Search results for: spot price

Authors: Debit Datta, Jeffrey J. Coleman, Scott A. Enebak, Lori G. Eckhardt

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Loblolly pine (Pinus taeda) is a leading productive timber species in the southeastern USA. Over the past three years, an emerging threat is expressed by successive needle defoliation followed by stunted growth and tree mortality in loblolly pine plantations. Considering economic significance, it has now become a rising concern among landowners, forest managers, and forest health state cooperators. However, the symptoms of the disease were perplexed somewhat with root disease(s) and recurrently attributed to invasive Phytophthora species due to the similarity of disease nature and devastation. Therefore, the study investigated the potential causal agent of this disease and characterized the fungi associated with loblolly pine needle defoliation in the southeastern USA. Besides, 70 trees were selected at seven long-term monitoring plots at Chatom, Alabama, to monitor and record the annual disease incidence and severity. Based on colony morphology and ITS-rDNA sequence data, a total of 28 species of fungi representing 17 families have been recovered from diseased loblolly pine needles. The native brown-spot pathogen, Lecanosticta acicola, was the species most frequently recovered from unhealthy loblolly pine needles in combination with some other common needle cast and rust pathogen(s). Identification was confirmed using morphological similarity and amplification of translation elongation factor 1-alpha gene region of interest. Tagged trees were consistently found chlorotic and defoliated from 2019 to 2020. The current emergence of the brown-spot pathogen causing loblolly pine mortality necessitates the investigation of the role of changing climatic conditions, which might be associated with increased pathogen pressure to loblolly pines in the southeastern USA.

Keywords: brown-spot needle blight, loblolly pine, needle defoliation, plantation forestry

Procedia PDF Downloads 106

Authors: Jiahau Guo, Yihe Zhang

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This paper proposes solutions for pricing options on stocks paying discrete dividends in markets with daily price limits. We first extend the intraday density function of Guo and Chang (2020) to a multi-day one and use the framework of Haug et al. (2003) to value European options on stocks paying discrete dividends. Next, we adopt the fast Fourier transform (FFT) to derive accurate and efficient formulae for American options and further employ the three-point Richardson extrapolation to accelerate the computation. Finally, the accuracy of our proposed methods is verified by simulations.

Keywords: daily price limit, discrete dividend, early exercise, fast Fourier transform, multi-day density function, Richardson extrapolation

Procedia PDF Downloads 136

Authors: Sucihatiningsih Dian Wisika Prajanti, Himawan Arif Susanto

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Fruits are perishable products and have relatively low price, especially at harvest time. Generally, farmers only sell the products shortly after the harvest time without any processing. Farmers also only play role as price takers leading them to have less power to set the price. Sometimes, farmers are manipulated by middlemen, especially during abundant harvest. Therefore, it requires an effort to cultivate fruits and create innovation to make them more durable and have higher economic value. The purpose of this research is how to increase the added- value of fruits that have high economic value. The research involved 60 farmers of Salak fruit as the sample. Then, descriptive analysis was used to analyze the data in this study. The results showed the selling price of Salak fruit is very low. Hence, to increase the added-value of the fruits, fruit processing is carried out by freezing - frying which can cause the fruits last longer. In addition to increase these added-value, the products can be accommodated for further processed without worrying about their crops rotted or unsold.

Keywords: fruits processing, Salak fruit, freezing frying, farmer’s welfare, Sleman, Yogyakarta

Procedia PDF Downloads 323

Authors: Hamidreza Joodaki, Sara Farzaneh, Jaleh Afshar Qhazvin

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Tourism industry is considered as the greatest and most various industry in the world. Most of these countries know this dynamic industry as main source of income, occupation, growth of private sector and development of infrastructure. One of the old methods of investment in countries such as Iran have transitional economy, is buying land and house, sometimes is resulted to high profit and of course for this reason hustler's are very interested in this background. Nowadays buying and selling land in the areas with pleasant climate in our country is considered. Since, Shahmirzad is a city with fair and desired environmental attractions is located in the border of deserted cities, mainly has special climatic position and these conditions are resulted to attraction of passenger, tourist for passing their leisure hours from Semnan and other cities of the area and from other provinces in hot seasons and with regard to these suitable conditions in the city buying land and housing also have been considered by most of residents of Semnan and cities around Shahmirzad by now. The aim of present research is investigation the role of tourism in increasing price of land and housing in Shahmirzad city. By studying on price of land and housing especially in central area, that gardens of the city are located in this area, we have concluded that role of tourism have caused in price of land and housing specially these prices in central and old areas are more expensive than towns around the city.

