Search results for: artificial rainfall

Authors: Marwa Ahmed Abd El Hamied

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In this paper In this paper, a new technique based on Artificial Immunity System (AIS) technique has been developed to track Maximum Power Point (MPP). AIS system is implemented in a photovoltaic system that is subjected to variable temperature and insulation condition. The proposed novel is simulated using Mat Lab program. The results of simulation have been compared to those who are generated from Observation Controller. The proposed model shows promising results as it provide better accuracy comparing to classical model.

Keywords: component, artificial immunity technique, solar energy, perturbation and observation, power based methods

Procedia PDF Downloads 406

Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

Procedia PDF Downloads 134

Authors: Tarek Aboueldahab, Hanan Farag

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Parallel Job Shop Scheduling Problem (JSP) is a multi-objective and multi constrains NP- optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution, so we propose a hybrid Artificial Intelligence model (AI) with Discrete Breeding Swarm (DBS) added to traditional Artificial Intelligence to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: parallel job shop scheduling problem, artificial intelligence, discrete breeding swarm, car sequencing and operator allocation, cost minimization

Procedia PDF Downloads 151

Authors: Fatema Akram, Mohammad Golam Rasul, Mohammad Masud Kamal Khan, Md. Sharif Imam Ibne Amir

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A variety of routing techniques are available to develop surface runoff hydrographs from rainfall. The selection of runoff routing method is very vital as it is directly related to the type of watershed and the required degree of accuracy. There are different modelling softwares available to explore the rainfall-runoff process in urban areas. XPSTORM, a link-node based, integrated storm-water modelling software, has been used in this study for developing surface runoff hydrograph for a Golf course area located in Rockhampton in Central Queensland in Australia. Four commonly used methods, namely SWMM runoff, Kinematic wave, Laurenson, and Time-Area are employed to generate runoff hydrograph for design storm of this study area. In runoff mode of XPSTORM, the rainfall, infiltration, evaporation and depression storage for sub-catchments were simulated and the runoff from the sub-catchment to collection node was calculated. The simulation results are presented, discussed and compared. The total surface runoff generated by SWMM runoff, Kinematic wave and Time-Area methods are found to be reasonably close, which indicates any of these methods can be used for developing runoff hydrograph of the study area. Laurenson method produces a comparatively less amount of surface runoff, however, it creates highest peak of surface runoff among all which may be suitable for hilly region. Although the Laurenson hydrograph technique is widely acceptable surface runoff routing technique in Queensland (Australia), extensive investigation is recommended with detailed topographic and hydrologic data in order to assess its suitability for use in the case study area.

Keywords: ARI, design storm, IFD, rainfall temporal pattern, routing techniques, surface runoff, XPSTORM

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Authors: Latika Choudhary

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“The potential benefits of Artificial Intelligence are huge, So are the dangers.” - Dave Water. Artificial intelligence is one of the facet of Information technology domain which despite several attempts does not have a clear definition or ambit. However it can be understood as technology to solve problems via automated decisions and predictions. Artificial intelligence is essentially an algorithm based technology which analyses the large amounts of data and then solves problems by detecting useful patterns. Owing to its automated feature it will not be wrong to say that humans & AI have more utility than humans alone or computers alone.1 For many decades AI experienced enthusiasm as well as setbacks, yet it has today become part and parcel of our everyday life, making it convenient or at times problematic. AI and related technology encompass Intellectual Property in multiple ways, the most important being AI technology for management of Intellectual Property, IP for protecting AI and IP as a hindrance to the transparency of AI systems. Thus the relationship between the two is of reciprocity as IP influences AI and vice versa. While AI is a recent concept, the IP laws for protection or even dealing with its challenges are relatively older, raising the need for revision to keep up with the pace of technological advancements. This paper will analyze the relationship between AI and IP to determine how beneficial or conflictual the same is, address how the old concepts of IP are being stretched to its maximum limits so as to accommodate the unwanted consequences of the Artificial Intelligence and propose ways to mitigate the situation so that AI becomes the friend it is and not turn into a potential foe it appears to be.

