Search results for: daily price limit

Authors: Victoria Masi Haruna Karatu, Nik Kamariah Nikmat

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In recent times awareness about the environment and green purchase has been on the increase across nations due to global warming. Previous researchers have attempted to determine what actually influences the purchase intention of consumers in this environmentally conscious epoch. The consumers too have become conscious of what to buy and who to buy from in their purchasing decisions as this action will reflect their concern about the environment and their personal well-being. This trend is a widespread phenomenon in most developed countries of the world. On the contrary evidence revealed that only 5% of the populations of Nigeria involve in green purchase activities thus making the country lag behind its counterparts in green practices. This is not a surprise as Nigeria is facing problems of inadequate green knowledge, non-enforcement of environmental regulations, sensitivity to the price of green products when compared with the conventional ones and distrust towards green products which has been deduced from prior studies of other regions. The main objectives of this study is to examine the direct antecedents of green purchase intention (green availability, government regulations, perceived green knowledge, perceived value and green price sensitivity) in Nigeria and secondly to establish the mediating role of perceived behavioral control on the relationship between these antecedents and green purchase intention. The study adopts quantitative method whereby 700 questionnaires were administered to lecturers in three Nigerian universities. 502 datasets were collected which represents 72 percent response rate. After screening the data only 440 were usable and analyzed using structural equation modeling (SEM) and bootstrapping. From the findings, three antecedents have significant direct relationships with green purchase intention (perceived green knowledge, perceived behavioral control, and green availability) while two antecedents have positive and significant direct relationship with perceived behavioral control (perceived value and green price sensitivity). On the other hand, PBC does not mediate any of the paths from the predictors to criterion variable. This result is discussed in the Nigerian context.

Keywords: Green Availability, Green Price Sensitivity, Green Purchase Intention, Perceived Green Knowledge, Perceived Value

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Authors: Marlen Acosta Alamo, Lisa Manne, Richard Veit

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Habitat loss due to land use change is one of the leading causes of biodiversity loss worldwide. This form of habitat loss is a non-random phenomenon since the same environmental factors that make an area suitable for supporting high local biodiversity overlap with those that make it attractive for urban development. We aimed to compare the effect of two non-random habitat loss predictors on the richness, abundance, and rarity of nature-affiliated and human-affiliated North American breeding birds. For each group of birds, we simulated the non-random habitat loss using two predictors: the House Price Index as a measure of the attractiveness of an area for humans and the Normalized Difference Vegetation Index as a proxy for primary productivity. We compared the results of the two non-random simulation sets and one set of random habitat loss simulations using an analysis of variance and followed up with a Tukey-Kramer test when appropriate. The attractiveness of an area for humans predicted estimates of richness loss and increase of rarity higher than primary productivity and random habitat loss for nature-affiliated and human-affiliated birds. For example, at 50% of habitat loss, the attractiveness of an area for humans produced estimates of richness at least 5% lower and of a rarity at least 40% higher than primary productivity and random habitat loss for both groups of birds. Only for the species abundance of nature-affiliated birds, the attractiveness of an area for humans did not outperform primary productivity as a predictor of biodiversity following habitat loss. We demonstrated the value of the House Price Index, which can be used in conservation assessments as an index of the risks of habitat loss for natural communities. Thus, our results have relevant implications for sustainable urban land-use planning practices and can guide stakeholders and developers in their efforts to conserve local biodiversity.

Keywords: biodiversity loss, bird biodiversity, house price index, non-random habitat loss

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Authors: Namasivayam Navaranjan, Eric Dimla

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The world needs more quality food, shelter and transportation to meet the demands of growing population and improving living standard of those who currently live below the poverty line. Materials are essential commodities for various applications including food and pharmaceutical packaging, building and automobile. Petroleum based plastics are widely used materials amongst others for these applications and their demand is expected to increase. Use of plastics has environment related issues because considerable amount of plastic used worldwide is disposed in landfills, where its resources are wasted, the material takes up valuable space and blights communities. Some countries have been implementing regulations and/or legislations to increase reuse, recycle, renew and remanufacture materials as well as to minimise the use of non-environmentally friendly materials such as petroleum plastics. However, issue of material waste is still a concern in the countries who have low environmental regulations. Development of materials, mostly bioplastics from renewable biomass resources has become popular in the last decade. It is widely believed that the potential for up to 90% substitution of total plastics consumption by bioplastics is technically possible. The global demand for bioplastics is estimated to be approximately six times larger than in 2010. Recently, standard polymers like polyethylene (PE), polypropylene (PP), Polyvinyl Chloride (PVC) or Polyethylene terephthalate (PET), but also high-performance polymers such as polyamides or polyesters have been totally or partially substituted by their renewable equivalents. An example is Polylactide (PLA) being used as a substitute in films and injection moulded products made of petroleum plastics, e.g. PET. The starting raw materials for bio-based materials are usually sugars or starches that are mostly derived from food resources, partially also recycled materials from food or wood processing. The risk in lower food availability by increasing price of basic grains as a result of competition with biomass-based product sectors for feedstock also needs to be considered for the future bioplastic production. Manufacturing of bioplastic materials is often still reliant upon petroleum as an energy and materials source. Life Cycle Assessment (LCA) of bioplastic products has being conducted to determine the sustainability of a production route. However, the accuracy of LCA depends on several factors and needs improvement. Low oil price and high production cost may also limit the technically possible growth of these plastics in the coming years.

