Search results for: agriculture yield prediction

Authors: Mohammad Saeri, Noor Rizkiyah, Kambang Vetrani Asie, Titin Apung Atikah

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The study aims to assess efficient specific-location soybean farming technology assembly by assisting the farmers in applying the suggested technology. Superimposed trial was conducted to know NPK fertilizer effect toward soybean growth and yield and soybean improved variety test for the dissemination of improved variety. The assessment was conducted at the farmers group of Sumber Rejeki, Kepulungan Village, Gempol Sub-district, Pasuruan Regency as the soybean central at Pasuruan area. The number of farmers involved in the study was 38 people with 25 ha soybean area. This study was held from July to October 2012.  The recommended technology package agreed at the socialization time and used in this research were: using Argomulyo variety seeds of 40 kg/ha, planting by drilling, planting by distance of 40x10 cm, deciding the seeds amount of 2-3 seeds per hole, and giving fertilization based on recommendation of East Java AIAT of 50 kg Urea, 100 kg SP-36 and 50 kg KCl.  Farmers around the research location were used as control group. Assessment on soybean farming system was considered effective because it could increase the production up to 38%. The farming analysis showed that the result collaborator farmers gained were positively higher than non-collaborator farmers with RC ratio of 2.03 and 1.54, respectively. Argomulyo variety has the prospect to be developed due to the high yield of about 2 tons/ha and the larger seeds. The NPK fertilization test at the soybean plants showed that the fertilization had minor effect on the yield.

Keywords: farming system, soybean, variety, location specific

Procedia PDF Downloads 176

Authors: Hadi Molaei, Yasavoli Davoud, Keshavarz, Mozhde Poordana

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The present study aimed was to prediction the academic performance based on academic motivation, self-efficacy and Resiliency in the students. The present study was descriptive and correlational. Population of the study consisted of all students in Arak schools in year 1393-94. For this purpose, the number of 304 schools students in Arak was selected using multi-stage cluster sampling. They all questionnaires, self-efficacy, Resiliency and academic motivation Questionnaire completed. Data were analyzed using Pearson correlation and multiple regressions. Pearson correlation showed academic motivation, self-efficacy, and Resiliency with academic performance had a positive and significant relationship. In addition, multiple regression analysis showed that the academic motivation, self-efficacy and Resiliency were predicted academic performance. Based on the findings could be conclude that in order to increase the academic performance and further progress of students must provide the ground to strengthen academic motivation, self-efficacy and Resiliency act on them.

Keywords: academic motivation, self-efficacy, resiliency, academic performance

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Authors: Mahmoud M. Tash, S. Alkahtani

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The present study was undertaken to investigate the effect of pre-aging and aging parameters (time and temperature) on the mechanical properties of Al-Mg-Zn (7075) alloys. Ultimate tensile strength, 0.5% offset yield strength and % elongation measurements were carried out on specimens prepared from cast and heat treated 7075 alloys. Aging treatments were carried out for the as solution treated (SHT) specimens (after quenching in warm water). The specimens were aged at different conditions; Natural aging was carried out at room temperature for different periods of time. Double aging was performed for SHT conditions (pre-aged at different time and temperature followed by high temperature aging). Ultimate tensile strength, yield strength and % elongation as a function of different pre-aging and aging parameters are analysed to acquire an understanding of the effects of these variables and their interactions on the mechanical properties of Be-treated 7075 alloys.

Keywords: duplex aging treatment, mechanical properties, Al-Mg-Zn (7075) alloys, manufacturing

Procedia PDF Downloads 238

Authors: Ajay Kumar Rana

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Predicting human behaviour, discerning monozygotic twins or left over remnant tissues/fluids of a single human source remains a big challenge in forensic science. Recent advances in the field of DNA methylations which are broadly chemical hallmarks in response to environmental factors can certainly help to identify and discriminate various single-source DNA samples collected from the crime scenes. In this review, cytosine methylation of DNA has been methodologically discussed with its broad applications in many challenging forensic issues like body fluid identification, race/ethnicity identification, monozygotic twins dilemma, addiction or behavioural prediction, age prediction, or even authenticity of the human DNA. With the advent of next-generation sequencing techniques, blooming of DNA methylation datasets and together with standard molecular protocols, the prospect of investigating and solving the above issues and extracting the exact nature of the truth for reconstructing the crime scene events would be undoubtedly helpful in defending and solving the critical crime cases.

