Search results for: machine and plant engineering

Authors: Mohammad Reza Fooladi

Abstract:

Because of the importance and effect of plant innovations on economy, industry, and especially agriculture, they have been on the core attention of legislators at the national level, and have been a topic of international documents related to intellectual innovations in the recent decades. For protection of plant innovations, two legal systems (i.e. particular system based on International Convention for protection of new variety of plants, and the patent system) have been considered. Ease of access to the support and the level of support in each of these systems are different. Our attempt in this paper, in addition to describing and analyzing the characteristics of each system, is to suggest the compatible system to the industry and agriculture of Afghanistan. Due to the lack of sufficient industrial infrastructure and academic research, the particular system based on the International Convention on the protection of new variety of plants is suggested. At the same time, appropriate industrial and legal infrastructures, as well as laboratories and research centers should be provided in order that plant innovations under the patent system could also be supported.

Keywords: new varieties of plant, patent, agriculture, Afghanistan

Procedia PDF Downloads 330

Authors: Anthony Okechukwu Ifediniru, Austin Ikechukwu Gbasouzor, Isidore Uche Uju

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This article is centered on the design, analysis, construction, and test of a locally made arc welding machine that operates on 250vac with 8 amp output taps ranging from 60vac to 250vac at a fixed frequency, which is of benefit to urban areas; while considering its cost-effectiveness, strength, portability, and mobility. The welding machine uses a power supply to create an electric arc between an electrode and the metal at the welding point. A current selector coil needed for current selection is connected to the primary winding. Electric power is supplied to the primary winding of its transformer and is transferred to the secondary winding by induction. The voltage and current output of the secondary winding are connected to the output terminal, which is used to carry out welding work. The output current of the machine ranges from 110amps for low current welding to 250amps for high current welding. The machine uses a step-down transformer configuration for stepping down the voltage in order to obtain a high current level for effective welding. The welder can adjust the output current within a certain range. This allows the welder to properly set the output current for the type of welding that is being performed. The constructed arc welding machine was tested by connecting the work piece to it. Since there was no shock or spark from the transformer’s laminated core and was successfully used to join metals, it confirmed and validated the design.

Keywords: AC current, arc welding machine, DC current, transformer, welds

Procedia PDF Downloads 181

Authors: Lubaina Soni, Claire Farrell, Christopher Szota, Tim D. Fletcher

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Green roofs are a promising engineered ecosystem for reducing stormwater runoff and restoring vegetation cover in cities. Plants can contribute to rainfall retention by rapidly depleting water in the substrate; however, this increases the risk of plant drought stress. Green roof configurations, therefore, need to provide plants the opportunity to efficiently deplete the substrate but also avoid severe drought stress. This study used green roof modules placed in a rainout shelter during a six-month rainfall regime simulated in Melbourne, Australia. Rainfall was applied equally with an overhead irrigation system on each module. Aside from rainfall, modules were under natural climatic conditions, including temperature, wind, and radiation. A single species, Ficinia nodosa, was planted with five different treatments and three replicates of each treatment. In this experiment, we tested the impact of three plant cover treatments (0%, 50% and 100%) on rainfall retention and plant drought stress. We also installed two runoff zone treatments covering 50% of the substrate surface for additional modules with 0% and 50% plant cover to determine whether directing rainfall resources towards plant roots would reduce drought stress without impacting rainfall retention. The retention performance for the simulated rainfall events was measured, quantifying all components for hydrological performance and survival on green roofs. We found that evapotranspiration and rainfall retention were similar for modules with 50% and 100% plant cover. However, modules with 100% plant cover showed significantly higher plant drought stress. Therefore, planting at a lower cover/density reduced plant drought stress without jeopardizing rainfall retention performance. Installing runoff zones marginally reduced evapotranspiration and rainfall retention, but by approximately the same amount for modules with 0% and 50% plant cover. This indicates that reduced evaporation due to the installation of the runoff zones likely contributed to reduced evapotranspiration and rainfall retention. Further, runoff occurred from modules with runoff zones faster than those without, indicating that we created a faster pathway for water to enter and leave the substrate, which also likely contributed to lower overall evapotranspiration and retention. However, despite some loss in retention performance, modules with 50% plant cover installed with runoff zones showed significantly lower drought stress in plants compared to those without runoff zones. Overall, we suggest that reducing plant cover represents a simple means of optimizing green roof performance but creating runoff zones may reduce plant drought stress at the cost of reduced rainfall retention.

