Search results for: crack growth prediction

Authors: M. Heydari Vini

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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

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Authors: Adia Fatima, Naila Chand, Rifat Ullah Khan

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The goal of this study was to determine how fennel seed supplementation affected broiler growth, carcass quality, antioxidant status, and antibody titer in heat-stressed broilers. A total of 720 one-day-old broiler chickens were weighed and assigned to 28-floor pens (25 broiler chickens per pen). The broiler chickens were housed in a thermoneutral (TN) environment and were exposed to heat stress (HS). For 23 hours, the broiler chickens were kept under fluorescent lighting. For 35d, HS broiler chickens were fed a control diet and three levels of fennel seeds powder at rates of 15g/kg (Fen-15), 20 g/kg (Fen-20), and 25 g/kg (Fen-25). Overall feed intake, weight gain, and dressing % were considerably greater (P < 0.05) in Fen-25 and TN, but FCR was significantly reduced (P<0.01) in the same groups. When TN, Fen-20, and Fen-25 were compared to the control, malondialdehyde (MDA), paraoxonase (PON1), and antibody titer against New Castle disease (ND) were considerably (P < 0.05) greater. Further, the linear and quadratic response was for feed intake, weight gain, FCR, MDA, PON1, and ND titer. It was concluded that Fen-20 and Fen-25 increased broiler growth, carcass quality, antioxidant status, and immunological response under HS conditions.

Keywords: heat stress, growth, antioxidant, immunity

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Authors: Muralisankar Thirunavukkarasu, Saravana Bhavan Periyakali, Santhanam Perumal

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The present study was performed to assess the effects of dietary copper (Cu) on growth, biochemical constituents, digestive enzyme activities, enzymatic antioxidant and metabolic enzymes of the freshwater prawn, Macrobrachium rosenbergii post larvae (PL). The Cu was supplemented at 0, 10, 20, 40, 60 and 80 mg kg-1 with the basal diets. Cu supplemented diets were fed to M. rosenbergii PL for a period of 90 days. At the end of the feeding experiment, 40 mg kg-1 Cu supplemented feeds fed PL showed significant (P < 0.05) improvement in survival, growth, digestive enzyme activities and concentrations of biochemical constituents. However, PL fed with 60 to 80 mg Cu kg-1 showed negative performance. Activities of enzymatic antioxidants, metabolic enzymes and lipid peroxidation in the muscle and hepatopancreas showed insignificant alterations (P > 0.05) up to 40 mg kg-1 Cu supplemented feeds fed PL. Whereas, 60 and 80 mg of Cu kg-1 supplemented feeds fed PL showed significant alterations on these antioxidants and metabolic enzymes levels. It indicates that beyond 40 mg Cu kg-1 diets were produced some toxic to M. rosenbergii PL. Therefore, the present study suggests that 40 mg Cu kg-1 can be supplemented in the diets of M. rosenbergii PL for regulating better survival and growth.

Keywords: antioxidants, biochemical constituents, copper, growth, Macrobrachium rosenbergii

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Authors: Biruhi Tesfaye, Avinash M. Potdar

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The laboratory test process to determine the California bearing ratio (CBR) of black cotton soils is not only overpriced but also time-consuming as well. Hence advanced prediction of CBR plays a significant role as it is applicable In pavement design. The prediction of CBR of treated soil was executed by Artificial Neural Networks (ANNs) which is a Computational tool based on the properties of the biological neural system. To observe CBR values, combined eggshell and waste glass was added to soil as 4, 8, 12, and 16 % of the weights of the soil samples. Accordingly, the laboratory related tests were conducted to get the required best model. The maximum CBR value found at 5.8 at 8 % of eggshell waste glass powder addition. The model was developed using CBR as an output layer variable. CBR was considered as a function of the joint effect of liquid limit, plastic limit, and plastic index, optimum moisture content and maximum dry density. The best model that has been found was ANN with 5, 6 and 1 neurons in the input, hidden and output layer correspondingly. The performance of selected ANN has been 0.99996, 4.44E-05, 0.00353 and 0.0067 which are correlation coefficient (R), mean square error (MSE), mean absolute error (MAE) and root mean square error (RMSE) respectively. The research presented or summarized above throws light on future scope on stabilization with waste glass combined with different percentages of eggshell that leads to the economical design of CBR acceptable to pavement sub-base or base, as desired.

