Search results for: flavor forecasting

Authors: Youness Laaroussi, Zine Elabidine Guennoun, Amine Amar

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Modeling and forecasting dynamics of rainfall occurrences constitute one of the major topics, which have been largely treated by statisticians, hydrologists, climatologists and many other groups of scientists. In the same issue, we propose in the present paper a new hybrid method, which combines Extreme Values and fractal theories. We illustrate the use of our methodology for transformed Emberger Index series, constructed basing on data recorded in Oujda (Morocco). The index is treated at first by Peaks Over Threshold (POT) approach, to identify excess observations over an optimal threshold u. In the second step, we consider the resulting excess as a fractal object included in one dimensional space of time. We identify fractal dimension by the box counting. We discuss the prospect descriptions of rainfall data sets under Generalized Pareto Distribution, assured by Extreme Values Theory (EVT). We show that, despite of the appropriateness of return periods given by POT approach, the introduction of fractal dimension provides accurate interpretation results, which can ameliorate apprehension of rainfall occurrences.

Keywords: extreme values theory, fractals dimensions, peaks Over threshold, rainfall occurrences

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Authors: H. H. Goh, Q. S. Chua, S. W. Lee, B. C. Kok, K. C. Goh, K. T. K. Teo

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At present, the evaluation of voltage stability assessment experiences sizeable anxiety in the safe operation of power systems. This is due to the complications of a strain power system. With the snowballing of power demand by the consumers and also the restricted amount of power sources, therefore, the system has to perform at its maximum proficiency. Consequently, the noteworthy to discover the maximum ability boundary prior to voltage collapse should be undertaken. A preliminary warning can be perceived to evade the interruption of power system’s capacity. The effectiveness of line voltage stability indices (LVSI) is differentiated in this paper. The main purpose of the indices is used to predict the proximity of voltage instability of the electric power system. On the other hand, the indices are also able to decide the weakest load buses which are close to voltage collapse in the power system. The line stability indices are assessed using the IEEE 14 bus test system to validate its practicability. Results demonstrated that the implemented indices are practically relevant in predicting the manifestation of voltage collapse in the system. Therefore, essential actions can be taken to dodge the incident from arising.

Keywords: critical line, line outage, line voltage stability indices (LVSI), maximum loadability, voltage collapse, voltage instability, voltage stability analysis

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Authors: Sabine Ambros, Ulrich Kulozik

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Berries such as blue- and raspberries belong to the most valuable fruits. To preserve the characteristic flavor and the high contents of vitamins and anthocyanins, the very sensitive berries are usually dried by lyophilization. As this method is very time- and energy-consuming, the dried fruit is extremely expensive. However, healthy snack foods are growing in popularity. Especially dried fruit free of any additives or additional sugar are more and more asked for. To make these products affordable, the fruits have to be dried by a method that is more energy-efficient than freeze drying but reveals the same high product quality. The additional insertion of microwaves to a freeze drying process was examined in this work to overcome the inconveniences of freeze drying. As microwaves penetrate the product volumetrically, sublimation takes place simultaneously all over the product and leads to a many times shorter process duration. A range of microwave and pressure settings was applied to find the optimum drying condition. The influence of the process parameters microwave power and chamber pressure on drying kinetics, product temperature and product quality was investigated to find the best condition for an energy-efficient process with high product quality. The product quality was evaluated by rehydration capacitiy, crispiness, shrinkage, color, vitamin C content and antioxidative capacity. The conclusion could be drawn that microwave freeze dried berries were almost equal to freeze dried fruit in all measured quality parameters or even could overcome it. Additionally, sensory evaluations could confirm the analytical studies. Drying time could be reduced by more than 75% at much lower energy consumption rates. Thus, an energy-efficient and cost saving method compared to the conventional freeze drying technique for the gentle production of tasty fruit or vegetable snacks has been found. This technique will make dried high-quality snacks available for many of consumers.

Keywords: blueberries, freeze drying, microwave freeze drying, process parameters, product quality

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Authors: Wullapa Wongsinlatam

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Back propagation algorithm (BP) is a widely used technique in artificial neural network and has been used as a tool for solving the time series problems, such as decreasing training time, maximizing the ability to fall into local minima, and optimizing sensitivity of the initial weights and bias. This paper proposes an improvement of a BP technique which is called IM-COH algorithm (IM-COH). By combining IM-COH algorithm with cuckoo search algorithm (CS), the result is cuckoo search improved control output hidden layer algorithm (CS-IM-COH). This new algorithm has a better ability in optimizing sensitivity of the initial weights and bias than the original BP algorithm. In this research, the algorithm of CS-IM-COH is compared with the original BP, the IM-COH, and the original BP with CS (CS-BP). Furthermore, the selected benchmarks, four time series samples, are shown in this research for illustration. The research shows that the CS-IM-COH algorithm give the best forecasting results compared with the selected samples.