Keywords: green roof, plant cover, plant drought stress, rainfall retention

Procedia PDF Downloads 115

Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

Procedia PDF Downloads 133

Authors: Aishatu Ibrahim Birma, Aminu Tukur, Abdulkarim Abbass Gora

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Machine translation plays a vital role in the Field of Natural Language Processing (NLP), breaking down language barriers and enabling communication across diverse communities. In the context of Hausa, a widely spoken language in West Africa, mainly in Nigeria, effective translation systems are essential for enabling seamless communication and promoting cultural exchange. However, due to the unique linguistic characteristics of Hausa, accurate translation remains a challenging task. The research proposes an approach to improving the machine learning translation of Hausa by integrating Named Entity Recognition (NER) techniques. Named entities, such as person names, locations, organizations, and dates, are critical components of a language's structure and meaning. Incorporating NER into the translation process can enhance the quality and accuracy of translations by preserving the integrity of named entities and also maintaining consistency in translating entities (e.g., proper names), and addressing the cultural references specific to Hausa. The NER will be incorporated into Neural Machine Translation (NMT) for the Hausa to English Translation.

Keywords: machine translation, natural language processing (NLP), named entity recognition (NER), neural machine translation (NMT)

Procedia PDF Downloads 43

Authors: Nivethika Ajeethan, Lord Abbey, Svetlana Yurge

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Riboflavin is a water-soluble vitamin and the direct precursor of the flavin cofactors flavin mononucleotide and flavin adenine dinucleotide. Flavins (FLs) are bioactive molecules that have a beneficial effect on plant growth and development. Sinorhizobium meliloti strain 1021 is an α-proteobacterium that forms agronomically important N₂-fixing symbiosis with Medicago plants and secretes a considerable amount of FLs (FL⁺ strain). This strain was also implicated in plant growth promotion in its association with non-legume host plants. However, the mechanism of this plant growth promotion is not well understood. In this study, we evaluated the growth and development of tomato plants inoculated with S. meliloti 1021 and its mutant (FL⁻ strain) with limited ability to secrete FLs. Our preliminary experiments indicated that inoculation with FL⁺ strain significantly increased seedlings' root and shoot length and surface area compared to those of plants inoculated with FL⁻ strain. For example, the root lengths of 9-day old seedlings inoculated with FL⁺ strain were 35% longer than seedlings inoculated with the mutant. Proteomic approaches combined with the analysis of plant physiological responses such as growth and photosynthetic rate, stomatal conductance, transpiration rate, and chlorophyll content will be used to evaluate the host-plant response to bacteria-derived FLs.

Keywords: flavin, plant growth promotion, riboflavin, Sinorhizobium meliloti

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Authors: Talal Alshammari, Nasser Alshammari, Mohamed Sedky, Chris Howard

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With the widespread adoption of the Internet-connected devices, and with the prevalence of the Internet of Things (IoT) applications, there is an increased interest in machine learning techniques that can provide useful and interesting services in the smart home domain. The areas that machine learning techniques can help advance are varied and ever-evolving. Classifying smart home inhabitants’ Activities of Daily Living (ADLs), is one prominent example. The ability of machine learning technique to find meaningful spatio-temporal relations of high-dimensional data is an important requirement as well. This paper presents a comparative evaluation of state-of-the-art machine learning techniques to classify ADLs in the smart home domain. Forty-two synthetic datasets and two real-world datasets with multiple inhabitants are used to evaluate and compare the performance of the identified machine learning techniques. Our results show significant performance differences between the evaluated techniques. Such as AdaBoost, Cortical Learning Algorithm (CLA), Decision Trees, Hidden Markov Model (HMM), Multi-layer Perceptron (MLP), Structured Perceptron and Support Vector Machines (SVM). Overall, neural network based techniques have shown superiority over the other tested techniques.