Keywords: CBR, artificial neural network, liquid limit, plastic limit, maximum dry density, OMC

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Authors: N. S. Aggangan, B. Dell, P. Jeffries

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Two moderately nickel-tolerant isolates of Pisolithus were compared with a non-Ni tolerant isolate for the ability to increase the growth of Eucalyptus urophylla seedlings in the presence of nickel (Ni) in pots in a glasshouse. Seedlings, either inoculated with mycorrhizal fungi or uninoculated, were transplanted into pots containing 3 kg steam-pasteurized yellow sand amended with five concentrations of nickel (0, 6, 12, 24 and 48 mg Ni kg-1 soil). Within a day after transplanting, all seedlings subjected to Ni rates greater than 12 mg Ni kg-1 showed symptoms of wilting and all died within two weeks. At lower nickel concentrations, inoculation with all 3 Pisolithus strains increased rates of seedling survival after 12 weeks. Inoculation with all 3 isolates Pisolithus significantly increased the growth of plants in Ni-free soils between 2 to 4 fold dependent on isolate. However, seedlings growing in soils containing 12 mg Ni kg-1 grew poorly, mycorrhizal development was inhibited and no beneficial effects of inoculation were noted. In contrast, in soils containing 6mg Ni kg-1, inoculated seedlings did not show the reduced root growth and severe toxicity symptoms (chlorosis on young leaves and shoot tips) of uninoculated seedlings. Only the Ni-tolerant Pisolithus strains conferred a significant growth benefit compared to non-inoculated controls, and plants inoculated with one of these strains grew twice the size as those inoculated with the other Ni-tolerant strain. Inorganic plant analysis revealed that inoculation increased plant growth through improved P uptake but did not prevent Ni uptake. However, toxicity may have been minimized by dilution due to an increase in plant biomass. The results suggest that only one of the Ni-tolerant strains of Pisolithus has the potential to improve the growth and survival of E. urophylla seedlings in serpentine soils in the Philippines.

Keywords: ectomycorrhizas, Eucalyptus urophylla, nickel tolerance, pisolithus

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Authors: Nastaran Moosavi, Mohammad Mokhtari

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Seismic inversion is a technique which has been in use for years and its main goal is to estimate and to model physical characteristics of rocks and fluids. Generally, it is a combination of seismic and well-log data. Seismic inversion can be carried out through different methods; we have conducted and compared post-stack and pre- stack seismic inversion methods on real data in one of the fields in the Persian Gulf. Pre-stack seismic inversion can transform seismic data to rock physics such as P-impedance, S-impedance and density. While post- stack seismic inversion can just estimate P-impedance. Then these parameters can be used in reservoir identification. Based on the results of inverting seismic data, a gas reservoir was detected in one of Hydrocarbon oil fields in south of Iran (Persian Gulf). By comparing post stack and pre-stack seismic inversion it can be concluded that the pre-stack seismic inversion provides a more reliable and detailed information for identification and prediction of hydrocarbon reservoirs.

Keywords: density, p-impedance, s-impedance, post-stack seismic inversion, pre-stack seismic inversion

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Authors: Naif Albelwi, Alan Kwan, Yacine Rezgui

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Cities with rapid growth face tremendous challenges not only to provide services to meet this growth but also to assure that this growth occurs in a sustainable way. The consumption of material, energy, and water resources is inextricably linked to population growth with a unique impact in urban areas, especially in light of significant investments in infrastructure to support urban development. Urban Metabolism (UM) is becoming popular as it provides a framework accounting the mass and energy flows through a city. The objective of this study is to determine the energy and material flows of Riyadh, Saudi Arabia using locally generated data from 1996 and 2012 and analyzing the temporal trends of energy and material flows. Preliminary results show that while the population of Riyadh grew 90% since 1996, the input and output flows have increased at higher rate. Results also show increasing in energy mobile consumption from 61k TJ in 1996 to 157k TJ in 2012 which points to Riyadh’s inefficient urban form. The study findings highlight the importance to develop effective policies for improving the use of resources.

Keywords: energy and water consumption, sustainability, urban development, urban metabolism

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Authors: Varun A Baheti, Sanjay Kashyap, Kamanio Chattopadhyay, Praveen Kumar, Aloke Paul

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Ni–Sn system finds applications in the microelectronics industry, especially with respect to flip–chip or direct chip, attach technology. Here the region of interest is under bump metallization (UBM), and solder bump (Sn) interface due to the formation of brittle intermetallic phases there. Understanding the growth of these phases at UBM/Sn interface is important, as in many cases it controls the electro–mechanical properties of the product. Cu and Ni are the commonly used UBM materials. Cu is used for good bonding because of fast reaction with solder and Ni often acts as a diffusion barrier layer due to its inherently slower reaction kinetics with Sn–based solders. Investigation on the growth kinetics of phases in Ni–Sn system is reported in this study. Just for simplicity, Sn being major solder constituent is chosen. Ni–Sn electroplated diffusion couples are prepared by electroplating pure Sn on Ni substrate. Bulk diffusion couples prepared by the conventional method are also studied along with Ni–Sn electroplated diffusion couples. Diffusion couples are annealed for 25–1000 h at 50–215°C to study the phase evolutions and growth kinetics of various phases. The interdiffusion zone was analysed using field emission gun equipped scanning electron microscope (FE–SEM) for imaging. Indexing of selected area diffraction (SAD) patterns obtained from transmission electron microscope (TEM) and composition measurements done in electron probe micro−analyser (FE–EPMA) confirms the presence of various product phases grown across the interdiffusion zone. Time-dependent experiments indicate diffusion controlled growth of the product phase. The estimated activation energy in the temperature range 125–215°C for parabolic growth constants (and hence integrated interdiffusion coefficients) of the Ni₃Sn₄ phase shed light on the growth mechanism of the phase; whether its grain boundary controlled or lattice controlled diffusion. The location of the Kirkendall marker plane indicates that the Ni₃Sn₄ phase grows mainly by diffusion of Sn in the binary Ni–Sn system.