Keywords: intellectual property rights, information technology, algorithm, artificial intelligence

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Authors: L. Mouzai, M. Bouhadef

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Rills begin to be generated once overland flow shear capacity overcomes the soil surface resistance. This resistance depends on soil texture, the arrangement of soil particles and on chemical and physical properties. The rill density could affect soil erosion, especially when the distance between the rills (interrill) contributes to the variation of the rill characteristics, and consequently on sediment concentration. To investigate this point, agricultural sandy soil, a soil tray of 0.2x1x3m³ and a piece of hardwood rectangular in shape to build up rills were the base of this work. The results have shown that small lines have been developed between the rills and the flow acceleration increased in comparison to the flow on the flat surface (interrill). Sediment concentration increased with increasing rill number (density).

Keywords: artificial rainfall, experiments, rills, soil erosion, transport capacity

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Authors: A. L. Qureshi, A. A. Mahessar, R. K. Dashti, S. M. Yasin

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In this paper, effect of marginal quality groundwater on yield of cotton crop and soil salinity was studied. In this connection, three irrigation treatments each with four replications were applied. These treatments were use of canal water, use of marginal quality groundwater from tube well, and conjunctive use by mixing with the ratio of 1:1 of canal water and marginal quality tubewell water. Water was applied to the crop cultivated in Kharif season 2011; its quantity has been measured using cut-throat flume. Total 11 watering each of 50 mm depth have been applied from 20th April to 20th July, 2011. Further, irrigations were stopped from last week of July, 2011 due to monsoon rainfall. Maximum crop yield (seed cotton) was observed under T1 which was 1,516.8 kg/ha followed by T3 (mixed canal and tube well water) having 1009 kg/ha and 709 kg/ha for T2 i.e. marginal quality groundwater. This concludes that crop yield in T2 and T3 with in comparison to T1was reduced by about 53 and 30% respectively. It has been observed that yield of cotton crop is below potential limit for three treatments due to unexpected rainfall at the time of full flowering season; thus the yield was adversely affected. However, salt deposition in soil profiles was not observed that is due to leaching effect of heavy rainfall occurred during monsoon season.

Keywords: conjunctive use, cotton crop, groundwater, soil salinity status, water use efficiency

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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: Maria Jędrzejczak, Patryk Pieniążek

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The European legal reality is on the eve of significant change. In European Union law, there is talk of a “fourth industrial revolution”, which is driven by massive data resources linked to powerful algorithms and powerful computing capacity. The above is closely linked to technological developments in the area of artificial intelligence, which has prompted an analysis covering both the legal environment as well as the economic and social impact, also from an ethical perspective. The discussion on the regulation of artificial intelligence is one of the most serious yet widely held at both European Union and Member State level. The literature expects legal solutions to guarantee security for fundamental rights, including privacy, in artificial intelligence systems. There is no doubt that personal data have been increasingly processed in recent years. It would be impossible for artificial intelligence to function without processing large amounts of data (both personal and non-personal). The main driving force behind the current development of artificial intelligence is advances in computing, but also the increasing availability of data. High-quality data are crucial to the effectiveness of many artificial intelligence systems, particularly when using techniques involving model training. The use of computers and artificial intelligence technology allows for an increase in the speed and efficiency of the actions taken, but also creates security risks for the data processed of an unprecedented magnitude. The proposed regulation in the field of artificial intelligence requires analysis in terms of its impact on the regulation on personal data protection. It is necessary to determine what the mutual relationship between these regulations is and what areas are particularly important in the personal data protection regulation for processing personal data in artificial intelligence systems. The adopted axis of considerations is a preliminary assessment of two issues: 1) what principles of data protection should be applied in particular during processing personal data in artificial intelligence systems, 2) what regulation on liability for personal data breaches is in such systems. The need to change the regulations regarding the rights and obligations of data subjects and entities processing personal data cannot be excluded. It is possible that changes will be required in the provisions regarding the assignment of liability for a breach of personal data protection processed in artificial intelligence systems. The research process in this case concerns the identification of areas in the field of personal data protection that are particularly important (and may require re-regulation) due to the introduction of the proposed legal regulation regarding artificial intelligence. The main question that the authors want to answer is how the European Union regulation against data protection breaches in artificial intelligence systems is shaping up. The answer to this question will include examples to illustrate the practical implications of these legal regulations.