Keywords: tourism, climate conditions, price of land and housing, Shahmirzad

Procedia PDF Downloads 267

Authors: Carmen Cantuarias-Villessuzanne, Jeffrey Blain, Radmila Pineau

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Our research project aims at analyzing the sensitiveness of French households to urban biodiversity and urban ecosystem services (UES). Opinion surveys show that the French population is sensitive to biodiversity and ecosystem services loss, but the value given to these issues within urban fabric and real estate market lacks evidence. Using GIS data and economic evaluation, by hedonic price methods, weassess the isolated contribution of the explanatory variables of biodiversityand UES on the price of residential real estate. We analyze the variation of the valuefor three urban ecosystem services - flood control, proximity to green spaces, and refreshment - on the price of real estate whena property changes ownership. Our modeling and mapping focus on the price at theIRIS scale (statistical information unit) from 2014 to 2019. The main variables are internal characteristics of housing (area, kind of housing, heating), external characteristics(accessibility and infrastructure, economic, social, and physical environmentsuch as air pollution, noise), and biodiversity indicators and urban ecosystemservices for the Ile-de-France region. Moreover, we compare environmental values on the enhancement of greenspaces and their impact on residential choices. These studies are very useful for real estate developers because they enable them to promote green spaces, and municipalities to become more attractive.

Keywords: urban ecosystem services, sustainable real estate, urban biodiversity perception, hedonic price, environmental values

Procedia PDF Downloads 102

Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

Procedia PDF Downloads 497

Authors: Renzo Munoz-Najar, Cristina Wong, Daniel De La Torre Ugarte

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The literature on price transmission usually deals with asymmetries related to different commodities and/or the short and long term. The role of domestic regional differences and the relationship with asymmetries within a country are usually left out. This paper looks at the asymmetry in the transmission of rice prices from the international price to the farm gate prices in four northern regions of Peru for the last period 2001-2016. These regions are San Martín, Piura, Lambayeque and La Libertad. The relevance of the study lies in its ability to assess the need for policies aimed at improving the competitiveness of the market and ensuring the benefit of producers. There are differences in planting and harvesting dates, as well as in geographic location that justify the hypothesis of the existence of differences in the price transition asymmetries between these regions. Those differences are due to at least three factors geography, infrastructure development, and distribution systems. For this, the Threshold Vector Error Correction Model and the Autoregressive Vector Model with Threshold are used. Both models, collect asymmetric effects in the price adjustments. In this way, it is sought to verify that farm prices react more to falls than increases in international prices due to the high bargaining power of intermediaries. The results of the investigation suggest that the transmission of prices is significant only for Lambayeque and La Libertad. Likewise, the asymmetry in the transmission of prices for these regions is checked. However, these results are not met for San Martin and Piura, the main rice producers nationwide. A significant price transmission is verified only in the Lambayeque and La Libertad regions. San Martin and Piura, in spite of being the main rice producing regions of Peru, do not present a significant transmission of international prices; a high degree of self-sufficient supply might be at the center of the logic for this result. An additional finding is the short-term adjustment with respect to international prices, it is higher in La Libertad compared to Lambayeque, which could be explained by the greater bargaining power of intermediaries in the last-mentioned region due to the greater technological development in the mills.

Keywords: asymmetric price transmission, rice prices, price transmission, regional economics

Procedia PDF Downloads 187

Authors: Juan Manuel Sanchez-Cartas, Gonzalo Leon

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A price competition algorithm for ABMs based on game theory principles is proposed to deal with the simulation of theoretical market models. The algorithm is applied to the classical Hotelling’s model and to a two-sided market model to show it leads to the optimal behavior predicted by theoretical models. However, when theoretical models fail to predict the equilibrium, the algorithm is capable of reaching a feasible outcome. Results highlight that the algorithm can be implemented in other simulation models to guarantee rational users and endogenous optimal behaviors. Also, it can be applied as a tool of verification given that is theoretically based.

Keywords: agent-based models, algorithmic game theory, multi-sided markets, price optimization

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Authors: Shu-Bo Yang, Wei Wu, Yuan-Heng Liu

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This research aims to design a heat-exchanger type of methanol reformer coupled with a preheating design in gPROMS® environment. The endothermic methanol steam reforming reaction (MSR) and the exothermic preferential oxidation reaction (PROX) occur in the inner tube and the outer tube of the reformer, respectively. The effective heat transfer manner between the inner and outer tubes is investigated. It is verified that the countercurrent-flow type reformer provides the higher hydrogen yield than the cocurrent-flow type. Since the hot spot temperature appears in the outer tube, an improved scheme is proposed to suppress the hot spot temperature by splitting the excess air flowing into two sites. Finally, an optimization algorithm for maximizing the hydrogen yield is employed to determine optimal operating conditions.