Keywords: bioplastics, plastics, renewable resources, biomass

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Authors: Randy Price, Harbir Antil, Rainald Löhner, Fumiya Togashi

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Chemically reacting flows are common in engineering applications such as hypersonic flow, combustion, explosions, manufacturing process, and environmental assessments. The number of reactions in combustion simulations can exceed 100, making a large number of flow and combustion problems beyond the capabilities of current supercomputers. Motivated by this, deep neural networks (DNNs) will be introduced with the goal of eventually replacing the existing chemistry software packages with DNNs. The DNNs used in this paper are motivated by the Residual Neural Network (ResNet) architecture. In the continuum limit, ResNets become an optimization problem constrained by an ODE. Such a feature allows the use of ODE control techniques to enhance the DNNs. In this work, DNNs are constructed, which update the species un at the nᵗʰ timestep to uⁿ⁺¹ at the n+1ᵗʰ timestep. Parallel DNNs are trained for each species, taking in uⁿ as input and outputting one component of uⁿ⁺¹. These DNNs are applied to multiple species and reactions common in chemically reacting flows such as H₂-O₂ reactions. Experimental results show that the DNNs are able to accurately replicate the dynamics in various situations and in the presence of errors.

Keywords: chemical reacting flows, computational fluid dynamics, ODEs, residual neural networks, ResNets

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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: Deniz Peksen

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This thesis aims to predict trend movement of closing price of stock and to maximize portfolio by utilizing the predictions. In this context, the study aims to define a stock portfolio strategy from models created by using Logistic Regression, Gradient Boosting and Random Forest. Recently, predicting the trend of stock price has gained a significance role in making buy and sell decisions and generating returns with investment strategies formed by machine learning basis decisions. There are plenty of studies in the literature on the prediction of stock prices in capital markets using machine learning methods but most of them focus on closing prices instead of the direction of price trend. Our study differs from literature in terms of target definition. Ours is a classification problem which is focusing on the market trend in next 20 trading days. To predict trend direction, fourteen years of data were used for training. Following three years were used for validation. Finally, last three years were used for testing. Training data are between 2002-06-18 and 2016-12-30 Validation data are between 2017-01-02 and 2019-12-31 Testing data are between 2020-01-02 and 2022-03-17 We determine Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate as benchmarks which we should outperform. We compared our machine learning basis portfolio return on test data with return of Hold Stock Portfolio, Best Stock Portfolio and USD-TRY Exchange rate. We assessed our model performance with the help of roc-auc score and lift charts. We use logistic regression, Gradient Boosting and Random Forest with grid search approach to fine-tune hyper-parameters. As a result of the empirical study, the existence of uptrend and downtrend of five stocks could not be predicted by the models. When we use these predictions to define buy and sell decisions in order to generate model-based-portfolio, model-based-portfolio fails in test dataset. It was found that Model-based buy and sell decisions generated a stock portfolio strategy whose returns can not outperform non-model portfolio strategies on test dataset. We found that any effort for predicting the trend which is formulated on stock price is a challenge. We found same results as Random Walk Theory claims which says that stock price or price changes are unpredictable. Our model iterations failed on test dataset. Although, we built up several good models on validation dataset, we failed on test dataset. We implemented Random Forest, Gradient Boosting and Logistic Regression. We discovered that complex models did not provide advantage or additional performance while comparing them with Logistic Regression. More complexity did not lead us to reach better performance. Using a complex model is not an answer to figure out the stock-related prediction problem. Our approach was to predict the trend instead of the price. This approach converted our problem into classification. However, this label approach does not lead us to solve the stock prediction problem and deny or refute the accuracy of the Random Walk Theory for the stock price.

