodiversity hot spots. The governmental authorities are well informed about the environmental challenges for the sustainable development of the city. Together with the scientific community of the Technical University of Raipur many engineering solutions are discussed for implementation of the future. The case study helped to point out the importance environmental measures that support the ecosystem services of green infrastructure. The fast process of urbanization is difficult to control. Uncontrolled creation of urban housing leads to difficulties in unsustainable use of natural resources. This is the major threat for the urban biodiversity.

Keywords: India, novel ecosystems, plant diversity, urban ecology

Procedia PDF Downloads 277

Authors: Subrato Saha, Yun-Hyun Cho

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In electric power steering (EPS), spoke type brushless ac (BLAC) motors offer distinct advantages over other electric motor types in terms torque smoothness, reliability and efficiency. This paper deals with the shape optimization of spoke type BLAC motor, in order to reduce cogging torque. This paper examines 3 steps skewing rotor angle, optimizing rotor core edge and rotor overlap length for reducing cogging torque in spoke type BLAC motor. The methods were applied to existing machine designs and their performance was calculated using finite- element analysis (FEA). Prototypes of the machine designs were constructed and experimental results obtained. It is shown that the FEA predicted the cogging torque to be nearly reduce using those methods.

Keywords: EPS, 3-Step skewing, spoke type BLAC, cogging torque, FEA, optimization

Procedia PDF Downloads 491

Authors: Dalila Bouamra, Chekib Arslane Baki, Abdelhamid Bouchebour, Fatiha Koussa, Amel Benamara, Seoussen Kada

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The activity of methanolic extract from Anacyclus pyrethrum was evaluated using λ-carrageenan 1% induced paw edema in Wistar Albinos rats. The oral administration of 200 mg/kg, 400 mg/kg and 600 mg/kg, body weight of methanolic extract, one hour before induction of inflammation, exerted a significant inhibition effect of 47%, 57% and 62% respectively after 4h λ-carrageenan treatment and highly significant inhibition effect of 57%, 66% and 75% respectively after 8h λ-carrageenan treatment, compared to non treated group (100%) and that treated with aspirin, a standard anti-inflammatory drug. On the other hand, the effect of the plant extract on stomach was macroscopically and microscopically studied. The plant extract has an impact on the loss ratio of granulocytes that have invaded the stomach after a period of inflammation at a dose of 600 mg/kg body weight.

Keywords: inflammation, Anacyclus pyrethrum, gastritis, Wistar Albinos rats

Procedia PDF Downloads 487

Authors: Hai Zeng, Anqing Liu, Shuguang Dan, Ying Zhang, Yan Li, Zihang Zeng

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Children are strong; the country strong, the development of children Basketball is a strategic advantage. Common forms of basketball equipment has been difficult to meet the needs of young children teaching the game of basketball, basketball development for 3-6 years old children in the form of appropriate teaching aids is a breakthrough basketball game teaching children bottlenecks, improve teaching critical path pleasure, but also the development of early childhood basketball a necessary requirement. In this study, literature, questionnaires, focus group interviews, comparative analysis, for domestic and foreign use of 12 kinds of basketball teaching aids (cloud computing MINI basketball, adjustable basketball MINI, MINI basketball court, shooting assist paw print ball, dribble goggles, dribbling machine, machine cartoon shooting, rebounding machine, against the mat, elastic belt, ladder, fitness ball), from fun and improve early childhood shooting technique, dribbling technology, as well as offensive and defensive rebounding against technology conduct research on conversion technology. The results show that by using appropriate forms of teaching children basketball aids, can effectively improve children's fun basketball game, targeted to improve a technology, different types of aids from different perspectives enrich the connotation of children basketball game. Recommended for children of color psychology, cartoon and environmentally friendly material production aids, and increase research efforts basketball aids children, encourage children to sports teachers aids applications.

