Search results for: flavor forecasting

Authors: Sahar Heidary

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Breast cancer is a main public common obstacle global. Additionally, it is the second top reason for tumor death across women. Considering breast cancer cure choices can aid private doctors in precaution for their patients through future cancer treatment. This article reviews usual management centered on stage, histology, and biomarkers. The growth of breast cancer is a multi-stage procedure including numerous cell kinds and its inhibition residues stimulating in the universe. Timely identification of breast cancer is one of the finest methods to stop this illness. Entirely chief therapeutic administrations mention screening mammography for women aged 40 years and older. Breast cancer metastasis interpretations for the mainstream of deaths from breast cancer. The discovery of breast cancer metastasis at the initial step is essential for managing and estimate of breast cancer development. Developing methods consuming the exploration of flowing cancer cells illustrate talented outcomes in forecasting and classifying the initial steps of breast cancer metastasis in patients. In public, mammography residues are the key screening implement though the efficiency of medical breast checks and self-checkup is less. Innovative screening methods are doubtful to exchange mammography in the close upcoming for screening the overall people.

Keywords: breast cancer, screening, metastasis, methods

Procedia PDF Downloads 128

Authors: Qamar Iqbal

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Companies strive to improve their forecasting methods to predict the fluctuations in customer demand. These fluctuation and variation in demand affect the manufacturing operations and can limit a company’s ability to fulfill customer demand on time. Companies keep the inventory buffer and maintain the stocking levels to reduce the impact of demand variation. A mid-size company deals with thousands of stock keeping units (skus). It is neither easy and nor efficient to control and manage each sku. Inventory classification provides a tool to the management to increase their ability to support customer demand. The paper presents a framework that shows how inventory classification can play a role to increase supply chain responsiveness. A case study will be presented to further elaborate the method both for build-to-order and build-to-forecast manufacturing environments. Results will be compared that will show which manufacturing setting has advantage over another under different circumstances. The outcome of this study is very useful to the management because this will give them an insight on how inventory classification can be used to increase their ability to respond to changing customer needs.

Keywords: inventory classification, supply chain responsiveness, forecast, manufacturing environment

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi

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The probability distributions are the best method for forecasting of extreme hydrologic phenomena such as rainfall and flood flows. In this research, in order to determine suitable probability distribution for estimating of annual extreme rainfall and flood flows (discharge) series with different return periods, precipitation with 40 and discharge with 58 years time period had been collected from Karkheh River at Iran. After homogeneity and adequacy tests, data have been analyzed by Stormwater Management and Design Aid (SMADA) software and residual sum of squares (R.S.S). The best probability distribution was Log Pearson Type III with R.S.S value (145.91) and value (13.67) for peak discharge and Log Pearson Type III with R.S.S values (141.08) and (8.95) for maximum discharge in Jelogir Majin and Pole Zal stations, respectively. The best distribution for maximum precipitation in Jelogir Majin and Pole Zal stations was Log Pearson Type III distribution with R.S.S values (1.74&1.90) and then Pearson Type III distribution with R.S.S values (1.53&1.69). Overall, the Log Pearson Type III distributions are acceptable distribution types for representing statistics of extreme hydrologic phenomena in Karkheh River at Iran with the Pearson Type III distribution as a potential alternative.

Keywords: Karkheh River, Log Pearson Type III, probability distribution, residual sum of squares

Procedia PDF Downloads 176

Authors: Antonello Troncone, Luigi Pugliese, Andrea Parise, Enrico Conte

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The present study deals with the landslides periodically reactivated by groundwater level fluctuations owing to rainfall. The main type of movement which generally characterizes these landslides consists in sliding with quite small-displacement rates. Another peculiar characteristic of these landslides is that soil deformations are essentially concentrated within a thin shear band located below the body of the landslide, which, consequently, undergoes an approximately rigid sliding. In this context, a simple method is proposed in the present study to forecast the movements of this type of landslides owing to rainfall. To this purpose, the landslide body is schematized by means of a two-block model. Some analytical solutions are derived to relate rainfall measurements with groundwater level oscillations and these latter, in turn, to landslide mobility. The proposed method is attractive for engineering applications since it requires few parameters as input data, many of which can be obtained from conventional geotechnical tests. To demonstrate the predictive capability of the proposed method, the application to a well-documented landslide periodically reactivated by rainfall is shown.

