Search results for: machine and plant engineering

Authors: Ibrahim Khan, Waqas Khalid

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The Healthcare sector is known to consume a higher proportion of total energy consumption in the HVAC market owing to an excessive cooling and heating requirement in maintaining human thermal comfort in indoor conditions, catering to patients undergoing treatment in hospital wards, rooms, and intensive care units. The indoor thermal comfort conditions in selected hospitals of Islamabad, Pakistan, were measured on a real-time basis with the collection of first-hand experimental data using calibrated sensors measuring Ambient Temperature, Wet Bulb Globe Temperature, Relative Humidity, Air Velocity, Light Intensity and CO2 levels. The Experimental data recorded was analyzed in conjunction with the Thermal Comfort Questionnaire Surveys, where the participants, including patients, doctors, nurses, and hospital staff, were assessed based on their thermal sensation, acceptability, preference, and comfort responses. The Recorded Dataset, including experimental and survey-based responses, was further analyzed in the development of a correlation between operative temperature, operative relative humidity, and other measured operative parameters with the predicted mean vote and adaptive predicted mean vote, with the adaptive temperature and adaptive relative humidity estimated using the seasonal data set gathered for both summer – hot and dry, and hot and humid as well as winter – cold and dry, and cold and humid climate conditions. The Machine Learning Logistic Regression Algorithm was incorporated to train the operative experimental data parameters and develop a correlation between patient sensations and the thermal environmental parameters for which a new ML-based adaptive thermal comfort model was proposed and developed in our study. Finally, the accuracy of our model was determined using the K-fold cross-validation.

Keywords: predicted mean vote, thermal comfort, energy management, logistic regression, machine learning

Procedia PDF Downloads 63

Authors: Noshaba Dilbar, Hina Ashraf

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Medicinal plants are considered as rich source of ingredients which can be used in drug development and synthesis. Moreover, these plants play a vital role in the development of human culture of using ayurvedic medicines around the whole world. Among all plants, dessert plants are being proved as effective source of ayurvedic medicines and remedy against many diseases. Considering the fact, two plant species Limium indicum and Euphorbia granulata were taken from Cholistan dessert of Bahawalpur, Pakistan. Firstly, phytochemical screening was done by making dry and fresh plant extracts in five different solvents i.e Petroleum ether, benzene, chloroform, ethanol and methanol. Standard confirmation tests for all compounds were applied for analysis. Results revealed the presence of high range of bioactive compounds such as alakaloids, terpenoids, glycosides, steroids, flavonoids, saponins, phytosterols, oxalic acid, anthocyanin and quinone in both plants. Best results were obtained by methanolic, chloroform and petroleum ether extracts and methanolic, ethanolic and benzene extracts of Limium indicum and Euphorbia granulate respectively. Considering the results, methanolic extracts of both plants were further analysed for antibacterial activity. Plants were analysed against four pathogens including Escherchia coli, Proteus vulgaris, Klebsiella pneumonia and Pseudomonas aruginosa using disc diffusion method. Limium indicum showed highly significant activity against all pathogens while Euphorbia granulata showed significant activity against Klebsiella pneumonia and Proteus vulgaris but lesser against Escherchia coli and Pseudomonas aruginosa. MIC of extracts against each positive bacterium was calculated and recorded. Present plants can be considered for making useful drugs but further studies are needed to isolate active agents from plant extracts for drug development.

Keywords: antibacterial activity, Euphorbia granulata, Limium indicum, medicinal plants, phytochemical screening

Procedia PDF Downloads 117

Authors: Gargi Prasad, Ashiho A. Mao, Deepu Vijayan, S. Mandal

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The main objective of the present study was to optimize and develop an efficient protocol for in vitro propagation of a medicinally important orchid Cephalantheropsis obcordata (Lindl.) Ormerod along with genetic stability analysis of regenerated plants. This plant has been traditionally used in Chinese folk medicine and the decoction of whole plant is known to possess anticancer activity. Nodal segments used as explants were inoculated on Murashige and Skoog (MS) medium supplemented with various concentrations of isopentenyl adenine (2iP). The rooted plants were successfully acclimatized in the greenhouse with 100% survival rate. Inter-simple sequence repeats (ISSR) markers were used to assess the genetic fidelity of in vitro raised plants and the mother plant. It was revealed that monomorphic bands showing the absence of polymorphism in all in vitro raised plantlets analyzed, confirming the genetic uniformity among the regenerants. Phytochemical analysis was done to compare the antioxidant activities and HPLC fingerprinting assay of 80% aqueous ethanol extract of the leaves and stem of in vitro and in vivo grown C. obcordata. The extracts of the plants were examined for their antioxidant activities by using free radical 1, 1-diphenyl-2-picryl hydrazyl (DPPH) scavenging method, 2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging ability, reducing power capacity, estimation of total phenolic content, flavonoid content and flavonol content. A simplified method for the detection of ascorbic acid, phenolic acids and flavonoids content was also developed by using reversed phase high-performance liquid chromatography (HPLC). This is the first report on the micropropagation, genetic integrity study and quantitative phytochemical analysis of in vitro regenerated plants of C. obcordata.