Keywords: DNA methylation, differentially methylated regions, human identification, forensics

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Authors: Pooria Norouzi, Nicholas Tsang, Adam van der Goes, Joseph Yu, Douglas Zheng, Sirine Maleej

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In this study, virtual meters will be designed and used for energy balance measurements of an air handling unit (AHU). The method aims to replace traditional physical sensors in heating, ventilation, and air conditioning (HVAC) systems with simulated virtual meters. Due to the inability to manage and monitor these systems, many HVAC systems have a high level of inefficiency and energy wastage. Virtual meters are implemented and applied in an actual HVAC system, and the result confirms the practicality of mathematical sensors for alternative energy measurement. While most residential buildings and offices are commonly not equipped with advanced sensors, adding, exploiting, and monitoring sensors and measurement devices in the existing systems can cost thousands of dollars. The first purpose of this study is to provide an energy consumption rate based on available sensors and without any physical energy meters. It proves the performance of virtual meters in HVAC systems as reliable measurement devices. To demonstrate this concept, mathematical models are created for AHU-07, located in building NE01 of the British Columbia Institute of Technology (BCIT) Burnaby campus. The models will be created and integrated with the system’s historical data and physical spot measurements. The actual measurements will be investigated to prove the models' accuracy. Based on preliminary analysis, the resulting mathematical models are successful in plotting energy consumption patterns, and it is concluded confidently that the results of the virtual meter will be close to the results that physical meters could achieve. In the second part of this study, the use of virtual meters is further assisted by artificial intelligence (AI) in the HVAC systems of building to improve energy management and efficiency. By the data mining approach, virtual meters’ data is recorded as historical data, and HVAC system energy consumption prediction is also implemented in order to harness great energy savings and manage the demand and supply chain effectively. Energy prediction can lead to energy-saving strategies and considerations that can open a window in predictive control in order to reach lower energy consumption. To solve these challenges, the energy prediction could optimize the HVAC system and automates energy consumption to capture savings. This study also investigates AI solutions possibility for autonomous HVAC efficiency that will allow quick and efficient response to energy consumption and cost spikes in the energy market.

Keywords: virtual meters, HVAC, artificial intelligence, energy consumption prediction

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Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Kamran Baksh Soomro, Amin Talei, Sina Alaghmand

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Due to the declining freshwater availability to agriculture in many areas, the utilization of saline irrigation requires more consideration. For this purpose, the effects of saline irrigation on the economics of crop yield and soil salinity should be understood. A two-year field experiment was carried out during 2017-18 with three replications to investigate the effect of saline groundwater on the economics of bitter gourd production and soil salinity status after harvesting the crop. Two irrigation treatments, i.e., fresh quality irrigation water (IT₁ EC 0.56 dS.m⁻¹ (control) and other is saline groundwater ( IT₂ EC 2.56 dS.m⁻¹) were used under drip system of irrigation. Cost-benefit analysis is often used to assess adaptation approaches. In this study, it has been observed that the salts under IT₁ (fresh quality water) and IT₂ (saline groundwater) did not accumulate in the wetted zone. However, the salts were observed deposited at wetted periphery under both the treatments after the crop end at all the three sampling depths under drip system of irrigation. Moreover, the costs and benefits associated with different irrigation treatments for two consecutive seasons for bitter-gourd cultivation were also investigated, and it was found that the average gross returns per hectare in season 1 were USD 5008.22 and 4454.78 under irrigation treatment IT₁ and IT₂ respectively. Whereas in season 2 the average gross returns per hectare were 3713.47 and 3140.51 under IT₁ and IT₂ respectively.