Keywords: rainfall, water level fluctuations, landslide mobility, two-block model

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Authors: L. Rashidian, M. Rajabali

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Climate changes have unpredictable and costly effects on water resources of various basins. The impact of atmospheric phenomena on human life and the environment is so significant that only knowledge of management can reduce its consequences. In this study, using LARS.WG model and down scaling of general circulation climate model HADCM-3 and according to the IPCC scenarios, including series A1b, A2 and B1, we simulated data from 2010 to 2040 in order to using them for long term forecasting of climate parameters of the Caspian Sea and its impact on sea level. Our research involves collecting data on monthly precipitation amounts, minimum and maximum temperature and daily sunshine hours, from meteorological organization for Caspian Sea coastal station such as Gorgan, Ramsar, Rasht, Anzali, Astara and Ghaemshahr since their establishment until 2010. Considering the fact that the fluctuation range of water level in the Caspian Sea has various ups and downs in different times, there is an increase in minimum and maximum temperature for all the mentioned scenarios, which will last until 2040. Overall, the amount of rainfall in cities bordering the Caspian Sea was studied based on the three scenarios, which shows an increase in the amount. However, there will be a decrease in water level of the Caspian Sea till 2040.

Keywords: IPCC, climate change, atmospheric circulation, Caspian Sea, HADCM3, sea level

Procedia PDF Downloads 214

Authors: Muhammad Ajmal

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- Thermal processing and subsequent storage of ultra-heat treated (UHT) milk leads to alteration in protein profile, Maillard reaction and lipid oxidation. Concentration of carbohydrates in normal and flavored version of UHT milk is considerably different. Transition in protein profile, Maillard reaction and lipid oxidation in UHT flavored milk was determined for 90 days at ambient conditions and analyzed at 0, 45 and 90 days of storage. Protein profile, hydroxymethyl furfural, furosine, Nε-carboxymethyl-l-lysine, fatty acid profile, free fatty acids, peroxide value and sensory characteristics were determined. After 90 days of storage, fat, protein, total solids contents and pH were significantly less than the initial values determined at 0 day. As compared to protein profile normal UHT milk, more pronounced changes were recorded in different fractions of protein in UHT milk at 45 and 90 days of storage. Tyrosine content of flavored UHT milk at 0, 45 and 90 days of storage were 3.5, 6.9 and 15.2 µg tyrosine/ml. After 45 days of storage, the decline in αs1-casein, αs2-casein, β-casein, κ-casein, β-lactoglobulin, α-lactalbumin, immunoglobulin and bovine serum albumin were 3.35%, 10.5%, 7.89%, 18.8%, 53.6%, 20.1%, 26.9 and 37.5%. After 90 days of storage, the decline in αs1-casein, αs2-casein, β-casein, κ-casein, β-lactoglobulin, α-lactalbumin, immunoglobulin and bovine serum albumin were 11.2%, 34.8%, 14.3%, 33.9%, 56.9%, 24.8%, 36.5% and 43.1%. Hydroxy methyl furfural content of UHT milk at 0, 45 and 90 days of storage were 1.56, 4.18 and 7.61 (µmol/L). Furosine content of flavored UHT milk at 0, 45 and 90 days of storage intervals were 278, 392 and 561 mg/100g protein. Nε-carboxymethyl-l-lysine content of UHT flavored milk at 0, 45 and 90 days of storage were 67, 135 and 343mg/kg protein. After 90 days of storage of flavored UHT milk, the loss of unsaturated fatty acids 45.7% from the initial values. At 0, 45 and 90 days of storage, free fatty acids of flavored UHT milk were 0.08%, 0.11% and 0.16% (p<0.05). Peroxide value of flavored UHT milk at 0, 45 and 90 days of storage was 0.22, 0.65 and 2.88 (MeqO²/kg). Sensory analysis of flavored UHT milk after 90 days indicated that appearance, flavor and mouth feel score significantly decreased from the initial values recorded at 0 day. Findings of this investigation evidenced that in flavored UHT milk more pronounced changes take place in protein profile, Maillard reaction products and lipid oxidation as compared to normal UHT milk.

Keywords: UHT flavored milk , hydroxymethyl furfural, lipid oxidation, sensory properties

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Authors: Marc S. Paolella, Pawel Polak

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The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery.