Keywords: stock prediction, portfolio optimization, data science, machine learning

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Authors: Yongqi Dong, Zuo Zhang, Rui Fu, Li Li

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The records generated by taxicabs which are equipped with GPS devices is of vital importance for studying human mobility behavior, however, here we are focusing on taxi drivers' operation strategies between workdays and weekends temporally and spatially. We identify a group of valuable characteristics through large scale drivers' behavior in a complex metropolis environment. Based on the daily operations of 31,000 taxi drivers in New York City, we classify drivers into top, ordinary and low-income groups according to their monthly working load, daily income, daily ranking and the variance of the daily rank. Then, we apply big data analysis and visualization methods to compare the different characteristics among top, ordinary and low income drivers in selecting of working time, working area as well as strategies between workdays and weekends. The results verify that top drivers do have special operation tactics to help themselves serve more passengers, travel faster thus make more money per unit time. This research provides new possibilities for fully utilizing the information obtained from urban taxicab data for estimating human behavior, which is not only very useful for individual taxicab driver but also to those policy-makers in city authorities.

Keywords: big data, operation strategies, comparison, revenue, temporal, spatial

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Authors: Jamal A. Mayouf, Hashim Salih Al Bayati

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Green tea is one of the most common drinks in all cities of Libyan. Heavy metal contents such as cadmium (Cd), lead (Pb), nickel (Ni) and zinc (Zn) were determined in four green tea samples collected from Libyan market and their tea infusions by using atomic emission spectrophotometry after acid digestion. The results obtained indicate that the concentrations of Cd, Pb, Ni, and Zn in tea infusions samples ranged from 0.07-0.12, 0.19-0.28, 0.09-0.15, 0.18-0.43 mg/l after boiling for 5 min., 0.06-0.08, 0.18-0.23, 0.08-0.14, 0.17-0.27 mg/l after boiling for 10 min., 0.07-0.11, 0.18-0.24, 0.08-0.14, 0.21-0.34 mg/l after boiling for 15 min. respectively. On the other hand, the concentrations of the same element mentioned above obtained in tea leaves ranged from 6.0-18.0, 36.0-42.0, 16.0-20.0, 44.0-132.0 mg/kg respectively. The concentrations of Cd, Pb, Ni and Zn in tea leaves samples were higher than Prevention of Food Adulteration (PFA) limit and World Health Organization(WHO) permissible limit.

Keywords: tea, infusion, metals, Libya

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Authors: Dupe Olufunke Ogunbosoye, Thaofik Badmos Mustapha, Lanre Shaffihy Adeaga, R. O. Imam

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Efforts have been made to reduce ruminants' methane emissions while improving animal productivity. Hence, an experiment was conducted to investigate the in vitro fermentation pattern, methane production, and performance of West African dwarf (WAD) rams-fed diets at graded levels of ginger. Sixteen (16) rams were randomly allocated into four dietary treatments with four animals per treatment in a completely randomized design for 84 days. Ginger powder was added at 0.00%, 0.25%, 0.50% and 0.75% as T1, T2, T3 and T4 respectively. The results indicated that at the 24-hour diet incubation, gas production, methane, metabolizable energy (ME), organic matter digestibility (OMD), and short-chain fatty acids (SCFA) concentrations decreased with the increasing level of ginger. Conversely, the sheep-fed T4 recorded the highest daily weight gain (47.61g/day), while the least daily weight gain (17.86g/day) was recorded in ram-fed T1. The daily weight gain of the rams fed T3 and T4 was similar but significantly different from the daily weight gain in T1 (17.86g/day) and T2 (29.76g/day). Daily feed intake was not significantly different across the treatments. T4 recorded the best response regarding feed conversion ratio (18.59) compared with other treatments. Based on the results obtained, rams fed T4 perform best in terms of growth and methane production. It is therefore concluded that the addition of ginger powder into the diet of sheep up to 0.75% enhances the growth rate of WAD sheep and reduces enteric methane production to create a smart nutrition system in ruminant animal production.

Keywords: enteric methane, growth, in vitro, sheep, nutrition system

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Authors: I. Hossain, Huda H. Kassim, Fadhil I. Sharrad, Said A. Mansour

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In this conference, we apply the interacting boson model-1 (IBM-1) formula for U(5) symmetry in order to calculate the energy levels and reduced transition probabilities for a few yrast transitions in Ru with neutron N=60, 62. The neutron rich even-even isotopes of Ru are very interesting to investigate using IBM-1, because even 104,106Ru isotopes are great consequence due to excited near the magic number 50. The calculation of ground state band and B(E2) values using IBM-1 for Z=44 are not calculated to describe the valuable information of nuclear structure by U(5) limit. The parameters in the formula are deduced based on the experimental energy level and value of B(E2, 2+->0+). The yrast states and transition strength B(E2) from 1st 4+ to 1st 2+, 1st 6+ to 1st 4+ and 1st 8+ to 1st 6+ states of Ru for even N= 60, 62 were calculated. The quadrupole moments, deformation parameters and U(5) limit were discussed for those nuclei.