Keywords: activities of daily living, classification, internet of things, machine learning, prediction, smart home

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Authors: Cheba Ben Amar

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Bacillus and Paenibacillus are Gram-positive aerobic endospore-forming bacteria (AEFB) and most abundant in the rhizosphere, they mediated plant growth promotion and disease protection by several complex and interrelated processes involving direct and indirect mechanisms that include nitrogen fixation, phosphate solubilization, siderophores production, phytohormones production and plant diseases control. In addition to their multiple PGPR properties, high secretory capacity, spore forming ability and spore resistance to unfavorable conditions enabling their extended commercial applications for long shelf-life. Due to these unique advantages, Bacillus species were the most an ideal candidate for developing efficient PGPR products such as biopesticides, fungicides and fertilizers. This review list all studied and reported plant growth promoting Bacillus species and strains, discuss their capacities to enhance plant growth and protection with special focusing on the most frequent species Bacillus subtilis, B. pumilus ,B. megaterium, B. amyloliquefaciens , B. licheniformis and B. sphaericus, furthermore we recapitulate the beneficial activities and mechanisms of several species and strains of the genus Paenibacillus involved in plant growth stimulation and plant disease control.

Keywords: bacillus, paenibacillus, PGPR, beneficial activities, mechanisms, growth promotion, disease control, agrobiotechnology

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Authors: Ercan Ceyhan, Ali Kahraman, Hasan Dalgıç

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This research was made during the 2011 and 2012 growing periods according to “Randomized Blocks Design” with 3 replications. Research material was the following chickpea genotype: CA119, CA128, CA149, CA150, CA222, CA250, CA254 and other 2 commercial varieties named as Gökçe and Yaşa. Some agronomical characteristics such as plant height (cm), number of pod per plant, number of seed per pod, number of seed per plant, 1000 seed weight (g) and seed yield (kg ha-1) were determined. Statistically significant variations were found amongst the genotypes for all variables except seeds per pod. Means of the two years showed the range for plant height was from 52.83 cm (Gökçe) to 73.00 cm (CA150), number of pod per plant was from 14.00 (CA149) to 26.83 (CA261), number of seed per pod was from 1.10 (Gökçe) to 1.19 (CA149 ve CA250), number of seed per plant was from 16.28 (CA149) to 31.65 (CA261), 1000 seed weight was from 295.85 g (CA149) to 437.80 g (CA261) and seed yield was from 1342.73 kg ha-1 (CA261) to 2161.50 kg ha-1 (CA128). Results of the research implicated that the new developed lines were superior compared with the control (commercial) varieties by means of most of the characteristics.

Keywords: agricultural characters, chickpea, seed yield, genotype variations

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

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An experiment was conducted to study the effect of different growth regulators on seed germination and vegetative propagation by cuttings of an endangered medicinal plant species, Celastrus paniculatus Willd. at College of Horticulture, Mudigere during June- Sept 2014. Various growth parameters were recorded for seed germination and significantly higher results for Rate of germination (0.78), Plant vigour (2082.74), Plant height (22.10cm), number of leaves (7.83) fresh weight (136.58mg) and dry weight of plant (59.16mg) noticed in seeds treated with GA3 400 ppm when compared to control. In vegetative propagation the cuttings treated with IBA 2000 ppm recorded significantly highest sprouting percentage (98.00) when compared to control (71.00). The results of present investigation will be helpful for large scale multiplication of the species. It will also help for cultivation and conservation of this endangered species.

Keywords: Celastrus paniculatus Willd, seeds, germination, cuttings

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Authors: Yavuz Bağcı, Levent Keskin

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The study was carried out in 2011-2014 period to determine plant-based hand tools uses of plants in Kadınhanı (Konya) and surrounding villages. A total of 153 individuals, who lived or were living during this study in 4 towns, 37 villages and 9 neighborhood were interviewed. It was found that of a total about 20 plants belonging to 10 families in the study area, about 60 hand-made goods were used by peoples for various purposes.