Keywords: methanol reformer, methanol steam reforming, optimization, simulation

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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The Greek Energy Market is structured as a mandatory pool where the producers make their bid offers in day-ahead basis. The System Operator solves an optimization routine aiming at the minimization of the cost of produced electricity. The solution of the optimization problem leads to the calculation of the System Marginal Price (SMP). Accurate forecasts of the SMP can lead to increased profits and more efficient portfolio management from the producer`s perspective. Aim of this study is to provide a comparative analysis of various machine learning models such as artificial neural networks and neuro-fuzzy models for the prediction of the SMP of the Greek market. Machine learning algorithms are favored in predictions problems since they can capture and simulate the volatilities of complex time series.

Keywords: deregulated energy market, forecasting, machine learning, system marginal price

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Authors: Muhammad Siddique, Volodymyr Lugovskyy

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This paper explores how nations might mitigate the unfavorable impacts of oil price volatility on economic growth by switching to renewable energy sources. The impacts of uncertain factor prices on economic activity are examined by looking at the Realized Volatility (RV) of oil prices rather than the more traditional method of looking at oil price shocks. The United States of America (USA), China (C), India (I), United Kingdom (UK), Germany (G), Malaysia (M), and Pakistan (P) are all included to round out the traditional literature's examination of selected nations, which focuses on oil-importing and exporting economies. Granger Causality Tests (GCT), Impulse Response Functions (IRF), and Variance Decompositions (VD) demonstrate that in a Vector Auto-Regressive (VAR) scenario, the negative impacts of oil price volatility extend beyond what can be explained by oil price shocks alone for all of the nations in the sample. Different nations have different levels of vulnerability to changes in oil prices and other factors that may play a role in a sectoral composition and the energy mix. The conventional method, which only takes into account whether a country is a net oil importer or exporter, is inadequate. The potential economic advantages of initiatives to decouple the macroeconomy from volatile commodities markets are shown through simulations of volatility shocks in alternative energy mixes (with greater proportions of renewables). It is determined that in developing countries like Pakistan, increasing the use of renewable energy sources might lessen an economy's sensitivity to changes in oil prices; nonetheless, a country-specific study is required to identify particular policy actions. In sum, the research provides an innovative justification for mitigating economic growth's dependence on stable oil prices in our sample countries.

Keywords: oil price volatility, renewable energy, economic growth, developed and developing economies

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Authors: Amine M. Benmehaia, Amine Oulmane

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The supply response of agricultural commodities to changes in price incentives is an important issue for the success of any policy reform in the agricultural sector. This study aims to quantify the responsiveness of producers of some livestock commodities to price incentives in Algerian context. Time series analysis is used on annual data for a period of 52 years (1966-2018). Both co-integration and vector error correction model (VECM) are used through the Nerlove model of partial adjustment. The study attempts to determine the long-run and short-run relationships along with the magnitudes of disequilibria in the selected commodities. Results show that the short-run price elasticities are low in cow and sheep meat sectors (8.7 and 8% respectively), while their respective long-run elasticities are 16.5 and 10.5, whereas eggs and milk have very high short-run price elasticities (82 and 90% respectively) with long-run elasticities of 40 and 46 respectively. The error correction coefficient, reflecting the speed of adjustment towards the long-run equilibrium, is statistically significant and have the expected negative sign. Its estimates are 12.7 for cow meat, 33.5 for sheep meat, 46.7 for eggs and 8.4 for milk. It seems that cow meat and milk producers have a weak feedback of about 12.7% and 8.4% respectively of the previous year's disequilibrium from the long-run price elasticity, whereas sheep meat and eggs producers adjust to correct long run disequilibrium with a high speed of adjustment (33.5% and 46.7 % respectively). The implication of this is that much more in-depth research is needed to identify those factors that affect agricultural supply and to describe the effect of factors that shift supply in response to price incentives. This could provide valuable information for government in the use of appropriate policy measures.