Keywords: B(E2), energy level, ¹⁰⁴Ru, ¹⁰⁶Ru

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Siphat Lim

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This study used Autoregressive Distributed Lag (ARDL) approach to cointegration. In the long-run the general price level and exchange rate have a positively significant effect on domestic output. The estimated result further revealed that fiscal stimulus help stimulate domestic output in the long-run, but not in the short-run, while monetary expansion help to stimulate output in both short-run and long-run. The result is complied with the theory which is the macroeconomic policies, fiscal and monetary policy; help to stimulate domestic output in the long-run. The estimated result of the Vector Error Correction Model (VECM) has indicated more clearly that the consumer price index has a positive effect on output with highly statistically significant. Increasing in the general price level would increase the competitiveness among producers than increase in the output. However, the exchange rate also has a positive effect and highly significant on the gross domestic product. The exchange rate depreciation might increase export since the purchasing power of foreigners has increased. More importantly, fiscal stimulus would help stimulate the domestic output in the long-run since the coefficient of government expenditure is positive. In addition, monetary expansion would also help stimulate the output and the result is highly significant. Thus, fiscal stimulus and monetary expansionary would help stimulate the domestic output in the long-run in Cambodia.

Keywords: fiscal policy, monetary policy, ARDL, VECM

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Authors: Lin-Han Chiang Hsieh, Hsiao-Yi Lin

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This study aims to determine the impact of the disclosure of flood potential map on housing prices. The disclosure is supposed to mitigate the market failure by reducing information asymmetry. On the other hand, opponents argue that the official disclosure of simulated results will only create unnecessary disturbances on the housing market. This study identifies the impact of the disclosure of the flood potential map by comparing the hedonic price of flood potential before and after the disclosure. The flood potential map used in this study is published by Taipei municipal government in 2015, which is a result of a comprehensive simulation based on geographical, hydrological, and meteorological factors. The residential property sales data of 2013 to 2016 is used in this study, which is collected from the actual sales price registration system by the Department of Land Administration (DLA). The result shows that the impact of flood potential on residential real estate market is statistically significant both before and after the disclosure. But the trend is clearer after the disclosure, suggesting that the disclosure does have an impact on the market. Also, the result shows that the impact of flood potential differs by the severity and frequency of precipitation. The negative impact for a relatively mild, high frequency flood potential is stronger than that for a heavy, low possibility flood potential. The result indicates that home buyers are of more concern to the frequency, than the intensity of flood. Another contribution of this study is in the methodological perspective. The classic hedonic price analysis with OLS regression suffers from two spatial problems: the endogeneity problem caused by omitted spatial-related variables, and the heterogeneity concern to the presumption that regression coefficients are spatially constant. These two problems are seldom considered in a single model. This study tries to deal with the endogeneity and heterogeneity problem together by combining the spatial fixed-effect model and geographically weighted regression (GWR). A series of literature indicates that the hedonic price of certain environmental assets varies spatially by applying GWR. Since the endogeneity problem is usually not considered in typical GWR models, it is arguable that the omitted spatial-related variables might bias the result of GWR models. By combing the spatial fixed-effect model and GWR, this study concludes that the effect of flood potential map is highly sensitive by location, even after controlling for the spatial autocorrelation at the same time. The main policy application of this result is that it is improper to determine the potential benefit of flood prevention policy by simply multiplying the hedonic price of flood risk by the number of houses. The effect of flood prevention might vary dramatically by location.

Keywords: flood potential, hedonic price analysis, endogeneity, heterogeneity, geographically-weighted regression

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Authors: S. Sakthivel

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Currently, the global economy is based on the hydrocarbon economy, which is referencing the global hydrocarbon industry. Problems of using these fossil fuels (like oil, NG, coal) are emitting greenhouse gases (GHGs) and price fluctuation, supply/distribution, etc. These challenges can be overcome by using clean energy as hydrogen. The hydrogen economy is the use of hydrogen as a low carbon fuel, particularly for hydrogen vehicles, alternative industrial feedstock, power generation, and energy storage, etc. Engineering consulting firms have a significant role in this ambition and green hydrogen value chain (i.e., integration of renewables, production, storage, and distribution to end-users). Typically, the cost of green hydrogen is a function of the price of electricity needed, the cost of the electrolyser, and the operating cost to run the system. This article focuses on economic viability and explores the alluring business scenarios globally. Break-even analysis was carried out for green hydrogen production and in order to evaluate and compare the impact of the electricity price on the production costs of green hydrogen and relate it to fossil fuel-based brown/grey/blue hydrogen costs. It indicates that the cost of green hydrogen production will fall drastically due to the declining costs of renewable electricity prices and along with the improvement and scaling up of electrolyser manufacturing. For instance, in a scenario where electricity prices are below US$ 40/MWh, green hydrogen cost is expected to reach cost competitiveness.