Keywords: Cephalantheropsis obcordata, genetic fidelity, ISSR markers, HPLC

Procedia PDF Downloads 156

Authors: Yusuf Ersoy Yildirim, Ismail Tas, Ceren Gorgusen, Tugba Yeter, Aysegul Boyacioglu, K. Mehmet Tugrul, Murat Tugrul, Ayten Namli, H. Sabri Ozturk, M. Onur Akca

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In recent years, a lot of research has been done for the sustainable use of scarce resources in the world. Especially, effective and sustainable use of water resources has been researched for many years. Sub-surface drip irrigation (SDI) is one of the most effective irrigation methods in which efficient and sustainable use of irrigation water can be achieved. When the literature is taken into consideration, it is often emphasized that, besides its numerous advantages, it also allows the application of irrigation water to the plant root zone along with air. It is stated in different studies that the air applied to the plant root zone with irrigation water has a positive effect on the root zone. Plants need sufficient oxygen for root respiration as well as for the metabolic functions of the roots. Decreased root respiration due to low oxygen content reduces transpiration, disrupts the flow of ions, and increases the ingress of salt reaching toxic levels, seriously affecting plant growth. Lack of oxygen (Hypoxia) can affect the survival of plants. The lack of oxygen in the soil is related to the exchange of gases in the soil with the gases in the atmosphere. Soil aeration is an important physical parameter of a soil. It is highly dynamic and is closely related to the amount of water in the soil and its bulk weight. Subsurface drip irrigation; It has higher water use efficiency compared to irrigation methods such as furrow irrigation and sprinkler irrigation. However, in heavy clay soils, subsurface drip irrigation creates continuous wetting fronts that predispose the rhizosphere region to hypoxia or anoxia. With subsurface drip irrigation, the oxygen is limited for root microbial respiration and root development, with the continuous spreading of water to a certain region of the root zone. In this study, the change in sugar beet yield caused by air application in the SDI system will be explained.

Keywords: sugar beet, subsurface drip irrigation, air application, irrigation efficiency

Procedia PDF Downloads 81

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

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This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

Procedia PDF Downloads 114

Authors: Lucas Paganin, Viliam Makis

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With the advent of globalization, the market competition has become a major issue for most companies. One of the main strategies to overcome this situation is the quality improvement of the product at a lower cost to meet customers’ expectations. In order to achieve the desired quality of products, it is important to control the process to meet the specifications, and to implement the optimal maintenance policy for the machines and the production lines. Thus, the overall objective is to reduce process variation and the production and maintenance costs. In this paper, an integrated model involving Statistical Process Control (SPC) and maintenance is developed to achieve this goal. Therefore, the main focus of this paper is to develop the jointly optimal maintenance and statistical process control policy minimizing the total long run expected average cost per unit time. In our model, the production process can go out of control due to either the deterioration of equipment or other assignable causes. The equipment is also subject to failures in any of the operating states due to deterioration and aging. Hence, the process mean is controlled by an Xbar control chart using equidistant sampling epochs. We assume that the machine inspection epochs are the times when the control chart signals an out-of-control condition, considering both true and false alarms. At these times, the production process will be stopped, and an investigation will be conducted not only to determine whether it is a true or false alarm, but also to identify the causes of the true alarm, whether it was caused by the change in the machine setting, by other assignable causes, or by both. If the system is out of control, the proper actions will be taken to bring it back to the in-control state. At these epochs, a maintenance action can be taken, which can be no action, or preventive replacement of the unit. When the equipment is in the failure state, a corrective maintenance action is performed, which can be minimal repair or replacement of the machine and the process is brought to the in-control state. SMDP framework is used to formulate and solve the joint control problem. Numerical example is developed to demonstrate the effectiveness of the control policy.

Keywords: maintenance, semi-Markov decision process, statistical process control, Xbar control chart

Procedia PDF Downloads 91

Authors: Zhila Hemati, Seyed Sajjad Hosseni, Sohrab Zamzami

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In nature, forests serve a multitude of functions. They stabilize and nourish soil, store carbon, clean the air and water, and support biodiverse ecosystems. A natural disaster that can affect forests and ecosystems locally or globally is wildfires. Iran experiences annual forest fires that affect roughly 6000 hectares, with the Arasbaran forest being the most affected. These fires may be generated unnaturally by human activity in the forests, or they could occur naturally as a result of climate change. These days, wildfires pose a major natural risk. Wildfires significantly reduce the amount of property and human life in ecosystems globally. Concerns regarding the immediate and longterm effects have been raised by the rise in fire activity in various Iranian regions in recent decades. Natural ecosystem abundance, quality, and health will all be impacted by pasture and forest fires. Monitoring is the first line of defense against and control for forest fires. To determine the spatial-temporal variations of these occurrences in the vegetation regions of Arasbaran, this study was carried out to estimate the areas affected by fires. The findings indicated that July through September, which spans over 130000 hectares, is when fires in Arasbaran's vegetation areas occur to their greatest extent. A significant portion of the nation's forests caught fire in 2024, particularly in the northwest of the Arasbaran vegetation area. On the other hand, January through March sees the least number of fire locations in the Arasbaran vegetation areas. The Arasbaran forest experiences its greatest number of forest fires during the hot, dry months of the year. As a result, the linear association between the burned and active fire regions in the Arasbaran forest indicates a substantial relationship between species abundance and plant species. This link demonstrates that some of the active forest fire centers are the burned regions in Arasbaran's vegetation areas.

Keywords: wildfire, vegetation, plant species, forest

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Authors: N. Benahmed, A. Cheriti

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Kidney stones of infectious origin, in particular, the phosphate amoniaco-magnesian hexahydrate or struvite are one of the risk factors that most often leads of renal insufficiency. Many plants species, described in pharmacopoeias of several countries is used as a remedy for urinary stones, the latter is a disease resulting from the presence of stones in the kidneys or urinary tract. Our research is based on the existing relationship between the effect of extracts of medicinal plant used for the cure of urinary tract diseases in the region of Algeria south-west on urolithiasis especially Ammonium-Magnesium Phosphate Hexahydrate (Struvite). We have selected Zea mays L. (POACEAE) for this study. On the first stage, we have studied the crystallisation of struvite 'in vitro' without inhibitors, after we have compared to crystallization with inhibitors. Most of The organic and aqueous extracts of this plant give an effect on the crystal size of struvite. It is a very significant reduction in the size of the crystals of struvite in the presence of hexane and ethanol extract (12 to 5-6 μm). We’ve observed a decrease in the size of the aggregates in the presence of all the extracts. This reduction is important for the aqueous, acetone and chloroform extract (45 to 10-16μm). Finally, a deep study was conducted on the effective extract of Zea mays L.; for determine the influence of inhibitory phytochemical compounds.