Keywords: Algeria, cointegration, livestock, supply response, vector error correction model

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Authors: Şakir Bingöl, Abdullah Emre Aydemir, Abdullah Saado, Ahmet Akıl, Elif Canbaz, Feyza Nur Bulgurcu, Gizem Uzun, Günsu Bilge Dal, Muhammedcan Pirinççi

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Smart contracts, which can be applied in many different areas, from financial applications to the internet of things, come to the fore with their security, low cost, and self-executing features. In this paper, it is focused on peer-to-peer (P2P) energy trading and the implementation of the smart contract on the Ethereum blockchain. It is assumed a microgrid consists of consumers and prosumers that can produce solar and wind energy. The proposed architecture is a system where the prosumer makes the purchase or sale request in the smart contract and the maximum price obtained through the distribution system operator (DSO) by forecasting. It is aimed to forecast the hourly maximum unit price of energy by using deep learning instead of a fixed pricing. In this way, it will make the system more reliable as there will be more dynamic and accurate pricing. For this purpose, Istanbul's energy generation, energy consumption and market clearing price data were used. The consistency of the available data and forecasting results is observed and discussed with graphs.

Keywords: energy trading smart contract, deep learning, microgrid, forecasting, Ethereum, peer to peer

Procedia PDF Downloads 86

Authors: Jayanwita Sarkar, Usha Chakraborty, Bishwanath Chakraborty

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Plants are constantly interacting with various abiotic and biotic stresses. In changing climate scenario plants are continuously modifying physiological processes to adapt to changing environmental conditions which profoundly affect plant-pathogen interactions. Spot blotch in wheat is a fast-rising disease in the warmer plains of South Asia where the rise in minimum average temperature over most of the year already affecting wheat production. Hence, the study was undertaken to explore the role of elevated temperature in spot blotch disease development and modulation of antioxidative responses by plant growth promoting rhizobacteria (PGPR) for biocontrol of spot blotch at high temperature. Elevated temperature significantly increases the susceptibility of wheat plants to spot blotch causing pathogen Bipolaris sorokiniana. Two PGPR Bacillus safensis (W10) and Ochrobactrum pseudogrignonense (IP8) isolated from wheat (Triticum aestivum L.) and blady grass (Imperata cylindrical L.) rhizophere respectively, showing in vitro antagonistic activity against Bipolaris sorokiniana were tested for growth promotion and induction of resistance against spot blotch in wheat. GC-MS analysis showed that Bacillus safensis (W10) and Ochrobactrum pseudogrignonense (IP8) produced antifungal and antimicrobial compounds in culture. Seed priming with these two bacteria significantly increase growth, modulate antioxidative signaling and induce resistance and eventually reduce disease incidence in wheat plants at optimum as well as elevated temperature which was further confirmed by indirect immunofluorescence assay using polyclonal antibody raised against Bipolaris sorokiniana. Application of the PGPR led to enhancement in activities of plant defense enzymes- phenylalanine ammonia lyase, peroxidase, chitinase and β-1,3 glucanase in infected leaves. Immunolocalization of chitinase and β-1,3 glucanase in PGPR primed and pathogen inoculated leaf tissue was further confirmed by transmission electron microscopy using PAb of chitinase, β-1,3 glucanase and gold labelled conjugates. Activity of ascorbate-glutathione redox cycle related enzymes such as ascorbate peroxidase, superoxide dismutase and glutathione reductase along with antioxidants such as carotenoids, glutathione and ascorbate and osmolytes like proline and glycine betain accumulation were also increased during disease development in PGPR primed plant in comparison to unprimed plants at high temperature. Real-time PCR analysis revealed enhanced expression of defense genes- chalcone synthase and phenyl alanineammonia lyase. Over expression of heat shock proteins like HSP 70, small HSP 26.3 and heat shock factor HsfA3 in PGPR primed plants effectively protect plants against spot blotch infection at elevated temperature as compared with control plants. Our results revealed dynamic biochemical cross talk between elevated temperature and spot blotch disease development and furthermore highlight PGPR mediated array of antioxidative and molecular alterations responsible for induction of resistance against spot blotch disease at elevated temperature which seems to be associated with up-regulation of defense genes, heat shock proteins and heat shock factors, less ROS production, membrane damage, increased expression of redox enzymes and accumulation of osmolytes and antioxidants.