Keywords: comfort, financial crises, portfolio optimization, risk monitoring

Procedia PDF Downloads 497

Authors: Paul Han, Jun-Geol Baek

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In the flat panel display industry, the scheduler and dispatching system to meet production target quantities and the deadline of production are the major production management system which controls each facility production order and distribution of WIP (Work in Process). In dispatching system, delivery time is a key factor for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors and a forecasting model of delivery time. Of survival analysis techniques to select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the Accelerated Failure Time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the Mean Square Error (MSE) criteria, the AFT model decreased by 33.8% compared to the existing prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing a delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

Keywords: delivery time, survival analysis, Cox PH model, accelerated failure time model

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Authors: Rawaa H. El-Bidweihy, Hisham M. Abdelsalam, Ihab A. El-Khodary

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In an era where machines run and shape our world, the need for a stable, non-ending source of energy emerges. In this study, the focus was on the solar energy in Egypt as a renewable source, the most important factors that could affect the solar energy’s market share throughout its life cycle production were analyzed and filtered, the relationships between them were derived before structuring a Bayesian network. Also, forecasted models were built for multiple factors to predict the states in Egypt by 2035, based on historical data and patterns, to be used as the nodes’ states in the network. 37 factors were found to might have an impact on the use of solar energy and then were deducted to 12 factors that were chosen to be the most effective to the solar energy’s life cycle in Egypt, based on surveying experts and data analysis, some of the factors were found to be recurring in multiple stages. The presented Bayesian network could be used later for scenario and decision analysis of using solar energy in Egypt, as a stable renewable source for generating any type of energy needed.

Keywords: ARIMA, auto correlation, Bayesian network, forecasting models, life cycle, partial correlation, renewable energy, SARIMA, solar energy

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Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

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A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

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Authors: Cem Isik Dogru, Salih Tekin, Kursad Derinkuyu

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Electric vehicle (EV) carsharing is an alternative method to tackle urban transportation problems. This method can be applied by several options. One of the options is the one-way carsharing, which allow an EV to be taken at a designated location and leaving it on another specified location customer desires. Although it may increase users’ satisfaction, the issues, namely, demand dissatisfaction, relocation of EVs and charging schedules, must be dealt with. Also, excessive electricity has to be stored in batteries of EVs. To cope with aforementioned issues, two-step mixed integer programming (MIP) model is proposed. In first step, the integer programming model is used to determine amount of electricity to be sold to the grid in terms of time periods for extra profit. Determined amounts are provided from the batteries of EVs. Also, this step works in day-ahead electricity markets with forecast of periodical electricity prices. In second step, other MIP model optimizes daily operations of one-way carsharing: charging-discharging schedules, relocation of EVs to serve more demand and renting to maximize the profit of EV fleet owner. Due to complexity of the models, heuristic methods are introduced to attain a feasible solution and different price information scenarios are compared.

Keywords: electric vehicles, forecasting, mixed integer programming, one-way carsharing

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Authors: Bahareh Sarmadi, Paul A. Kilmartin, Leandro D. Araújo, Brandt P. Bastow

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Aim: Sauvignon blanc is the most substantial variety cultivated in almost 62% of all producing vineyards of New Zealand. The popularity of New Zealand Sauvignon blanc is due to its unique taste. It is the most famous wine characterized by its aroma profile derived from mercaptans. 3-mercaptohexan-1-ol (3MH) and 3-mercaptohexyl acetate (3MHA) are two of the most important volatile mercaptans found in Sauvignon blanc wines. “Viticultural” and “Enological” factors such as machine-harvesting, the most common harvesting practice used in New Zealand, can be among the reasons for this distinct flavor. Elemental sulfur is commonly sprayed in the fields to protect berries against powdery mildew. Although it is not the only source of sulfur, this practice creates a source of elemental sulfur that can be transferred into the must and eventually into wines. Despite the clear effects of residual elemental sulfur present in the must on the quality and aroma of the final wines, its measurement before harvest or fermentation is not a regular practice in the wineries. This can be due to the lack of accessible and applicable methods for the equipment at most commercial wineries. This study aims to establish a relationship between the number and frequency of elemental sulfur applications and the concentration of polyfunctional mercaptans in the final wines. Methods: An apparatus was designed to reduce elemental sulfur to sulfide, then an ion-selective electrode to measure sulfide concentration. During harvest 2022, we explored a wider range of residual elemental sulfur levels than what typically applies in the vineyards. This has been done through later manual elemental sulfur applications in the vineyard. Additional sulfur applications were made 20, 10 and 5 days prior to harvesting the treated grapes, covering long and short pre-harvest intervals (PHI). The grapes were processed into juice and fermented into wine; then, they were analyzed to find the correlation between polyfunctional mercaptans concentrations in the wines and residual elemental sulfur in the juice samples. Results: The research showed that higher 3MH/3MHA was formed when elemental sulfur was applied more frequent in the vineyards and supported the proposed pathway in which elemental sulfur is a source of 3MH formation in wines.