Keywords: medicinal plants, struvite, urolithiasis, zea mays

Procedia PDF Downloads 449

Authors: Oscar Armando González-Marin, Jhon F. Rodríguez-León, Oscar Mota-Pérez, Jorge Pineda-Piñón, Roberto S. Velázquez-González., Julio C. Sosa-Savedra

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Over the past six years, the World Meteorological Organization (WMO) has recorded the warmest years since 1880, primarily attributed to climate change. In addition, the overexploitation of agricultural lands, combined with food and water scarcity, has highlighted the urgent need for sustainable cultivation methods. Hydroponics has emerged as a sustainable farming technique that enables plant cultivation using nutrient solutions without the requirement for traditional soil. Among hydroponic methods, the Nutrient Film Technique (NFT) facilitates plant growth by circulating a nutrient solution continuously. This approach allows the monitoring and precise control of nutritional parameters, with potential for automation and technological integration. This study aims to present the state of the art of automated NFT hydroponic systems, discussing their design methodologies and considerations for implementation. Moreover, a medium-scale NFT system developed at CICATA-QRO is introduced, detailing its current manual management and progress toward automation.

Keywords: automation, hydroponics, nutrient film technique, sustainability

Procedia PDF Downloads 39

Authors: Siva Chander Chabattula, Piyush Kumar Gupta, Debashis Chakraborty, Rama Shanker Verma

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Green nanoparticles have gotten a lot of attention because of their potential applications in tissue regeneration, bioimaging, wound healing, and cancer therapy. The physical and chemical methods to synthesize metal oxide nanoparticles have an environmental impact, necessitating the development of an environmentally friendly green strategy for nanoparticle synthesis. In this study, we used Annona muricata plant leaf extract to synthesize Zinc Oxide nanoparticles (Am-ZnO NPs), which were evaluated using UV/Visible spectroscopy, FTIR spectroscopy, X-Ray Diffraction, DLS, and Zeta potential. Nanoparticles had an optical absorbance of 355 nm and a net negative surface charge of ~ - 2.59 mV. Transmission Electron Microscope characterizes the Shape and size of the nanoparticles. The obtained Am-ZnO NPs are biocompatible and hemocompatible in nature. These nanoparticles caused an anti-cancer therapeutic effect in MIA PaCa2 and MOLT4 cancer cells by inducing oxidative stress, and a change in mitochondrial membrane potential leads to programmed cell death. Further, we observed a reduction in the size of lung cancer spheroids (act as tumor micro-environment) with doxorubicin as a positive control.

Keywords: Biomaterials, nanoparticle, anticancer activity, ZnO nanoparticles

Procedia PDF Downloads 204

Authors: А. I. Dovgyalo, S. O. Nekrasova, D. V. Sarmin, A. A. Shimanov, D. A. Uglanov

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Usually liquefied natural gas (LNG) gasification is performed due to atmospheric heat. In order to produce a liquefied gas a sufficient amount of energy is to be consumed (about 1 kW∙h for 1 kg of LNG). This study offers a number of solutions, allowing using a cold energy of LNG. In this paper it is evaluated the application turbines installed behind the evaporator in LNG complex due to its work additional energy can be obtained and then converted into electricity. At the LNG consumption of G=1000kg/h the expansion work capacity of about 10 kW can be reached. Herewith-open Rankine cycle is realized, where a low capacity cryo-pump (about 500W) performs its normal function, providing the cycle pressure. Additionally discussed an application of Stirling engine within the LNG complex also gives a possibility to realize cold energy. Considering the fact, that efficiency coefficient of Stirling engine reaches 50 %, LNG consumption of G=1000 kg/h may result in getting a capacity of about 142 kW of such a thermal machine. The capacity of the pump, required to compensate pressure losses when LNG passes through the hydraulic channel, will make 500 W. Apart from the above-mentioned converters, it can be proposed to use thermoelectric generating packages (TGP), which are widely used now. At present, the modern thermoelectric generator line provides availability of electric capacity with coefficient of efficiency up to 15%. In the proposed complex, it is suggested to install the thermoelectric generator on the evaporator surface is such a way, that the cold end is contacted with the evaporator’s surface, and the hot one – with the atmosphere. At the LNG consumption of G=1000 kgг/h and specified coefficient of efficiency the capacity of the heat flow Qh will make about 32 kW. The derivable net electric power will be P=4,2 kW, and the number of packages will amount to about 104 pieces. The carried out calculations demonstrate the research perceptiveness in this field of propulsion plant development, as well as allow realizing the energy saving potential with the use of liquefied natural gas and other cryogenics technologies.

Keywords: cold energy, gasification, liquefied natural gas, electricity

Procedia PDF Downloads 273

Authors: Nemat Sokhandan Bashir

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Threats from viruses to agricultural crops could be even larger than the losses caused by the other pathogens because, in many cases, the viral infection is latent but crucial from an epidemic point of view. Wild vegetation can be a source of many viruses that eventually find their destiny in crop plants. Although often asymptomatic in wild plants due to adaptation, they can potentially cause serious losses in crops. Therefore, exploring viruses in wild vegetation is very important. Recently, omics have been quite useful for exploring plant viruses from various plant sources, especially wild vegetation. For instance, we have discovered viruses such as Ambrossia asymptomatic virus I (AAV-1) through the application of metagenomics from Oklahoma Prairie Reserve. Accordingly, extracts from randomly-sampled plants are subjected to high speed and ultracentrifugation to separated virus-like particles (VLP), then nucleic acids in the form of DNA or RNA are extracted from such VLPs by treatment with phenol—chloroform and subsequent precipitation by ethanol. The nucleic acid preparations are separately treated with RNAse or DNAse in order to determine the genome component of VLPs. In the case of RNAs, the complementary cDNAs are synthesized before submitting to DNA sequencing. However, for VLPs with DNA contents, the procedure would be relatively straightforward without making cDNA. Because the length of the nucleic acid content of VPLs can be different, various strategies are employed to achieve sequencing. Techniques similar to so-called "chromosome walking" may be used to achieve sequences of long segments. When the nucleotide sequence data were obtained, they were subjected to BLAST analysis to determine the most related previously reported virus sequences. In one case, we determined that the novel virus was AAV-l because the sequence comparison and analysis revealed that the reads were the closest to the Indian citrus ringspot virus (ICRSV). AAV—l had an RNA genome with 7408 nucleotides in length and contained six open reading frames (ORFs). Based on phylogenies inferred from the replicase and coat protein ORFs of the virus, it was placed in the genus Mandarivirus.

