Search results for: quantitative precipitation forecasting

Authors: Kaniz Roksana, Aluthgun Hewage Shaini, Cheng Zhu

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Lead is environmentally hazardous because it may persist for a long time in soil, water, and air, and it can travel large distances when carried by wind or water. Lead is toxic to many different species of organisms and has the potential to disrupt ecosystem stability. Moreover, lead can contaminate crops and livestock, which can then have an adverse effect on human health. This study was conducted to use the enzyme-induced calcium carbonate precipitation (EICP) technique from soybean crude extract urease along coconut fiber derived biochar’s (CFB) to bioremediate lead. To study the desorption rates of heavy metals from the soil, lead (Pb) was added to the soil at load ratios of 50 and 100 mg/kg. There were five separate treatment soil columns created: control sample, only CFB, only EICP, EICP with 2% (w/w) CFB, and EICP with 4% (w/w) CFB. Laboratory scale experiment demonstrates significant lead removal from soil. The amount of CaCO₃ precipitated in the soil was measured using a gravimetric acid digestion test, which related heavy metal desorption to the amount of precipitated calcium carbonate. These findings were validated using a scanning electron microscope (SEM), which revealed calcium carbonate and lead coprecipitation. As a result, the study reveals that the EICP technique, in conjunction with coconut fiber biochar, could be an efficient alternative in the remediation of heavy metal ion-contaminated soils.

Keywords: enzyme induced calcium carbonate precipitation (EICP), coconut fiber derived biochar’s (CFB), bioremediation, heavy metal

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Namwinwelbere Dabire, Eugene C. Ezin, Adandedji M. Firmin

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The prediction of hydrological flows (rainfall-depth or rainfall-discharge) is becoming increasingly important in the management of hydrological risks such as floods. In this study, the Long Short-Term Memory (LSTM) network, a state-of-the-art algorithm dedicated to time series, is applied to predict the daily water level of Nokoue Lake in Benin. This paper aims to provide an effective and reliable method enable of reproducing the future daily water level of Nokoue Lake, which is influenced by a combination of two phenomena: rainfall and river flow (runoff from the Ouémé River, the Sô River, the Porto-Novo lagoon, and the Atlantic Ocean). Performance analysis based on the forecasting horizon indicates that LSTM can predict the water level of Nokoué Lake up to a forecast horizon of t+10 days. Performance metrics such as Root Mean Square Error (RMSE), coefficient of correlation (R²), Nash-Sutcliffe Efficiency (NSE), and Mean Absolute Error (MAE) agree on a forecast horizon of up to t+3 days. The values of these metrics remain stable for forecast horizons of t+1 days, t+2 days, and t+3 days. The values of R² and NSE are greater than 0.97 during the training and testing phases in the Nokoué Lake basin. Based on the evaluation indices used to assess the model's performance for the appropriate forecast horizon of water level in the Nokoué Lake basin, the forecast horizon of t+3 days is chosen for predicting future daily water levels.

Keywords: forecasting, long short-term memory cell, recurrent artificial neural network, Nokoué lake

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Authors: Anas Al-Ali, Mohammed Suleiman, Ayman Hussein

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Sulfur is an important element has many practical applications in present as nanoparticles. Nanosize sulfur particles also have many important applications like in pharmaceuticals, medicine, syn-thesis of nano-composites for lithium batteries, modification of carbon nano tubes. Different methods were used for nano-sized particle synthesis; among those, chemical precipitation, electrochemical method, micro emulsion technique, composing of oil, surfactant, co-surfactant, aqueous phases with the specific compositions and ultrasonic treatment of sulfur-cystine solution. In this work Sulfur nanoparticles (S NPs) were prepared by a quick precipitation method with and without using a surfactant to stabilize the formed S NPs. The synthesized S NPs were characterized by XRD, SEM and TEM in order to confirm their sizes and structures.Application of nanotechnology is suggested for diag-nosis and treatment of cancer. The anticancer activity of the prepared S NPs has been tested on various types of cancer cell clones including leukemia, kidney and colon cancers.