Keywords: data protection law, personal data, AI law, personal data breach

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Authors: Ali Khaleel Kareem, Shian Gao, Ahmed Qasim Ahmed

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A numerical investigation of unsteady mixed convection heat transfer in a 3D moving top wall enclosure, which has a central rotating cylinder and uses either artificial roughness on the bottom hot plate or smooth bottom hot plate to study the heat transfer enhancement, is completed for fixed circular cylinder, and anticlockwise and clockwise rotational speeds, -1 ≤ Ω ≤ 1, at Reynolds number of 5000. The top lid-driven wall was cooled, while the other remaining walls that completed obstructed cubic were kept insulated and motionless. A standard k-ε model of Unsteady Reynolds-Averaged Navier-Stokes (URANS) method is involved to deal with turbulent flow. It has been clearly noted that artificial roughness can strongly control the thermal fields and fluid flow patterns. Ultimately, the heat transfer rate has been dramatically increased by involving artificial roughness on the heated bottom wall in the presence of rotating cylinder.

Keywords: artificial roughness, lid-driven cavity, mixed convection heat transfer, rotating cylinder, URANS method

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Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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Authors: Azmat Farooq Ahmad Khurram, Sadaf Aslam

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This meta-analysis systematically examines the use of artificial intelligence (AI) in instructional methods across diverse educational settings through a thorough analysis of empirical research encompassing various disciplines, educational levels, and regions. This study aims to assess the effects of AI integration on teaching methodologies, classroom dynamics, teachers' roles, and student engagement. Various research methods were used to gather data, including literature reviews, surveys, interviews, and focus group discussions. Findings indicate paradigm shifts in teaching and education, identify emerging trends, practices, and the application of artificial intelligence in learning, and provide educators, policymakers, and stakeholders with guidelines and recommendations for effectively integrating AI in educational contexts. The study concludes by suggesting future research directions and practical considerations for maximizing AI's positive influence on pedagogical practices.

Keywords: artificial intelligence, teaching practices, meta-analysis, teaching-learning

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Authors: M. Irannezhad, E. H. N. Gashti, S. Mohammadighavam, M. Zarrini, B. Kløve

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Climate warming would increase rainfall by shifting precipitation falling form from snow to rain, and would accelerate snow cover disappearing by increasing snowpack. Using temperature and precipitation data in the temperature-index snowmelt model, we evaluated variability of snowfall and continuous snow cover duration(CSCD) during 1944-2010 over Pelso, central Finland. MannKendall non-parametric test determined that annual precipitation increased by 2.69 (mm/year, p<0.05) during the study period, but no clear trend in annual temperature. Both annual rainfall and snowfall increased by 1.67 and 0.78 (mm/year, p<0.05), respectively. CSCD was generally about 205 days from 14 October to 6 May. No clear trend was found in CSCD over Pelso. Spearman’s rank correlation showed most significant relationships of annual snowfall with the East Atlantic (EA) pattern, and CSCD with the East Atlantic/West Russia (EA/WR) pattern. Increased precipitation with no warming temperature caused the rainfall and snowfall to increase, while no effects on CSCD.

Keywords: variations, snowfall, snow cover duration, temperature-index snowmelt model, teleconnection patterns

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Authors: Mehrnoosh Abouzari, Shahrokh Sahraei

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Today, with the increase in quantity and quality and variety of crimes, the discussion of crime prevention has faced a serious challenge that human resources alone and with traditional methods will not be effective. One of the developments in the modern world is the presence of artificial intelligence in various fields, including criminal law. In fact, the use of artificial intelligence in criminal investigations and fighting crime is a necessity in today's world. The use of artificial intelligence is far beyond and even separate from other technologies in the struggle against crime. Second, its application in criminal science is different from the discussion of prevention and it comes to the prediction of crime. Crime prevention in terms of the three factors of the offender, the offender and the victim, following a change in the conditions of the three factors, based on the perception of the criminal being wise, and therefore increasing the cost and risk of crime for him in order to desist from delinquency or to make the victim aware of self-care and possibility of exposing him to danger or making it difficult to commit crimes. While the presence of artificial intelligence in the field of combating crime and social damage and dangers, like an all-seeing eye, regardless of time and place, it sees the future and predicts the occurrence of a possible crime, thus prevent the occurrence of crimes. The purpose of this article is to collect and analyze the studies conducted on the use of artificial intelligence in predicting and preventing crime. How capable is this technology in predicting crime and preventing it? The results have shown that the artificial intelligence technologies in use are capable of predicting and preventing crime and can find patterns in the data set. find large ones in a much more efficient way than humans. In crime prediction and prevention, the term artificial intelligence can be used to refer to the increasing use of technologies that apply algorithms to large sets of data to assist or replace police. The use of artificial intelligence in our debate is in predicting and preventing crime, including predicting the time and place of future criminal activities, effective identification of patterns and accurate prediction of future behavior through data mining, machine learning and deep learning, and data analysis, and also the use of neural networks. Because the knowledge of criminologists can provide insight into risk factors for criminal behavior, among other issues, computer scientists can match this knowledge with the datasets that artificial intelligence uses to inform them.