Keywords: artificial neural networks, back propagation algorithm, time series, local minima problem, metaheuristic optimization

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Authors: Aurelia Samuel

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Socio-economic modelling approaches linked to water management have contributed to impact assessments of agricultural policies and management practices on water quality at catchment level. With an increasing interest in informing water management policy that considers complex links between socioeconomic factors, climate change, agricultural production, and water quality, several models have been developed and applied in the literature to capture these relationships. This paper offers an overview of socio-economic approaches that have been incorporated within an integrated framework. It also highlights how data gaps on socio-economic factors have been addressed using forecasting techniques. Findings of the review show that while integrated frameworks have the potential to account for complexities within dynamic systems, they generally do not provide direct, measurable financial impact of socio-economic factors on biophysical water parameters that affect water quality. The paper concludes with a recommendation that modelling framework is kept simple to make it more transparent and easier to capture the most important relationship.

Keywords: financial impact, integrated framework, socio-economic modelling, water quality

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Authors: Luther Ollier, Sylvie Thiria, Anastase Charantonis, Carlos E. Mejia, Michel Crépon

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Sea Surface Height Anomaly (SLA) is a signature of the sub-mesoscale dynamics of the upper ocean. Sea Surface Temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)◦ in the North Atlantic Ocean (26.5-44.42◦N, -64.25–41.83◦E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at five days by using the SST fields as additional information. We obtained predictions of a 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days), respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.

Keywords: deep-learning, altimetry, sea surface temperature, forecast

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Authors: Timothy J. Considine, Edward J. M. Manderson

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This paper develops an econometric forecasting system of energy demand coupled with engineering-economic models of energy supply. The framework is used to quantify the impact of state-level renewable portfolio standards (RPSs) achieved predominately with solar generation on electricity rates, electricity consumption, and environmental quality. We perform the analysis using Arizona’s RPS as a case study. We forecast energy demand in Arizona out to 2035, and find by this time the state will require an additional 35 million MWh of electricity generation. If Arizona implements its RPS when supplying this electricity demand, we find there will be a substantial increase in electricity rates (relative to a business-as-usual scenario of reliance on gas-fired generation). Extending the current regime of tax credits can greatly reduce this increase, at the taxpayers’ expense. We find that by 2025 Arizona’s RPS will implicitly abate carbon dioxide emissions at a cost between $101 and $135 per metric ton, and by 2035 abatement costs are between $64 and $112 per metric ton (depending on the future evolution of nature gas prices).

Keywords: electricity demand, renewable portfolio standard, solar, carbon dioxide

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Authors: Nor Zila Abd Hamid, Mohd Salmi M. Noorani

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This study is focused on the development of prediction models of the Ozone concentration time series. Prediction model is built based on chaotic approach. Firstly, the chaotic nature of the time series is detected by means of phase space plot and the Cao method. Then, the prediction model is built and the local linear approximation method is used for the forecasting purposes. Traditional prediction of autoregressive linear model is also built. Moreover, an improvement in local linear approximation method is also performed. Prediction models are applied to the hourly ozone time series observed at the benchmark station in Malaysia. Comparison of all models through the calculation of mean absolute error, root mean squared error and correlation coefficient shows that the one with improved prediction method is the best. Thus, chaotic approach is a good approach to be used to develop a prediction model for the Ozone concentration time series.

Keywords: chaotic approach, phase space, Cao method, local linear approximation method

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: Josip Visković, Josip Arnerić, Ante Rozga

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Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modelling time varying volatility are GARCH type models. When financial returns exhibit sudden jumps that are due to structural breaks, standard GARCH models show high volatility persistence, i.e. integrated behaviour of the conditional variance. In such situations models in which the parameters are allowed to change over time are more appropriate. This paper compares different GARCH models in terms of their ability to describe structural changes in returns caused by financial crisis at stock markets of six selected central and east European countries. The empirical analysis demonstrates that Markov regime switching GARCH model resolves the problem of excessive persistence and outperforms uni-regime GARCH models in forecasting volatility when sudden switching occurs in response to financial crisis.