Keywords: sulfur nanoparticles (S-NPs), TEM, SEM, XRD

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Authors: Anas Al-Ali, Mohammed Suleiman, Ayman Hussein

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Sulfur is an important element has many practical applications in present as nanoparticles. Nanosize sulfur particles also have many important applications like in pharmaceuticals, medicine, synthesis of nanocomposites for lithium batteries, modification of carbon nanotubes. Different methods were used for nano-sized particle synthesis; among those, chemical precipitation, electrochemical method, micro-emulsion technique, composing of oil, surfactant, co-surfactant, aqueous phases with the specific compositions and ultrasonic treatment of sulfur-cystine solution. In this work, sulfur nanoparticles (S NPs) were prepared by a quick precipitation method with and without using a surfactant to stabilize the formed S NPs. The synthesized S NPs were characterized by XRD, SEM, and TEM in order to confirm their sizes and structures. Application of nanotechnology is suggested for diagnosis and treatment of cancer. The anticancer activity of the prepared S NPs has been tested on various types of cancer cell clones including leukemia, kidney and colon cancers.

Keywords: sulfur nanoparticles (S-NPs), TEM, SEM, anti cancer activity, XRD

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Authors: Nokwethaba Makhanya, Babatunde J. Abiodun, Piotr Wolski

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Climate change possibly intensifies hydrological droughts and reduces water availability in river basins. Despite this, most research on climate change effects in southern Africa has focused exclusively on meteorological droughts. This thesis projects the potential impact of climate change on the future characteristics of hydrological droughts in the Limpopo River Basin (LRB). The study uses regional climate model (RCM) measurements (from the Coordinated Regional Climate Downscaling Experiment, CORDEX) and a combination of hydrological simulations (using the Soil and Water Assessment Tool Plus model, SWAT+) to predict the impacts at four global warming levels (GWLs: 1.5℃, 2.0℃, 2.5℃, and 3.0℃) under the RCP8.5 future climate scenario. The SWAT+ model was calibrated and validated with a streamflow dataset observed over the basin, and the sensitivity of model parameters was investigated. The performance of the SWAT+LRB model was verified using the Nash-Sutcliffe efficiency (NSE), Percent Bias (PBIAS), Root Mean Square Error (RMSE), and coefficient of determination (R²). The Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) have been used to detect meteorological droughts. The Soil Water Index (SSI) has been used to define agricultural drought, while the Water Yield Drought Index (WYLDI), the Surface Run-off Index (SRI), and the Streamflow Index (SFI) have been used to characterise hydrological drought. The performance of the SWAT+ model simulations over LRB is sensitive to the parameters CN2 (initial SCS runoff curve number for moisture condition II) and ESCO (soil evaporation compensation factor). The best simulation generally performed better during the calibration period than the validation period. In calibration and validation periods, NSE is ≤ 0.8, while PBIAS is ≥ ﹣80.3%, RMSE ≥ 11.2 m³/s, and R² ≤ 0.9. The simulations project a future increase in temperature and potential evapotranspiration over the basin, but they do not project a significant future trend in precipitation and hydrological variables. However, the spatial distribution of precipitation reveals a projected increase in precipitation in the southern part of the basin and a decline in the northern part of the basin, with the region of reduced precipitation projected to increase with GWLs. A decrease in all hydrological variables is projected over most parts of the basin, especially over the eastern part of the basin. The simulations predict meteorological droughts (i.e., SPEI and SPI), agricultural droughts (i.e., SSI), and hydrological droughts (i.e., WYLDI, SRI) would become more intense and severe across the basin. SPEI-drought has a greater magnitude of increase than SPI-drought, and agricultural and hydrological droughts have a magnitude of increase between the two. As a result, this research suggests that future hydrological droughts over the LRB could be more severe than the SPI-drought projection predicts but less severe than the SPEI-drought projection. This research can be used to mitigate the effects of potential climate change on basin hydrological drought.

Keywords: climate change, CORDEX, drought, hydrological modelling, Limpopo River Basin

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Authors: Bandreddy Anand Babu, Srinivasa Rao Mandadi, C. Pradeep Reddy, N. Ramesh Babu

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In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and daptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating. The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab programming.