Keywords: ground-water, soil salinity, drip irrigation, wetted zone, wetted periphery, cost benefit analysis

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Authors: Rima Younis

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Agro-ecology is considered one of the agricultural approaches that is spreading across the world due to the practical solutions it provides that are in harmony with nature. These solutions target many agricultural problems, food production issues, and climate change. Agriculture and fertile soil in particular, play a vital role when it comes to food security and climate change. The organic substances, which mainly consist of carbon, in the soil contribute to the ecological system through 4 elements: Resistance to soil erosion, conserving water in soil, increasing soil fertility, and improving the biodiversity in it. Any small changes to the carbon storage in soil have a tremendous impact on both agricultural productivity and the greenhouse gas cycle, which is what agro-ecology aims to achieve. The importance of agro-ecology lies here, as it helps increase organic matter/carbon in the soil, on an ongoing basis, 15-20 times higher than nature’s rate in producing organic matter. Agro-ecology is set to increase the production of crops free of chemicals, develop organic matter, and establish carbon in soil, thus being a factor in limiting climate change, not just mitigating or adapting. Under the events of the rapid increase in population and the need to feed humans, agro-ecology stands in the first place as it surpasses the productivity of chemical agriculture per unit area, according to international and local experience. The introduction of agro-ecology to Palestine started 15 years ago, with modest beginnings faced with a lot of criticism and opposition, but is currently experiencing rapid growth among farmers and is becoming accepted among specialists. Even though the number of agro-ecologist farmers is still small, it reflects a state of turnover into a more sustainable, less polluting agriculture that works on renewing life and the elements of nature.

Keywords: toward to solidarity economy, food sovereignty, the introduction of agro-ecology to Palestine, the importance of agro-ecology

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Authors: Khosro Mohammadi

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In order to study the effect of different resources of farmyard manure, compost and biofertilizers on grain yield and quality of canola (Talaieh cultivar), an experiment was conducted at Kurdistan region. Experimental units were arranged in split-split plots design based on randomized complete blocks with three replications. Main plots consisted of two locations with difference in soil texture (L1): Agricultural Research Center of Sanandaj and (L2): Islamic Azad University of Sanandaj, as location levels. Also, five strategies for obtaining the base fertilizer requirement including (N1): farmyard manure; (N2): compost; (N3): chemical fertilizers; (N4): farm yard manure + compost and (N5): farm yard manure + compost + chemical fertilizers were considered in split plots. Four levels of biofertilizers were (B1): Bacillus lentus and Pseudomonas putida; (B2): Trichoderma harzianum; (B3): Bacillus lentus and Pseudomonas putida & Trichoderma harzianum; and (B4): control. Results showed that location, different resources of fertilizer and interactions of them have a significant effect on grain yield. The highest grain yield (4660 kg/ha) was obtained from treatment, that farmyard manure, compost and biofertilizers were co application in clay loam soil (Gerizeh station). Different methods of fertilization have a significant effect on leaf chlorophyll. Highest amount of chlorophyll (38 Spad) was obtained from co application of farmyard manure, chemical fertilizers and compost (N5 treatment). Location, basal fertilizers and biofertilizers have a significant effect on N, S and N/S of canola seed. Oil content was decreased in Gerizeh station, but oil yield had a significant increasing than Azad University station. Co application of compost and farmyard manure produced highest percent of oleic acid (61.5 %) and linoleic acid (22.9 %). Co application of compost and farmyard manure has a significant increase in oleic acid and linoleic acid. Finally, L1N5B3 treatment, that compost, farmyard manure and biofertilizers were co application in Gerizeh station in compare to other treatments, selected as a best treatment of experiment.

Keywords: soil texture, organic fertilizer, chemical fertilizer, oil, Canola

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 133

Authors: Serge Mudinga Lemika, Samuel Olukayode Akinwamide, Aribo Sunday, Babatunde Abiodun Obadele, Peter Apata Olubambi

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Use of Duplex stainless steel has become predominant in applications where excellent corrosion resistance is of utmost importance. Corrosion behavior of duplex stainless steel induced with varying stress in a chloride media were studied. Characterization of as received 2205 duplex stainless steels were carried out to reveal its structure and properties tensile sample produced from duplex stainless steel was initially subjected to tensile test to obtain the yield strength. Stresses obtained by various percentages (20, 40, 60 and 80%) of the yield strength was induced in DSS samples. Corrosion tests were carried out in magnesium chloride solution at room temperature. Morphologies of cracks observed with optical and scanning electron microscope showed that samples induced with higher stress had its austenite and ferrite grains affected by pitting.