Keywords: artificial intelligence, criminology, crime, prevention, prediction

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Authors: R. Islam, Rafiqul A. Tarefder

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This study presents the moisture variations of unbound layers from April 2012 to January 2014 in the Interstate 40 (I-40) pavement section in New Mexico. Three moisture probes were installed at different layers inside the pavement which measure the continuous moisture variations of the pavement. Data show that the moisture contents of unbound layers are typically constant throughout the day and month unless there is rainfall. Moisture contents of all unbound layers change with rainfall. Change in ground water table may affect the moisture content of unbound layers which has not investigated in this study. In addition, the Level 3 predictions of moisture contents using the Pavement Mechanistic-Empirical (ME) Design software are compared and found quite reasonable. However, results presented in the current study may not be applicable for pavement in other regions.

Keywords: asphalt pavement, moisture probes, resilient modulus, climate model

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Authors: Fitih Ademe, Kibebew Kibret, Sheleme Beyene, Mezgebu Getnet, Gashaw Meteke

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Soil moisture and nutrient availability are the two key edaphic factors that affect crop yields and are directly or indirectly affected by climate variability and change. The study examined climate-induced yield level, yield variability and gap of maize during 1981-2010 main growing season in the Central Rift Valley (CRV) of Ethiopia. Pearson correlation test was employed to see the relationship between climate variables and yield. The coefficient of variation (CV) was used to analyze annual yield variability. Decision Support System for Agro-technology Transfer cropping system model (DSSAT-CSM) was used to simulate the growth and yield of maize for the study period. The result indicated that maize grain yield was strongly (P<0.01) and positively correlated with seasonal rainfall (r=0.67 at Melkassa and r = 0.69 at Ziway) in the CRV while day temperature affected grain yield negatively (r= -0.44) at Ziway (P<0.05) during the simulation period. Variations in total seasonal rainfall at Melkassa and Ziway explained 44.9 and 48.5% of the variation in yield, respectively, under optimum nutrition. Following variation in rainfall, high yield variability (CV=23.5%, Melkassa and CV=25.3%, Ziway) was observed for optimum nutrient simulation than the corresponding nutrient limited simulation (CV=16%, Melkassa and 24.1%, Ziway) in the study period. The observed farmers’ yield was 72, 52 and 43% of the researcher-managed, water-limited and potential yield of the crop, respectively, indicating a wide maize yield gap in the region. The study revealed rainfed crop production in the CRV is prone to yield variabilities due to its high dependence on seasonal rainfall and nutrient level. Moreover, the high coefficient of variation in the yield gap for the 30-year period also foretells the need for dependable water supply at both locations. Given the wide yield gap especially during lower rainfall years across the simulation periods, it signifies the requirement for a more dependable application of irrigation water and a potential shift to irrigated agriculture; hence, adopting options that can improve water availability and nutrient use efficiency would be crucial for crop production in the area.

Keywords: climate variability, crop model, water availability, yield gap, yield variability

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Authors: Fatima Chaudhary, Rehan Ul Haq

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Spatial and anthropogenic factors influencing nest-site selection in birds need to be identified for effective conservative practices. Environmental attributes such as food availability, predator density, previous reproductive success, etc., provide information regarding the site's quality. An artificial nest box experiment was carried out to evaluate the effect of various factors on nest-site selection, as it is hard to assess the natural cavities. The experiment was conducted whereby half of the boxes were filled with old nest material. Artificial nest boxes created with different materials and different sizes and colors were installed at different heights. A total of 14 out of 60 nest boxes were occupied and four of them faced predation. The birds explored a total of 32 out of 60 nests, whereas anthropogenic factors destroyed 25 out of 60 nests. Birds chose empty nest boxes at higher rates however, there was no obvious avoidance of sites having high ectoparasites load due to old nest material. It is also possible that the preference towards the artificial nest boxes may differ from year to year because of several climatic factors and the age of old nest material affecting the parasite's survival. These variables may fluctuate from one season to another. Considering these factors, nest-site selection experiments concerning the effectiveness of artificial nest boxes should be carried out over several successive seasons. This topic may stimulate further studies, which could lead to a fully understanding the birds' evolutionary ecology. Precise information on these factors influencing nest-site selection can be essential from an economic point of view as well.