Keywords: ARIMA, ANFIS, fuzzy surmising tool stash, weather forecasting, MATLAB

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Authors: Chiara Cesali

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The effects of climate change are increasingly evident: increases in temperature (i.e. global warming), greater frequency of extreme weather events, i.e. storms, floods, which often affect transport infrastructures. Large-scale climatological models with long-term horizons (up to 2100) show the possibility of significant increases in precipitation in the future, according to the greenhouse gas emissions scenarios from IPCC. Consequently, the insufficiency of existing hydraulic works (i.e. bridges, culverts, drainage systems) may be more frequent, or those currently being designed may become insufficient in the future. Thus, the hydraulic design methods of transport infrastructure must begin to take into account the influence of climate change. To this purpose, criteria for applying to the hydraulic design of a railway infrastructure some of the approaches currently available for determining design rainfall intensity and/or peak discharge flow on the basis of possible climate change scenarios are defined and proposed in the paper. Some application cases are also described.

Keywords: climate change, hydraulic design, precipitation, railway

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Authors: Umberto Cerasani, Luca Cerasani

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Methods for earthquakes forecasting have been intensively investigated in the last decades, but there is still no universal solution agreed by seismologists. Rock failure is most often preceded by a tiny elastic movement in the failure area and by the appearance of micro-cracks. These micro-cracks could be detected at the soil surface and represent useful earth-quakes precursors. The aim of this study was to verify whether tiny raw acceleration signals (in the 10⁻¹ to 10⁻⁴ cm/s² range) prior to the arrival of main primary-waves could be exploitable and related to earthquakes magnitude. Mathematical tools such as Fast Fourier Transform (FFT), moving average and wavelets have been applied on raw acceleration data available on the ITACA web site, and the study focused on one of the most unpredictable earth-quakes, i.e., the August 24th, 2016 at 01H36 one that occurred in the central Italy area. It appeared that these tiny acceleration signals preceding main P-waves have different patterns both on frequency and time domains for high magnitude earthquakes compared to lower ones.

Keywords: earthquake, accelerometer, earthquake forecasting, seism

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Authors: Wei-Bo Hua, Zhuo Zheng, Xiao-Dong Guo, Ben-He Zhong

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The layered structure LiNi1/3Co1/3Mn1/3-xAlxO2 (x = 0 ~ 0.04) series cathode materials were synthesized by a carbonate co-precipitation method, followed by a high temperature calcination process. The influence of Al substitution on the microstructure and electrochemical performances of the prepared materials was investigated by X-Ray diffraction (XRD), scanning electron microscopy (SEM), and galvanostatic charge/discharge test. The results show that the LiNi1/3Co1/3Mn1/3-xAlxO2 has a well-ordered hexagonal "α" -NaFeO2 structure. Although the discharge capacity of Al-doped samples decreases as x increases, LiNi1/3Co1/3Mn1/3-0.02Al0.02O2 exhibits superior capacity retention at high voltage (4.6 V). Therefore, LiNi1/3Co1/3Mn1/3-0.02Al0.02O2 is a promising material for “green” vehicles.

Keywords: lithium ion battery, carbonate co-precipitation, doping, microstructure, electrochemical properties

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Authors: Siddhant Sahu, P. James Daniel Paul

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Technical analysis comprised by various technical indicators is a holistic way of representing price movement of stocks in the market. Various forms of indicators have evolved from the primitive ones in the past decades. There have been many attempts to introduce volume as a major determinant to determine strong patterns in market forecasting. The law of demand defines the relationship between the volume and price. Most of the traders are familiar with the volume game. Including the time dimension to the law of demand provides a different visualization to the theory. While attempting the same, it was found that there are different thresholds in the market for different companies. These thresholds have a significant influence on the price. This article is an attempt in determining the thresholds for companies using the three dimensional graphs for optimizing the portfolios. It also emphasizes on the magnitude of importance of volumes as a key factor for determining of predicting strong price movements, bullish and bearish markets. It uses a comprehensive data set of major companies which form a major chunk of the Indian automotive sector and are thus used as an illustration.