Keywords: wild, plant, novel, metagenomics

Procedia PDF Downloads 80

Authors: Gloria M. Ntuba-Jua, Afui M. Mih, Eneke E. T. Bechem

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Prunus africana (Hook. F.) Kalkman, commonly known as Pygeum or African cherry belongs to the Rosaceae family. It is a medium to large, evergreen tree with a spreading crown of 10 to 20 m. It is used by the traditional medical practitioners for the treatment of over 45ailments in Cameroon and sub-Sahara Africa. In modern medicine, it is used in the treatment of benign prostrate hyperplasia (BPH), prostate gland hypertrophy (enlarged prostate glands). This is possible because of its ability to produce some secondary metabolites which are believed to have bioactivity against these ailments. The ready international market for the sale of Prunus bark, uncontrolled exploitation, illegal harvesting using inappropriate techniques and poor timing of harvesting have contributed enormously to making the plant endangered. It is known to harbor a large number of endophytic fungi with the potential to produce similar secondary metabolites as the parent plant. Alternative sourcing of medicinal principles through endophytic fungi requires succinct knowledge of the endophytic fungi. This will serve as a conservation measure for Prunus africana by reducing dependence on Prunus bark for such metabolites. This work thus sought to compare the production of some major secondary metabolites produced by P. africana and some of its associated endophytic fungi. The leaves and stem bark of the plant from different provenances were soaked in methanol for 72 hrs to yield the methanolic crude extract. The phytochemical screening of the methanolic crude extracts using different standard procedures revealed the presence of tannins, flavonoids, terpenoids, saponins, phenolics and steroids. Pure cultures of some predominantly isolated endophyte species from the difference Prunus provenances such as Curvularia sp, and Morphospecies P001 were also grown in Potato Dextrose Broth (PDB) for 21 days and later extracted with Methylene dichloride (MDC) solvent after 24hrs to produce crude culture extracts. Qualitative assessment of crude culture extracts showed the presence of tannins, terpenoids, phenolics and steroids particularly β-Sitosterol, (a major bioactive metabolite) as did the plant tissues. Qualitative analysis by thin layer chromatography (TLC) was done to confirm and compare the production of β-Sitosterol (as marker compounds) in the crude extracts of the plant and endophyte. Samples were loaded on TLC silica gel aluminium barked plate (Kieselgel 60 F254, 0.2 mm, Merck) using acetone/hexane, (3.0:7.0) solvent system. They were visualized under an ultra violet lamp (UV254 and UV360). TLC revealed that leaves had a higher concentration of β-sitosterol in terms of band intensity than stem barks from the different provenances. The intensity of β-sitosterol bands in the culture extracts of endophytes was comparable to the plant extracts except for Curvularia sp (very minute) whose band was very faint. The ability of these fungi to make β-sitosterol was confirmed by TLC analysis with the compound having chromatographic properties (retention factor) similar to those of β-sitosterol standard. The ability of these major endophytes to produce secondary metabolites similar to the host has therefore been demonstrated. There is, therefore, the potential of developing the in vitro production system of Prunus secondary metabolites thereby enhancing its conservation.

Keywords: Caneroon, endophytic fungi, Prunus africana, secondary metabolite

Procedia PDF Downloads 231

Authors: S. Jayasinghe, R. B. N. Dissanayake

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Optimization modelling together with the Network models and Linear Programming techniques is a powerful tool in problem solving and decision making in real world applications. This study developed a mathematical model to optimize the net profit by minimizing the transportation cost. This model focuses the transportation among decentralized production plants to a centralized distribution centre and then the distribution among island wide agencies considering the customer satisfaction as a requirement. This company produces basically 9 types of food items with 82 different varieties and 4 types of non-food items with 34 different varieties. Among 6 production plants, 4 were located near the city of Mawanella and the other 2 were located in Galewala and Anuradhapura cities which are 80 km and 150 km away from Mawanella respectively. The warehouse located in the Mawanella was the main production plant and also the only distribution plant. This plant distributes manufactured products to 39 agencies island-wide. The average values and average amount of the goods for 6 consecutive months from May 2013 to October 2013 were collected and then average demand values were calculated. The following constraints are used as the necessary requirement to satisfy the optimum condition of the model; there was one source, 39 destinations and supply and demand for all the agencies are equal. Using transport cost for a kilometer, total transport cost was calculated. Then the model was formulated using distance and flow of the distribution. Network optimization and linear programming techniques were used to originate the model while excel solver is used in solving. Results showed that company requires total transport cost of Rs. 146, 943, 034.50 to fulfil the customers’ requirement for a month. This is very much less when compared with data without using the model. Model also proved that company can reduce their transportation cost by 6% when distributing to island-wide customers. Company generally satisfies their customers’ requirements by 85%. This satisfaction can be increased up to 97% by using this model. Therefore this model can be used by other similar companies in order to reduce the transportation cost.