Keywords: appropriate forms of children basketball, auxiliary equipment, appli, MINI basketball, 3-6 years old children, teaching

Procedia PDF Downloads 386

Authors: Min Kyu Kim, Eun Young Lee, Dong Woo Son, Yoon Seok Chang

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In this paper, we introduce a flexible box erecting machine (BEM) that swiftly and automatically transforms cardboard into a three dimensional box. Recently, the parcel service and home-shopping industries have grown rapidly, and there is an increasing need for various box types to ship various products. However, workers cannot fold thousands of boxes manually in a day. As such, automatic BEMs are garnering greater attention. This study takes equipment operation into consideration as well as mechanical improvements in order to design a BEM that is able to outperform its conventional counterparts. We analyzed six dispatching rules – First In First Out (FIFO), Shortest Processing Time (SPT), Earliest Due Date (EDD), Setup Avoidance, EDD + SPT, and EDD + Setup Avoidance – to determine which one was most suitable for BEM operation. Consequently, SPT and Setup Avoidance were found to be the most critical rules, followed by EDD + Setup Avoidance, EDD + SPT, EDD, and FIFO. This hierarchy was valid for both our conventional BEM and our new flexible BEM from the viewpoint of processing time. We believe that this research can contribute to flexible BEM management, which has the potential to increase productivity and convenience.

Keywords: automation, box erecting machine, dispatching rule, setup time

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Authors: Mohamud Ali Ibrahim, Ali Bayat, Ali Bolat

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Defoliant spraying is an important link in the mechanized cotton harvest because adequate and uniform spraying can improve defoliation quality and reduce cotton trash content. In defoliant application, application volume and spraying technology are extremely important. In this study, the effectiveness of defoliant application to cotton plant that has come to harvest with two different application volumes and three different types of nozzles with a standard field crop sprayer was determined. Experiments were carried in two phases as field area trials and laboratory analysis. Application rates were 250 l/ha and 400 L/ha, and spraying nozzles were (1) Standard flat fan nozzle (TP8006), (2) Air induction nozzle (AI 11002-VS), and (3) Dual Pattern nozzle (AI307003VP). A tracer (BSF) and defoliant were applied to mature cotton with approximately 60% open bolls and samplings for BSF deposition and spray coverage on the cotton plant were done at two plant height (upper layer, lower layer) of plant. Before and after spraying, bolls open and leaves rate on cotton plants were calculated, and filter papers were used to detect BSF deposition, and water sensitive papers (WSP) were used to measure the coverage rate of spraying methods used. Spectrofluorophotometer was used to detect the amount of tracer deposition on targets, and an image process computer programme was used to measure coverage rate on WSP. In analysis, conclusions showed that air induction nozzle (AI 11002-VS) achieved better results than the dual pattern and standard flat fan nozzles in terms of higher depositions, coverages, and leaf defoliations, and boll opening rates. AI nozzles operating at 250 L/ha application rate provide the highest deposition and coverage rate on applications of the defoliant; in addition, BSF as an indicator of the defoliant used reached on leaf beneath in merely this spray nozzle. After defoliation boll opening rate was 85% on the 7th and 12th days after spraying and falling rate of leaves was 76% at application rate of 250 L/ha with air induction (AI1102) nozzle.

Keywords: cotton defoliant, air induction nozzle, dual pattern nozzle, standard flat fan nozzle, coverage rate, spray deposition, boll opening rate, leaves falling rate

Procedia PDF Downloads 198

Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

Procedia PDF Downloads 357

Authors: Jae-Yong Lee, Gyu-Hyun Bae, Yun-Soo Chung, Dae-Sub Kim, Jae-Sung Bae

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In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics.