Keywords: central and east European countries, financial crisis, Markov switching GARCH model, transition probabilities

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Authors: Fahad Javed, Tasneem Akhter, Maria Zafar, Adnan Shafique

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Evaluation and quantification of techniques is critical element of research and development of technology. Simulations and models play an important role in providing the tools for such assessments. When we look at technologies which impact or is dependent on an average Joe consumer then modeling the socio-economic and psychological aspects of the consumer also gain an importance. For feed in tariff for home consumers which is being deployed for average consumer may force many consumers to be adapters of the technology. Understanding how consumers will adapt this technologies thus hold as much significance as evaluating how the techniques would work in consumer agnostic scenarios. In this paper we first build the case for simulators which accommodate socio-economic realities of the consumers to evaluate smart grid technologies, provide a glossary of data that can aid in this effort and present an abstract model to capture and simulate consumers' adaptation and behavioral response to smart grid technologies. We provide a case study to express the power of such simulators.

Keywords: smart grids, simulation, socio-economic parameters, feed in tariff (FiT), forecasting

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Authors: Mona Baniahmadi, Saied Haghanifar

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Radio Frequency Identification (RFID) is regarded as a promising technology for the optimization of supply chain processes since it improves manufacturing and retail operations from forecasting demand for planning, managing inventory, and distribution. This study precisely aims at learning to know the RFID technology and at explaining how it can concretely be used for supply chain management and how it can help improving it in the case of Hejrat Company which is located in Iran and works on the distribution of medical drugs and cosmetics. This study uses some statistical analysis to calculate the expected benefits of an integrated RFID system on supply chain obtained through competitive advantages increases with decreasing cost factor. The study investigates how the cost of storage process, labor cost, the cost of missing goods, inventory management optimization, on-time delivery, order cost, lost sales and supply process optimization affect the performance of the integrated RFID supply chain regarding cost factors and provides a competitive advantage.

Keywords: cost, competitive advantage, radio frequency identification, supply chain

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Authors: Enas R. Shouman

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Economic development with population growth is leading to a continuous increase in energy demand. At the same time, growing global concern for the environment is driving to decrease the use of conventional energy sources and to increase the use of renewable energy sources. The objective of this study is to present the market trends of solar energy photovoltaic technology over the world and to represent economics methods for PV financial analyzes on the basis of expectations for the expansion of PV in many applications. In the course of this study, detailed information about the current PV market was gathered and analyzed to find factors influencing the penetration of PV energy. The paper methodology depended on five relevant economic financial analysis methods that are often used for investment decisions maker. These methods are payback analysis, net benefit analysis, saving-to-investment ratio, adjusted internal rate of return, and life-cycle cost. The results of this study may be considered as a marketing guide that helps diffusion of using PV Energy. The study showed that PV cost is economically reliable. The consumers will pay higher purchase prices for PV system installation but will get lower electricity bill.

Keywords: photovoltaic, financial methods, solar energy, economics, PV panel

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Authors: Rusudan Kinkladze, Guguli Kurashvili, Ketevan Chitaladze

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Foreign direct investment is a driving force in the development of the interdependent national economies, and the study and analysis of investments is an urgent problem. It is particularly important for transitional economies, such as Georgia, and the study and analysis of investments is an urgent problem. Consequently, the goal of the research is the study and analysis of direct foreign investments in Georgia, and identification and forecasting of modern trends, and covers the period of 2006-2015. The study uses the methods of statistical observation, grouping and analysis, the methods of analytical indicators of time series, trend identification and the predicted values are calculated, as well as various literary and Internet sources relevant to the research. The findings showed that modern investment policy In Georgia is favorable for domestic as well as foreign investors. Georgia is still a net importer of investments. In 2015, the top 10 investing countries was led by Azerbaijan, United Kingdom and Netherlands, and the largest share of FDIs were allocated in the transport and communication sector; the financial sector was the second, followed by the health and social work sector, and the same trend will continue in the future. 