Keywords: ethnobotanic, handmade, Kadınhanı, Konya, plant-human relationship

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Authors: Peng Liu, Chyng Wen Tee, Xiaofei Xu

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This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike.

Keywords: multi-period portfolio optimization, look-ahead constrained optimization, machine learning, sequential decision making

Procedia PDF Downloads 48

Authors: Ankitsinh Chauhan, Vimal Patel, A. D. Parekh, Ishant patil

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This study delves into the crucial influence of cooling tower fan operation on the performance of a chiller plant, with a specific focus on the Chiller Plant at SVNIT. Continuous operation of the chiller plant led to unexpected damage to the cooling tower's belt drive, rendering the cooling tower fan non-operational. Consequently, the efficiency of heat transfer in the condenser was significantly impaired. In response, we analyzed and calculated several vital parameters, including the Coefficient of Performance (COP), heat rejection in the condenser (Qc), work required for the compressor (Wc), and heat absorbed by the refrigerant in the evaporator (Qe). Our findings revealed that in the absence of the cooling tower fan, relying solely on natural convection, the COP of the chiller plant reached a minimum value of 5.49. However, after implementing a belt drive to facilitate forced convection for the cooling tower fan, the COP of the chiller plant experienced a noteworthy improvement, reaching approximately 6.27. Additionally, the utilization of forced convection resulted in an impressive reduction of 8.9% in compressor work, signifying enhanced energy efficiency. This study underscores the critical role of cooling tower fan operation in optimizing chiller plant performance, with practical implications for energy-efficient HVAC systems. It highlights the potential benefits of employing forced convection mechanisms, such as belt drives, to ensure efficient heat transfer in the condenser, ultimately contributing to improved energy utilization and reduced operational costs in cooling.

Keywords: cooling tower, chiller Plant, cooling tower fan, energy efficiency, VCRS.

Procedia PDF Downloads 40

Authors: He Yuhai, Ahmad Ziad Bin Sulaiman

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Tongkat Ali, or Eurycoma longifolia, is a traditional Malay and Orang Asli herb used as aphrodisiac, general tonic, anti-Malaria, and anti-Pyretic. It has been recognized as a cashcrop by Malaysia due to its high value for the pharmaceutical use. In Tongkat Ali, eurycomanone, a quassinoid is usually chosen as a marker phytochemical as it is the most abundant phytochemical. In this research, ultrasound and enzyme were used to enhance the extraction of Eurycomanone from Tongkat Ali. Ultrasonic assisted extraction (USE) enhances extraction by facilitating the swelling and hydration of the plant material, enlarging the plant pores, breaking the plant cell, reducing the plant particle size and creating cavitation bubbles that enhance mass transfer in both the washing and diffusion phase of extraction. Enzyme hydrolyses the cell wall of the plant, loosening the structure of the cell wall, releasing more phytochemicals from the plant cell, enhancing the productivity of the extraction. Possible effects of ultrasound on the activity of the enzyme during the hydrolysis of the cell wall is under the investigation by this research. The extracts was analysed by high performance liquid chromatography for the yields of Eurycomanone. In this whole process, the conventional water extraction was used as a control of comparing the performance of the ultrasound and enzyme assisted extraction.

Keywords: ultrasound, enzymatic, extraction, Eurycoma longifolia

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Authors: Sudhir Kumar Singh, Debashish Chakravarty

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Slope stability analysis is an important aspect in the field of geotechnical engineering. It is also important from safety, and economic point of view as any slope failure leads to loss of valuable lives and damage to property worth millions. This paper aims at mitigating the risk of slope failure by studying the effect of different joint sets on the stability of mine pit slopes under the influence of various external factors, namely degree of saturation, rainfall intensity, and seismic coefficients. Supervised machine learning approach has been utilized for making accurate and reliable predictions regarding the stability of slopes based on the value of Factor of Safety. Numerous cases have been studied for analyzing the stability of slopes using the popular Finite Element Method, and the data thus obtained has been used as training data for the supervised machine learning models. The input data has been trained on different supervised machine learning models, namely Random Forest, Decision Tree, Support vector Machine, and XGBoost. Distinct test data that is not present in training data has been used for measuring the performance and accuracy of different models. Although all models have performed well on the test dataset but Random Forest stands out from others due to its high accuracy of greater than 95%, thus helping us by providing a valuable tool at our disposition which is neither computationally expensive nor time consuming and in good accordance with the numerical analysis result.