Keywords: camera type weft straightener, structure analysis, control, skew and bow roller

Procedia PDF Downloads 292

Authors: Mohammad Arabi

Abstract:

The development of advanced technologies in the field of signal processing and vibration analysis has enabled more accurate analysis and fault detection in electrical systems. This research investigates the application of temporal and frequency features in detecting faults in electric motor bearings, aiming to enhance fault detection accuracy and prevent unexpected failures. The use of methods such as deep learning algorithms and neural networks in this process can yield better results. The main objective of this research is to evaluate the efficiency and accuracy of methods based on temporal and frequency features in identifying faults in electric motor bearings to prevent sudden breakdowns and operational issues. Additionally, the feasibility of using techniques such as machine learning and optimization algorithms to improve the fault detection process is also considered. This research employed an experimental method and random sampling. Vibration signals were collected from electric motors under normal and faulty conditions. After standardizing the data, temporal and frequency features were extracted. These features were then analyzed using statistical methods such as analysis of variance (ANOVA) and t-tests, as well as machine learning algorithms like artificial neural networks and support vector machines (SVM). The results showed that using temporal and frequency features significantly improves the accuracy of fault detection in electric motor bearings. ANOVA indicated significant differences between normal and faulty signals. Additionally, t-tests confirmed statistically significant differences between the features extracted from normal and faulty signals. Machine learning algorithms such as neural networks and SVM also significantly increased detection accuracy, demonstrating high effectiveness in timely and accurate fault detection. This study demonstrates that using temporal and frequency features combined with machine learning algorithms can serve as an effective tool for detecting faults in electric motor bearings. This approach not only enhances fault detection accuracy but also simplifies and streamlines the detection process. However, challenges such as data standardization and the cost of implementing advanced monitoring systems must also be considered. Utilizing temporal and frequency features in fault detection of electric motor bearings, along with advanced machine learning methods, offers an effective solution for preventing failures and ensuring the operational health of electric motors. Given the promising results of this research, it is recommended that this technology be more widely adopted in industrial maintenance processes.

Keywords: electric motor, fault detection, frequency features, temporal features

Procedia PDF Downloads 48

Authors: Marzieh Fotovvat, Farzaneh Najafi, Ramazan Ali Khavari-Nejad, Daryush Talei, Farhad Rejali

Abstract:

Salvia leriifolia Benth. is one of the valuable and perennial medicinal plants of the Lamiaceae family, geographically growing in the south and tropical regions of Khorassan and Semnan provinces in Iran. In recent years, several medicinal properties such as antimicrobial, antifungal, anti-diabetic, analgesic, and anti-inflammatory effects have been reported from this plant. The use of elicitors such as chitosan and Arbuscular mycorrhizal fungi (AMF) symbiosis are the main methods for increasing the production of secondary metabolites, growth, and physiological factors in plants. The main aim of this study was to investigate the effects of foliar spraying applications by chitosan and/or the contribution of AMF (Glomus interaradices) on some growth factors and chlorophyll content of S. leriifolia under glasshouse conditions. The sterilized seeds were germinated by placing them into a cocopeat. After one month, seedlings that were in the 2-4 leaf stage were transferred to plastic pots (garden soil and pumice at 2:1) with or without mycorrhizal fungi. Chitosan (0, 50, 100, 200, and 400 mg L-1) was sprayed four times in the fourth month of the vegetative period. The results showed that fresh leaf weight, fresh root weight, root height, and chlorophyll content could change in the plant treated with chitosan and AMF symbiosis. So that the highest chlorophyll content and fresh weight of roots and leaves were observed in the interaction of chitosan and G. interaradices. In general, by optimizing the chitosan concentration and the use of appropriate AMF symbiosis, it is possible to improve the growth and quality of the medicinal plant S. leriifolia.

Keywords: chitosan, chlorophyll, growth factors, mycorrhiza

Procedia PDF Downloads 81

Authors: Mamdouh Y. Saleh, Gaber El Enany, Medhat H. Elzahar, Moustafa H. Omran

Abstract:

The discharge limits of industrial waste water effluents are subjected to regulations which are getting more restricted with time. A former research occurred in Port Said city studied the efficiency of treating industrial wastewater using the first stage (A-stage) of the multiple-stage plant (AB-system).From the results of this former research, the effluent treated wastewater has high rates of total dissolved solids (TDS) and chemical oxygen demand (COD). The purpose of this paper is to improve the treatment process in removing TDS and COD. Thus, a pilot plant was constructed at wastewater pump station in the industrial area in the south of Port Said. Experimental work was divided into several groups adding activated carbon with different dosages to waste water, and for each group waste water was filtered after being mixed with activated carbon. pH and TSS as variables were also studied. At the end of this paper, a comparison was made between the efficiency of using activated carbon and the efficiency of using limestone in the same circumstances.