Keywords: green hydrogen, cost analysis, break-even analysis, renewables, electrolyzer

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Authors: Disha Bhanot, Vinish Kathuria

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This study focusses on the issue of distress sale in horticulture sector in India, which faces unique challenges, given the perishable nature of horticulture crops, seasonal production and paucity of post-harvest produce management links. Distress sale, from a farmer’s perspective may be defined as urgent sale of normal or distressed goods, at deeply discounted prices (way below the cost of production) and it is usually characterized by unfavorable conditions for the seller (farmer). The small and marginal farmers, often involved in subsistence farming, stand to lose substantially if they receive lower prices than expected prices (typically framed in relation to cost of production). Distress sale maximizes price uncertainty of produce leading to substantial income loss; and with increase in input costs of farming, the high variability in harvest price severely affects profit margin of farmers, thereby affecting their survival. The objective of this study is to model the occurrence of distress sale by tomato cultivators in the Indian state of Maharashtra, against the background of differential access to set of factors such as - capital, irrigation facilities, warehousing, storage and processing facilities, and institutional arrangements for procurement etc. Data is being collected using primary survey of over 200 farmers in key tomato growing areas of Maharashtra, asking information on the above factors in addition to seeking information on cost of cultivation, selling price, time gap between harvesting and selling, role of middleman in selling, besides other socio-economic variables. Farmers selling their produce far below the cost of production would indicate an occurrence of distress sale. Occurrence of distress sale would then be modelled as a function of farm, household and institutional characteristics. Heckman-two-stage model would be applied to find the probability/likelihood of a famer falling into distress sale as well as to ascertain how the extent of distress sale varies in presence/absence of various factors. Findings of the study would recommend suitable interventions and promotion of strategies that would help farmers better manage price uncertainties, avoid distress sale and increase profit margins, having direct implications on poverty.

Keywords: distress sale, horticulture, income loss, India, price uncertainity

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Authors: Jamal A. Mayouf, Hashim Salih Al Bayati, Eltayeb M. Emmima

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Green tea is one of the most common drinks in all cities of Libyan. Heavy metal contents such as cadmium (Cd), lead (Pb), nickel (Ni) and zinc (Zn) were determined in four green tea samples collected from Libyan market and their tea infusions by using atomic emission spectrophotometry after acid digestion. The results obtained indicate that the concentrations of Cd, Pb, Ni and Zn in tea infusions samples ranged from 0.07-0.12, 0.19-0.28, 0.09-0.15, 0.18-0.43 mg/l after boiling for 5 min., 0.06-0.08, 0.18-0.23, 0.08-0.14, 0.17-0.27 mg/l after boiling for 10 min., 0.07-0.11, 0.18-0.24, 0.08-0.14, 0.21-0.34 mg/l after boiling for 15 min. respectively. On the other hand, the concentrations of the same element mentioned above obtained in tea leaves ranged from 6.0-18.0, 36.0-42.0, 16.0-20.0, 44.0-132.0 mg/kg respectively. The concentrations of Cd, Pb, Ni and Zn in tea leaves samples were higher than Prevention of Food Adulteration (PFA) limit and World Health Organization(WHO) permissible limit.

Keywords: boiling, infusion, metals, tea

Procedia PDF Downloads 356

Authors: Gamalath M. B. P. Abeysekara

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Primary objective of any organization is to enhance the bottom line profit. Strategic procurement is one of the prominent aspects in view of receiving this ultimate objective. Strategic procurement is an activity used in each and every organization in their operations. Pharmaceutical procurement is an especially significant task for any organizations, particularly state sector concerned. The whole pharmaceutical procurement requirement of the country is procured through the State Pharmaceutical Corporation (SPC) of Sri Lanka. They follow Pharmaceutical Procurement Guideline of 2006 as the procurement principle. The main objective of this project is to identify the importance of State Pharmaceutical Corporation supplier selection criteria and critical analysis of pharmaceutical procurement procedure. State Pharmaceutical Corporations applied net price, product quality, past performance, and delivery of suppliers’ as main criteria for the selection suppliers. Data collection for this study was taken place through a questionnaire, given to fifty doctors within the Colombo district attached to five main state hospitals. Data analysis is carried out with mean and standard deviation functions. The ultimate outcomes indicated product quality, net price, and delivery of suppliers’ are the most important criteria behind the selection of suppliers. Critical analysis proved State Pharmaceutical Corporation should focus on net price reduction, improving laboratory testing facilities and effective communication between up and down stream of supply chain.