Keywords: technical analysis, expert system, law of demand, stocks, portfolio analysis, Indian automotive sector

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Authors: Amir Hamzeh Haghiabi

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Water resource management is of global significance as it plays a key role in the socioeconomic development of all nations. On account of the fact that Iran is situated in a highly pressurized belt in the world, precipitation is limited, so that the average annual precipitation in the country is about 250 mm, only about one third to one quarter of the world average for rainfall. Karkheh basin is located in the semiarid and arid regions of Western Iran, an area with severe water scarcity. 70 % of rainfall is directly evaporated. The potential annual evaporation of the southern and northern regions is 3,600 mm 1,800 mm, respectively. In this paper, Some aspects of water resources management for this region, the specifications of the Karkheh reservoir dam & hydroelectric power plant as the biggest dam in history of Iran with total volume of reservoir 7.3 Bm3 are illustrated. Also the situation of water availability in the basin, surface and groundwater potential are considered.

Keywords: Iran, water availability, water resources, Zagros

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Authors: Zerarka Hizia, Akchiche Mustapha, Prunier Florent

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The landslide of Ain El Hammam (AEH) is characterized by a complex geology and a high hydrogeology hazard. AEH's perpetual reactivation compels us to look closely at its triggers and to better understand the mechanisms of its evolution in mass and in depth. This study builds a numerical model to simulate the influencing factors such as precipitation, non-saturation, and pore pressure fluctuations, using Plaxis software. For a finer analysis of instabilities, we use Hill's criterion, based on the sign of the second order work, which is the most appropriate material stability criterion for non-associated elastoplastic materials. The results of this type of calculation allow us, in theory, to predict the shape and position of the slip surface(s) which are liable to ground movements of the slope, before reaching the rupture given by the plastic limit of Mohr Coulomb. To validate the numerical model, an analysis of inclinometer measures is performed to confirm the direction of movement and kinematic of the sliding mechanism of AEH’s slope.

Keywords: landslide, second order work, precipitation, inclinometers

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Authors: Josip Arneric, Blanka Skrabic Peric

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Forecasting of volatility, i.e. returns fluctuations, has been a topic of interest to portfolio managers, option traders and market makers in order to get higher profits or less risky positions. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most common used models are GARCH type models. As standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance, it is difficult the predict volatility using standard GARCH models. Due to practical limitations of these models different approaches have been proposed in the literature, based on Markov switching models. In such situations models in which the parameters are allowed to change over time are more appropriate because they allow some part of the model to depend on the state of the economy. The empirical analysis demonstrates that Markov switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility for selected emerging markets.

Keywords: emerging markets, Markov switching, GARCH model, transition probabilities

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Authors: Rachel Y. Zhang, Christopher K. Anderson

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A critical aspect of revenue management is a firm’s ability to predict demand as a function of price. Historically hotels have used simple time series models (regression and/or pick-up based models) owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand – focusing on the opportunities created by machine learning approaches, including K-Nearest-Neighbors, Support vector machine, Regression Tree, and Artificial Neural Network algorithms. The out-of-sample performances of above approaches to forecasting hotel demand are illustrated by using a proprietary sample of the market level (24 properties) transactional data for Las Vegas NV. Causal predictive models can be built and evaluated owing to the availability of market level (versus firm level) data. This research also compares and contrast model accuracy of firm-level models (i.e. predictive models for hotel A only using hotel A’s data) to models using market level data (prices, review scores, location, chain scale, etc… for all hotels within the market). The prospected models will be valuable for hotel revenue prediction given the basic characters of a hotel property or can be applied in performance evaluation for an existed hotel. The findings will unveil the features that play key roles in a hotel’s revenue performance, which would have considerable potential usefulness in both revenue prediction and evaluation.

Keywords: hotel revenue, k-nearest-neighbors, machine learning, neural network, prediction model, regression tree, support vector machine

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Authors: Juliana Barcelos Cordeiro, Khashayar Mahani, Farbod Farzan, Mohsen A. Jafari

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Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.  

Keywords: energy consumption forecasting, energy efficiency, load disaggregation, pattern recognition approach

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Authors: Raja Kannusamy

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The research focuses on reducing food waste in the restaurant industry. A study has been conducted on the chain of retail restaurant outlets. It has been observed that the food wastages are due to the inefficient inventory management systems practiced in the restaurant outlets. The major food items which are wasted more in quantity are being selected across the retail chain outlets. A moving average forecasting method has been applied for the selected food items so that their future demand could be predicted accurately and food wastage could be avoided. It has been found that the moving average prediction method helps in predicting forecasts accurately. The demand values obtained from the moving average method have been compared to the actual demand values and are found to be similar with minimum variations. The inventory optimization technique helps in reducing food wastage in restaurant supply chain outlets.