Keywords: diffusion, equilibrium phase, metastable phase, the Ni-Sn system

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Authors: D. R. T. N. K. Dissanayake, H. M. S. K. Herath, H. K. S. G. Gunadasa, P. Weerasinghe

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Luffa is a tropical and subtropical vegetable, belongs to family Cucurbiteceae. It is predominantly monoecious in sex expression and provides an ample scope for utilization of hybrid vigor. Hybrid varieties develop through open pollination, produce higher yields due to its hybrid vigor. Naga F1 hybrid variety consists number of desirable traits other than higher yield such as strong and vigorous plants, fruits with long deep ridges, attractive green color fruits ,better fruit weight, length and early maturity compared to the local Luffa cultivars. Unavailability of fertilizer recommendations for hybrid cucurbit vegetables leads to an excess fertilizer application causing a vital environmental issue that creates undesirable impacts on nature and the human health. Main Objective of this research is to determine effect of different nitrogen and potassium fertilizer rates on growth and yield of Naga F1 Variety. Other objectives are, to evaluate specific growth parameters and yield, to identify the optimum nitrogen and potassium fertilizer levels based on growth and yield of hybrid Luffa variety. As well as to formulate the general fertilizer recommendation for hybrid Luffa -Naga F1 variety.

Keywords: hybrid, nitrogen, phosphorous, potassium

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Authors: Sang Heon Han, Daejun Chang, Won Yang

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NOx reduction characteristics of syngas fuel were numerically investigated for the 2MW pilot scale heavy oil furnace of KITECH (Korea Institute of Industrial Technology). The secondary fuel and syngas was fed into the furnace with two purposes- partial replacement of main fuel and reburning of NOx. Some portion of syngas was fed into the flame zone to partially replace the heavy oil, while the other portion was fed into the furnace downstream to reduce NOx generation. The numerical prediction was verified by comparing it with the experimental results. Syngas of KITECH’s experiment, assumed to be produced from biomass, had very low calorific value and contained 3% hydrocarbon. This study investigated the precise behavior of NOx generation and NOx reduction as well as thermo-fluidic characteristics inside the furnace, which was unavailable with experiment. In addition to 3% hydrocarbon syngas, 5%, and 7% hydrocarbon syngas were numerically tested as reburning fuels to analyze the effect of hydrocarbon proportion to NOx reduction. The prediction showed that the 3% hydrocarbon syngas is as much effective as 7% hydrocarbon syngas in reducing NOx.

Keywords: syngas, reburning, heavy oil, furnace

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Authors: A. Y. Benabdellah, W. Lakhdari, A. Dahliz, Y. Bouchikh, A. Soud, R. M'lik, H. Hammi

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The direct comparison tests on the culture medium, between Trichoderma sp. and Fusarium oxysporum f. sp. radicis-lycopersici revealed that the latest one could inhibit the growth of F. oxysporum mycelial over than 40% compared to the control and that after four days of incubation at 26° C. Moreover, beyond this period and at the end of six days, Trichoderma sp. invading the colonies of F. oxysporum on what it sporule, thus revealing its power is highly myco-parasitic. Almost similar results were obtained against Alternaria solani is also a pathogen which is not causing a lot of damage, but we found it more sensitive to Trichoderma sp. with a percentage of inhibition more than 50%. So due to the in vitro test of Trichoderma sp. against these aggressive pathogens by direct contact has been found that can inhibit their mycelial growth with high speed and a high inhibition rate.

Keywords: Trichoderma sp., Fusarium oxysporum f. sp. radicis-lycopersici, Alternaria solani, biological control, antagonist

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Authors: Jacob Green, Cecilia Cabrera, Maximilian Jakobs, Andrea Dimitracopoulos, Mark van der Wilk, Ryan Greenhalgh

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Predicting drug properties is key in drug discovery to enable de-risking of assets before expensive clinical trials and to find highly active compounds faster. Interest from the machine learning community has led to the release of a variety of benchmark datasets and proposed methods. However, it remains unclear for practitioners which method or approach is most suitable, as different papers benchmark on different datasets and methods, leading to varying conclusions that are not easily compared. Our large-scale empirical study links together numerous earlier works on different datasets and methods, thus offering a comprehensive overview of the existing property classes, datasets, and their interactions with different methods. We emphasise the importance of uncertainty quantification and the time and, therefore, cost of applying these methods in the drug development decision-making cycle. To the best of the author's knowledge, it has been observed that the optimal approach varies depending on the dataset and that engineered features with classical machine learning methods often outperform deep learning. Specifically, QSAR datasets are typically best analysed with classical methods such as Gaussian Processes, while ADMET datasets are sometimes better described by Trees or deep learning methods such as Graph Neural Networks or language models. Our work highlights that practitioners do not yet have a straightforward, black-box procedure to rely on and sets a precedent for creating practitioner-relevant benchmarks. Deep learning approaches must be proven on these benchmarks to become the practical method of choice in drug property prediction.