Keywords: mathematical model, network optimization, linear programming

Procedia PDF Downloads 346

Authors: B. M. Vishal, Monamie Basu, Gopinath M., Rose Havilah Pulla

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Though there are several methods of synthesizing silver nano particles, Green synthesis always has its own dignity. Ranging from the cost-effectiveness to the ease of synthesis, the process is simplified in the best possible way and is one of the most explored topics. This study of extracting secondary metabolites from Perilla frutescens and using them for therapeutic additives has its own significance. Unlike the other researches that have been done so far, this study aims to synthesize Silver nano particles from Perilla frutescens using three available forms of the plant: leaves, seed, and commercial leaf extract powder. Perilla frutescens, commonly known as 'Beefsteak Plant', is a perennial plant and belongs to the mint family. The plant has two varieties classed within itself. They are frutescens crispa and frutescens frutescens. The species, frutescens crispa (commonly known as 'Shisho' in Japanese), is generally used for edible purposes. Its leaves occur in two forms, varying on the colors. It is found in two different colors of red with purple streaks and green with crinkly pattern on it. This species is aromatic due to the presence of two major compounds: polyphenols and perillaldehyde. The red (purple streak) variety of this plant is due to the presence of a pigment, Perilla anthocyanin. The species, frutescens frutescens (commonly known as 'Egoma' in Japanese), is the main source for perilla oil. This species is also aromatic, but in this case, the major compound which gives the aroma is Perilla ketone or egoma ketone. Shisho grows short as compared with Wild Sesame and both produce seeds. The seeds of Wild Sesame are large and soft whereas that of Shisho is small and hard. The seeds have a large proportion of lipids, ranging about 38-45 percent. Excluding those, the seeds have a large quantity of Omega-3 fatty acids, linoleic acid, and an Omega-6 fatty acid. Other than these, Perilla leaf extract has gold and silver nano particles in it. The yield comparison in all the cases have been done, and the process’ optimal conditions were modified, keeping in mind the efficiencies. The characterization of secondary metabolites includes GC-MS and FTIR which can be used to identify the components of purpose that actually helps in synthesizing silver nano particles. The analysis of silver was done through a series of characterization tests that include XRD, UV-Vis, EDAX, and SEM. After the synthesis, for being used as therapeutic additives, the toxin analysis was done, and the results were tabulated. The synthesis of silver nano particles was done in a series of multiple cycles of extraction from leaves, seeds and commercially purchased leaf extract. The yield and efficiency comparison were done to bring out the best and the cheapest possible way of synthesizing silver nano particles using Perilla frutescens. The synthesized nano particles can be used in therapeutic drugs, which has a wide range of application from burn treatment to cancer treatment. This will, in turn, replace the traditional processes of synthesizing nano particles, as this method will prove effective in terms of cost and the environmental implications.

Keywords: nanoparticles, green synthesis, Perilla frutescens, characterisation, toxin analysis

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

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Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

Procedia PDF Downloads 67

Authors: Weiwei Cui, Dejian Lin, Leigang Zhang, Yao Wang, Zheng Sun, Lianfeng Li

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Built-in test (BIT) is an important technology in testability field, and it is widely used in state monitoring and fault diagnosis. With the improvement of modern equipment performance and complexity, the scope of BIT becomes larger, and it leads to the emergence of false alarm problem. The false alarm makes the health assessment unstable, and it reduces the effectiveness of BIT. The conventional false alarm suppression methods such as repeated test and majority voting cannot meet the requirement for a complicated system, and the intelligence algorithms such as artificial neural networks (ANN) are widely studied and used. However, false alarm has a very low frequency and small sample, yet a method based on ANN requires a large size of training sample. To recognize the false alarm, we propose a method based on multi-classification support vector machine (SVM) in this paper. Firstly, we divide the state of a system into three states: healthy, false-alarm, and faulty. Then we use multi-classification with '1 vs 1' policy to train and recognize the state of a system. Finally, an example of fault injection system is taken to verify the effectiveness of the proposed method by comparing ANN. The result shows that the method is reasonable and effective.

Keywords: false alarm, fault diagnosis, SVM, k-means, BIT

Procedia PDF Downloads 155

Authors: Fatemeh Mohsennezhad

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Air pollution is one of major problems for environment. Providing healthy food and protecting water sources from pollution has been one of the concerns of human societies and decision-making centers so that protecting food from pollution, detecting sources of pollution and measuring them become important. Nutritive and political significance of grape in this area, extensive use of leaf and fruit of this plant and development of urban areas around grape gardens and construction of Tabriz – Miandoab road, which is the most important link between East and West Azarbaijan, led us to examine the impact of this road construction and urban environment pollutants such as lead chromium and cadmium on the quality of this valuable crop. First, the samples were taken from different adjacent places and medium distances from the road, each place being located exactly by Google earth and GPS. Digestion was done through burning dry material and hydrochloric acid and their ashes were analyzed by atomic absorption to determine (Pb, Cr, Cd) accumulations. In this experiments effects of 2 following factors were examined as a variable: Garden distance from the main road with levels 1: For 50 meters, 2: For 120-200 meters, 3: For above 800 meters, and plant organ with levels 1: For fruit, 2: For leaves. At the end, the results were processed by SPSS software. 3.54 ppm, the most lead quantity, was at sample No. 54 in fruits with 800 meters distance from the road and 1.00 ppm was the least lead quantity at sample No. 50 in fruits with 1000 meters from the road. In leaves, the most lead quantity was 19.16 ppm at sample No. 15 with 50 meters distance from the road and the least quantity was 1.41 ppm at sample No. 31 with 50 meters from the road. Pb uptake is significantly different at 50 meters and 200 meters distance. It means that Pb uptake near the main road is the highest. But this result is not true for others elements. Distance has not a meaningful effect on Cr uptake. The result of analysis of variation in distance and plant organ for Cd showed that between fruit and leaf, Cd uptake is significantly different. But distance and interaction between distance and plant organ is not meaningful. There is neither meaningful interaction between these elements uptakes in fruits nor in leaves. If leaves and fruits, assumed all together, showed a very meaningful integration between heavy metal accumulations. It means that each of these elements causes uptake others without considering special organs. In the tested area, it became clear that, from the accumulation of heavy metals perspective, there is no meaningful difference in existing distance between road and garden. There is a meaningful difference among heavy metals accumulation. In other words, increase ratio of one metal to another was different from the resulted differences shown in corresponding graphs. Interaction among elements and distance between garden and road was not meaningful.