Keywords: foreign direct investments, methods, statistics, analysis

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Authors: Lima Antony

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In G. M. Hopkins’s poetics and aesthetics there is scope for a comparative study with Indian discourses on aesthetics, an area not adequately explored so far. This exploration will enrich the field of comparative study of diverse cultural expressions and their areas of similarity. A comparative study of aesthetic and religious experiences in diverse cultures will open up avenues for the discovery of similarities in self-experiences and their transcendence. Such explorations will reveal similar patterns in aesthetic and religious experiences. The present paper intends to prove this in the theories of Hopkins and Indian aesthetics. From the time of the Vedas Indian sages have believed that aesthetic enjoyment could develop into a spiritual realm. From the Natyasastra of Bharata, Indian aesthetics develops and reaches its culmination in later centuries into a consciousness of union with the mystery of the Ultimate Being, especially in Dhvanaāloka of Anandavardhana and Locana of Abhinavagupta. Dhvanyaloka elaborates the original ideas of rasa (mood or flavor) and dhvani (power of suggestion) in Indian literary theory and aesthetics. Hopkins was successful, like the ancient Indian alankarikas, in creating aesthetically superb patterns at various levels of sound and sense for which he coined the term ‘inscape’. So Hopkins’s aesthetic theory becomes suitable for transcultural comparative study with Indian aesthetics especially the dhvani theories of Anandavardhana and Abhinavagupta. Hopkins’s innovative approach to poetics and his selection of themes are quite suitable for analysis in the light of Indian literary theories. Indian philosophy views the ultimate reality called Brahman, as the 'soul,' or inner essence, of all reality. We see in Hopkins also a search for the essence of things and the chiming of their individuality with the Ultimate Being in multidimensional patterns of sound, sense and ecstatic experience. This search culminates in the realization of a synthesis of the individual self with the Ultimate Being. This is achieved through an act of surrender of the individuality of the self before the Supreme Being. Attempts to reconcile the immanent and transcendent aspects of the Ultimate Being can be traced in the Indian as well as Hopkins’s aesthetics which can contribute to greater understanding and harmony between cultures.

Keywords: Dhvani, Indian aesthetics, transcultural studies, Rasa

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Authors: Nady Farag Faragalla Hanna

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In the car dealership industry in Japan, the sales specialist is a key factor in the success of the company. I hypothesize that when a company understands the characteristics of sales professionals in its industry, it is easier to recruit and train salespeople effectively. Lean human resources management ensures the economic success and performance of companies, especially small and medium-sized companies.The purpose of the article is to determine the characteristics of sales specialists for small and medium-sized car dealerships using the chi-square test and the proximate variable model. Accordingly, the results show that career change experience, learning ability and product knowledge are important, while university education, career building through internal transfer, leadership experience and people development are not important for becoming a sales professional. I also show that the characteristics of sales specialists are perseverance, humility, improvisation and passion for business.

Keywords: electronics engineering, marketing, sales, E-commerce digitalization, interactive systems, sales process ARIMA models, sales demand forecasting, time series, R codetraits of sales professionals, variable precision rough sets theory, sales professional, sales professionals

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Authors: Tuji Jemal Ahmed

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Fresh produce is an essential component of a healthy diet. However, it can also be a potential source of pathogenic microorganisms that can cause foodborne illnesses. Traditional disinfection methods, such as washing with water and chlorine, have limitations and may not effectively remove or inactivate all microorganisms. This has led to the development of alternative/new methods of fresh produce disinfection, including physical and chemical methods. In this paper, we explore the physical and chemical new methods of fresh produce disinfection, their advantages and disadvantages, and their suitability for different types of produce. Physical methods of disinfection, such as ultraviolet (UV) radiation and high-pressure processing (HPP), are crucial in ensuring the microbiological safety of fresh produce. UV radiation uses short-wavelength UV-C light to damage the DNA and RNA of microorganisms, and HPP applies high levels of pressure to fresh produce to reduce the microbial load. These physical methods are highly effective in killing a wide range of microorganisms, including bacteria, viruses, and fungi. However, they may not penetrate deep enough into the product to kill all microorganisms and can alter the sensory characteristics of the product. Chemical methods of disinfection, such as acidic electrolyzed water (AEW), ozone, and peroxyacetic acid (PAA), are also important in ensuring the microbiological safety of fresh produce. AEW uses a low concentration of hypochlorous acid and a high concentration of hydrogen ions to inactivate microorganisms, ozone uses ozone gas to damage the cell membranes and DNA of microorganisms, and PAA uses a combination of hydrogen peroxide and acetic acid to inactivate microorganisms. These chemical methods are highly effective in killing a wide range of microorganisms, but they may cause discoloration or changes in the texture and flavor of some products and may require specialized equipment and trained personnel to produce and apply. In conclusion, the selection of the most suitable method of fresh produce disinfection should take into consideration the type of product, the level of microbial contamination, the effectiveness of the method in reducing the microbial load, and any potential negative impacts on the sensory characteristics, nutritional composition, and safety of the produce.