Keywords: activity (QSAR), ADMET, classical methods, drug property prediction, empirical study, machine learning

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Authors: James Purnama, Riri Fitri Sari

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We investigate the machine learning algorithm selection problem in the term of a depth image based eye gaze estimation, with respect to its essential difficulty in reducing the number of required training samples and duration time of training. Statistics based prediction accuracy are increasingly used to assess and evaluate prediction or estimation in gaze estimation. This article evaluates Root Mean Squared Error (RMSE) and R-Squared statistical analysis to assess machine learning methods on depth camera data for gaze estimation. There are 4 machines learning methods have been evaluated: Random Forest Regression, Regression Tree, Support Vector Machine (SVM), and Linear Regression. The experiment results show that the Random Forest Regression has the lowest RMSE and the highest R-Squared, which means that it is the best among other methods.

Keywords: gaze estimation, gaze tracking, eye tracking, kinect, regression model, orange python

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Authors: Fatemeh Abdollahi, Munn-Sann Lye, Jamshid Yazdani Charati, Mehran Zarghami

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Background: The negative consequences of intimate partner violence (IPV) on children have not been studied extensively. This study aimed to determine the prevalence of different types of IPV and its association with children’s growth and developmental problems. Methods: In a descriptive-analytical study, 596 mothers of one-year-old children referred to the primary health centers in Gonbad-e- Kavoos city were recruited (2018). The data were collected using the World Health Organization Domestic Violence, Ages and Stages Questionnaire-12 and the socio-economic, obstetrics, demographic and anthropometric characteristics related questionnaire. BMI Z-Score was categorized into three grades; thin (Z<-2), normal (-2≤Z<1), and overweight-obese (Z≥1). The data were analyzed using descriptive analysis, chi-square test, and regression. Results: The prevalence of physical, psychological, and sexual IPV was 7.4%, 29.5%, and 2.4%, respectively. Most of the children were of normal weight at one-year-old (91.7%). Similarly, the prevalence of overweight and obese was 13.3% and 8%, respectively. 2% of children had developmental problems at age one. There was a significant relationship between the father’s education and occupation and IPV and children’s delay in growth, respectively. There was no significant difference between BMI Z-Score and developmental disabilities in the children in women exposed and not exposed to all types of IPV. Conclusions: The prevalence of psychological IPV was common. IPV and children’s growth problems were influenced by the father’s socio-economic status. Preventing psychological IPV as a forerunner of other types of IPV and improving the economic situation may help in the reduction of these difficulties.

Keywords: children, development, growth, intimate partner violence

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Authors: Leila Azadbakht, Maedeh Moradi, Mohammad Reza Merasi, Farzaneh Jahangir

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Maternal dietary intake during lactation might affect the growth rate of an exclusive breastfed infant. The present study was conducted to evaluate the effect of maternal dietary patterns and quality during lactation on the growth of the exclusive breastfed infant. Methods: 484 healthy lactating mothers with their infant were enrolled in this study. Only exclusive breastfed infants were included in this study which was conducted in Iran. Dietary intake of lactating mothers was assessed using a validated and reliable semi-quantitative food frequency questionnaire. Diet quality indices such as alternative Healthy eating index (HEI), Dietary energy density (DED), and adherence to Mediterranean dietary pattern score, Nordic and dietary approaches to stop hypertension (DASH) eating pattern were created. Anthropometric features of infant (weight, height, and head circumference) were recorded at birth, two and four months. Results: Weight, length, weight for height and head circumference of infants at two months and four months age were mostly in the normal range among those that mothers adhered more to the HEI in lactation period (normal weight: 61%; normal height: 59%). The prevalence of stunting at four months of age among those whose mothers adhered more to the HEI was 31% lower than those with the least adherence to HEI. Mothers in the top tertiles of HEI score had the lowest frequency of having underweight infants (18% vs. 33%; P=0.03). Odds ratio of being overweight or obese at four months age was the lowest among those infants whose mothers adhered more to the HEI (OR: 0.67 vs 0.91; Ptrend=0.03). However, there was not any significant association between adherence of mothers to Mediterranean diet as well as DASH diet and Nordic eating pattern and the growth of infants (none of weight, height or head circumference). Infant weight, length, weight for height and head circumference at two months and four months did not show significant differences among different tertile categories of mothers’ DED. Conclusions: Higher diet quality indices and more adherence of lactating mother to HEI (as an indicator of diet quality) may be associated with better growth indices of the breastfed infant. However, it seems that DED of the lactating mother does not affect the growth of the breastfed infant. Adherence to the different dietary patterns such as Mediterranean, DASH or Nordic among mothers had no different effect on the growth indices of the infants. However, higher diet quality indices and more adherence of lactating mother to HEI may be associated with better growth indices of the breastfed infant. Breastfeeding is a complete way that is not affected much by the dietary patterns of the mother. However, better diet quality might be associated with better growth.