Keywords: Vitis vinifera L., phytoremediation, heavy metals accumulation, lead, chromium, cadmium

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Authors: R. Ramesh, K. K. Shivaraman

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The main aim of this paper is to handle the energy requirement in an efficient manner by merging the advanced digital communication and control technologies for smart grid applications. In order to reduce user home load during peak load hours, utility applies several incentives such as real-time pricing, time of use, demand response for residential customer through smart meter. However, this method provides inconvenience in the sense that user needs to respond manually to prices that vary in real time. To overcome these inconvenience, this paper proposes a convolutional neural network (CNN) with k-means clustering machine learning model which have ability to forecast energy requirement in short term, i.e., hour of the day or day of the week. By integrating our proposed technique with home energy management based on Bluetooth low energy provides predicted value to user for scheduling appliance in advanced. This paper describes detail about CNN configuration and k-means clustering algorithm for short-term energy forecasting.

Keywords: convolutional neural network, fuzzy logic, k-means clustering approach, smart home energy management

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Authors: M. H. Arslan, U. K. Sahin, H. Acikgoz-Tufan, I. Gocek, I. Erdem

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Various exterior factors have perpetual effects on textile materials during wear, use and laundering in everyday life. In accordance with their frequency of use, textile materials are required to be laundered at certain intervals. The medium in which the laundering process takes place have inevitable detrimental physical and chemical effects on textile materials caused by the unique parameters of the process inherently existing. Connatural structures of various textile materials result in many different physical, chemical and mechanical characteristics. Because of their specific structures, these materials have different behaviors against several exterior factors. By modeling the behavior of commercial and test textiles as group-wise against laundering process, it is possible to disclose the relation in between these two groups of materials, which will lead to better understanding of their behaviors in terms of similarities and differences against the washing parameters of the laundering. Thus, the goal of the current research is to examine the behavior of two groups of textile materials as commercial textiles and as test textiles towards the main washing machine parameters during laundering process such as temperature, load quantity, mechanical action and level of water amount by concentrating on shrinkage, pilling, sewing defects, collar abrasion, the other defects other than sewing, whitening and overall properties of textiles. In this study, cotton fabrics were preferred as commercial textiles due to the fact that garments made of cotton are the most demanded products in the market by the textile consumers in daily life. Full factorial experimental set-up was used to design the experimental procedure. All profiles always including all of the commercial and the test textiles were laundered for 20 cycles by commercial home laundering machine to investigate the effects of the chosen parameters. For the laundering process, a modified version of ‘‘IEC 60456 Test Method’’ was utilized. The amount of detergent was altered as 0.5% gram per liter depending on varying load quantity levels. Datacolor 650®, EMPA Photographic Standards for Pilling Test and visual examination were utilized to test and characterize the textiles. Furthermore, in the current study the relation in between commercial and test textiles in terms of their performance was deeply investigated by the help of statistical analysis performed by MINITAB® package program modeling their behavior against the parameters of the laundering process. In the experimental work, the behaviors of both groups of textiles towards washing machine parameters were visually and quantitatively assessed in dry state.

Keywords: behavior against washing machine parameters, performance evaluation of textiles, statistical analysis, commercial and test textiles

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Authors: Haikal Achmad Thaha, Pujo Laksono, Dhamma Nibbana Putra

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Customer service is one of the core interest in a service sector of a company, whether as the core business or as service part of the operation. Most of the time, the people and the previous research in service industry is focused on finding the best business model solution for the service sector, usually to decide between total in house customer service, outsourcing, or something in between. Conventionally, to take this decision is some important part of the management job, and this is a process that usually takes some time and staff effort, meanwhile market condition and overall company needs may change and cause loss of income and temporary disturbance in the companies operation . However, in this paper we have offer a new concept model to assist decision making process in service industry. This model will featured information platform as central tool to integrate service industry operation. The result is service information model which would ideally increase response time and effectivity of the decision making. it will also help service industry in switching the service solution system quickly through machine learning when the companies growth and the service solution needed are changing.

Keywords: service industry, customer service, machine learning, decision making, information platform

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Authors: Ayu Nabila Kusuma Pradana, Aprinaldi Jasa Mantau, Tomohiko Akahoshi

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The incidence of Disseminated intravascular coagulation (DIC) following gastrointestinal surgery has a poor prognosis. Therefore, it is important to determine the factors that can predict the prognosis of DIC. This study will investigate the factors that may influence the outcome of DIC in patients after gastrointestinal surgery. Eighty-one patients were admitted to the intensive care unit after gastrointestinal surgery in Kyushu University Hospital from 2003 to 2021. Acute DIC scores were estimated using the new Japanese Association for Acute Medicine (JAAM) score from before and after surgery from day 1, day 3, and day 7. Acute DIC scores will be compared with The Sequential Organ Failure Assessment (SOFA) score, platelet count, lactate level, and a variety of biochemical parameters. This study applied machine learning algorithms to predict the prognosis of DIC after gastrointestinal surgery. The results of this study are expected to be used as an indicator for evaluating patient prognosis so that it can increase life expectancy and reduce mortality from cases of DIC patients after gastrointestinal surgery.