Keywords: food wastage, restaurant supply chain, inventory optimisation, demand forecasting

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Authors: Ahmad Shayeq Azizi, Yuji Toda

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In Afghanistan, floods are the most frequent and recurrent events among other natural disasters. On the other hand, lack of monitoring data is a severe problem, which increases the difficulty of making the appropriate flood countermeasures of flood forecasting. This study is carried out to simulate the flood inundation in Harirud River Basin by application of distributed hydrological model, Integrated Flood Analysis System (IFAS) and 2D hydrodynamic model, International River Interface Cooperative (iRIC) based on satellite rainfall combined with historical peak discharge and global accessed data. The results of the simulation can predict the inundation area, depth and velocity, and the hardware countermeasures such as the impact of levee installation can be discussed by using the present method. The methodology proposed in this study is suitable for the area where hydrological and geographical data including river survey data are poorly observed.

Keywords: distributed hydrological model, flood inundation, hydrodynamic model, ungauged basins

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Authors: Farzad Shirzad, Bohlol Alijani, Mehry Akbary, Mohammad Saligheh

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Climate change and global warming are very important issues today. The process of climate change, especially changes in temperature and precipitation, is the most important issue in the environmental sciences. Climate change means changing the averages in the long run. Iran is located in arid and semi-arid regions due to its proximity to the equator and its location in the subtropical high pressure zone. In this respect, the Hyrcanian forest is a green necklace between the Caspian Sea and the south of the Alborz mountain range. In the forty-third session of UNESCO, it was registered as the second natural heritage of Iran. Beech is one of the most important tree species and the most industrial species of Hyrcanian forests. In this research, using dendroclimatology, the width of the tree ring, and climatic data of temperature and precipitation from Shanderman meteorological station located in the study area, And non-parametric Mann-Kendall statistical method to investigate the trend of climate change over a time series of 202 years of growth ringsAnd Pearson statistical method was used to correlate the growth of "ring" growth rings of beech trees with climatic variables in the region. The results obtained from the time series of beech growth rings showed that the changes in beech growth rings had a downward and negative trend and were significant at the level of 5% and climate change occurred. The average minimum, medium, and maximum temperatures and evaporation in the growing season had an increasing trend, and the annual precipitation had a decreasing trend. Using Pearson method during fitting the correlation of diameter of growth rings with temperature, for the average in July, August, and September, the correlation is negative, and the average temperature in July, August, and September is negative, and for the average The average maximum temperature in February was correlation-positive and at the level of 95% was significant, and with precipitation, in June the correlation was at the level of 95% positive and significant.

Keywords: climate change, dendroclimatology, hyrcanian forest, beech

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Authors: Chattrarat Hotrawaisaya

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This research aims to formulate the logistics collaborative model which is the management tool for orchid flower exporter. The researchers study logistics activities in orchid supply chain that stakeholders can collaborate and develop, including demand forecasting, inventory management, warehouse and storage, order-processing, and transportation management. The research also explores logistics collaboration implementation into orchid’s stakeholders. The researcher collected data before implementation and after model implementation. Consequently, the costs and efficiency were calculated and compared between pre and post period of implementation. The research found that the results of applying the logistics collaborative model to orchid exporter reduces inventory cost and transport cost. The model also improves forecasting accuracy, and synchronizes supply chain of exporter. This research paper contributes the uniqueness logistics collaborative model which value to orchid industry in Thailand. The orchid exporters may use this model as their management tool which aims in competitive advantage.