Keywords: government procurement procedure, pharmaceutical procurement supplier selection criteria, importance of SPC supplier selection criteria

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Authors: Qasim Kriri, Harsh B. Desai

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Water supply and sanitation in Saudi Arabia is portrayed by difficulties and accomplishments. One of the fundamental difficulties is water shortage. With a specific end goal to beat water shortage, significant ventures have been attempted in sea water desalination, water circulation, sewerage, and wastewater treatment. The motivation behind Statistical Process Control (SPC) is to decide whether the execution of a procedure is keeping up an acceptable quality level [AQL]. SPC is an analytical decision-making method. A fundamental apparatus in the SPC is the Control Charts, which follow the inconstancy in the estimations of the item quality attributes. By utilizing the suitable outline, administration can decide whether changes should be made with a specific end goal to keep the procedure in charge. The two most important quality factors in the distilled water which were taken into consideration were pH (Potential of Hydrogen) and TDS (Total Dissolved Solids). There were three stages at which the quality checks were done. The stages were as follows: (1) Water at the source, (2) water after chemical treatment & (3) water which is sent for packing. The upper specification limit, central limit and lower specification limit are taken as per Saudi water standards. The procedure capacity to accomplish the particulars set for the quality attributes of Berain water Factory chose to be focused by the proposed SPC system.

Keywords: acceptable quality level, statistical quality control, control charts, process charts

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Authors: Endang Lukitaningsih, Fathul Jannah, Arief R. Hakim, Ratna D. Puspita, Zullies Ikawati

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Rifampicin is a semisynthetic antibiotic derivative of rifamycin B produced by Streptomyces mediterranei. RIF has been used worldwide as first line drug-prescribed throughout tuberculosis therapy. This study aims to develop and to validate an HPLC method couple with a UV detection for determination of rifampicin in spiked human plasma and its application for bioequivalence study. The chromatographic separation was achieved on an RP-C18 column (LachromHitachi, 250 x 4.6 mm., 5μm), utilizing a mobile phase of phosphate buffer/acetonitrile (55:45, v/v, pH 6.8 ± 0.1) at a flow of 1.5 mL/min. Detection was carried out at 337 nm by using spectrophotometer. The developed method was statistically validated for the linearity, accuracy, limit of detection, limit of quantitation, precise and specifity. The specifity of the method was ascertained by comparing chromatograms of blank plasma and plasma containing rifampicin; the matrix and rifampicin were well separated. The limit of detection and limit of quantification were 0.7 µg/mL and 2.3 µg/mL, respectively. The regression curve of standard was linear (r > 0.999) over a range concentration of 20.0 – 100.0 µg/mL. The mean recovery of the method was 96.68 ± 8.06 %. Both intraday and interday precision data showed reproducibility (R.S.D. 2.98% and 1.13 %, respectively). Therefore, the method can be used for routine analysis of rifampicin in human plasma and in bioequivalence study. The validated method was successfully applied in pharmacokinetic and bioequivalence study of rifampicin tablet in a limited number of subjects (under an Ethical Clearance No. KE/FK/6201/EC/2015). The mean values of Cmax, Tmax, AUC(0-24) and AUC(o-∞) for the test formulation of rifampicin were 5.81 ± 0.88 µg/mL, 1.25 hour, 29.16 ± 4.05 µg/mL. h. and 29.41 ± 4.07 µg/mL. h., respectively. Meanwhile for the reference formulation, the values were 5.04 ± 0.54 µg/mL, 1.31 hour, 27.20 ± 3.98 µg/mL.h. and 27.49 ± 4.01 µg/mL.h. From bioequivalence study, the 90% CIs for the test formulation/reference formulation ratio for the logarithmic transformations of Cmax and AUC(0-24) were 97.96-129.48% and 99.13-120.02%, respectively. According to the bioequivamence test guidelines of the European Commission-European Medicines Agency, it can be concluded that the test formulation of rifampicin is bioequivalence with the reference formulation.

Keywords: validation, HPLC, plasma, bioequivalence

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Authors: Ozgu Turgut

Abstract:

Water scarcity is a problem for many regions which requires immediate action, and solutions cannot be postponed for a long time. It is known that farming consumes a significant portion of usable water. Although in recent years, the efforts to make the transition to dripping or spring watering systems instead of using surface watering started to pay off. It is also known that this transition is not necessarily translated into an increase in the capacity dedicated to other water consumption channels such as city water or power usage. In order to control and allocate the water resource more purposefully, new watering systems have to be used with monitoring abilities that can limit the usage capacity for each farm. In this study, a decision support model which relies on a bi-objective stochastic linear optimization is proposed, which takes crop yield and price volatility into account. The model generates annual planting plans as well as water usage limits for each farmer in the region while taking the total value (i.e., profit) of the overall harvest. The mathematical model is solved using the L-shaped method optimally. The decision support model can be especially useful for regional administrations to plan next year's planting and water incomes and expenses. That is why not only a single optimum but also a set of representative solutions from the Pareto set is generated with the proposed approach.