Keywords: finite element method, geotechnical engineering, machine learning, slope stability

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Authors: Seyedmilad Kazemisangdehi, Seyedmehdi Kazemisangdehi

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Flux switching permanent magnet machines are considered for wide range of applications because of their outstanding merits including high torque/power densities, high efficiency, simple and robust rotor structure. Therefore, several topologies have been proposed like the PM exited flux switching machine, hybrid excited flux switching type, and so on. Recently, a novel laminated segmented rotor flux switching permanent magnet machine was introduced. It features flux barriers on rotor structure to enhance the performances of machine including torque ripple reduction and also torque and efficiency improvements at the same time. This is while, the design of barriers was not optimized by the authors. Therefore, in this paper three coefficients regarding the position of the barriers are considered for optimization. The effect of each coefficient on the performance of this machine is investigated by finite element method and finally an optimized design of flux barriers based on these three coefficients is proposed from different points of view including electromagnetic torque maximization and cogging torque/torque ripple minimization. At optimum design from maximum developed torque aspect, this machine generates 0.65 Nm torque higher than that of the not-optimized design with an almost 0.4 % improvement in efficiency.

Keywords: finite element analysis, FSPM, laminated segmented rotor flux switching permanent magnet machine, optimization

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: Gürcan D. Baysal, Ali Tanış

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In this study, samples of plant types including rose hip (Rosa canina L.), jujube (Ziziphus jujube), sea buckthorn (Hippophae rhamnoides), elderberry (Sambucus nigra), apricot (Prunus armeniaca), scots pine (Pinus sylvestris), and cedar of Lebanon (Cedrus libani) were grown using plant supporting units called Ekobox and drip irrigation systems in the Karapınar, Konya region of Turkey to reveal the efficiency of Ekobox and drip irrigation compared against a control with no irrigation. The plant diameter, height, and survival rates were determined, compared with each other, and statistically analyzed. According to the statistical analysis of the results, Ekobox applications resulted in the highest values for survival rate, diameter, and height measurements whereas the lowest values were determined in the control groups. These results indicate that the cultivation of plants with Ekobox may help protect against the loss of fertile soils as an effective mechanism for combating erosion and desertification. These advantages may also lead to a lasting economic effect on the cultivation of plants by locals of the Karapınar, Konya province who suffer from an ever-decreasing underground water level as a result of agricultural consumption.

Keywords: drip irrigation, ekobox, plant diameter, plant height, plant survival rate

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Authors: H. Zou, B. Wang

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Thermal distortion of the machine tool plays a critical role in its machining accuracy. This study investigates the thermal performance of a high-precision machine frame with future-oriented mineral casting components. A thermo-mechanical finite element model (FEM) was established to evaluate the thermal behavior of the frame under environmental thermal fluctuations. The validity of the presented FEM model was confirmed experimentally by a series of laser interferometer tests. Good agreement between numerical and experimental results demonstrates that the proposed model can accurately predict the thermal deformation of the frame with thermo-mechanical coupling effect. The results also show that keeping the workshop in thermally stable conditions is crucial for improving the machine accuracy of the system with large scale components. The goal of this paper is to investigate the feasibility of innovative mineral casting material applied in high-precision drilling machine and to provide a strategy for machine tool industry seeking a perfect substitute for classic frame materials such as cast iron and granite.