Keywords: breastfeeding, growth, infant, maternal diet

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Authors: M. Karimpour, N. Elkhoury, L. Hitihamillage, S. Moridpour, R. Hesami

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There is a necessity among rail transportation authorities for a superior understanding of the rail track degradation overtime and the factors influencing rail degradation. They need an accurate technique to identify the time when rail tracks fail or need maintenance. In turn, this will help to increase the level of safety and comfort of the passengers and the vehicles as well as improve the cost effectiveness of maintenance activities. An accurate model can play a key role in prediction of the long-term behaviour of railroad tracks. An accurate model can decrease the cost of maintenance. In this research, the rail track degradation is predicted using an autoregressive moving average with exogenous input (ARMAX). An ARMAX has been implemented on Melbourne tram data to estimate the values for the tram track degradation. Gauge values and rail usage in Million Gross Tone (MGT) are the main parameters used in the model. The developed model can accurately predict the future status of the tram tracks.

Keywords: ARMAX, dynamic systems, MGT, prediction, rail degradation

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Authors: Surita Maini, Sanjay Dhanka

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Machine learning (ML) involves developing algorithms and statistical models that enable computers to learn and make predictions or decisions based on data without being explicitly programmed. Because of its unlimited abilities ML is gaining popularity in medical sectors; Medical Imaging, Electronic Health Records, Genomic Data Analysis, Wearable Devices, Disease Outbreak Prediction, Disease Diagnosis, etc. In the last few decades, many researchers have tried to diagnose Breast Cancer (BC) using ML, because early detection of any disease can save millions of lives. Working in this direction, the authors have proposed a hybrid ML technique RBF SVM, to predict the BC in earlier the stage. The proposed method is implemented on the Breast Cancer UCI ML dataset with 569 instances and 32 attributes. The authors recorded performance metrics of the proposed model i.e., Accuracy 98.24%, Sensitivity 98.67%, Specificity 97.43%, F1 Score 98.67%, Precision 98.67%, and run time 0.044769 seconds. The proposed method is validated by K-Fold cross-validation.

Keywords: breast cancer, support vector classifier, machine learning, hyper parameter tunning

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Authors: Xiaolei Hu, Enrico Ferrera, Riccardo Tomasi, Claudio Pastrone

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Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is easy yet effective to apply Load Forecasting at larger geographic scale, i.e. Smart Micro Grids, wherein the lower available grid flexibility makes accurate prediction more critical in Demand Response applications. This paper analyses the application of short-term load forecasting in a concrete scenario, proposed within the EU-funded GreenCom project, which collect load data from single loads and households belonging to a Smart Micro Grid. Three short-term load forecasting techniques, i.e. linear regression, artificial neural networks, and radial basis function network, are considered, compared, and evaluated through absolute forecast errors and training time. The influence of weather conditions in Load Forecasting is also evaluated. A new definition of Gain is introduced in this paper, which innovatively serves as an indicator of short-term prediction capabilities of time spam consistency. Two models, 24- and 1-hour-ahead forecasting, are built to comprehensively compare these three techniques.

Keywords: short-term load forecasting, smart micro grid, linear regression, artificial neural networks, radial basis function network, gain

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Authors: A. Ejah Umraeni Salam, M. Tola, M. Selintung, F. Maricar

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Clean water is an essential and fundamental human need. Therefore, its supply must be assured by maintaining the quality, quantity and water pressure. However the fact is, on its distribution system, leakage happens and becomes a common world issue. One of the technical causes of the leakage is a leaking pipe. The purpose of the research is how to use the Radial Basis Function Neural (RBFNN) model to detect the location and the magnitude of the pipeline leakage rapidly and efficiently. In this study the RBFNN are trained and tested on data from EPANET hydraulic modeling system. Method of Radial Basis Function Neural Network is proved capable to detect location and magnitude of pipeline leakage with of the accuracy of the prediction results based on the value of RMSE (Root Meant Square Error), comparison prediction and actual measurement approaches 0.000049 for the whole pipeline system.