Keywords: sauvignon blanc, elemental sulfur, polyfunctional mercaptans, varietal thiols

Procedia PDF Downloads 73

Authors: Ashish Ashish

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In the last few decades, the discrete chaos of difference equations has gained a massive attention of academicians and scholars due to its tremendous applications in each and every branch of science, such as cryptography, traffic control models, secure communications, weather forecasting, and engineering. In this article, a generalized logistic discrete map is established and discrete chaos is reported through period doubling bifurcation, period three orbit and Lyapunov exponent. It is interesting to see that the generalized logistic map exhibits superior chaos due to the presence of an extra degree of freedom of an ordered parameter. The period doubling bifurcation and Lyapunov exponent are demonstrated for some particular values of parameter and the discrete chaos is determined in the sense of Devaney's definition of chaos theoretically as well as numerically. Moreover, the study discusses an extended chaos based image encryption and decryption scheme in cryptography using this novel system. Surprisingly, a larger key space for coding and more sensitive dependence on initial conditions are examined for encryption and decryption of text messages, images and videos which secure the system strongly from external cyber attacks, coding attacks, statistic attacks and differential attacks.

Keywords: chaos, period-doubling, logistic map, Lyapunov exponent, image encryption

Procedia PDF Downloads 118

Authors: Mani Venkatesh, Catarina Delgado, Purvishkumar Patel

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The sustainable supply chain is gaining popularity among practitioners because of increased environmental degradation and stakeholder awareness. On the other hand supply chain, risk management is very crucial for the practitioners as it potentially disrupts supply chain operations. Prediction and addressing the risk caused by social issues in the supply chain is paramount importance to the sustainable enterprise. More recently, the usage of Big data analytics for forecasting business trends has been gaining momentum among professionals. The aim of the research is to explore the application of big data, predictive analytics in successfully mitigating supply chain social risk and demonstrate how such mitigation can help in achieving sustainability (environmental, economic & social). The method involves the identification and validation of social issues in the supply chain by an expert panel and survey. Later, we used a case study to illustrate the application of big data in the successful identification and mitigation of social issues in the supply chain. Our result shows that the company can predict various social issues through big data, predictive analytics and mitigate the social risk. We also discuss the implication of this research to the body of knowledge and practice.

Keywords: big data, sustainability, supply chain social sustainability, social risk, case study

Procedia PDF Downloads 373

Authors: Sirilak Areerachakul

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Water quality has initiated serious management efforts in many countries. Artificial Neural Network (ANN) models are developed as forecasting tools in predicting water quality trend based on historical data. This study endeavors to automatically classify water quality. The water quality classes are evaluated using 6 factor indices. These factors are pH value (pH), Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Nitrate Nitrogen (NO3N), Ammonia Nitrogen (NH3N) and Total Coliform (T-Coliform). The methodology involves applying data mining techniques using multilayer perceptron (MLP) neural network models. The data consisted of 11 sites of Saen Saep canal in Bangkok, Thailand. The data is obtained from the Department of Drainage and Sewerage Bangkok Metropolitan Administration during 2007-2011. The results of multilayer perceptron neural network exhibit a high accuracy multilayer perception rate at 94.23% in classifying the water quality of Saen Saep canal in Bangkok. Subsequently, this encouraging result could be combined with GIS data improves the classification accuracy significantly.

Keywords: artificial neural network, geographic information system, water quality, computer science