Keywords: logistics, orchid, supply chain, collaboration

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Authors: L. L. Tundisi, A. Pessoa Jr., E. B. Tambourgi, E. Silveira, P. G. Mazzola

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L-asparaginase (L-ASNase) is the gold standard treatment for acute lymphoblastic leukemia that mainly affects pediatric patients; treatment increases survival from 20% to 90%. The characterization of other L-Asparaginases, apart from the most used from Escherichia coli and Erwinia chrysanthemi, has been reported, but the choice of the most appropriate is still under debate. This choice should be based on its pharmacokinetics, immune hypersensitivity, doses, prices, pharmacodynamics. The main factors influencing the antileukemic activity of ASNase are enzymatic activity, Km, glutaminase activity, clearance of the enzyme and development of resistance. However, most of the commercialized enzyme present an intrinsic glutaminase activity, which is responsible for some side effects. In this study, glutaminase free asparaginase produced from Aspergillus oryzae was precipitated in different percentages of ethanol (0–80%), until optimum ethanol concentration of 60% (w/w) was found. Following, precipitation of crude L-ASNase was performed in a single step, using 60% (w/w) ethanol, under constant agitation and temperature. It presented activity of 135.45 U/mg and after gel filtration chromatography with Sephadex G-the enzymatic activity was 322.02 U/mg. The apparent molecular mass of the purified L-ASNase fraction was estimated by 10% SDS-PAGE. Proteins were stained with Coomassie Brilliant Blue R-250. The molar mass range was from 10 kDa to 250 kDa. L-ASNase from Aspergillus oryzae was characterized aiming possible therapeutic use. Four different buffers (phosphate-citrate buffer pH 2.6 to 5.8; phosphate buffer pH 5.8 to 7.4; Tris - HCl pH 7.4 to 9.0; and carbonate buffer pH 9.8 to 10.6) were used to measure the optimum pH for L-ASNase activity. The optimum temperature for enzyme activity was measured at optimal pH conditions (Tris-HCl and phosphate buffer, pH 7.4) at different temperatures ranging from 5 to 55°C. All activities were calculated by quantifying the free ammonia, using the Nessler reagent. The kinetic parameters calculation, e.g. Michaelis-Menten constant (Km), maximum velocity (Vmax) and Hills coefficient (n), were performed by incubating the enzyme in different concentrations of the substrate at optimum conditions of pH and fitted on Hill’s equation. This glutaminase free asparaginase showed a low Km (3.39 mM and 3.81 mM) and enzymatic activity of 135.45 U/mg after precipitation with ethanol. After gel filtration chromatography it rose to 322.02 U/mg. Optimum activity was found between pH 5.8 - 9.0, best activity results with phosphate buffer pH 7.4 and Tris-HCl pH 7.4 and showed activity from 5°C to 55°C. These results indicate that L-ASNase from A. oryzae has the potential for human use.

Keywords: biopharmaceuticals, bioprocessing, bioproducts, biotechnology, enzyme activity, ethanol precipitation

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Snezhana G. Gocheva-Ilieva, Maya P. Stoimenova

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Ambient air pollution with fine particulate matter (PM10) is a systematic permanent problem in many countries around the world. The accumulation of a large number of measurements of both the PM10 concentrations and the accompanying atmospheric factors allow for their statistical modeling to detect dependencies and forecast future pollution. This study applies the classification and regression trees (CART) method for building and analyzing PM10 models. In the empirical study, average daily air data for the city of Pleven, Bulgaria for a period of 5 years are used. Predictors in the models are seven meteorological variables, time variables, as well as lagged PM10 variables and some lagged meteorological variables, delayed by 1 or 2 days with respect to the initial time series, respectively. The degree of influence of the predictors in the models is determined. The selected best CART models are used to forecast future PM10 concentrations for two days ahead after the last date in the modeling procedure and show very accurate results.

Keywords: cross-validation, decision tree, lagged variables, short-term forecasting

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Authors: Dawd Ahmed, Venkatesh Uddameri