Keywords: thermo-mechanical model, finite element method, laser interferometer, mineral casting frame

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Authors: Nazakat Ismailzada, Carol Jones

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Tomato drilling moth Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) is the main pest of tomato plants in many countries. The larvae of tomato leaves, the stems inside, in the end buds, they opened the gallery in green and ripe fruit. In this way the harmful products can be fed with all parts of the tomato plant can cause damage to 80-100%. Pest harms all above ground parts of the tomato plant. After the seedlings are planted in areas and during blossoming holder traps with tomato moth’s rubber capsule inside should be placed in the area by using five-tomato moth’s feremon per ha. Then there should be carried out observations in the fields in every three days regularly. During the researches, it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms. Therefore it was showed that in field condition Carogen 20 SC besides high-level biological efficiency also has low ecological load for environment, and should be used against tomato moth in farms with insecticide expenditure norm 320 qr\ha. In farms should be used plant rotation, plant fields should be plowed on the 25-30 sm depth, before sowing seeds should be proceeded by insecticides. As element of integrated plant protection activities, should be used pheromones trap. In tomato plant fields as an insecticide should be used AGROSAN 240 SC and Carogen 20 SP.

Keywords: lepidoptera, Tuta absoluta, chemical control, integrated pest management

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Authors: P. C. Tewari, Parveen Kumar

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The paper describes the availability analysis of milling system of a rice milling plant using probabilistic approach. The subsystems under study are special purpose machines. The availability analysis of the system is carried out to determine the effect of failure and repair rates of each subsystem on overall performance (i.e. steady state availability) of system concerned. Further, on the basis of effect of repair rates on the system availability, maintenance repair priorities have been suggested. The problem is formulated using Markov Birth-Death process taking exponential distribution for probable failures and repair rates. The first order differential equations associated with transition diagram are developed by using mnemonic rule. These equations are solved using normalizing conditions and recursive method to drive out the steady state availability expression of the system. The findings of the paper are presented and discussed with the plant personnel to adopt a suitable maintenance policy to increase the productivity of the rice milling plant.

Keywords: availability modeling, Markov process, milling system, rice milling plant

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Authors: Zehra Aytac, Nurdilek Gulmezoglu

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Safflower has been grown for centuries for many purposes worldwide. Especially it is important for the orange-red dye from its petal and for its high-quality oil obtained from the seeds. The crop is high adaptable to areas with insufficient rainfall and poor soil conditions. The plant has a deep taproot that can draw moisture and plant nutrients from deep to the subsoil. The research was carried out to study the nitrogen (N) uptake of different safflower cultivars and lines at different stages of growth and different plant parts in the experimental field of Faculty of Agriculture, Eskişehir Osmangazi University under semi-arid conditions. Different safflower cultivars and lines of varied origins were used as the material. The cultivars and lines were planted in a Randomized Complete Block Design with three replications. Two different growth stages (flowering and harvest) and three different plant parts (head, stem+leaf and seed) were determined. The nitrogen concentration of different plant parts was determined by the Kjeldahl method. Statistical analysis were performed by analysis of variance for each growth stage and plant parts taking a level of p < 0.05 and p < 0.01 as significant according to the LSD test. As a result, N concentration showed significant differences among different plant parts and different growth stages for different safflower genotypes of varied origins.

Keywords: Carthamus tinctorius L., growth stages, head N, leaf N, N uptake, seed N, Safflower

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Authors: Babak Mohajeri, Sen Bao, Timo Nyberg

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Customer experience is increasingly the differentiator between successful companies and those who struggle. Currently, customer experiences become more dynamic; and they advance with each interaction between the company and a customer. Every customer conversation and any effort to evolve these conversations would be beneficial and should ultimately result in a positive customer experience. The aim of this paper is to analyze the evolving customer experience management landscape and the relevant challenges and opportunities. A case study on the “paper machine” companies is chosen. Hence, this paper analyzes the challenges and opportunities in customer experience management of paper machine companies for the case of “road to steel”. Road to steel shows the journey of steel from raw material to end product (i.e. paper machine in this paper). ALPHA (Steel company) and BETA (paper machine company), are chosen and their efforts to evolve the customer experiences are investigated. Semi-structured interviews are conducted with experts in those companies to identify the challenges and opportunities of the evolving customer experience management from their point of view. The findings of this paper contribute to the theory and business practices in the realm of the evolving customer experience management landscape.