Keywords: adsorption, COD removal, filtration, TDS removal

Procedia PDF Downloads 498

Authors: Anahita Izadyar

Abstract:

Using a recombinant enzyme derived from corn and a simple modification, we are fabricating a facile, fast, and cost-beneficial novel biosensor to measure glucose. We are applying Plant Produced Mn Peroxidase (PPMP), glucose oxidase (GOx), polyaniline (PANI) as conductive polymer and gold nanoparticles (AuNPs) on Au electrode using electrochemical response to detect glucose. We applied the entrapment method of enzyme composition, which is generally used to immobilize conductive polymer and facilitate electron transfer from the enzyme oxidation-reduction center to the sample solution. In this work, the oxidation of glucose on the modified gold electrode was quantified with Linear Sweep Voltammetry(LSV). We expect that the modified biosensor has the potential for monitoring various biofluids.

Keywords: plant-produced manganese peroxidase, enzyme-based biosensors, glucose, modified gold nanoparticles electrode, polyaniline

Procedia PDF Downloads 200

Authors: Bahaa Eltahawy, Mikko Ylihärsilä, Reino Virrankoski, Esko Petäjä

Abstract:

Sheet metal processing is automated, but the step from product models to the production machine control still requires human intervention. This may cause time consuming bottlenecks in the production process and increase the risk of human errors. In this paper we present a system, which automatically recognizes features from the CAD-model of the sheet metal product. By using these features, the system produces a complete model of the particular sheet metal product. Then the model is used as an input for the sheet metal processing machine. Currently the system is implemented, capable to recognize more than 11 of the most common sheet metal structural features, and the procedure is fully automated. This provides remarkable savings in the production time, and protects against the human errors. This paper presents the developed system architecture, applied algorithms and system software implementation and testing.

Keywords: feature recognition, automation, sheet metal manufacturing, CAD, CAM

Procedia PDF Downloads 355

Authors: Jiwan Kumar, Pooja, Sandeep Negi, Anjum Rouf, Amit Kumar, Naveen Lakra

Abstract:

In modern times where, health issues are increasing day by day, breast cancer is also one of them, which is very crucial and really important to find in the early stages. Doctors can use this model in order to tell their patients whether a cancer is not harmful (benign) or harmful (malignant). We have used the knowledge of machine learning in order to produce the model. we have used algorithms like Logistic Regression, Random forest, support Vector Classifier, Bayesian Network and Radial Basis Function. We tried to use the data of crucial parts and show them the results in pictures in order to make it easier for doctors. By doing this, we're making ML better at finding breast cancer, which can lead to saving more lives and better health care.

Keywords: Bayesian network, radial basis function, ensemble learning, understandable, data making better, random forest, logistic regression, breast cancer

Procedia PDF Downloads 53

Authors: M. Duran Toksari, Berrin Ucarkus

Abstract:

In this paper, we propose two algorithms to optimally solve makespan and total completion time scheduling problems with learning effect and job dependent delivery times in a single machine environment. The delivery time is the extra time to eliminate adverse effect between the main processing and delivery to the customer. In this paper, we introduce the job dependent delivery times for some single machine scheduling problems with position dependent learning effect, which are makespan are total completion. The results with respect to two algorithms proposed for solving of the each problem are compared with LINGO solutions for 50-jobs, 100-jobs and 150-jobs problems. The proposed algorithms can find the same results in shorter time.

Keywords: delivery Times, learning effect, makespan, scheduling, total completion time

Procedia PDF Downloads 469

Authors: Birmohan Singh, V.K.Jain

Abstract:

Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

Procedia PDF Downloads 491