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Agriculture in Ethiopia is the main economic sector influenced by agricultural drought. A simultaneous assessment of drought trends using multiple drought indicators is useful for drought planning and management. Intra-season and seasonal drought trends in Ethiopia were studied using a suite of drought indicators. Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), and Z-index for long-rainy, dry, and short-rainy seasons are used to identify drought-causing mechanisms. The Statistical software package R version 3.5.2 was used for data extraction and data analyses. Trend analysis indicated shifts in late-season long-rainy season precipitation into dry in the southwest and south-central portions of Ethiopia. Droughts during the dry season (October–January) were largely temperature controlled. Short-term temperature-controlled hydrologic processes exacerbated rainfall deficits during the short rainy season (February–May) and highlight the importance of temperature- and hydrology-induced soil dryness on the production of short-season crops such as tef. Droughts during the long-rainy season (June–September) were largely driven by precipitation declines arising from the narrowing of the intertropical convergence zone (ITCZ). Increased dryness during long-rainy season had severe consequences on the production of corn and sorghum. PDSI was an aggressive indicator of seasonal droughts suggesting the low natural resilience to combat the effects of slow-acting, moisture-depleting hydrologic processes. The lack of irrigation systems in the nation limits the ability to combat droughts and improve agricultural resilience. There is an urgent need to monitor soil moisture (a key agro-hydrologic variable) to better quantify the impacts of meteorological droughts on agricultural systems in Ethiopia.

Keywords: autocorrelation, climate change, droughts, Ethiopia, food security, palmer z-index, PDSI, SPEI, SPI, trend analysis

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Authors: Vinay C. Doranalu, Amba Shetty

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In recent days due to climate change, there is a large variation in spatial distribution of daily rainfall within a small region. Rainfall is one of the main end climatic variables which affect spatio-temporal patterns of water availability. The real task postured by the change in climate is identification, estimation and understanding the uncertainty of rainfall. This study intended to analyze the spatial variations and temporal trends of daily precipitation using high resolution (0.25º x 0.25º) gridded data of Indian Meteorological Department (IMD). For the study, 38 grid points were selected in the study area and analyzed for daily precipitation time series (113 years) over the period 1901-2013. Grid points were divided into two zones based on the elevation and situated location of grid points: Low Land (exposed to sea and low elevated area/ coastal region) and High Land (Interior from sea and high elevated area/western Ghats). Time series were applied to examine the spatial analysis and temporal trends in each grid points by non-parametric Mann-Kendall test and Theil-Sen estimator to perceive the nature of trend and magnitude of slope in trend of rainfall. Pettit-Mann-Whitney test is applied to detect the most probable change point in trends of the time period. Results have revealed remarkable monotonic trend in each grid for daily precipitation of the time series. In general, by the regional cluster analysis found that increasing precipitation trend in shoreline region and decreasing trend in Western Ghats from recent years. Spatial distribution of rainfall can be partly explained by heterogeneity in temporal trends of rainfall by change point analysis. The Mann-Kendall test shows significant variation as weaker rainfall towards the rainfall distribution over eastern parts of the Western Ghats region of Karnataka.

Keywords: change point analysis, coastal region India, gridded rainfall data, non-parametric

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Authors: R. J. Kuo, W. C. Cheng, W. C. Lien, T. J. Yang

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Taiwan is a hyper endemic area for the Hepatitis B virus (HBV). The estimated total number of HBsAg carriers in the general population who are more than 20 years old is more than 3 million. Therefore, a case record review is conducted from January 2003 to June 2007 for all patients with a diagnosis of acute hepatitis who were admitted to the Emergency Department (ED) of a well-known teaching hospital. The cost for the use of medical resources is defined as the total medical fee. In this study, principal component analysis (PCA) is firstly employed to reduce the number of dimensions. Support vector regression (SVR) and artificial neural network (ANN) are then used to develop the forecasting model. A total of 117 patients meet the inclusion criteria. 61% patients involved in this study are hepatitis B related. The computational result shows that the proposed PCA-SVR model has superior performance than other compared algorithms. In conclusion, the Child-Pugh score and echogram can both be used to predict the cost of medical resources for patients with acute hepatitis in the ED.

Keywords: acute hepatitis, medical resource cost, artificial neural network, support vector regression

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Authors: Mitra Arami

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The main objective of this paper is to identify the factors that influence Kuwaiti consumers’ behavior towards online shopping. A survey was conducted among B2C e-commerce customers using a structured self-administered questionnaire. The findings of this study show that B2C e-commerce customer behavior in Kuwait is strongly influenced by customer entertainment but weakly influenced by customer trust. While the overall research project involves exploratory research using mixed methods, the focus of this paper is on a quantitative analysis of responses obtained from a survey of Kuwaiti customers, with the design of the questionnaire instrument being based on the findings of a qualitative analysis. The main findings of the analysis include a list of key factors that affect Kuwait online shoppers, and quantitative indications of the relative strengths of the various relationships. This study provides a basis for further research and more in depth studies to find the scope of online shopping in Kuwait especially, the influence of hedonic and utilitarian motivations on user engagement.