Keywords: antioxidative enzymes, defense enzymes, elevated temperature, heat shock proteins, PGPR, Real-Time PCR, spot blotch, wheat

Procedia PDF Downloads 136

Authors: Mohammad Rahim Rahnama

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The study employs Monte Carlo method for simulation of rented housing in Mashhad second largest city in Iran. A total number of 334 rental residential units in Mashhad, including both apartments and houses (villa), were randomly selected from advertisements placed in Khorasan Newspapers during the months of July and August of 2015. In order to simulate the monthly rent price, the rent index was calculated through combining the mortgage and the rent price. In the next step, the relation between the variables of the floor area and that of the number of bedrooms for each unit, in both apartments and houses(villa), was calculated through multivariate regression using SPSS and was coded in XML. The initial model was called using simulation button in SPSS and was simulated using triangular and binominal algorithms. The findings revealed that the average simulated rental index was 548.5$ per month. Calculating the sensitivity of rental index to a number of bedrooms we found that firstly, 97% of units have three bedrooms, and secondly as the number of bedrooms increases from one to three, for the rent price of less than 200$, the percentage of units having one bedroom decreases from 10% to 0. Contrariwise, for units with the rent price of more than 571.4$, the percentage of bedrooms increases from 37% to 48%. In the light of these findings, it becomes clear that planning to build rental residential units, overseeing the rent prices, and granting subsidies to rental residential units, for apartments with two bedrooms, present a felicitous policy for regulating residential units in Mashhad.

Keywords: Mashhad, Monte Carlo, simulation, rent price, residential unit

Procedia PDF Downloads 240

Authors: Dongqi Sun, Juan Tao, Yingying Wu

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This paper reexamines the appropriateness of contrarian trades as a proxy of informed trades, using high frequency Chinese stock data. Employing this measure for 5 minute intervals, a U-shaped intraday pattern of probability of informed trades (PIN) is found for the CSI300 stocks, which is consistent with previous findings for other markets. However, while dividing the trades into different sizes, a reversed U-shaped PIN from large-sized trades, opposed to the U-shaped pattern for small- and medium-sized trades, is observed. Drawing from the mixed evidence with different trade sizes, the price impact of trades is further investigated. By examining the relationship between trade imbalances and unexpected returns, larges-sized trades are found to have significant price impact. This implies that in those intervals with large trades, it is non-contrarian trades that are more likely to be informed trades. Taking account of the price impact of large-sized trades, non-contrarian trades are used to proxy for informed trading in those intervals with large trades, and contrarian trades are still used to measure informed trading in other intervals. A stronger U-shaped PIN is demonstrated from this modification. Auto-correlation and information advantage tests for robustness also support the modified informed trading measure.

Keywords: contrarian trades, informed trading, price impact, trade imbalance

Procedia PDF Downloads 133

Authors: Rania E. Ramadan

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Dental esthetics is subjective, can be reported by the dentist and not noticed by the patient. However, if there is any imperfection seen by both the dentist and the patient, it is considered as an unesthetic like white and/or brown spot lesions. Many patients nowadays have been concerned about dental esthetics. Esthetic rehabilitation of anterior teeth and even maxillary premolars aid a lot in patients’ satisfaction of their smile consequently, gaining positive psychological impact for the patients. Many cases need esthetic rehabilitation such as diastema closure, spaced teeth and masking discolored teeth. Dental fluorosis and enamel hypo calcification can be presented as white and/or brown spot lesions. There are many treatment options for the management of these spotted teeth. Treatment options range from bleaching, microabrasion, direct composite restorations, porcelain veneers, and complete coverage crowns. The selection of certain options depends on many factors: the patient’s age, socioeconomic status and the severity of the lesion. In this clinical report, a 22-year-old male patient has been presented to the Department of Prosthodontics in Alexandria University, Egypt. His chief complaint was, “I was unpleased by white and brown spots in my teeth and I want to close the space between the two maxillary central.” Upon medical history, clinical examination, diagnostic photographs, and digital smile design by Exocad software, lithium disilicate veneers were chosen as the treatment of choice in maxillary anterior and first premolars.

Keywords: flourosis, ceramic veneers, case report, diastema closure

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Authors: Ming Liu, Zhihui Sun, Xiaoning Zhang

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This paper studies a deterministic container transportation problem, jointly optimizing the berth allocation, quay crane assignment and yard storage allocation at container ports. The problem is formulated as an integer program to coordinate the decisions. Because of the large scale, it is then transformed into a set partitioning formulation, and a framework of branchand- price algorithm is provided to solve it.