Keywords: the survival rate, gastrointestinal surgery, JAAM score, neural network, machine learning, disseminated intravascular coagulation (DIC)

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Authors: Jehad Dumireih, Malak Dmirieh, Michael Wink

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The present work is aimed to use plant cell suspension cultures of Crataegus monogyna for biosynthesis of valuable natural products by using quercetin as an inexpensive precursor. Suspension cell cultures of C. monogyna were established by using Murashige and Skoog medium (MS) supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid and 1 mg/L kinetin. Cells were harvested from the cultures and extracted by using methanol and ethyl acetate; then the extracts were used for the identification of isoquercetin by HPLC and by mass spectrometry. The incubation of the cells with 0.24 mM quercetin for one week resulted in an 16 fold increase of isoquercetin biosynthesis; the growth rate of the cells increased by 20%. Moreover, the biosynthesis of isoquercetin was enhanced by 40% when we divided the added quercetin into three portions each one with concentration 0.12 mM supplied at 3 days intervals. In addition, we didn’t find any positive effects of adding different concentrations the precursors phenylalanine (0.2 mM) and galactose to the cell cultures. In conclusion, the efficiency of the biotransformation of quercetin into isoquercetin depended on the concentration quercetin, its incubation time and the way of its administration. The results of the present work suggest that the biotechnological methods such as cell suspension cultures could be successfully used to obtain highly valuable natural product starting from inexpensive compound.

Keywords: biosynthesis, biotransformation, Crataegus, isoquercetin

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Authors: Man Fung Ho, Lap So, Jiaqi Zhang, Yuheng Zhao, Huiyang Lu, Tat Shing Choi, K. Y. Michael Wong

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Nowadays, a tremendous amount of data is available in the transportation system, enabling the development of various machine learning approaches to make short-term latency predictions. A natural question is then the choice of relevant information to enable accurate predictions. Using traffic data collected from the Taiwan Freeway System, we consider the prediction of short-term latency of a freeway segment with a length of 17 km covering 5 measurement points, each collecting vehicle-by-vehicle data through the electronic toll collection system. The processed data include the past latencies of the freeway segment with different time lags, the traffic conditions of the individual segments (the accumulations, the traffic fluxes, the entrance and exit rates), the total accumulations, and the weekday latency profiles obtained by Gaussian process regression of past data. We arrive at several important conclusions about how data should be refined to obtain accurate predictions, which have implications for future system-wide latency predictions. (1) We find that the prediction of median latency is much more accurate and meaningful than the prediction of average latency, as the latter is plagued by outliers. This is verified by machine-learning prediction using XGBoost that yields a 35% improvement in the mean square error of the 5-minute averaged latencies. (2) We find that the median latency of the segment 15 minutes ago is a very good baseline for performance comparison, and we have evidence that further improvement is achieved by machine learning approaches such as XGBoost and Long Short-Term Memory (LSTM). (3) By analyzing the feature importance score in XGBoost and calculating the mutual information between the inputs and the latencies to be predicted, we identify a sequence of inputs ranked in importance. It confirms that the past latencies are most informative of the predicted latencies, followed by the total accumulation, whereas inputs such as the entrance and exit rates are uninformative. It also confirms that the inputs are much less informative of the average latencies than the median latencies. (4) For predicting the latencies of segments composed of two or three sub-segments, summing up the predicted latencies of each sub-segment is more accurate than the one-step prediction of the whole segment, especially with the latency prediction of the downstream sub-segments trained to anticipate latencies several minutes ahead. The duration of the anticipation time is an increasing function of the traveling time of the upstream segment. The above findings have important implications to predicting the full set of latencies among the various locations in the freeway system.

Keywords: data refinement, machine learning, mutual information, short-term latency prediction

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Authors: S. Ben-Shabat, T. Turgeman, O. Leubinski, N. Roth-Bejerano, V. Kagan-Zur, Y. Sitrit

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The ectomycorrhizal (ECM) desert truffle Terfezia boudieri produces edible fruit bodies and forms symbiosis with its host plant Helianthemum sessiliflorum (Cistaceae) in the Negev desert of Israel. The symbiosis is vital for both partners' survival under desert conditions. Under desert habitat conditions, ECMs must form symbiosis before entering the dry season. To secure a successful encounter, in the course of evolution, both partners have responded by evolving special signals exchange that facilitates recognition. Members of the Cistaceae family serve as host plants for many important truffles. Conceivably, during evolution a common molecule present in Cistaceae plants was recruited to facilitate successful encounter with ectomycorrhizas. Arbuscular vesicular fungi (AM) are promiscuous in host preferences, in contrast, ECM fungi show specificity to host plants. Accordingly, we hypothesize that H. sessiliflorum secretes a chemotrophic-signaling, which is common to plants hosting ECM fungi belonging to the Pezizales. However, thus far no signaling molecules have been identified in ECM fungi. We developed a bioassay for chemotrophic activity. Fractionation of root exudates revealed a substance with chemotrophic activity and molecular mass of 534. Following the above concept, screening the transcriptome of Terfezia, grown under chemoattraction, discovered genes showing high homology to G proteins-coupled receptors of plant pathogens involved in positive chemotaxis and chemotaxis suppression. This study aimed to identify the active molecule using analytical methods (LC-MS, NMR etc.). This should contribute to our understanding of how ECM fungi communicate with their hosts in the rhizosphere. In line with the ability of Terfezia to form also endomycorrhizal symbiosis like AM fungi, analysis of the mechanisms may likewise be applicable to AM fungi. Developing methods to manipulate fungal growth by the chemoattractant can open new ways to improve inoculation of plants.