Keywords: duplex stainless steel, hardness, nanoceramics, spark plasma sintering

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

Procedia PDF Downloads 135

Authors: Sidra Shaheen, Nadir Naveed Siddiqui, Shah Ali Ul Qader

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In recent years, significant progress has been made in discovering and developing new bacterial polysaccharides producing organisms possessing extremely functional properties. Levan is a natural biopolymer of fructose which is produced by transfructosylation reaction in the presence of levansucrase. It is one of the industrially promising enzymes that offer a variety of industrial applications in the field of cosmetics, foods and pharmaceuticals. Although levan has significant applications but the yield of levan produced is not equal to other biopolymers due to the inefficiency of producer microorganism. Among wide range of levansucrase producing microorganisms, Zymomonas mobilis is considered as a potential candidate for large scale production of this natural polysaccharide. The present investigation is concerned with the isolation of levansucrase producing natural isolate having maximum enzyme production. Furthermore, production parameters were optimized to get higher enzyme yield. Levansucrase was partially purified and characterized to study its applicability on industrial scale. The results of this study revealed that the bacterial strain Z. mobilis KIBGE-IB14 was the best producer of levansucrase. Bacterial growth and enzyme production was greatly influenced by physical and chemical parameters. Maximum levansucrase production was achieved after 24 hours of fermentation at 30°C using modified medium of pH-6.5. Contrary to other levansucrases, the one presented in the current study is able to produce high amount of products in relatively short period of time with optimum temperature at 35°C. Due to these advantages, this enzyme can be used on large scale for commercial production of levan and other important metabolites.

Keywords: levansucrase, metabolites, polysaccharides, transfructosylation

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Authors: Fazal Said, Mian Inayatullah

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Various insect visitors in common and honeybees in particular are considered to be accountable for 80-85% of pollination services for numerous crops worldwide. Pollinators not only increase crop yield but also improve quality of produce as well. The present investigation is therefore, an endeavor to assess the visitation pattern of honeybees, Apis florea (Hymenopterae: Apidae) in sunflower (Helianthus annuus L.). The current research trial was carried out at New Developmental Farm (NDF), The University of Agriculture Peshawar, (34.01° N, 71.53° E) Khyber Pakhtunkhwa-Pakistan during 2012 and 2013. Different observations on the foraging behavior of A. florea’s individuals were made from 0800 hr in the morning and continued until 1800 hr in the evening. Hence, total duration of foraging activity of A. florea individuals was comprised of 10 hours. It was found that two peaks of visitation/foraging occurred between 1400 to 1600 hr of the day. First peak of foraging was recorded at 1600hr, where 15 individuals of honeybees/3 m2 were counted to be engaged in foraging sunflower blooms. Second peak visitation was recorded with a total of 12 bees/3 m2 at 1400 hrs of the day. Visitations of A. florea were observed to its minimum intensity of only 07 individuals during late hours of the day as evening approached after 1800 hrs. Similarly, due to more number of pollens and nectars on flowers, high frequency of A. florea were found engaged in foraging during 20th and 25th day after initiation of blooms on sunflower. Minimum numbers of honeybees were recorded during initial and very last days of flowering due to less number of plants with blooms and less availability of pollen and nectar on flowers.

Keywords: apis florea, days after flowering, daily hours, sunflower, visitation pattern

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Authors: Moudi Almousa

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This research paper explores Saudi Arabian female consumers’ experiences using 3D body scanning technology and their level of acceptance of possible market applications of this technology to adopt for apparel online shopping. Data was collected for 82 women after being scanned then viewed a short video explaining three possible scenarios of 3D body scanning applications, which include size prediction, customization, and virtual try-on, before completing the survey questionnaire. Although respondents have strong positive responses towards the scanning experience, the majority were concerned about their privacy during the scanning process. The results indicated that size prediction and virtual try on had greater market application potential and a higher chance of crossing the gap based on consumer interest. The results of the study also indicated a strong positive correlation between respondents’ concern with inability to try on apparel products in online environments and their willingness to use the 3D possible market applications.