Keywords: Customer Experience Management, Paper Machine , Value Chain Management, Risk Analysis

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Authors: Jenq-Shyong Chen, Ben-Fong Yu

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CNC(computer numerical control) machining centers have been widely used for machining different metal components for various industries. For a specific CNC machine, its everyday job is assigned to cut different products with quite different attributes such as material type, workpiece weight, geometry, tooling, and cutting conditions. Theoretically, the dynamic characteristics of the CNC machine should be properly tuned match each machining job in order to get the optimal machining performance. However, most of the CNC machines are set with only a standard set of CNC parameters. In this study, we have developed an auto-tuning system which can automatically change the CNC parameters and in hence change the machine dynamic characteristics according to the selection of machining modes which are set by the mixed combination of three machine performance indexes: the HO (high surface quality) index, HP (high precision) index and HS (high speed) index. The acceleration, jerk, corner error tolerance, oscillation and dynamic bandwidth of machine’s feed axes have been changed according to the selection of the machine performance indexes. The proposed auto-tuning system of the CNC parameters has been implemented on a PC-based CNC controller and a three-axis machining center. The measured experimental result have shown the promising of our proposed auto-tuning system.

Keywords: auto-tuning, CNC parameters, machining mode, high speed, high accuracy, high surface quality

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Authors: Harshkumar K. Desai, Anuj K. Desai, Jay P. Patel, Snehal V. Trivedi, Yogendrasinh Parmar

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The need of continuous improvement in a product or process in this era of global competition leads to apply value engineering for functional and aesthetic improvement in consideration with economic aspect too. Solar industries located at G.I.D.C., Makarpura, Vadodara, Gujarat, India; a manufacturer of variety of CNC Machines had a challenge to analyze the structural design of column, base, carriage and table of CNC Milling Machine in the account of reduction of overall weight of a machine without affecting the rigidity and accuracy at the time of operation. The identified task is the first attempt to validate and optimize the proposed design of ribbed structure statically using advanced modeling and analysis tools in a systematic way. Results of stress and deformation obtained using analysis software are validated with theoretical analysis and found quite satisfactory. Such optimized results offer a weight reduction of the final assembly which is desired by manufacturers in favor of reduction of material cost, processing cost and handling cost finally.

Keywords: CNC milling machine, optimization, finite element analysis (FEA), weight reduction

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Authors: John Gaber, Youssef Ahmed, Hossam A.Gabbar, Jing Ren

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Nuclear Power Plant (NPP) operators have a large responsibility on their shoulders. They must allow the plant to generate a high amount of energy while inspecting and maintaining the safety of the plant. This type of occupation comes with high amounts of mental fatigue, and a small mistake can have grave consequences. Electroencephalography (EEG) is a method of gathering the electromagnetic waves emitted by a human brain. We propose a safety system by monitoring brainwaves for signs of mental fatigue. This requires an analysis of the tasks and mental models of the NPP operator, as well as risk factors on mental fatigue and attention that NPP operators face when performing their tasks. The brain waves generated from experiencing mental fatigue can then be monitored for. These factors are analyzed, developing an EEG-based monitoring system, which aims to alert NPP operators when levels of mental fatigue and attention start affecting their performance in task completion.

Keywords: EEG, power plant operator, psychology, task analysis

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Authors: Azene Zenebe

Abstract:

Deep learning is a subset of machine learning which incorporates techniques for the construction of artificial neural networks and found to be useful for modeling complex problems with large dataset. Deep learning requires a very high power computational and longer time for training. By aggregating computing power, high performance computer (HPC) has emerged as an approach to resolving advanced problems and performing data-driven research activities. Cyber threat intelligence (CIT) is actionable information or insight an organization or individual uses to understand the threats that have, will, or are currently targeting the organization. Results of review of literature will be presented along with results of experimental study that compares the performance of tree-based and function-base machine learning including deep learning algorithms using secondary dataset collected from darknet.