Keywords: branch-and-price, container terminal, joint scheduling, maritime logistics

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Authors: P. S. Jain, K. D. Bobade, S. J. Surana

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A simple, selective, precise and Stability-indicating High-performance thin-layer chromatographic method for analysis of Naftopidil both in a bulk and in pharmaceutical formulation has been developed and validated. The method employed, HPTLC aluminium plates precoated with silica gel as the stationary phase. The solvent system consisted of hexane: ethyl acetate: glacial acetic acid (4:4:2 v/v). The system was found to give compact spot for Naftopidil (Rf value of 0.43±0.02). Densitometric analysis of Naftopidil was carried out in the absorbance mode at 253 nm. The linear regression analysis data for the calibration plots showed good linear relationship with r2=0.999±0.0001 with respect to peak area in the concentration range 200-1200 ng per spot. The method was validated for precision, recovery and robustness. The limits of detection and quantification were 20.35 and 61.68 ng per spot, respectively. Naftopidil was subjected to acid and alkali hydrolysis, oxidation and thermal degradation. The drug undergoes degradation under acidic, basic, oxidation and thermal conditions. This indicates that the drug is susceptible to acid, base, oxidation and thermal conditions. The degraded product was well resolved from the pure drug with significantly different Rf value. Statistical analysis proves that the method is repeatable, selective and accurate for the estimation of investigated drug. The proposed developed HPTLC method can be applied for identification and quantitative determination of Naftopidil in bulk drug and pharmaceutical formulation.

Keywords: naftopidil, HPTLC, validation, stability, degradation

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Authors: Qaiser Munir, Hamid Hussain

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Oil prices are believed to have a major impact on several economic indicators, leading to several instances where a comparison between oil prices and a trade deficit of oil-importing countries have been carried out. Building upon the narrative, this paper sheds light on the ongoing debate by inquiring upon the possibility of asymmetric linkages between oil prices, industrial production, exchange rate, whole price index, and trade deficit. The analytical tool used to further understand the complexities of a recent approach called nonlinear auto-regressive distributed lag model (NARDL) is utilised. Our results suggest that there are significant asymmetric effects among the main variables of interest. Further, our findings indicate that any variation in oil prices, industrial production, exchange rate, and whole price index on trade deficit tend to fluctuate in the long run. Moreover, the long-run picture denotes that increased oil price leads to a negative impact on the trade deficit, which, in its true essence, is a disproportionate impact. In addition to this, the Wald test simultaneously conducted concludes the absence of any significant evidence of the asymmetry in the oil prices impact on the trade balance in the short-run.

Keywords: trade deficit, oil prices, developing economy, NARDL

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Authors: Pratheeba Subramanian, Anjula Gurtoo, Mary Mathew

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Transaction of patents in emerging patent markets is gaining momentum. Pricing patents for a transaction say patent sale remains a challenge. Patents vary in their pricing with some patents fetching higher prices than others. Sale of patents in portfolios further complicates pricing with multiple patents playing a role in pricing a bundle. In this paper, a set of 138 US patents sold individually as single invention lots and 462 US patents sold in bundles of 120 portfolios are investigated to understand the dynamics of selling prices of singletons and portfolios and their determinants. Firstly, price variations when patents are sold individually as singletons and portfolios are studied. Multivariate statistical techniques are used for analysis both at the lot level as well as at the individual patent level. The results show portfolios fetching higher prices than singletons at the lot level. However, at the individual patent level singletons show higher prices than per patent price of individual patent members within the portfolio. Secondly, to understand the price determinants, the effect of patent, seller, and bundling related characteristics on selling prices is studied separately for singletons and portfolios. The results show differences in the set of characteristics determining prices of singletons and portfolios. Selling prices of singletons are found to be dependent on the patent related characteristics, unlike portfolios whose prices are found to be dependent on all three aspects – patent, seller, and bundling. The specific patent, seller and bundling characteristics influencing selling price are discussed along with the implications.

Keywords: auction, patents, portfolio bundling, seller type, selling price, singleton

Procedia PDF Downloads 299

Authors: Mohamed Fathy Heweage, Xiao Wen, Ayman Mokhtar, Ahmed Eldamarawy

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In this paper, we are interested in modeling, simulation, and measurement of the laser spot position with a quadrant detector. We enhance detection and tracking of semi-laser weapon decoding system based on microcontroller. The system receives the reflected pulse through quadrant detector and processes the laser pulses through a processing circuit, a microcontroller decoding laser pulse reflected by the target. The seeker accuracy will be enhanced by the decoding system, the laser detection time based on the receiving pulses number is reduced, a gate is used to limit the laser pulse width. The model is implemented based on Pulse Repetition Frequency (PRF) technique with two microcontroller units (MCU). MCU1 generates laser pulses with different codes. MCU2 decodes the laser code and locks the system at the specific code. The codes EW selected based on the two selector switches. The system is implemented and tested in Proteus ISIS software. The implementation of the full position determination circuit with the detector is produced. General system for the spot position determination was performed with the laser PRF for incident radiation and the mechanical system for adjusting system at different angles. The system test results show that the system can detect the laser code with only three received pulses based on the narrow gate signal, and good agreement between simulation and measured system performance is obtained.