Keywords: radial basis function neural network, leakage pipeline, EPANET, RMSE

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Authors: Peace Nnadi

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This research was aimed at using seed extraction methods, soil media and planting density to enhance seed germination and seedling growth of Chrysophyllum albidum commonly known as star apple. The experiment was conducted in two stages, mature, healthy ripe fruits were used and the seeds were extracted from the fruits. The experiment involves the extraction of uniform number of seeds of pulpled and depulped, planted into the various soil media. Result on planting density also showed that Depulped seeds/ seedlings at (p=0.05), recorded significant increase in germination percentage and seedling growth. The finding shows that when seeds are depulped, they enhance germination percentage and addition of poultry manure to the soil media encourages plant growth.

Keywords: germination, seedling, soil media, extraction

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Authors: Hannah Dean

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The growth ideology, and its association with progress, is an important construct in the narrative of modernity. This ideology is embedded in neoclassical economic growth theory which conceptualises growth as linear and predictable, and the entrepreneur as a rational economic manager. This conceptualisation has been critiqued for reinforcing the managerial discourse in entrepreneurship studies. Despite these critiques, both the neoclassical growth theory and its adjacent managerial discourse dominate entrepreneurship studies notably the literature on female entrepreneurs. The latter is the focus of this paper. Given this emphasis on growth, female entrepreneurs are portrayed as problematic because their growth lags behind their male counterparts. This image which ignores the complexity and diversity of female entrepreneurs’ experience persists in the literature due to the lack of studies that analyse the process and contextual factors surrounding female entrepreneurs’ experience. This study aims to address the subordination of female entrepreneurs by questioning the hegemonic logic of economic growth and the managerial discourse as a true representation for the entrepreneurial experience. This objective is achieved by drawing on Schumpeter’s theorising and narrative inquiry. This exploratory study undertakes in depth interviews to gain insights into female entrepreneurs’ experience and the impact of the economic growth model and the managerial discourse on their performance. The narratives challenge a number of assumptions about female entrepreneurs. The participants occupied senior positions in the corporate world before setting up their businesses. This is at odds with much writing which assumes that women underperform because they leave their career without gaining managerial experience to achieve work-life balance. In line with Schumpeter, who distinguishes the entrepreneur from the manager, the participants’ main function was innovation. They did not believe that the managerial paradigm governing their corporate careers was applicable to their entrepreneurial experience. Formal planning and managerial rationality can hinder their decision making process. The narratives point to the gap between the two worlds which makes stepping into entrepreneurship a scary move. Schumpeter argues that the entrepreneurial process is evolutionary and that failure is an integral part of it. The participants’ entrepreneurial process was in fact irregular. The performance of new combinations was not always predictable. They therefore relied on their initiative. The inhibition to deploy these traits had an adverse effect on business growth. The narratives also indicate that over-reliance on growth threaten the business survival as it faces competing pressures. The study offers theoretical and empirical contributions to (female) entrepreneurship studies by presenting Schumpeter’s theorising as an alternative theoretical framework to the neoclassical economic growth theory. The study also reduces entrepreneurs’ vulnerability by making them aware of the negative influence that the linear growth model and the managerial discourse hold upon their performance. The study has implications for policy makers as it generates new knowledge that incorporates the current social and economic changes in the context of entrepreneurs that can no longer be sustained by the linear growth models especially in the current economic climate.

Keywords: economic growth, female entrepreneurs, managerial discourse, Schumpeter

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Authors: Shang-Han Tsai, Yong-Hong Lin, Chung-Ruey Yen

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Wax apple is an important tropical fruit in Taiwan. The famous producing area is located on the coast in Pingtung county. Land subsidence and climate change will tend to soil alkalization more seriously. This study was to evaluate the effects of NaCl in wax apple seedlings. NaCl salinity reduced wax apple shoot growth, it may due to reducing relative water content in leaf and new shoot. Leaf Cl and Na concentration were increased but K, Ca, and Mg content had no significant difference after irrigated with NaCl for six weeks. In roots, Na and Cl content increase significantly with 90 mM NaCl treatment, but K, Ca, and Mg content was reduced. 30-90 mM Nacl treatment do not affect K/Na, Ca/Na, and Mg/Na ratio, but decrease significantly in 90 mM treatment in roots. The leaf and root electrolyte leakage were significantly affected by 90 mM NaCl treatment. Suggesting 90 mM was optimum concentration for sieve out other tolerance wax apple verities.

Keywords: growth, NaCl stress, nutrient, wax apple

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Authors: Lavanya Annadi, Fahimeh Jafari

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Transportation brings immense benefits to society, but it also has its costs. Costs include such as the cost of infrastructure, personnel and equipment, but also the loss of life and property in traffic accidents on the road, delays in travel due to traffic congestion and various indirect costs in terms of air transport. More research has been done to identify the various factors that affect road accidents, such as road infrastructure, traffic, sociodemographic characteristics, land use, and the environment. The aim of this research is to predict the probabilistic crash prediction of vehicles using machine learning due to natural and structural reasons by excluding spontaneous reasons like overspeeding etc., in the United States. These factors range from weather factors, like weather conditions, precipitation, visibility, wind speed, wind direction, temperature, pressure, and humidity to human made structures like road structure factors like bump, roundabout, no exit, turning loop, give away, etc. Probabilities are dissected into ten different classes. All the predictions are based on multiclass classification techniques, which are supervised learning. This study considers all crashes that happened in all states collected by the US government. To calculate the probability, multinomial expected value was used and assigned a classification label as the crash probability. We applied three different classification models, including multiclass Logistic Regression, Random Forest and XGBoost. The numerical results show that XGBoost achieved a 75.2% accuracy rate which indicates the part that is being played by natural and structural reasons for the crash. The paper has provided in-deep insights through exploratory data analysis.