Keywords: 3D body scanning, market applications, online, apparel fit

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Authors: Fatima Bello, Ibrahim Sani

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Medicinal plants are the most important sources of life saving drugs for the majority of world’s population. People of all continents have used hundreds to thousands of indigenous plants in curing and management of many diseases. Sesame (Sesamum indicum L.) is one of the most widely cultivated species for its nutritious and medicinal seeds and oil. This research was carried out to determine the flavonoid content and antioxidant potential of two varieties of sesame seeds oil. Oil extraction was done using Soxhlet apparatus. The percentage oil yield for white and brown seeds were 47.85% and 20.72%, respectively. Flavonoid was present in both seeds with concentration of 480 mg/g and 360 mg/g in white and brown sesame seeds, respectively. The antioxidant potential was determined at different oil volume; 1.00, 0.75, 0.50 and 0.25ml. The results for the white and brown sesame seed oils were 96.8 and 70.7, 91.0 and 65.2, 83.1 and 55.4, 77.9 and 50.2, respectively. The white seed oil has higher oil yield than the brown seed oil. Likewise, the white seed oil has more flavonoid content than the brown seed oil and also better reducing power than the brown seed oil.

Keywords: antioxidant potential, brown sesame seeds, flavonoid content, sesame seed oil, Sesamum indicum L., white sesame seeds

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Authors: Thidathit Prachanukool, Chaiyaporn Yuksen, Welawat Tienpratarn, Sorravit Savatmongkorngul, Panvilai Tangkulpanich, Chetsadakon Jenpanitpong, Yuranan Phootothum, Malivan Phontabtim, Promphet Nuanprom

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Background: Ruptured appendicitis has a high morbidity and mortality and requires immediate surgery. The Alvarado Score is used as a tool to predict the risk of acute appendicitis, but there is no such score for predicting rupture. This study aimed to developed the prediction score to determine the likelihood of ruptured appendicitis in an Asian population. Methods: This study was diagnostic, retrospectively cross-sectional and exploratory model at the Emergency Medicine Department in Ramathibodi Hospital between March 2016 and March 2018. The inclusion criteria were age >15 years and an available pathology report after appendectomy. Clinical factors included gender, age>60 years, right lower quadrant pain, migratory pain, nausea and/or vomiting, diarrhea, anorexia, fever>37.3°C, rebound tenderness, guarding, white blood cell count, polymorphonuclear white blood cells (PMN)>75%, and the pain duration before presentation. The predictive model and prediction score for ruptured appendicitis was developed by multivariable logistic regression analysis. Result: During the study period, 480 patients met the inclusion criteria; of these, 77 (16%) had ruptured appendicitis. Five independent factors were predictive of rupture, age>60 years, fever>37.3°C, guarding, PMN>75%, and duration of pain>24 hours to presentation. A score > 6 increased the likelihood ratio of ruptured appendicitis by 3.88 times. Conclusion: Using the Ramathibodi Welawat Ruptured Appendicitis Score. (RAMA WeRA Score) developed in this study, a score of > 6 was associated with ruptured appendicitis.

Keywords: predictive model, risk score, ruptured appendicitis, emergency room

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Authors: Jing Li, Li Jin, Fulin Wang, Jie Dong, Wenjiang Ding