Keywords: fresh produce, pathogenic microorganisms, foodborne illnesses, disinfection methods

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Authors: Yasmina Bendaali, Cristian Vaquero, Carlos Escott, Carmen González, Antonio Morata

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Consumer tendencies to healthy eating habits and request for organic beverages led to the production of new drinks from fruit juices as a source of nutrients and bioactive compounds. Grape juice is a rich source of sugars, organic acids, and phenolic compounds, which define its beneficial effect on health and the attractive sensory profile for consumers' choices (color, taste, flavor). Thus, grape juice was used as a source of sugars, avoiding the addition of sweeteners by diluting it with mineral water to obtain the sugar concentration recommended for isotonic drinks (6% to 8%) to provide energy during physical activities. In addition, phenolic compounds of grape juice are associated with many human health benefits, mainly antioxidant activity, which helps to prevent different diseases associated with oxidative stress, including cancers and cardiovascular and neurodegenerative diseases. Furthermore, physical exercise has been shown to increase the production of free radicals and other reactive oxygen species. Thus, athletes need to improve their antioxidant defense systems to prevent oxidative damage. Different studies have demonstrated the positive effect of grape juice consumption during physical activities, which improves antioxidant activity and performance, protects against oxidative damage, and reduces inflammation. Thus, the use of grape juice to develop isotonic drinks can provide isotonic drinks with antioxidant and biological activities in addition to their principal role of rehydration and replacement of minerals and carbohydrates during physical exercises. Moreover, attractive sensory characteristics, mainly color, which is provided by anthocyanin content, have a great contribution to making the drinks more natural and help to dispense the use of synthetic dyes in addition to the health benefits which will be a novel product in the field of healthy beverages responding on the demand of consumers for new, innovative, and healthy products.

Keywords: grape juice, isotonic, antioxidants, anthocyanins, natural, sport

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Authors: Hira Lal Gope, Hidekazu Fukai

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The aim of this study is to develop a system which can identify and sort peaberries automatically at low cost for coffee producers in developing countries. In this paper, the focus is on the classification of peaberries and normal coffee beans using image processing and machine learning techniques. The peaberry is not bad and not a normal bean. The peaberry is born in an only single seed, relatively round seed from a coffee cherry instead of the usual flat-sided pair of beans. It has another value and flavor. To make the taste of the coffee better, it is necessary to separate the peaberry and normal bean before green coffee beans roasting. Otherwise, the taste of total beans will be mixed, and it will be bad. In roaster procedure time, all the beans shape, size, and weight must be unique; otherwise, the larger bean will take more time for roasting inside. The peaberry has a different size and different shape even though they have the same weight as normal beans. The peaberry roasts slower than other normal beans. Therefore, neither technique provides a good option to select the peaberries. Defect beans, e.g., sour, broken, black, and fade bean, are easy to check and pick up manually by hand. On the other hand, the peaberry pick up is very difficult even for trained specialists because the shape and color of the peaberry are similar to normal beans. In this study, we use image processing and machine learning techniques to discriminate the normal and peaberry bean as a part of the sorting system. As the first step, we applied Deep Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) as machine learning techniques to discriminate the peaberry and normal bean. As a result, better performance was obtained with CNN than with SVM for the discrimination of the peaberry. The trained artificial neural network with high performance CPU and GPU in this work will be simply installed into the inexpensive and low in calculation Raspberry Pi system. We assume that this system will be used in under developed countries. The study evaluates and compares the feasibility of the methods in terms of accuracy of classification and processing speed.

Keywords: convolutional neural networks, coffee bean, peaberry, sorting, support vector machine

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Authors: Longqing Li

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The paper addresses the inefficiency of the classical model in measuring the Value-at-Risk (VaR) using a normal distribution or a Student’s t distribution. Specifically, the paper focuses on the one day ahead Value-at-Risk (VaR) of major stock market’s daily returns in US, UK, China and Hong Kong in the most recent ten years under 95% confidence level. To improve the predictable power and search for the best performing model, the paper proposes using two leading alternatives, Extreme Value Theory (EVT) and a family of GARCH models, and compares the relative performance. The main contribution could be summarized in two aspects. First, the paper extends the GARCH family model by incorporating EGARCH and TGARCH to shed light on the difference between each in estimating one day ahead Value-at-Risk (VaR). Second, to account for the non-normality in the distribution of financial markets, the paper applies Generalized Error Distribution (GED), instead of the normal distribution, to govern the innovation term. A dynamic back-testing procedure is employed to assess the performance of each model, a family of GARCH and the conditional EVT. The conclusion is that Exponential GARCH yields the best estimate in out-of-sample one day ahead Value-at-Risk (VaR) forecasting. Moreover, the discrepancy of performance between the GARCH and the conditional EVT is indistinguishable.