Keywords: road safety, crash prediction, exploratory analysis, machine learning

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Authors: Kalpana Jamdhade, Anita Chorey, Bharti Tijare, V. M. Bhale

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A field experiment was conducted during summer season of 2011 at Agronomy research farm, Dr. PDKV, Akola, to study the effect of irrigation regime and nitrogen levels on growth and productivity of summer sesame. The experiment was laid out in split plot Design in which three irrigation scheduling on the basis of IW/CPE ratio viz., irrigation at 0.6, 0.8 and 1.0 IW/CPE ratios (I1, I2 and I3, respectively) and one irrigation scheduling based on critical growth stages of sesame (I4), in main plot and three nitrogen levels 0, 30 and 60 kg N ha-1 (N0, N1 and N2, respectively) in subplot. The result showed that plant height, number of leaves plant-1, leaf area and dry matter accumulation were maximum in irrigation scheduling at 1.0 IW/CPE ratio, which significantly superior over 0.6 IW/CPE ratio and irrigation at critical growth stages but were statistically at par with irrigation at 0.8 IW/CPE ratio. Nitrogen levels, application of 60 kg N ha-1 was recorded significantly superior all growth parameters over treatment 30 kg N ha-1 and 0 kg N ha-1. In case of yield attributes viz., No. of capsules plant-1, Test wt., grain yield and Stalk yield (qha-1) were maximum in irrigation scheduling at 1.0 IW/CPE ratio and were significantly superior over 0.8 IW/CPE ratio, 0.6 IW/CPE ratio and irrigation at critical growth stages. Application of 60 kg N ha-1 increased all yield attributing characters over application of 30 and 0 kg N ha-1. In case of economics of crop same trend was found and the highest B:C ration was obtained in irrigation scheduling at 1.0 IW/CPE ratio. Whereas, application of 30 kg N ha-1 was recorded highest B:C ration over application of 60 and 0 kg N ha-1. Interaction effect of irrigation and nitrogen levels were found to be non significant in summer season.

Keywords: irrigation regimes, nitrogen levels, summer sesame, agricultural technology

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Authors: Mohd Azman Abdul Latif, Erwan Basiron

Abstract:

A flaw or drift from expected operational performance in one component (NAND, PMIC, controller, DRAM, etc.) may affect the reliability of the entire Solid State Drive (SSD) system. Therefore, it is important to ensure the required quality of each individual component through qualification testing specified using standards or user requirements. Qualification testing is time-consuming and comes at a substantial cost for product manufacturers. A highly technical team, from all the eminent stakeholders is embarking on reliability prediction from beginning of new product development, identify critical to reliability parameters, perform full-blown characterization to embed margin into product reliability and establish control to ensure the product reliability is sustainable in the mass production. The paper will discuss a comprehensive development framework, comprehending SSD end to end from design to assembly, in-line inspection, in-line testing and will be able to predict and to validate the product reliability at the early stage of new product development. During the design stage, the SSD will go through intense reliability margin investigation with focus on assembly process attributes, process equipment control, in-process metrology and also comprehending forward looking product roadmap. Once these pillars are completed, the next step is to perform process characterization and build up reliability prediction modeling. Next, for the design validation process, the reliability prediction specifically solder joint simulator will be established. The SSD will be stratified into Non-Operating and Operating tests with focus on solder joint reliability and connectivity/component latent failures by prevention through design intervention and containment through Temperature Cycle Test (TCT). Some of the SSDs will be subjected to the physical solder joint analysis called Dye and Pry (DP) and Cross Section analysis. The result will be feedbacked to the simulation team for any corrective actions required to further improve the design. Once the SSD is validated and is proven working, it will be subjected to implementation of the monitor phase whereby Design for Assembly (DFA) rules will be updated. At this stage, the design change, process and equipment parameters are in control. Predictable product reliability at early product development will enable on-time sample qualification delivery to customer and will optimize product development validation, effective development resource and will avoid forced late investment to bandage the end-of-life product failures. Understanding the critical to reliability parameters earlier will allow focus on increasing the product margin that will increase customer confidence to product reliability.