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Mg-Li dual-phase alloy exhibits better combination of yield strength and elongation than the Mg single-phase alloy. To exploit its deformation behavior, the deformation mechanisms of Mg-2Gd alloy with or without Li addition, i.e., Mg-6Li-2Gd and Mg-2Gd alloy, have been studied at both microscale and mesoscale. EBSD-assisted slip trace, twin trace, and texture evolution analysis show that the α-Mg phase of Mg-6Li-2Gd alloy exhibits different microscopic deformation mechanisms with the Mg-2Gd alloy, i.e., mainly prismatic slip in the former one, while basal slip, prismatic slip and extension twin in the latter one. Further Schmid factor analysis results attribute this different intra-phase deformation mechanisms to the higher critical resolved shear stress (CRSS) value of extension twin and lower ratio of CRSSprismatic /CRSSbasal in the α-Mg phase of Mg-6Li-2Gd alloy. Additionally, Li addition can induce dual-phase microstructure in the Mg-6Li-2Gd alloy, leading to the formation of hetero-deformation induced (HDI) stress at the mesoscale. This can be evidenced by the hysteresis loops appearing during the loading-unloading-reloading (LUR) tensile tests and the activation of multiple slip activity in the α-Mg phase neighboring β-Li phase. The Mg-6Li-2Gd alloy shows higher yield strength is due to the harder α-Mg phase arising from solid solution hardening of Li addition, as well asthe strengthening of soft β-Li phase by the HDI stress during yield stage. Since the strain hardening rate of Mg-6Li-2Gd alloy is lower than that of Mg-2Gd alloy after ~2% strain, which is partly due to the weak contribution of HDI stress, Mg-6Li-2Gd alloy shows no obvious increase of uniform elongation than the Mg-2Gd alloy.But since the β-Li phase is effective in blunting the crack tips, the Mg-6Li-2Gd alloy shows ununiform elongation, which, thus, leads to the higher total elongation than the Mg-2Gd alloy.

Keywords: Mg-Li-Gd dual-phase alloy, phase boundary, HDI stress, dislocation slip activity, mechanical properties

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Authors: Salam Alrekabi, A. B. Cundy, Mohammed Haloob Al-Majidi

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Novel multiscale hybrid reinforced cementitious composites based on carbon nanotubes (MHRCC-CNT), and carbon nanofibers (MHRCC-CNF) are new types of cement-based material fabricated with micro steel fibers and nanofilaments, featuring superior strain hardening, ductility, and energy absorption. This study focused on established models to predict the compressive strength, and direct and splitting tensile strengths of the produced cementitious composites. The analysis was carried out based on the experimental data presented by the previous author’s study, regression analysis, and the established models that available in the literature. The obtained models showed small differences in the predictions and target values with experimental verification indicated that the estimation of the mechanical properties could be achieved with good accuracy.

Keywords: multiscale hybrid reinforced cementitious composites, carbon nanotubes, carbon nanofibers, mechanical strength prediction

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Authors: Ayesha Sanjana Kawser Parsha

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S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

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Authors: Farzad Hosseini Hossein Abadi, Cristina Prieto Sierra, Cesar Álvarez Díaz

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Predicting streamflow accurately in flashy catchments prone to floods is a major research and operational challenge in hydrological modeling. Recent advancements in deep learning, particularly Long Short-Term Memory (LSTM) networks, have shown to be promising in achieving accurate hydrological predictions at daily and hourly time scales. In this work, a multi-timescale LSTM (MTS-LSTM) network was applied to the context of regional hydrological predictions at an hourly time scale in flashy catchments. The case study includes 40 catchments allocated in the Basque Country, north of Spain. We explore the impact of hyperparameters on the performance of streamflow predictions given by regional deep learning models through systematic hyperparameter tuning - where optimal regional values for different catchments are identified. The results show that predictions are highly accurate, with Nash-Sutcliffe (NSE) and Kling-Gupta (KGE) metrics values as high as 0.98 and 0.97, respectively. A principal component analysis reveals that a hyperparameter related to the length of the input sequence contributes most significantly to the prediction performance. The findings suggest that input sequence lengths have a crucial impact on the model prediction performance. Moreover, employing catchment-scale analysis reveals distinct sequence lengths for individual basins, highlighting the necessity of customizing this hyperparameter based on each catchment’s characteristics. This aligns with well known “uniqueness of the place” paradigm. In prior research, tuning the length of the input sequence of LSTMs has received limited focus in the field of streamflow prediction. Initially it was set to 365 days to capture a full annual water cycle. Later, performing limited systematic hyper-tuning using grid search, revealed a modification to 270 days. However, despite the significance of this hyperparameter in hydrological predictions, usually studies have overlooked its tuning and fixed it to 365 days. This study, employing a simultaneous systematic hyperparameter tuning approach, emphasizes the critical role of input sequence length as an influential hyperparameter in configuring LSTMs for regional streamflow prediction. Proper tuning of this hyperparameter is essential for achieving accurate hourly predictions using deep learning models.