Keywords: four quadrant detector, pulse code detection, laser guided weapons, pulse repetition frequency (PRF), Atmega 32 microcontrollers

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Authors: Widya Anggraini Pamungkas, Rosana Budi Setyawati, Awaludin Fitroh Rifai, Candra Pangesti Setiawan, Anatta Wahyu Budiiman, Inayati, Joko Waluyo, Sunu Herwi Pranolo

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The increase in LPG consumption in Indonesia is not balanced with the amount of supply. The high demand for LPG due to the success of the government's kerosene-to-LPG conversion program and the Covid-19 pandemic in 2020 led to an increase in LPG consumption in the household sector and caused Indonesia's trade balance to experience a deficit. The high consumption of LPG encourages the need for alternative fuels as a substitute or which aims to substitute LPG; one of the materials that can be used is Dimethyl Ether (DME). Dimethyl ether (DME) is an organic compound with the chemical formula CH 3. OCH 3 has a high cetane number and has characteristics similar to LPG. DME can be produced from various sources, such as coal, biomass and natural gas. Based on the economic analysis conducted at 10% IRR, coal has the largest NPV of Rp. 20,034,837,497,241 with a payback period of 3.86 years, then biomass with an NPV of Rp. 10,401,526,072,850 and a payback period of 5.16. the latter is natural gas with an NPV of IDR 7,401,272,559,191 and a payback period of 6.17 years. Of the three sources of raw materials used, if the sensitivity is calculated using the selling price of DME equal to the selling price of LPG, it will get an NPV value that is greater than the NPV value when using the current DME price. The advantages of coal as a raw material for DME are not only because it is profitable, namely: low price and abundant resources, but has high greenhouse gas emissions.

Keywords: LPG, DME, coal, biomass, natural gas

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Authors: Marjan Golmaryami, Marzieh Behzadi

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Recent developments in stock exchange highlight the need for an efficient and accurate method that helps stockholders make better decision. Since stock markets have lots of fluctuations during the time and different effective parameters, it is difficult to make good decisions. The purpose of this study is to employ artificial neural network (ANN) which can deal with time series data and nonlinear relation among variables to forecast next day stock price. Unlike other evolutionary algorithms which were utilized in stock exchange prediction, we trained our proposed neural network with metaheuristic bat algorithm, with fast and powerful convergence and applied it in stock price prediction for the first time. In order to prove the performance of the proposed method, this research selected a 7 year dataset from Parsian Bank stocks and after imposing data preprocessing, used 3 types of ANN (back propagation-ANN, particle swarm optimization-ANN and bat-ANN) to predict the closed price of stocks. Afterwards, this study engaged MATLAB to simulate 3 types of ANN, with the scoring target of mean absolute percentage error (MAPE). The results may be adapted to other companies stocks too.

Keywords: artificial neural network (ANN), bat algorithm, particle swarm optimization algorithm (PSO), stock exchange

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Authors: Christopher Reichstein, Ralf-Christian Härting, Martina Häußler

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With a continuously increasing speed of information exchange on the World Wide Web, retailers in the E-Commerce sector are faced with immense possibilities regarding different online purchase processes like dynamic price settings. By use of Dynamic Pricing, retailers are able to set short time price changes in order to optimize producer surplus. The empirical research illustrates the basics of Dynamic Pricing and identifies six influencing factors of Dynamic Pricing. The results of a structural equation modeling approach show five main drivers increasing the potential of dynamic price settings in the E-Commerce. Influencing factors are the knowledge of customers’ individual willingness to pay, rising sales, the possibility of customization, the data volume and the information about competitors’ pricing strategy.

Keywords: e-commerce, empirical research, experts, dynamic pricing (DP), influencing factors, potentials

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Authors: Tim Steinhaus, Christian Beidl

Abstract:

Urban air quality and climate protection are two major challenges for future mobility systems. Despite the steady reduction of pollutant emissions from vehicles over past decades, local immission load within cities partially still reaches heights, which are considered hazardous to human health. Although traffic-related emissions account for a major part of the overall urban pollution, modeling the exact interaction remains challenging. In this paper, a novel approach for the determination of the emission-immission interaction on the basis of neural network modeling for traffic induced NO2-immission load within a near-traffic hot-spot scenario is presented. In a detailed sensitivity analysis, the significance of relevant influencing variables on the prevailing NO2 concentration is initially analyzed. Based on this, the generation process of the model is described, in which not only environmental influences but also the vehicle fleet composition including its associated segment- and certification-specific real driving emission factors are derived and used as input quantities. The validity of this approach, which has been presented in the past, is re-examined in this paper using updated data on vehicle emissions and recent immission measurement data. Within the framework of a final scenario analysis, the future development of the immission load is forecast for different developments in the vehicle fleet composition. It is shown that immission levels of less than half of today’s yearly average limit values are technically feasible in hot-spot situations.