Keywords: e2e reliability prediction, SSD, TCT, solder joint reliability, NUDD, connectivity issues, qualifications, characterization and control

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Authors: Ilsun Pang, Kwanghun Kim, Sungsun Baik

Abstract:

Large diameter Si wafer is used as semiconductor substrate. Large diameter Si crystal ingot should be needed in order to increase wafer size. To make convection of large silicon melt stable, magnetic field is normally applied, but magnetic field is expensive and it is not proper to stabilize the large Si melt. To solve the problem, we propose a continuous Czochralski process which can be applied to small melt without magnetic field. We used granule poly, which has size distribution of 1~3 mm and is easily supplied in double crucible during silicon ingot growth. As the result, we produced 420 mm diameter ingot. In this paper, we describe an experimental study on crystal growth of large diameter silicon by Continuous Czochralski process.

Keywords: Czochralski, ingot, silicon crystal, wafer

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Authors: Shienna Marie Esteban

Abstract:

The economic growth experience of the Philippines is one of the fastest in Asia. However, the said growth has not yet trickled down to every Filipino. This is evident to agricultural-dependent population. Moreover, the contribution of the agriculture sector to GDP has been dwindling while large number of labor force is still dependent on a relatively small share of GDP. As a result, poverty incidence worsened among rural poor causing hunger and malnutrition. Therefore, the existing agricultural policies in the Philippines are pushing to achieve greater food production and productivity to alleviate poverty and food insecurity. Through a review of related literature and collection and analysis of secondary data from DA, DBM, BAS - CountrySTAT, PSA, NSCB, PIDS, IRRI, UN-FAO, IFPRI, and World Bank among others, the study revealed that Philippines is still far from its goals of poverty reduction and food security. In addition, the agricultural sector is underperforming. The productivity growth of the sector comes out mediocre. The common observation is that weakness is attributed to the failures of policy and institutional environments of the agriculture sector. The policy environment failed to create a structure appropriate for the rapid growth of the sector due to institutional and governance weaknesses. A recommendation is to go through institutional and policy reforms through legislative or executive mandates should take form to improve the implementation and enforcement of existing policies.

Keywords: agriculture, food security, policy, poverty

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Authors: Gokhan Erkal, Gulsen Aydin, Muge Yuce, Lokman Sahin

Abstract:

This study aims to analyze the impacts of involving in mediation of conflicts in the Middle East from the perspective of the economic growth of the mediators. The Middle East is a highly volatile region of the world with rampant crises whose affects spill beyond its borders. Therefore, management and resolution of the conflicts in the region are of great significance. Mediation is an instrument used for abating violence and settling dispute. The recourse to mediation has grown to an important degree in recent years. However, for mediators, it is a daunting task to involve in the mediation of the deadlocks in the Middle East. This study tries to shed light on the positive correlation between economic growth of the mediator and the successful outcome of the mediation process to provide motivation for mediators. To this end, first, it briefly introduces the conflicts ongoing in the region and their negative impacts. Second, the methodology, time series analysis, and the data to be used, International Crisis Behavior Project Data, are presented. Third, the empirical test is carried out and the findings are evaluated. The conclusion highlights the benefits of successful mediation for the economic growth of the mediators of Middle Eastern crises.

Keywords: international crises, mediation, Middle East, times series analysis

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Authors: Gaurav Sancheti

Abstract:

This study presents an impacting approach of Artificial Neural Networks (ANNs) in determining the quantity of High Tensile Steel (HTS) strands required in post-tensioned (PT) slabs. Various PT slab configurations were generated by varying the span and depth of the slab. For each of these slab configurations, quantity of required HTS strands were recorded. ANNs with backpropagation algorithm and varying architectures were developed and their performance was evaluated in terms of Mean Square Error (MSE). The recorded data for the quantity of HTS strands was used as a feeder database for training the developed ANNs. The networks were validated using various validation techniques. The results show that the proposed ANNs have a great potential with good prediction and generalization capability.

Keywords: artificial neural networks, back propagation, conceptual design, high tensile steel strands, post tensioned slabs, validation techniques

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Authors: Torchane Lazhar

Abstract:

In this work, our task consists in optimizing the nitriding treatment at low-temperature of the steel 32CrMoV13 by the way of the mixtures of ammonia gas, nitrogen and hydrogen to improve the mechanical properties of the surface (good wear resistance, friction and corrosion), and of the diffusion layer of the nitrogen (good resistance to fatigue and good tenacity with heart). By limiting our work to the pure iron and to the alloys iron-chromium and iron-chrome-carbon, we have studied the various parameters which manage the nitriding: flow rate and composition of the gaseous phase, the interaction chromium-nitrogen and chromium-carbon by the help of experiments of nitriding realized in the laboratory by thermogravimetry. The acquired knowledge have been applied by the mastery of the growth of the combination layer on the diffusion layer in the case of the industrial steel 32CrMoV13.

Keywords: diffusion of nitrogen, gaseous nitriding, layer growth kinetic, steel

Procedia PDF Downloads 394