Keywords: chemotrophic signal, Helianthemum sessiliflorum, Terfezia boudieri, ECM

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Authors: Ahmad Badawi Saluy

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This study aims to develop strategies for the prevention or elimination of environmental pollution as well as changes in external variables of the environment in order to implement the environmental management system ISO 14001:2004 by integrating analysis of environmental issues data, RKL-RPL transactional data and regulation as part of ERP on the management dashboard. This research uses a quantitative descriptive approach with analysis method comparing with air quality standard (PP 42/1999, LH 21/2008), water quality standard (permenkes RI 416/1990, KepmenLH 51/2004, kepmenLH 55/2013 ), and biodiversity indicators. Based on the research, the parameters of RPL monitoring have been identified, among others, the quality of emission air (SO₂, NO₂, dust, particulate) due to the influence of fuel quality, combustion performance in a combustor and the effect of development change around the generating area. While in water quality (TSS, TDS) there was an increase due to the flow of water in the cooling intake carrying sedimentation from the flow of Banjir Kanal Timur. Including compliance with the ISO 14001:2004 clause on application design significantly contributes to the improvement of the quality of power plant management.

Keywords: environmental management systems, power plant management, regulatory compliance , enterprises resource planning

Procedia PDF Downloads 179

Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Mujahid Farid, Shafaqat Ali, Muhammad Bilal Shakoor

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Heavy metals pollution of soil is a prevalent global problem and oilseed rape (Brassica napus L.) are considered useful for the restoration of metal contaminated soils. Phytoextraction is an in-situ environment-friendly technique for the clean-up of contaminated soils. Response to cadmium (Cd) toxicity in combination with a chelator, Ethylenediamminetetraacetic acid (EDTA) was studied in oilseed rape grown hydroponically in greenhouse conditions under three levels of Cd (0, 10, and 50 µM) and two levels of EDTA (0 and 2.5 mM). Cd decreased plant growth, biomass and chlorophyll concentrations while the application of EDTA enhanced plant growth by reducing Cd-induced effects in Cd-stressed plants. Significant decrease in photosynthetic parameters was found by the Cd alone. Addition of EDTA improved the net photosynthetic and gas exchange capacity of plants under Cd stress. Cd at 10 and 50 μM significantly increased electrolyte leakage, the production of hydrogen peroxidase (H2O2) and malondialdehyde (MDA) and a significant reduction was observed in the activities of catalase (CAT), guaiacol peroxidase (POD), ascorbate peroxidase (APX), and superoxide dismutase under Cd stress plants. Application of EDTA at the rate of 2.5 mM alone and with combination of Cd increased the antioxidant enzymes activities and reduced the electrolyte leakage and production of H2O2 and MDA. Oilseed rape (Brassica napus L.) actively accumulated Cd in roots, stems and leaves and the addition of EDTA boosted the uptake and accumulation of Cd in oil seed rape by dissociating Cd in culture media. The present results suggest that under 8 weeks Cd-induced stress, application of EDTA significantly improve plant growth, chlorophyll content, photosynthetic, gas exchange capacity, improving enzymes activities and increased the metal uptake in roots, stems and leaves of oilseed rape (Brassica napus L.) respectively.

Keywords: antioxidant enzymes, cadmium, chelator, EDTA, growth, oilseed rape

Procedia PDF Downloads 392

Authors: P. Larpsomboonchai

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Agro-industry is one of major industries that has strong impacts on national economic incomes, growth, stability, and sustainable development. Moreover, this industry also has strong influences on social, cultural and political issues. Furthermore, this industry, as producing primary and secondary products, is facing challenges from such diverse factors such as demand inconsistency, intense international competition, technological advancements and new competitors. In order to maintain and to improve industry’s competitiveness in both domestics and international markets, science and technology are key factors. Besides hard sciences and technologies, modern industrial engineering concepts such as Just in Time (JIT) Total Quality Management (TQM), Quick Response (QR), Supply Chain Management (SCM) and Lean can be very effective to supportant to increase efficiency and effectiveness of these agricultural products on world stage. Onion is one of Thailand’s major export products which brings back national incomes. But, it also facing challenges in many ways. This paper focused its interests in onion packing process and its related activities such as storage and shipment from one of major packing plant and storage in Mae Wang District, Chiang Mai, Thailand, by applying Toyota Production System (TPS) or Lean concepts, to improve process capability throughout the entire packing and distribution process which will be profitable for the whole onion supply chain. And it will be beneficial to other related agricultural products in Thailand and other ASEAN countries.

Keywords: packing process, Toyota Production System (TPS), lean concepts, waste reduction, lean in agro-industries activities

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Authors: H. Khomri, A. Bentellis

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Since century of the Development Activities extraction and a dispersed mineral processing Toxic metals and much more contaminated vast areas occupied by what they natural outcrops. New types of metalliferous habitats are so appeared. A species that is Lygeum spartum attracted our curiosity because apart from its valuable role in desertification, it is apparently able to exclude antimony and other metals can be. This species, green leaf blades which are provided as cattle feed, would be a good subject for phytoremediation of mineral soils. The study of absorption and translocation of chromium by the Lygeum spartum in the mining region of Djebel Hamimat and the interaction soil-plant, revealed that soils of this species living in this region are alkaline, calcareous majority in their fine texture medium and saline in their minority. They have normal levels of organic matter. They are moderately rich in nitrogen. They contain total chromium content reaches a maximum of 66,80 mg Kg^(-1) and a total absence of soluble chromium. The results of the analysis of variance of the difference between bare soils and soils appear Lygeum spartum made a significant difference only for the silt and organic matter. But for the other variables analyzed this difference is not significant. Thus, this plant has only one action on the amendment, only the levels of silt and organic matter in soils. The results of the multiple regression of the chromium content of the roots according to all soil variables studied did appear that among the studied variables included in the model, only the electrical conductivity and clay occur in the explanation of contents chromium in roots. The chromium content of the aerial parts analyzed by regression based on all studied soil variables allows us to see only the variables: electrical conductivity and content of chromium in the root portion involved in the explanation of the content chromium in the aerial part.

Keywords: absorption, translocation, analysis of variance, chrome, Lygeum spartum, multiple regression, the soil variables

Procedia PDF Downloads 270