Keywords: air quality, emission, emission-immission-interaction, immission, NO2, zero impact

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Authors: Luis A. Anchordoqui, Jorge F. Soriano, Diego F. Torres

Abstract:

Auger data show evidence for a correlation between ultrahigh-energy cosmic rays (UHECRs) and nearby starburst galaxies. This intriguing correlation is consistent with data collected by the Telescope Array, which have revealed a much more pronounced directional 'hot spot' in arrival directions not far from the starburst galaxy M82. In this work, we assume starbursts are sources of UHECRs, and we investigate the prospects to use the observed distribution of UHECR arrival directions to constrain galactic magnetic field models. We show that if the Telescope Array hot spot indeed originates on M82, UHECR data would place a strong constraint on the turbulent component of the galactic magnetic field.

Keywords: galactic magnetic field, Pierre Auger observatory, telescope array, ultra-high energy cosmic rays

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Authors: Samira Mohabbatdar, Abbas Ahmadi, Mohsen S. Sajadieh

Abstract:

This paper deals with a two-level supply chain consisted of one manufacturer and one retailer for single-type product. The demand function of the customers depends on price. We consider an integrated production inventory system where the manufacturer processes raw materials in order to deliver finished product with imperfect quality to the retailer. Then retailer inspects the products and after that delivers perfect products to customers. The proposed model is based on the joint total profit of both the manufacturer and the retailer, and it determines the optimal ordering lot-size, number of shipment and selling price of the retailer. A numerical example is provided to analyse and illustrate the behaviour and application of the model. Finally, sensitivity analysis of the key parameters are presented to test feasibility of the model.

Keywords: supply chain, pricing policy, defective quality, joint economic lot sizing

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Authors: Zhang Pinliang, Chen Chuan, Song Guangming, Wu Qiang, Gong Zizheng, Li Ming

Abstract:

The active removal of space debris and asteroid defense are important issues in human space activities. Both of them need a detumbling process, for almost all space debris and asteroid are in a rotating state, and it`s hard and dangerous to capture or remove a target with a relatively high tumbling rate. So it`s necessary to find a method to reduce the angular rate first. The laser ablation method is an efficient way to tackle this detumbling problem, for it`s a contactless technique and can work at a safe distance. In existing research, a laser rotational control strategy based on the estimation of the instantaneous angular velocity of the target has been presented. But their calculation of control torque produced by a laser, which is very important in detumbling operation, is not accurate enough, for the method they used is only suitable for the plane or regularly shaped target, and they did not consider the influence of irregular shape and the size of the spot. In this paper, based on the triangulation reconstruction of the target surface, we propose a new method to calculate the impulse of the irregularly shaped target under both the covered irradiation and spot irradiation of the laser and verify its accuracy by theoretical formula calculation and impulse measurement experiment. Then we use it to study the process of detumbling cylinder and asteroid by laser. The result shows that the new method is universally practical and has high precision; it will take more than 13.9 hours to stop the rotation of Bennu with 1E+05kJ laser pulse energy; the speed of the detumbling process depends on the distance between the spot and the centroid of the target, which can be found an optimal value in every particular case.

Keywords: detumbling, laser ablation drive, space target, space debris remove

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Authors: Sungzoon Cho, Hye Jin Lee, Sungwhan Jeon, Dongyoung Min, Sungwon Lyu

Abstract:

Major public corporations worldwide use conference calls to report their quarterly earnings. These 'earnings' conference calls allow for questions from stock analysts. We investigated if it is possible to identify sentiment from the call script and use it to predict stock price movement. We analyzed call scripts from six companies, two each from Korea, China and Indonesia during six years 2011Q1 – 2017Q2. Random forest with Frequency-based sentiment scores using Loughran MacDonald Dictionary did better than control model with only financial indicators. When the stock prices went up 20 days from earnings release, our model predicted correctly 77% of time. When the model predicted 'up,' actual stock prices went up 65% of time. This preliminary result encourages us to investigate advanced sentiment scoring methodologies such as topic modeling, auto-encoder, and word2vec variants.

Keywords: earnings call script, random forest, sentiment analysis, stock price prediction

Procedia PDF Downloads 262