Keywords: e-commerce, online shopping, customer behavior, quantitative analysis, Kuwait

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Authors: Michele Catalano, Jacopo Di Domenico, Luca Riccetti, Andrea Teglio

Abstract:

We develop a data-driven agent based model (ABM) for the Italian economy. We calibrate the model for the initial condition and parameters. As a preliminary step, we replicate the Monte-Carlo simulation for the Austrian economy. Then, we evaluate the dynamic properties of the model: the long-run equilibrium and the allocative efficiency in terms of disequilibrium patterns arising in the search and matching process for final goods, capital, intermediate goods, and credit markets. In this perspective, we use a randomized initial condition approach. We perform a robustness analysis perturbing the system for different parameter setups. We explore the empirical properties of the model using a rolling window forecast exercise from 2010 to 2022 to observe the model’s forecasting ability in the wake of the COVID-19 pandemic. We perform an analysis of the properties of the model with a different number of agents, that is, with different scales of the model compared to the real economy. The model generally displays transient dynamics that properly fit macroeconomic data regarding forecasting ability. We stress the model with a large set of shocks, namely interest policy, fiscal policy, and exogenous factors, such as external foreign demand for export. In this way, we can explore the most exposed sectors of the economy. Finally, we modify the technology mix of the various sectors and, consequently, the underlying input-output sectoral interdependence to stress the economy and observe the long-run projections. In this way, we can include in the model the generation of endogenous crisis due to the implied structural change, technological unemployment, and potential lack of aggregate demand creating the condition for cyclical endogenous crises reproduced in this artificial economy.

Keywords: agent-based models, behavioral macro, macroeconomic forecasting, micro data

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Authors: Jiahua Zhang, Qing Chang, Fengmei Yao

Abstract:

Studying on the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics andthe adaptation of ecosystems to global change. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) dataset and climate data were used to analyze the dynamics of grassland phenology as well as their correlation with climatic factors in different eco-geographic regions and elevation units across the Tibetan Plateau. The results showed that during 2003–2012, the start of the grassland greening season (SOS) appeared later while the end of the growing season (EOS) appeared earlier following the plateau’s precipitation and heat gradients from southeast to northwest. The multi-year mean value of SOS showed differences between various eco-geographic regions and was significantly impacted by average elevation and regional average precipitation during spring. Regional mean differences for EOS were mainly regulated by mean temperature during autumn. Changes in trends of SOS in the central and eastern eco-geographic regions were coupled to the mean temperature during spring, advancing by about 7d/°C. However, in the two southwestern eco-geographic regions, SOS was delayed significantly due to the impact of spring precipitation. The results also showed that the SOS occurred later with increasing elevation, as expected, with a delay rate of 0.66 d/100m. For 2003–2012, SOS showed an advancing trend in low-elevation areas, but a delayed trend in high-elevation areas, while EOS was delayed in low-elevation areas, but advanced in high-elevation areas. Grassland SOS and EOS changes may be influenced by a variety of other environmental factors in each eco-geographic region.

Keywords: grassland, phenology, MODIS, eco-geographic regions, elevation, climatic factors, Tibetan Plateau

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Authors: L. Korpinen, P. Taskinen, P. Rautio

Abstract:

This study aimed to describe procedures that we developed to use in the quantitative, bone-specific SPECT/CT at our hospital. Our procedures included the following questions for choosing imaging protocols, which were based on a clinical doctor's referral: (1) Is she/he a cancer patient or not? (2) Are there any indications of inflammatory rheumatoid arthritis? We performed about 1,106 skeletal scintigraphies over two years. About 394 patients were studied with quantitative bone-specific single-photon emission computed tomography/computerized tomography (SPECT/CT) (i.e., about 36% of all bone scintigraphies). Approximately 64% of the patients were studied using the conventional Anterior-Posterior/Posterior-Anterior imaging. Our procedure has improved efficiency and decreased cycle times.

Keywords: skeletal scintigraphy, SPECT/CT, imaging, procedure

Procedia PDF Downloads 124