Keywords: LSTMs, streamflow, hyperparameters, hydrology

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Authors: Munu Fritz-Austin Ndam

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Although the Cameroonian system of farming is considered as the first generation, the primary actors here involved have not yet understood the meaning of adding value to the product or produce they produce. The challenge here is for everyone who practices agriculture as an income generating activity in Cameroon to be able to understand the concept of value-added products and to know how to go about it. Recent studies have shown that these farmers who depend on agriculture as their main income generation activity make a great loss out of it because they don’t have the means to either transport their produce to the appropriate market, they don’t the knowledge on how to transform it, or they don’t have a means of conserving the product for a longer duration. It is important to note that after a thorough evaluation of the activity carried out, the final added value product sold is of great benefit not only to the producer but the buyer and the population at large. In my proposed prestation, I will discuss how the transformation activity will have a positive impact on the lives of farmers and the buyers and, most importantly, describe the methodology and procedure which is followed before the tubers (banana, Plantain, potato)is transformed into the finished or semi-finished product.

Keywords: transformation, sustainability, development, consumption

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Authors: Muhammet Baldan, Emel Timuçin

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

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

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Authors: Min Chen, Rubi Gupta

Abstract:

Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

Procedia PDF Downloads 156

Authors: Deokyeong Choe, Keonwook Nam, Young Hoon Roh

Abstract:

Wood waste is a potentially significant resource for economic and environment-friendly recycling. Wood waste represents a key sustainable source of biomass for transformation into bioethanol. Unfortunately, wood waste is highly recalcitrant for biotransformation, which limits its use and prevents economically viable conversion into bioethanol. As a result, an effective pretreatment is necessary to degrade cellulose of the wood waste, which improves the accessibility of cellulase. In this work, a H₃PO₄-acetone pretreatment was selected among the various pretreatment methods and used to dissolve cellulose and lignin. When the H₃PO₄ and acetone were used, 5–6% of the wood waste was found to be very appropriate for saccharification. Also, when the enzymatic saccharification was conducted in the mixture of the wood waste and 0.05 M citrate buffer solution, glucose and xylose were measured to be 80.2 g/L and 9.2 g/L respectively. Furthermore, ethanol obtained after 70 h of fermentation by S. cerevisiae was 30.4 g/L. As a result, the conversion yield from wood waste to bioethanol was calculated to be 57.4%. These results show that the pretreated wood waste can be used as good feedstocks for bioethanol production and that the H₃PO₄-acetone pretreatment can effectively increase the yield of ethanol production.

Keywords: wood waste, H₃PO₄-acetone, bioethanol, fermentation

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Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

Abstract:

In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: convolutional neural networks, deep learning, digital image processing, precision agriculture, semantic segmentation, unmanned aerial vehicles

Procedia PDF Downloads 258

Authors: Sang-Ryong Lee, Min-Woon Jung, Deugwoo Han, Kiyong Kim

Abstract:

This study laid an emphasis on the possible employment of biochar generated from pyrolysis of animal excreta to establish a green platform for producing biodiesel. To this end, the pseudo-catalytic transesterification reaction using chicken manure biochar and waste cooking oil was investigated. Compared with a commercial porous material (SiO2), chicken manure biochar generated from 350 C showed better performance, resulting in 95.6% of the FAME yield at 350C. The Ca species in chicken manure biochar imparted strong catalytic capability by providing the basicity for transesterification. The identified catalytic effect also led to the thermal cracking of unsaturated FAMEs, which decreased the overall FAME yield. For example, 40–60% of converted FAMEs were thermally degraded. To avoid undesirable thermal cracking arising from the high content of the Ca species in chicken manure biochar, the fabrication of chicken manure biochar at temperatures ≥350C was highly recommended.