Keywords: Value-at-Risk, Extreme Value Theory, conditional EVT, backtesting

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Authors: Lule Basha, Eralda Gjika

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The exchange rate is one of the most important economic variables, especially for a small, open economy such as Albania. Its effect is noticeable in one country's competitiveness, trade and current account, inflation, wages, domestic economic activity, and bank stability. This study investigates the fluctuation of Albania’s exchange rates using monthly average foreign currency, Euro (Eur) to Albanian Lek (ALL) exchange rate with a time span from January 2008 to June 2021, and the macroeconomic factors that have a significant effect on the exchange rate. Initially, the Random Forest Regression algorithm is constructed to understand the impact of economic variables on the behavior of monthly average foreign currencies exchange rates. Then the forecast of macro-economic indicators for 12 months was performed using time series models. The predicted values received are placed in the random forest model in order to obtain the average monthly forecast of the Euro to Albanian Lek (ALL) exchange rate for the period July 2021 to June 2022.

Keywords: exchange rate, random forest, time series, machine learning, prediction

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Gajaanuja Megalathan, Banuka Athuraliya

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Crime is one of our society's most intimidating and threatening challenges. With the majority of the population residing in cities, many experts and data provided by local authorities suggest a rapid increase in the number of crimes committed in these cities in recent years. There has been an increasing graph in the crime rates. People living in Sri Lanka have the right to know the exact crime rates and the crime rates in the future of the place they are living in. Due to the current economic crisis, crime rates have spiked. There have been so many thefts and murders recorded within the last 6-10 months. Although there are many sources to find out, there is no solid way of searching and finding out the safety of the place. Due to all these reasons, there is a need for the public to feel safe when they are introduced to new places. Through this research, the author aims to develop a mobile application that will be a solution to this problem. It is mainly targeted at tourists, and people who recently relocated will gain advantage of this application. Moreover, the Arima Model combined with ANN is to be used to predict crime rates. From the past researchers' works, it is evidently clear that they haven’t used the Arima model combined with Artificial Neural Networks to forecast crimes.

Keywords: arima model, ANN, crime prediction, data analysis

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Authors: Meesun Sun, Yongmin Kim

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Estimating maintenance cost is crucial in defense management because it affects military budgets and availability of equipment. When it comes to estimating maintenance cost of the deployed equipment, time series forecasting can be applied with the actual historical cost data. It is more difficult issue to estimate maintenance cost of new equipment for which the actual costs are not provided. In this underlying context, this study proposes an analytical method for maintenance cost estimating relationships (CERs) development of helicopters using linear programming. The CERs can be applied to a new helicopter because they use non-cost independent variables such as the number of engines, the empty weight and so on. In the Republic of Korea, the maintenance cost of new equipment has been usually estimated by reflecting maintenance cost to unit price ratio of the legacy equipment. This study confirms that the CERs perform well for the 10 types of airmobile helicopters in terms of mean absolute percentage error by applying leave-one-out cross-validation. The suggested method is very useful to estimate the maintenance cost of new equipment and can help in the affordability assessment of acquisition program portfolios for total life cycle systems management.

Keywords: affordability analysis, cost estimating relationship, helicopter, linear programming, maintenance cost

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Authors: Ubaidillah Zuhdi, Shunsuke Mori, Kazuhisa Kamegai

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The purpose of this study is to forecast the influences of Information and Communication Technology (ICT) on the structural changes of Japanese economies based on Leontief Input-Output (IO) coefficients. This study establishes a statistical analysis to predict the future interrelationships among industries. We employ the Constrained Multivariate Regression (CMR) model to analyze the historical changes of input-output coefficients. Statistical significance of the model is then tested by Likelihood Ratio Test (LRT). In our model, ICT is represented by two explanatory variables, i.e. computers (including main parts and accessories) and telecommunications equipment. A previous study, which analyzed the influences of these variables on the structural changes of Japanese industrial sectors from 1985-2005, concluded that these variables had significant influences on the changes in the business circumstances of Japanese commerce, business services and office supplies, and personal services sectors. The projected future Japanese economic structure based on the above forecast generates the differentiated direct and indirect outcomes of ICT penetration.