Procedia PDF Downloads 320

Authors: Alefsi D. Sanchez-Reinoso, Leonardo Lombardini, Hermann Restrepo

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Colombian coffee is recognized worldwide for its mild flavor and aroma. Its cultivation generates a large amount of waste, such as fresh pulp, which leads to environmental, health, and economic problems. Obtaining biochar (BC) by pyrolysis of coffee pulp and its incorporation to the soil can be a complement to the crop mineral nutrition. The objective was to evaluate the effect of the application of BC obtained from coffee pulp on the physiology and agronomic performance of the Castillo variety coffee crop (Coffea arabica L.). The research was developed in field condition experiment, using a three-year-old commercial coffee crop, carried out in Tolima. Four doses of BC (0, 4, 8 and 16 t ha-1) and four levels of chemical fertilization (CF) (0%, 33%, 66% and 100% of the nutritional requirements) were evaluated. Three groups of variables were recorded during the experiment: i) physiological parameters such as Gas exchange, the maximum quantum yield of PSII (Fv/Fm), biomass, and water status were measured; ii) physical and chemical characteristics of the soil in a commercial coffee crop, and iii) physiochemical and sensorial parameters of roasted beans and coffee beverages. The results indicated that a positive effect was found in plants with 8 t ha-1 BC and fertilization levels of 66 and 100%. Also, a positive effect was observed in coffee trees treated with 8 t ha-1 BC and 100%. In addition, the application of 16 t ha-1 BC increased the soil pHand microbial respiration; reduced the apparent density and state of aggregation of the soil compared to 0 t ha-1 BC. Applications of 8 and 16 t ha-1 BC and 66%-100% chemical fertilization registered greater sensitivity to the aromatic compounds of roasted coffee beans in the electronic nose. Amendments of BC between 8 and 16 t ha-1 and CF between 66% and 100% increased the content of total soluble solids (TSS), reduced the pH, and increased the titratable acidity in beverages of roasted coffee beans. In conclusion, 8 t ha-1 BC of the coffee pulp can be an alternative to supplement the nutrition of coffee seedlings and trees. Applications between 8 and 16 t ha-1 BC support coffee soil management strategies and help the use of solid waste. BC as a complement to chemical fertilization showed a positive effect on the aromatic profile obtained for roasted coffee beans and cup quality attributes.

Keywords: crop yield, cup quality, mineral nutrition, pyrolysis, soil amendment

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Authors: Satish Kumar Pappu

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The most common questions which are frequently asked in oil and gas industry are how much is the current production rate from a particular well and what is the approximate predicted life of that well. These questions can be answered through forecasting of important realistic data like flowing tubing hole pressures FTHP, Production decline curves which are used predict the future performance of a well in a reservoir. With the advent of directional drilling, cluster well drilling has gained much importance and in-fact has even revolutionized the whole world of oil and gas industry. An oil or gas reservoir can generally be described as a collection of several overlying, producing and potentially producing sands in to which a number of wells are drilled depending upon the in-place volume and several other important factors both technical and economical in nature, in some sands only one well is drilled and in some, more than one. The aim of this study is to derive important information from the data collected over a period of time at regular intervals on a depleted well in a reservoir sand and apply this information to predict the performance of other wells in that reservoir sand. The depleted wells are the most common observations when an oil or gas field is being visited, w the application of this study more realistic in nature.

Keywords: decline curve analysis, estimation of future gas reserves, reservoir sands, reservoir risk profile

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Authors: Igor Jerkovic

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Owing to the attractive sensory properties and low odour thresholds, norisoprenoids (degraded carotenoid-like structures with 3,5,5-trimethylcyclohex-2-enoic unit) have been identified as aroma contributors in a number of different matrices. C₁₃-Norisoprenoids have been found among volatile organic compounds of various honey types as well as C₉//C₁₀-norisoprenoids or C₁₄/C₁₅-norisoprenoids. Besides degradation of abscisic acid (which produces, e.g., dehydrovomifoliol, vomifoliol, others), the cleavage of the C(9)=C(10) bond of other carotenoid precursors directly generates nonspecific C₁₃-norisoprenoids such as trans-β-damascenone, 3-hydroxy-trans-β-damascone, 3-oxo-α-ionol, 3-oxo-α-ionone, β-ionone found in various honey types. β-Damascenone and β-ionone smelling like honey, exhibit the lowest odour threshold values of all C₁₃-norisoprenoids. The presentation is targeted on two uncommon C₁₃-norisoprenoids in the honey flavor that could be used as specific or nonspecific chemical markers of the botanical origin. Namely, after screening of different honey types, the focus was directed on Centaruea cyanus L. and Allium ursinum L. honey. The samples were extracted by headspace solid-phase microextraction (HS-SPME) and ultrasonic solvent extraction (USE) and the extracts were analysed by gas chromatography and mass spectrometry (GC-MS). SPME fiber with divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) coating was applied for the research of C. cyanus honey headspace and predominant identified compound was 3,4-dihydro-3-oxoedulan (2,5,5,8a-tetramethyl-2,3,5,6,8,8a-hexahydro-7H-chromen-7-one also known as 2,3,5,6,8,8a-hexahydro-2,5,5,8a-tetramethyl-7H-1-benzo-pyran-7-one). The oxoedulan structure contains epoxide and it is more volatile in comparison with its hydroxylated precursors. This compound has not been found in other honey types and can be considered specific for C. cyanus honey. The dichloromethane extract of A. ursinum honey contained abundant (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one that was previously isolated as dominant substance from the ether extracts of New Zealand thyme honey. Although a wide variety of degraded carotenoid-like substances have been identified from different honey types, this appears to be rare situation where 3,4-dihydro-3-oxoedulan and (E)-4-(r-1',t-2',c-4'-trihydroxy-3',6',6'-trimethylcyclohexyl)-but-3-en-2-one have been found that is of great importance for chemical fingerprinting and identification of the chemical biomarkers that can complement the pollen analysis as the major method for the honey classification.