Keywords: Trasesterification, Animal excreta, FAME, Biochar, Chicken manure

Procedia PDF Downloads 197

Authors: K. Nagaz, F. El Mokh, M. Masmoudi, N. Ben Mechlia, M. O. Baba Sy, G. Ghiglieri

Abstract:

Field experiments on deficit irrigation (DI) were performed in Médenine, Tunisia on drip-irrigated olive, orange and grapevine orchards during 2013 and 2014. Four irrigation treatments were compared: full irrigation (FI), which was irrigated at 100% of ETc for the whole season; two deficit irrigation (DI) strategies -DI75 and DI50- which received, respectively, 25 and 50% less water than FI; and traditional farming management (FM) - with water input much less than actually needed. The traditional farming (FM) applied 11, 18, 30 and 33% less water than the FI treatment, respectively, in orange, grapevine and table and oil olive orchards, indicating that the farmers practices represent a form of unintended deficit irrigation. Yield was reduced when deficit irrigation was applied and there were significant differences between DI75, DI50 and FM treatments. Significant differences were not observed between DI50 and FM treatments even though numerically smaller yield was observed in the former (DI50) as compared to the latter (FM). The irrigation water productivity (IWP) was significantly affected by irrigation treatments. The smallest IWP was recorded under the FI treatment, while the largest IWP was obtained under the deficit irrigation treatment (DI50). The DI50 and FM treatments reduced the economic return compared to the full treatment (FI), while the DI75 treatment resulted in a better economic return in respect to DI50 and FM. Full irrigation (FI) could be recommended for olive, orange and grapevine irrigation under the arid climate of Tunisia. Nevertheless, the treatment DI75 can be applied as a strategy under water scarcity conditions in commercial olive, orange and grapevine orchards allowing water savings up to 25% but with some reduction in yield and net return. The results would be helpful in adopting deficit irrigation in ways that enhance net financial returns.

Keywords: water productivity, deficit irrigation, drip irrigation, orchards

Procedia PDF Downloads 222

Authors: Vijay S. Katta, Vinod Kushwah, Rudraksh Tiwari, Mulayam Singh Gaur, Priti Dimri, Ashok Kumar Sharma

Abstract:

The formation process of seismic activities because of abrupt slip on faults, tectonic plate moments due to accumulated stress in the Earth’s crust. The prediction of seismic activity is a very challenging task. We have studied the changes in surface latent heat temperatures which are observed prior to significant earthquakes have been investigated and could be considered for short term earthquake prediction. We analyzed the surface latent heat temperature (SLHT) variation for inland earthquakes occurred in Chamba, Himachal Pradesh (32.5 N, 76.1E, M-4.5, depth-5km) nearby the main boundary fault region, the data of SLHT have been taken from National Center for Environmental Prediction (NCEP). In this analysis, we have calculated daily variations with surface latent heat temperature (0C) in the range area 1⁰x1⁰ (~120/KM²) with the pixel covering epicenter of earthquake at the center for a three months period prior to and after the seismic activities. The mean value during that period has been considered in order to take account of the seasonal effect. The monthly mean has been subtracted from daily value to study anomalous behavior (∆SLHT) of SLHT during the earthquakes. The results found that the SLHTs adjacent the epicenters all are anomalous high value 3-5 days before the seismic activities. The abundant surface water and groundwater in the epicenter and its adjacent region can provide the necessary condition for the change of SLHT. To further confirm the reliability of SLHT anomaly, it is necessary to explore its physical mechanism in depth by more earthquakes cases.

Keywords: surface latent heat temperature, satellite data, earthquake, magnetic storm

Procedia PDF Downloads 131

Authors: M. Heydari Vini

Abstract:

There is a complicated relation between effective input parameters of cold rolling and output rolling force and exit thickness of strips.in many mathematical models, the effect of some rolling parameters have been ignored and the outputs have not a desirable accuracy. In the other hand, there is a special relation among input thickness of strips,the width of the strips,rolling speeds,mandrill tensions and the required exit thickness of strips with rolling force and the real exit thickness of the rolled strip. First of all, in this paper the effective parameters of cold rolling process modeled using an artificial neural network according to the optimum network achieved by using a written program in MATLAB,it has been shown that the prediction of rolling stand parameters with different properties and new dimensions attained from prior rolled strips by an artificial neural network is applicable.

Keywords: cold rolling, artificial neural networks, rolling force, real rolled thickness of strips

Procedia PDF Downloads 504