Keywords: forecast, ICT, industrial structural changes, statistical analysis

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Authors: Yakubu Bala Mohammed, Nadire Chavus, Mohammed Bulama

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Generative Pre-trained Transformer (ChatGPT) represents an artificial intelligence (AI) tool capable of swiftly generating comprehensive responses to prompts and follow-up inquiries. This emerging AI tool was introduced in November 2022 by OpenAI firm, an American AI research laboratory, utilizing substantial language models. This present study aims to delve into the potential future consequences of ChatGPT usage in education using AI-based algorithms. The paper will bring forth the likely potential risks of ChatGBT utilization, such as academic integrity concerns, unfair learning assessments, excessive reliance on AI, and dissemination of inaccurate information using four machine learning algorithms: eXtreme-Gradient Boosting (XGBoost), Support vector machine (SVM), Emotional artificial neural network (EANN), and Random forest (RF) would be used to analyze the study collected data due to their robustness. Finally, the findings of the study will assist education stakeholders in understanding the future implications of ChatGPT usage in education and propose solutions and directions for upcoming studies.

Keywords: machine learning, ChatGPT, education, learning, implications

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Authors: K. Nazan Turhan, Tuğçe Ersan

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The population of the world is approximately 7.6 billion, and it increases uncontrollably and irrepressibly, leading to an increase in consumption. This situation causes crucial problems, and food waste is one of these. Wasting food endangers natural resources and causes hunger. For instance, excessive amounts of food waste cause greenhouse gas emissions, contributing to global warming. Therefore, waste management has been gaining significance in the last few decades at both local and global levels due to the expected scarcity of resources for the increasing population of the world. There are several ways to recover food waste. Bioethanol, biodiesel, biogas, agricultural fertilizer and animal feed can be obtained from food waste that is generated by different food industries. In this project, feeding animals was selected as a food waste recovery method and food waste of a plant was used to provide ingredient uniformity. Grasshoppers were used as a protein source. In other words, the project was performed to develop a dog food product by recovering the plant’s food waste after following some steps. The collected food waste and purchased grasshoppers were sterilized, dried, and pulverized. Then, they were all mixed with 60 g agar-agar solution (4%w/v). 3 different aromas were added separately to the samples to enhance flavor quality. Since there are differences in the required amounts of different species of dogs, fulfilling all nutritional needs is one of the problems. In other words, there is a wide range of nutritional needs in terms of carbohydrates, protein, fat, sodium, calcium and so on. Furthermore, the requirements differ depending on age, gender, weight, height and species. Therefore, the product that was developed contains average amounts of each substance to not cause any deficiency or surplus. On the other hand, it contains more protein than similar products in the market. The product was evaluated in terms of contamination and nutritional content. For contamination risk, detection of E. coli and Salmonella experiments were performed, and the results were negative. For the nutritional value test, protein content analysis was done. The protein contents of different samples vary between 33.68% and 26.07%. In addition, water activity analysis was performed, and the water activity (aw) values of different samples ranged between 0.2456 and 0.4145.

Keywords: food waste, dog food, animal nutrition, food waste recovery

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Authors: Fatemeh Haghiralsadat, Mohadese Hashemi, Elham Akhoundi Kharanaghi, Mojgan Yazdani, Mahboobe Sharafodini, Omid Javani

Abstract:

Niosomes are colloidal particles formed from the self-assembly of non-ionic surfactants in aqueous medium resulting in closed bilayer structures. As a consequence of this hydrophilic and hydrophobic structure, niosomes have the capacity to entrap compounds of different solubilities. Niosomes are promising vehicle for drug delivery which protect sensitive drugs and improve the therapeutic index of drugs by restricting their action to target cells. Essential oils are complex mixtures of volatile compounds such as terpenoids, phenol-derived aromatic components that have been used for many biological properties including bactericidal, fungicidal, insecticidal, antioxidant, anti-tyrosinase and other medicinal properties. Encapsulation of essential oils in niosomes can be an attractive method to overcome their limitation such as volatility, easily decomposition by heat, humidity, light, or oxygen. Cuminum cyminum L. (Cumin) is an aromatic plant included in the Apiaceae family and is used to flavor foods, added to fragrances, and for medical preparations which is indigenous to Egypt, the Mediterranean region, Iran and India. The major components of the Cumin oil were reported as cuminaldehyde, γ -terpinene, β-pinene, p-cymene, p-mentha-1, 3-dien-7-al, and p-mentha-1, 4-dien-7-al which provide the antimicrobial and antioxidant activity. The aim of this work was to formulate Cumin essential oil-loaded niosomes to improve water solubility of natural product and evaluate its physico-chemical features and stability. Cumin oil was obtained through steam distillation using a clevenger-type apparatus and GC/MS was applied to identify the main components of the essential oil. Niosomes were prepared by using thin film hydration method and nanoparticles were characterized for particle size, dispersity index, zeta potential, encapsulation efficiency, in vitro release, and morphology.