Keywords: artificial nesting, nest box, old nest material, birds

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Authors: Aayush Anant, Roshni Thendiyath

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Aridity impacts on agriculture, as well as ecological, human health, and economic activities. In the present study, the effect of climate change on aridity index has been analysed in South Bihar for the past 30 year period by five types of aridity indices as Lang AI, De-Martonne AI, Erinc AI, Pinna combinative AI and UNEP AI. For the study of aridity index, the analysis of rainfall and temperature is significant. Rainfall was analysed for 30 year period from data of 23 gridded stations in for the period 1991-2020. The results show that rainfall pattern was decreasing with respect to previous decades for majority of stations. Trend of maximum, minimum and mean annual temperature has been observed, which shows increasing trend. Structural breakpoint was observed for mean annual temperature data series in year 2004. In period 1991-2004 mean annual temperature was 25.25 ºC, and in period 2005-2020, mean annual temperature was 25.7 ºC. Average aridity index has been calculated by all the above mentioned methods for whole 30 period. Lang AI shows that eastern part of study area is Humid type, and rest all is semi arid. De-Martonne AI also reveals that east part is humid, but rest of the study area is moist sub humid. According to Erinc AI and Pinna, combinative AI shows that whole south Bihar is dry sub humid and semi dry, respectively. UNEP AI shows most of the part as sub humid, and very small part in west is semi arid, while small part of east is humid. Temporal distribution of all the aridity indices shows a decreasing trend. This indicates a decrease in the humid areas in south Bihar for the selected time period.

Keywords: drought, aridity index, climate change, rainfall, temperature

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

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The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

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Authors: Elana Mahboub, Lamar Bakhurji, Hind Alhindi, Sara Alesayi

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Duolingo is a revolutionary language learning platform that offers an interactive and accessible learning experience. Its gamified approach makes language learning engaging and enjoyable, with a diverse range of languages available. The platform's adaptive learning system tailors lessons to individual proficiency levels, ensuring a personalized and efficient learning journey. The incorporation of multimedia elements enhances the learning experience and promotes practical language application. Duolingo's success is attributed to its mobile accessibility, offering basic access to language courses for free, with optional premium features for those seeking additional resources. Research shows positive outcomes for users, and the app's global impact extends beyond individual learning to formal language education initiatives. Duolingo is a transformative force in language education, breaking down barriers and making language learning an attainable goal for millions worldwide.

Keywords: duolingo, artificial intelligence, artificial intelligence in duolingo, benefit of artificial intelligence

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Authors: Fong-Zuo Lee, Jihn-Sung Lai, Yung-Bin Lin, Xiaoqin Liu, Kuo-Chun Chang, Zhi-Xian Yang, Wen-Dar Guo, Jian-Hao Hong

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In recent years, climate change is a major factor to increase rainfall intensity and extreme rainfall frequency. The increased rainfall intensity and extreme rainfall frequency will increase the probability of flash flood with abundant sediment transport in a river basin. The floods caused by heavy rainfall may cause damages to the bridge, embankment, hydraulic works, and the other disasters. Therefore, the foundation scouring of bridge pier, embankment and spur dike caused by floods has been a severe problem in the worldwide. This severe problem has happened in many East Asian countries such as Taiwan and Japan because of these areas are suffered in typhoons, earthquakes, and flood events every year. Results from the complex interaction between fluid flow patterns caused by hydraulic works and the sediment transportation leading to the formation of river morphology, it is extremely difficult to develop a reliable and durable sensor to measure river bed degradation and bridge pier scouring. Therefore, an innovative scour monitoring sensor using vibration-based Micro-Electro Mechanical Systems (MEMS) was developed. This vibration-based MEMS sensor was packaged inside a stainless sphere with the proper protection of the full-filled resin, which can measure free vibration signals to detect scouring/deposition processes at the bridge pier. In addition, a friendly operational system includes rainfall runoff model, one-dimensional and two-dimensional numerical model, and the applicability of sediment transport equation and local scour formulas of bridge pier are included in this research. The friendly operational system carries out the simulation results of flood events that includes the elevation changes of river bed erosion near the specified bridge pier and the erosion depth around bridge piers. In addition, the system is developed with easy operation and integrated interface, the system can supplies users to calibrate and verify numerical model and display simulation results through the interface comparing to the scour monitoring sensors. To achieve the forecast of the erosion depth of river bed and main bridge pier in the study area, the system also connects the rainfall forecast data from Taiwan Typhoon and Flood Research Institute. The results can be provided available information for the management unit of river and bridge engineering in advance.