Keywords: decision support, farming, water, tactical planning, optimization, stochastic, pareto

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Authors: Oliver Takawira, Thembi Hope

Abstract:

This study will analyse the relationship between Bitcoin price movements and the Johannesburg stock exchange (JSE). The aim is to determine whether Bitcoin price movements affect the stock market performance. As crypto currencies continue to gain prominence as a safe asset during periods of economic distress, this raises the question of whether Bitcoin’s prosperity could affect investment in the stock market. To identify the existence of a short run and long run linear relationship, the study will apply the Autoregressive Distributed Lag Model (ARDL) bounds test and a Vector Error Correction Model (VECM) after testing the data for unit roots and cointegration using the Augmented Dicker Fuller (ADF) and Phillips-Perron (PP). The Non-Linear Auto Regressive Distributed Lag (NARDL) will then be used to check if there is a non-linear relationship between bitcoin prices and stock market prices.

Keywords: bitcoin, stock market, interest rates, ARDL

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

Abstract:

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Carla Ferreira, Helena Barros

Abstract:

The paper termed “Design of reinforced concrete with Eurocode 2” presents the theory regarding the design of reinforced concrete sections and the development of the tables and abacuses to verify the concrete section to the ultimate limit and service limit states. This paper is a complement of it, showing how to use the previous tools. Different numerical results are shown, proving the capability of the methodology. When a section of a beam is already chosen, the computer program presents the reinforcing steel in many locations along the structure, and it is the engineer´s task to choose the layout available for the construction, considering the maximum regular kind of reinforcing bars. There are many computer programs available for this task, but the interest of the present kind of tools is the fast and easy way of making the design and choose the optimal solution. Another application of these design tools is in the definition of the section dimensions, in a way that when stresses are evaluated, the final design is acceptable. In the design offices, these are considered by the engineers a very quick and useful way of designing reinforced concrete sections, employing variable strength concrete and higher steel classes. Examples of nonlinear analyses and redistribution of the bending moment will be considered, according to the Eurocode 2 recommendations, for sections under bending moment and axial forces. Examples of the evaluation of the service limit state will be presented.

Keywords: design examples, eurocode 2, reinforced concrete, section design

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

Abstract:

The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: Cameron Truong, Mikhail Bhatia, Yangyang Chen, Viet Nga Cao

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Using a sample of U.S. firms between 1996 and 2011, this paper documents a positive association between options trading volume and future stock price crash risk. This relation is evidently more pronounced among firms with higher information asymmetry, business uncertainty, and short-sale constraints. In a dichotomous cross-sectional setting, we also document that firms with options trading have higher future crash risk than firms without options trading. We further show in a difference-in-difference analysis that firms experience an increase in crash risk immediately after the listing of options. The results suggest that options traders are able of identifying bad news hoarding by management and choose to trade in a liquid options market in anticipation of future crashes.

Keywords: bad news hoarding, cross-sectional setting, options trading, stock price crash

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Authors: Siti Rodliyah, Andrik Purwasito, Bani Sudardi, Abdullah Wakit

Abstract:

This study employs sociological-ethnographic basic method and the cultural studies paradigm as the approach in understanding the habitus within the customary marriage of the East Nusa Tenggara society who require belis as a bride-price. The conceptual basis underlying the application of habitus theory and symbolic power in East Nusa Tenggara (NTT) society refers to the Bourdieu’s framework. This study is a result of participatory observation on habitus of a marital system using belis observed by the NTT society as a cognitive structure which connects individuals to the social activities of the customary marriage and makes it unquestionable habits. Knowledge of the social world under the pretext of prosperity for the recipients (family) of a bride-price can be a political instrument for the sustainability of power relations. The ritual-mythical system in the society has never been fully present as a neutral habit. The habitus reflected in the marital relationship among the NTT society enables the men to obtain and exercise their power relations. The sustainability of power relations can be seen from the representation of the social status of a girl and the properties attached to her. This is what gave birth to a symbolic rule, in which the social rules about bride-price or belis eventually will serve the interests of those who occupy a dominant position in the social structure, namely the rich men.

Keywords: belis, habitus, East Nusa Tenggara, marital system, power, symbolic

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Authors: Maryam Ghoreishi, Christian Larsen

Abstract:

In this paper, we investigate the effect of stochastic inputs on problem of joint optimal pricing and lot-sizing decisions where the inventory cycle is divided into advance and spot sales periods. During the advance sales period, customer can make reservations while customer with reservations can cancel their order. However, during the spot sales period customers receive the order as soon as the order is placed, but they cannot make any reservation or cancellation during that period. We assume that the inter arrival times during the advance sales and spot sales period are exponentially distributed where the arrival rate is decreasing function of price. Moreover, we assume that the number of cancelled reservations is binomially distributed. In addition, we assume that deterioration process follows an exponential distribution. We investigate two cases. First, we consider two-state case where we find the optimal price during the spot sales period and the optimal price during the advance sales period. Next, we develop a generalized case where we extend two-state case also to allow dynamic prices during the spot sales period. We apply the Markov decision theory in order to find the optimal solutions. In addition, for the generalized case, we apply the policy iteration algorithm in order to find the optimal prices, the optimal lot-size and maximum advance sales amount.