Keywords: Cuminum cyminum L., Cumin, niosome, essential oil, encapsulation

Procedia PDF Downloads 494

Authors: Abul Hashem

Abstract:

The experiment was conducted to find out the effect of different levels of Moringa oleifiera leaf extract and synthetic antioxidant (Beta Hydroxyl Anisole) on fresh and preserved beef meatballs. For this purpose, ground beef samples were divided into five treatment groups. They are treated as control, synthetic antioxidant, 0.1%, 0.2% and 0.3% Moringa oleifera leaf extract as T1, T2, T3, T4 and T5, respectively. Five kinds of meatballs were made and biscuit crushed and egg albumin was mixed with beef meatballs and cooking was practiced properly. Proximate analysis, sensory tests (color, flavor, tenderness, juiciness, overall acceptability), cooking loss, pH value, free fatty acids (FFA), thiobarbituric acid values (TBARS), peroxide value(POV) and microbiological examination were determined in order to evaluate the effect of Moringa oleifiera leaf extract as natural antioxidant & antimicrobial activities in comparing to BHA (Beta Hydroxyl Anisole) at first day before freezing and for maintaining meatballs qualities on the shelf life of beef meat balls stored for 60 days under frozen condition. Freezing temperature was -20˚C. Days of intervals of experiment were on 0, 15th, 30th, and 60th days. Dry matter content of all the treatment groups differ significantly (p<0.05). On the contrary, DM content increased significantly (p<0.05) with the advancement of different days of intervals. CP content of all the treatments were increased significantly (p<0.05) among the different treatment groups. EE content at different treatment levels differ significantly (p<0.05). Ash content at different treatment levels was also differ significantly (p<0.05). FFA values, TBARS, POV were decreased significantly (p<0.05) at different treatment levels. Color, odor, tenderness, juiciness, overall acceptability, raw PH, cooked pH were increased at different treatment levels significantly (p<0.05). The cooking loss (%) at different treatment levels were differ significantly (p<0.05). TVC (logCFU/g), TCC (logCFU/g) and TYMC (logCFU/g) was decreased significantly (p<0.05) at different treatment levels comparison to control. Considering CP, tenderness, juiciness, overall acceptability, cooking loss, FFA, POV, TBARS and microbial parameters it can be concluded that Moringa oleifera leaf extract at 0.1%, 0.2% and 0.3% can be used instead of 0.1% synthetic antioxidant BHA in beef meatballs.

Keywords: antioxidant, beef meatball, BHA, moringa leaf extract, quality

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Authors: Renalda El Samra, Elie Bou-Zeid, Hamza Kunhu Bangalath, Georgiy Stenchikov, Mutasem El Fadel

Abstract:

The regional-scale impact of climate change over complex terrain was examined through high-resolution dynamic downscaling conducted using the Weather Research and Forecasting (WRF) model, with initial and boundary conditions from a High-Resolution Atmospheric Model (HiRAM). The analysis was conducted over the eastern Mediterranean, with a focus on the country of Lebanon, which is characterized by a challenging complex topography that magnifies the effect of orographic precipitation. Four year-long WRF simulations, selected based on HiRAM time series, were performed to generate future climate projections of extreme temperature and precipitation over the study area under the conditions of the Representative Concentration Pathway (RCP) 4.5. One past WRF simulation year, 2008, was selected as a baseline to capture dry extremes of the system. The results indicate that the study area might be exposed to a temperature increase between 1.0 and 3ºC in summer mean values by 2050, in comparison to 2008. For extreme years, the decrease in average annual precipitation may exceed 50% at certain locations in comparison to 2008.

Keywords: HiRAM, regional climate modeling, WRF, Representative Concentration Pathway (RCP)

Procedia PDF Downloads 376