Keywords: flash flood, river bed degradation, bridge pier scouring, a friendly operational system

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Authors: Justin Orimisan Ijigbade

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The study was conducted to assess the effect of climate variability on honeybee’s production in Ondo State, Nigeria. Multistage sampling technique was employed to collect the data from 60 beekeepers across six Local Government Areas in Ondo State. Data collected were subjected to descriptive statistics and multiple regression model analyses. The results showed that 93.33% of the respondents were male with 80% above 40 years of age. Majority of the respondents (96.67%) had formal education and 90% produced honey for commercial purpose. The result revealed that 90% of the respondents admitted that low temperature as a result of long hours/period of rainfall affected the foraging efficiency of the worker bees, 73.33% claimed that long period of low humidity resulted in low level of nectar flow, while 70% submitted that high temperature resulted in improper composition of workers, dunes and queen in the hive colony. The result of multiple regression showed that beekeepers’ experience, educational level, access to climate information, temperature and rainfall were the main factors affecting honey bees production in the study area. Therefore, beekeepers should be given more education on climate variability and its adaptive strategies towards ensuring better honeybees production in the study area.

Keywords: climate variability, honeybees production, humidity, rainfall and temperature

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Authors: Masoumeh Asadi Aghbolaghi, Beata Dedicova, Farzad Sharifzadeh, Mansoor Omidi, Ulrika Egertsdotter

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Stipagrostis pennata is one of the valuable fodder plants and is very resistant to drought, due to the low capacity of seed production, the use of asexual reproduction methods, including somatic embryogenesis and artificial seed, can increase its reproduction on a large scale. This study was conducted in order to obtain optimal treatments for the production of artificial seeds of this plant through the somatic embryo encapsulating. Embryonic calluses were encapsulated using sodium alginate and calcium chloride and then sowed in a germination medium. The experiment was conducted as a factorial based on a completely randomized design with three replications. The treatments include three concentrations of sodium alginate (1.5, 2.5, and 3.5 percent), two ion exchange times (20 and 30 minutes,) and two artificial seed germination media (hormone free MS and MS containing zeatin riboside and L-proline). Germination percentage and number of days until the beginning of germination were investigated. The highest percentage of artificial seed germination was obtained when 2.5% sodium alginate was used for 30 minutes (ion exchange time) and the seeds were placed on the germination medium containing zeatin riboside and L-proline.

Keywords: somatic embryogenesis, Stipagrostis pennata, synthetic seed, tissue culture

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Yosra Mefteh Rekik, Younes Boujelbene

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We introduce in this paper a new conceptual model representing the stock market dynamics. This model is essentially based on cognitive behavior of the intelligence investors. In order to validate our model, we build an artificial stock market simulation based on agent-oriented methodologies. The proposed simulator is composed of market supervisor agent essentially responsible for executing transactions via an order book and various kinds of investor agents depending to their profile. The purpose of this simulation is to understand the influence of psychological character of an investor and its neighborhood on its decision-making and their impact on the market in terms of price fluctuations. Therefore, the difficulty of the prediction is due to several features: the complexity, the non-linearity and the dynamism of the financial market system, as well as the investor psychology. The Artificial Neural Networks learning mechanism take on the role of traders, who from their futures return expectations and place orders based on their expectations. The results of intensive analysis indicate that the existence of agents having heterogeneous beliefs and preferences has provided a better understanding of price dynamics in the financial market.