Keywords: 3, 4-dihydro-3-oxoedulan, (E)-4-(r-1', t-2', c-4'-trihydroxy-3', 6', 6'-trimethylcyclohexyl)-but-3-en-2-one, honey flavour, C₁₃-norisoprenoids

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Authors: Ali Reza Sattarzadeh, Ronny J. Kutadinata, Pubudu N. Pathirana, Van Thanh Huynh

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Traffic congestion has become a severe worldwide problem, affecting everyday life, fuel consumption, time, and air pollution. The primary causes of these issues are inadequate transportation infrastructure, poor traffic signal management, and rising population. Traffic flow forecasting is one of the essential and effective methods in urban congestion and traffic management, which has attracted the attention of researchers. With the development of technology, undeniable progress has been achieved in existing methods. However, there is a possibility of improvement in the extraction of temporal and spatial features to determine the importance of traffic flow sequences and extraction features. In the proposed model, we implement the convolutional neural network (CNN) and long short-term memory (LSTM) deep learning models for mining nonlinear correlations and their effectiveness in increasing the accuracy of traffic flow prediction in the real dataset. According to the experiments, the results indicate that implementing Conv-LSTM networks increases the productivity and accuracy of deep learning models for traffic flow prediction.

Keywords: deep learning algorithms, intelligent transportation systems, spatiotemporal features, traffic flow prediction

Procedia PDF Downloads 136

Authors: Thanh Van Hoang, Tien Yin Chou, Yao Min Fang, Yi Min Huang, Xuan Linh Nguyen

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Reliable information on the spatial distribution of annual rainfall and temperature is essential in research projects relating to urban and regional planning. This research presents results of a classification of temperature and rainfall in the Red River Delta of northern Vietnam based on measurements from seven meteorological stations (Ha Nam, Hung Yen, Lang, Nam Dinh, Ninh Binh, Phu Lien, Thai Binh) in the river basin over a thirty-years period from 1982-2011. The average accumulated rainfall trends in the delta are analysed and form the basis of research essential to weather and climate forecasting. This study employs interpolation based on the Kriging Method for daily rainfall (min and max) and daily temperature (min and max) in order to improve the understanding of sources of variation and uncertainly in these important meteorological parameters. To the Kriging method, the results will show the different models and the different parameters based on the various precipitation series. The results provide a useful reference to assist decision makers in developing smart agriculture strategies for the Red River Delta in Vietnam.

Keywords: spatial interpolation method, ArcGIS, temperature variability, rainfall variability, Red River Delta, Vietnam

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Authors: Shoaib Yousaf

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The main purpose of this research is to the empirical investigation of the bullwhip effect under sensitivity analysis in the two-tier supply chain. The simulation modeling technique has been applied in this research as a research methodology to see the sensitivity analysis of the bullwhip effect in the rice industry of Pakistan. The research comprises two case studies that have been chosen as a sample. The results of this research have confirmed that reduction in production delay reduces the bullwhip effect, which conforms to the time compressing paradigm and the significance of the reduction in production delay to lessen demand amplification. The result of this research also indicates that by increasing the value of time to adjust inventory decreases the bullwhip effect. Furthermore, by decreasing the value of alpha increases the damping effect of the exponential smoother, it is not surprising that it also reduces the bullwhip effect. Moreover, by reducing the value of time to work in progress also reduces the bullwhip effect. This research will help practitioners and operation managers to reduces the major costs of their products in three ways. They can reduce their i) inventory levels, ii) better utilize their capacity and iii) improve their forecasting techniques. However, this study is based on two tier supply chain, while in reality the supply chain has got many tiers. Hence, future work will be extended across more than two-tier supply chains.