Keywords: inventory control, pricing, Markov decision theory, advance sales system

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Authors: Christabel Ihedike, John Mooney, Monica Price

Abstract:

Summary: This study aims to critically evaluate effect of photochemical smog on respiratory health in Nigeria. Cohort of chronic obstructive pulmonary disease (COPD) patients was recruited from two large hospitals in Abuja Nigeria. Respiratory health questionnaires, daily diaries, dyspnoea scale and lung function measurement were used to obtain health data and investigate the relationship with air quality data (principally ozone, NOx and particulate pollution). Concentrations of air pollutants were higher than WHO and Nigerian air quality standard. The result suggests a correlation between measured air quality and exacerbation of respiratory illness. Introduction: Photochemical smog is a significant health challenge in most cities and its effect on respiratory health is well acknowledged. This type of pollution is most harmful to the elderly, children and those with underlying respiratory disease. This study aims to investigate impact of increasing temperature and photo-chemically generated secondary air pollutants on respiratory health in Abuja Nigeria. Method and Result: Health data was collected using spirometry to measure lung function on routine attendance at the clinic, daily diaries kept by patients and information obtained using respiratory questionnaire. Questionnaire responses (obtained using an adapted and internally validated version of St George’s Hospital Respiratory Questionnaire), shows that ‘time of wheeze’ showed an association with participants activities: 30% had worse wheeze in the morning: 10% cannot shop, 15% take long-time to get washed, 25% walk slower, 15% if hurry have to stop and 5% cannot take-bath. There was also a decrease in Forced expiratory volume in the first second and Forced Vital Capacity, and daily change in the afternoon–morning may be associated with the concentration level of pollutants. Also, dyspnoea symptoms recorded that 60% of patients were on grade 3, 25% grade 2 and 15% grade 1. Daily frequency of the number of patients in the cohort that cough /brought sputum is 78%. Air pollution in the city is higher than Nigerian and WHO standards with NOx and PM10 concentrations of 693.59ug/m-3 and 748ugm-3 being measured respectively. The result shows that air pollution may increase occurrence and exacerbation of respiratory disease. Conclusion: High temperature and local climatic conditions in urban Nigeria encourages formation of Ozone, the major constituent of photochemical smog, resulting also in the formation of secondary air pollutants associated with health challenges. In this study we confirm the likely potency of the pattern of secondary air pollution in exacerbating COPD symptoms in vulnerable patient group in urban Nigeria. There is need for better regulation and measures to reduce ozone, particularly when local climatic conditions favour development of photochemical smog in such settings. Climate change and likely increasing temperatures add impetus and urgency for better air quality standards and measures (traffic-restrictions and emissions standards) in developing world settings such as Nigeria.

Keywords: Abuja-Nigeria, effect, photochemical smog, respiratory health

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Authors: Chang-Jen Lan

Abstract:

Current Level of Service (LOS) index adopted in Highway Capacity Manual (HCM) for signalized intersections on surface streets is based on the intersection average delay. The delay thresholds for defining LOS grades are subjective and is unrelated to critical traffic condition. For example, an intersection delay of 80 sec per vehicle for failing LOS grade F does not necessarily correspond to the intersection capacity. Also, a specific measure of average delay may result from delay minimization, delay equality, or other meaningful optimization criteria. To that end, a reliability version of the intersection critical degree of saturation (v/c) as the LOS index is introduced. Traditionally, the level of saturation at a signalized intersection is defined as the ratio of critical volume sum (per lane) to the average saturation flow (per lane) during all available effective green time within a cycle. The critical sum is the sum of the maximal conflicting movement-pair volumes in northbound-southbound and eastbound/westbound right of ways. In this study, both movement volume and saturation flow are assumed log-normal distributions. Because, when the conditions of central limit theorem obtain, multiplication of the independent, positive random variables tends to result in a log-normal distributed outcome in the limit, the critical degree of saturation is expected to be a log-normal distribution as well. Derivation of the risk index predictive limits is complex due to the maximum and absolute value operators, as well as the ratio of random variables. A fairly accurate functional form for the predictive limit at a user-specified significant level is yielded. The predictive limit is then compared with the designated LOS thresholds for the intersection critical degree of saturation (denoted as X

Keywords: reliability analysis, level of service, intersection critical degree of saturation, risk based index

Procedia PDF Downloads 111