Keywords: artificial intelligence methods, artificial stock market, behavioral modeling, multi-agent based simulation

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Authors: Aroob Binhimd, Lyan Sayoti, Rana Almansour

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In recent years, artificial intelligence has grown drastically. This has led to the growth of educational programs to help students in solving educational problems and assist them in understanding certain topics. The purpose of this report is to investigate the Mathway application. Mathway is a mathematics software that teaches students how to solve and handle mathematical issues. The app allows students to insert questions manually on the platform or take a picture of the question, and then they get an answer to this mathematical question. It helps students enhance their performance in mathematics. This app can also be used to verify or check if their answers are correct. The report will include a questionnaire to collect data and analyze the users of this application.

Keywords: artificial intelligence, Mathway, mathematics, mathematical problems

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Authors: Marilyn S. Painagan

Abstract:

This general objective of the study was to apply the AquaCrop model to the conditions in the municipality of Carmen, North Cotabato in terms of predicting corn yields in this area and determine the influence of rainfall and soil depth on simulated yield. The study revealed wide disparity in monthly yields as a consequence of similarly varying monthly rainfall magnitudes. It also found out that simulated yield varies with the depth of soil, which in this case was clay loam, the predominant soil in the study area. The model was found to be easy to use even with limited data and shows a vast potential for various farming and policy applications, such as formulation of a cropping calendar.

Keywords: aquacrop, evapotranspiration, crop modelling, crop simulation

Procedia PDF Downloads 221

Authors: Hanadi Mohamed Shawgi Gamal, Ashraf Mohamed Ahmed Abdalla

Abstract:

Sudan is endowed by a great diversity of tree species; nevertheless, the utilization of wood resources has traditionally concentrated on a few number of species. With the great variation in the climatic zones of Sudan, great variations are expected in the anatomical properties between and within species. This variation needs to be fully explored in order to suggest the best uses for the species. Modern research on wood has substantiated that the climatic condition where the species grow has significant effect on wood properties. Understanding the extent of variability of wood is important because the uses for each kind of wood are related to its characteristics; furthermore, the suitability or quality of wood for a particular purpose is determined by the variability of one or more of these characteristics. The present study demonstrates the effect of rainfall zones in some anatomical properties of Acacia seyal var. seyal growing in Sudan. For this purpose, twenty healthy trees were collected randomly from two zones (ten trees per zone). One zone with relatively low rainfall (273mm annually) which represented by North Kordofan state and White Nile state and the second with relatively high rainfall (701 mm annually) represented by Blue Nile state and South Kordofan state. From each sampled tree, a stem disc (3 cm thick) was cut at 10% from stem height. One radius was obtained in central stem dices. Two representative samples were taken from each disc, one at 10% distance from pith to bark, the second at 90% in order to represent the juvenile and mature wood. The investigated anatomical properties were fibers length, fibers and vessels diameter, lumen diameter, and wall thickness as well as cell proportions. The result of the current study reveals significant differences between zones in mature wood vessels diameter and wall thickness, as well as juvenile wood vessels, wall thickness. The higher values were detected in the drier zone. Significant differences were also observed in juvenile wood fiber length, diameter as well as wall thickness. Contrary to vessels diameter and wall thickness, the fiber length, diameter as well as wall thickness were decreased in the drier zone. No significant differences have been detected in cell proportions of juvenile and mature wood. The significant differences in some fiber and vessels dimension lead to expect significant differences in wood density. From these results, Acacia seyal var. seyal seems to be well adapted with the change in rainfall and may survive in any rainfall zone.

Keywords: Acacia seyal var. seyal, anatomical properties, rainfall zones, variation

Procedia PDF Downloads 115

Authors: Preeyaphorn Kosa, Thanutch Sukwimolseree

Abstract:

The climate change is a main parameter which affects the element of hydrological cycle especially runoff. Then, the purpose of this study is to determine the impact of the climate change on surface runoff using land use map on 2008 and daily weather data during January 1, 1979 to September 30, 2010 for SWAT model. SWAT continuously simulate time model and operates on a daily time step at basin scale. The results present that the effect of temperature change cannot be clearly presented on the change of runoff while the rainfall, relative humidity and evaporation are the parameters for the considering of runoff change. If there are the increasing of rainfall and relative humidity, there is also the increasing of runoff. On the other hand, if there is the increasing of evaporation, there is the decreasing of runoff.

Keywords: climate, runoff, SWAT, upper Mun River basin

Procedia PDF Downloads 368