Keywords: bullwhip effect, rice industry, supply chain dynamics, simulation, sensitivity analysis

Procedia PDF Downloads 114

Authors: Belkacem Selma, Boumediene Selma, Tourkia Guerzou, Abbes Labdelli

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Natural disasters have occurred and will continue to cause human and material damage. Therefore, the idea of "preventing" natural disasters will never be possible. However, their prediction is possible with the advancement of technology. Even if natural disasters are effectively inevitable, their consequences may be partly controlled. The rapid growth and progress of artificial intelligence (AI) had a major impact on the prediction of natural disasters and risk assessment which are necessary for effective disaster reduction. The Earthquakes prediction to prevent the loss of human lives and even property damage is an important factor; that is why it is crucial to develop techniques for predicting this natural disaster. This present study aims to analyze the ability of artificial neural networks (ANNs) to predict earthquakes that occur in a given area. The used data describe the problem of high energy (higher than 10^4J) seismic bumps forecasting in a coal mine using two long walls as an example. For this purpose, seismic bumps data obtained from mines has been analyzed. The results obtained show that the ANN with high accuracy was able to predict earthquake parameters; the classification accuracy through neural networks is more than 94%, and that the models developed are efficient and robust and depend only weakly on the initial database.

Keywords: earthquake prediction, ANN, seismic bumps

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Authors: Jiemiao Chen, Shuoxun Xu

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The main goal of our research is to apply the Vine copula measuring dependency between price, temperature, and precipitation indices to calculate a fair crop insurance premium. This research is focused on Worth, Iowa, United States, over the period from 2000 to 2020, where the farmers are dependent on precipitation and average temperature during the growth period of corn. Our proposed insurance considers both the natural risk and the price risk in agricultural production. We first estimate the distributions of crops using parametric methods based on Goodness of Fit tests, and then Vine Copula is applied to model dependence between yield price, crop yield, and weather indices. Once the vine structure and its parameters are determined based on AIC/BIC criteria and forecasting price and yield are obtained from the ARIMA model, we calculate this crop insurance premium using the simulation data generated from the vine copula by the Monte Carlo Simulation method. It is shown that, compared with traditional crop insurance, our proposed insurance is more fair and thus less costly for the farmers and government.

Keywords: vine copula, weather index, crop insurance premium, insurance risk management, Monte Carlo simulation

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Authors: Kashif Hassan, M.A. Ahanger

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Floods have become more frequent and severe due to effects of global climate change and human alterations of the natural environment. Flood prediction/ forecasting and control is one of the greatest challenges facing the world today. The forecast of floods is achieved by the use of hydraulic models such as HEC-RAS, which are designed to simulate flow processes of the surface water. Extreme flood events in river Jhelum , lasting from a day to few are a major disaster in the State of Jammu and Kashmir, India. In the present study HEC-RAS model was applied to two different reaches of river Jhelum in order to estimate the flood levels corresponding to 25, 50 and 100 year return period flood events at important locations and to deduce flood vulnerability of important areas and structures. The flow rates for the two reaches were derived from flood-frequency analysis of 50 years of historic peak flow data. Manning's roughness coefficient n was selected using detailed analysis. Rating Curves were also generated to serve as base for determining the boundary conditions. Calibration and Validation procedures were applied in order to ensure the reliability of the model. Sensitivity analysis was also performed in order to ensure the accuracy of Manning's n in generating water surface profiles.

Keywords: flood plain, HEC-RAS, Jhelum, return period

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Authors: Ana Beltran Sanahuja, A. Valdés García, Saray Lopez De Pablo Gallego, Maria Soledad Prats Moya