Keywords: deep-learning, cyber security, cyber threat modeling, tree-based machine learning, function-based machine learning, data science

Procedia PDF Downloads 153

Authors: P. S. Jagadeesh Kumar, Mingmin Pan, Yang Yung, Tracy Lin Huan

Abstract:

Glaucoma is a group of visual maladies represented by the scheduled optic nerve neuropathy; means to the increasing dwindling in vision ground, resulting in loss of sight. In this paper, a novel support vector machine based retinal therapeutic for glaucoma using machine learning algorithm is conservative. The algorithm has fitting pragmatism; subsequently sustained on correlation clustering mode, it visualizes perfect computations in the multi-dimensional space. Support vector clustering turns out to be comparable to the scale-space advance that investigates the cluster organization by means of a kernel density estimation of the likelihood distribution, where cluster midpoints are idiosyncratic by the neighborhood maxima of the concreteness. The predicted planning has 91% attainment rate on data set deterrent on a consolidation of 500 realistic images of resolute and glaucoma retina; therefore, the computational benefit of depending on the cluster overlapping system pedestal on machine learning algorithm has complete performance in glaucoma therapeutic.

Keywords: machine learning algorithm, correlation clustering mode, cluster overlapping system, glaucoma, kernel density estimation, retinal therapeutic

Procedia PDF Downloads 254

Authors: Hüseyin Biçer

Abstract:

There are more than a thousand endemic plants growing in Turkey. On the other hand, apart from these plantsAnatolia is home to more plant diversitythan the neighboring countries due to its transitional zone. These plants become a part of traditional medicine in the hope of curing the people with whom they have lived for thousands of years. No matter how important the climate is for the plant, the diseases of the region have an important place in the plant's life. While the plants used for tea are in the foreground in regions with heavy winters, the use of raw plants and fruits is common in some gastrointestinal problems. The aim of this study is explaining using the area of endemic plants in Anatolia.

Keywords: anatolian traditional medicine, traditional medicine, anatolian medicine, herbs

Procedia PDF Downloads 179

Authors: Priyanka Govender, S. Mtshali, Theresa Moonsamy, Zanele Mkwanazi, L. Mthembu

Abstract:

Chemically enhanced primary treatment (CEPT) can be used at wastewater works to improve the quality of the final effluent discharge, provided that the plant has spare anaerobic digestion capacity. CEPT can transfer part of the organic load to the digesters thereby effectively relieving the hydraulic loading on the plant and in this way can allow the plant to continue operating long after the hydraulic capacity of the plant has been exceeded. This can allow a plant to continue operating well beyond its original design capacity, requiring only fairly simple and inexpensive modifications to the primary settling tanks as well as additional chemical costs, thereby delaying or even avoiding the need for expensive capital upgrades. CEPT can also be effective at plants where high organic loadings prevent the wastewater discharge from meeting discharge standards, especially in the case of COD, phosphates and suspended solids. By increasing removals of these pollutants in the primary settling tanks, CEPT can enable the plant to conform to specifications without the need for costly upgrades. Laboratory trials were carried out recently at the Umbilo WWTW in Durban and these were followed by a baseline assessment of the current plant performance and a subsequent full scale trial on the Conventional plant i.e. West Plant. The operating conditions of the plant are described and the improvements obtained in COD, phosphate and suspended solids, are discussed. The PST and plant overall suspended solids removal efficiency increased by approximately 6% during the trial. Details regarding the effect that CEPT had on sludge production and the digesters are also provided. The cost implications of CEPT are discussed in terms of capital costs as well as operation and maintenance costs and the impact of Ferric chloride on the infrastructure was also studied and found to be minimal. It was concluded that CEPT improves the final quality of the discharge effluent, thereby improving the compliance of this effluent with the discharge license. It could also allow for a delay in upgrades to the plant, allowing the plant to operate above its design capacity. This will be elaborated further upon presentation.

Keywords: chemically enhanced, ferric, wastewater, primary

Procedia PDF Downloads 301