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Tomato consumption has greatly increased worldwide in the last few years, mostly due to a growing demand for products like sofrito. In this sense, regular consumption of tomato-based products has been consistently associated with a reduction in the incidence of chronic degenerative diseases. The sofrito is a homemade tomato sauce typical of the Mediterranean area, which contains as main ingredients: tomato, onion, garlic and olive oil. There are also sofrito’s variations by adding other spices which bring at the same time not only color, flavor, smell and or aroma; they also provide medicinal properties, due to their antioxidant power. This protective effect has mainly been attributed to the predominant bioactive compounds present in sofrito, such as lycopene and other carotenoids as well as more than 40 different polyphenols. Regarding the cooking process, it is known that it can modify the properties and the availability of nutrients in sofrito; however, there is not enough information regarding this issue. For this reason, the aim of the present work is to evaluate the cooking effect on the antioxidant capacity of different variants of tomato-based sofrito combined with other spices, through the analysis of total phenols content (TPC) and to evaluate the antioxidant capacity by using the method of free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH). Based on the results obtained, it can be confirmed that the basic sofrito composed of tomato, onion, garlic and olive oil and the sofrito with 1 g of rosemary added, are the ones with the highest content of phenols presenting greater antioxidant power than other industrial sofrito, and that of other variables of sofrito with added thyme or higher amounts of garlic. Moreover, it has been observed that in the elaboration of the tomato-based sofrito, it is possible to cook until 60 minutes, since the cooking process increases the bioavailability of the carotenoids when breaking the cell walls, which weakens the binding forces between the carotenoids and increases the levels of antioxidants present, confirmed both with the TPC and DPPH methods. It can be concluded that the cooking process of different variants of tomato-based sofrito, including spices, can improve the antioxidant capacity. The synergistic effects of different antioxidants may have a greater protective effect; increasing, also, the digestibility of proteins. In addition, the antioxidants help to deactivate the free radicals of diseases such as atherosclerosis, aging, immune suppression, cancer, and diabetes.

Keywords: antioxidants, cooking process, phenols sofrito

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

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Floods have huge environmental and economic impact. Therefore, flood prediction is given a lot of attention due to its importance. This study analysed the annual maximum streamflow (discharge) (AMS or AMD) of Karkheh River in Karkheh River Basin for flood predicting using ARIMA model. For this purpose, we use the Box-Jenkins approach, which contains four-stage method model identification, parameter estimation, diagnostic checking and forecasting (predicting). The main tool used in ARIMA modelling was the SAS and SPSS software. Model identification was done by visual inspection on the ACF and PACF. SAS software computed the model parameters using the ML, CLS and ULS methods. The diagnostic checking tests, AIC criterion, RACF graph and RPACF graphs, were used for selected model verification. In this study, the best ARIMA models for Annual Maximum Discharge (AMD) time series was (4,1,1) with their AIC value of 88.87. The RACF and RPACF showed residuals’ independence. To forecast AMD for 10 future years, this model showed the ability of the model to predict floods of the river under study in the Karkheh River Basin. Model accuracy was checked by comparing the predicted and observation series by using coefficient of determination (R²).

Keywords: time series modelling, stochastic processes, ARIMA model, Karkheh river

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Authors: Nasrin Bakhshizadeh, Ashkan Forootan

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A major problem that affects the quality control of polymer in the industrial polymerization is the lack of suitable on-line measurement tools to evaluate the properties of the polymer such as melt and density indices. Controlling the polymerization in ordinary method is performed manually by taking samples, measuring the quality of polymer in the lab and registry of results. This method is highly time consuming and leads to producing large number of incompatible products. An online application for estimating melt index and density proposed in this study is a neural network based on the input-output data of the polyethylene production plant. Temperature, the level of reactors' bed, the intensity of ethylene mass flow, hydrogen and butene-1, the molar concentration of ethylene, hydrogen and butene-1 are used for the process to establish the neural model. The neural network is taught based on the actual operational data and back-propagation and Levenberg-Marquart techniques. The simulated results indicate that the neural network process model established with three layers (one hidden layer) for forecasting the density and the four layers for the melt index is able to successfully predict those quality properties.

Keywords: polyethylene, polymerization, density, melt index, neural network

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Mohammad Lemar Zalmai, Osman Hurol Turkakin, Cemil Akcay, Ekrem Manisali

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Most of the construction projects are exposed to time and cost overruns due to various factors and this is a major problem. As a solution to this, the Earned Value Management (EVM) method is considered. EVM is a powerful and well-known method used in monitoring and controlling the project. EVM is a technique that project managers use to track the performance of their project against project baselines. EVM gives an early indication that either project is delayed or not, and the project is either over budget or under budget at any particular day by tracking it. Thus, it helps to improve the management control system of a construction project, to detect and control the problems in potential risk areas and to suggest the importance and purpose of monitoring the construction work. This paper explains the main parameters of the EVM system involved in the calculation of time and cost for construction projects. In this study, the project management software Primavera P6 is used to deals with the project monitoring process of a seven-storeyed (G+6) faculty building whose construction is in progress at Istanbul, Turkey. A comparison between the planned progress of construction activities and actual progress is performed, and the analysis results are interpreted. This case study justifies the benefits of using EVM for project cash flow analysis and forecasting.

Keywords: earned value management (EVM), construction cost management, construction planning, primavera P6, project management, project scheduling

Procedia PDF Downloads 197