Search results for: rainfall threshold

Authors: Imen Dhaou

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This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets.

Keywords: CVaR, Dow Jones Islamic index, GJR-GARCH-EVT-pair copula, portfolio optimization

Procedia PDF Downloads 256

Authors: Aboubakr Gambo, Adama Diaw, Tobias Wunscher

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This study attempts to identify factors affecting rural households’ resilience to food insecurity in Niger. For this, we first create a resilience index by using Principal Component Analysis on the following five variables at the household level: income, food expenditure, duration of grain held in stock, livestock in Tropical Livestock Units and number of farms exploited and second apply Structural Equation Modelling to identify the determinants. Data from the 2010 National Survey on Households’ Vulnerability to Food Insecurity done by the National Institute of Statistics is used. The study shows that asset and social safety nets indicators are significant and have a positive impact on households’ resilience. Climate change approximated by long-term mean rainfall has a negative and significant effect on households’ resilience to food insecurity. The results indicate that to strengthen households’ resilience to food insecurity, there is a need to increase assistance to households through social safety nets and to help them gather more resources in order to acquire more assets. Furthermore, early warning of climatic events could alert households especially farmers to be prepared and avoid important losses that they experience anytime an uneven climatic event occur.

Keywords: food insecurity, principal component analysis, structural equation modelling, resilience

Procedia PDF Downloads 361

Authors: Merouane Salhi, Toufik Zebbiche

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When the stagnation pressure of perfect gas increases, the specific heat and their ratio do not remain constant anymore and start to vary with this pressure. The gas doesn’t remain perfect. Its state equation change and it becomes for a real gas. In this case, the effects of molecular size and intermolecular attraction forces intervene to correct the state equation. The aim of this work is to show and discuss the effect of stagnation pressure on supersonic thermodynamical, physical and geometrical flow parameters, to find a general case for real gas. With the assumptions that Berthelot’s state equation accounts for the molecular size and intermolecular force effects, expressions are developed for analyzing supersonic flow for thermally and calorically imperfect gas lower than the dissociation molecules threshold. The designs parameters for supersonic nozzle like thrust coefficient depend directly on stagnation parameters of the combustion chamber. The application is for air. A computation of error is made in this case to give a limit of perfect gas model compared to real gas model.

Keywords: supersonic flow, real gas model, Berthelot’s state equation, Simpson’s method, condensation function, stagnation pressure

Procedia PDF Downloads 447

Authors: Rana Gill, Chandandeep Kaur

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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.

Keywords: segmentation, edge detection, text, extracting characters

Procedia PDF Downloads 426

Authors: Obasi Igwe Oscar, Udokure Ubong James, Echebiri Raphael Ndubuisi

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The study examined the determinants of the welfare of itinerant palm oil marketers in Akwa Ibom State, Nigeria. Multistage sampling techniques were adopted to select 120 itinerant palm oil marketers for the study. Primary data were obtained using a structured questionnaire. Data were analyzed using the cost and returns formula and multiple regression model. Results showed that itinerant palm oil marketing was profitable and 57.39% efficient. The respondents' monthly expenditure of N111,787.90 on food and non-food items indicated that they live above the extreme poverty threshold of $2.15 per person per day, with a daily spending of over $2. Net income (P<0.05), age (P<0.01), educational level (P<0.01), household size (P<0.01), credit amount (P<0.01), market information (P<0.05), amount of tax paid (P<0.01) and the level of market participation (P<0.05) were the significant determinants of the welfare of itinerant traders in the study area. The study recommended that government and non-governmental organizations should make available marketing facilities and enhance transportation networks to reduce inefficiencies and lower transaction costs for itinerant palm oil traders in Akwa Ibom state.

Keywords: determinants, welfare, itinerant, palm oil, marketers

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Authors: Manoj Kumar

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Ionization cross sections of molecules due to electron impact play an important role in chemical processes in various branches of applied physics, such as radiation chemistry, gas discharges, plasmas etching in semiconductors, planetary upper atmospheric physics, mass spectrometry, etc. In the present work, we have calculated the total ionization cross sections for Adenine (C₅H₅N₅), a biologically important molecule, by electron impact in the incident electron energy range from ionization threshold to 2 keV employing a well-known Jain-Khare semiempirical formulation based on Bethe and Möllor cross sections. In the non-availability of the experimental results, the present results are in good agreement qualitatively as well as quantitatively with available theoretical results. The present results drive our confidence for further investigation of complex bio-molecule with better accuracy. Notwithstanding, the present method can deduce reliable cross-sectional data for complex targets with adequate accuracy and may facilitate the acclimatization of calculated cross-sections into atomic molecular cross-section data sets for modeling codes and other applications.

Keywords: electron impact ionization cross-sections, oscillator strength, jain-khare semiempirical approach

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Authors: Tejeet Singh, Ishavneet Singh

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The present study focused on carrying out the creep analysis in an isotropic thick-walled composite cylindrical pressure vessel composed of aluminum matrix reinforced with silicon-carbide in particulate form. The creep behavior of the composite material has been described by the threshold stress based creep law. The values of stress exponent appearing in the creep law were selected as 3, 5 and 8. The constitutive equations were developed using well known von-Mises yield criteria. Models were developed to find out the distributions of creep stress and strain rate in thick-walled composite cylindrical pressure vessels under internal pressure. In order to obtain the stress distributions in the cylinder, the equilibrium equation of the continuum mechanics and the constitutive equations are solved together. It was observed that the radial stress, tangential stress and axial stress increases along with the radial distance. The cross-over was also obtained almost at the middle region of cylindrical vessel for tangential and axial stress for different values of stress exponent. The strain rates were also decreasing in nature along the entire radius.

Keywords: steady state creep, composite, cylinder, pressure

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Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu

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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.

Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning

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Authors: Mohsen Rezaei Aghdam

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Recombinant DNA technology has meaningfully augmented the conventional crop improvement and has a great possibility to contribution plant breeders to encounter the augmented food request foretold for the 21st century. Predictable changes in weather and its erraticism, chiefly extreme fevers and vicissitudes in rainfall are expected to brand crop upgrading even more vital for food manufacture. Tissue attitude has been downtrodden to create genetic erraticism from which harvest plants can be better, to improve the state of health of the recognized physical and to upsurge the number of wanted germplasms obtainable to the plant breeder. This appraisal delivers an impression of the chances obtainable by the integration of vegetable biotechnology into plant development efforts and increases some of the social subjects that need to be considered in their application. Public-private companies offer chances to catalyze new approaches and investment while accelerating integrated research and development and commercial supply chain-based solutions. Novel varieties derivative by encouraged mutatgenesis are used commonly: rice in Thailand. These paper combinations obtainable data about the influence of change breeding-derived crop changes around the world, traveler magnetism the possibility of mutation upbringing as a flexible and feasible approach appropriate to any crop if that suitable objectives and selection approaches are used.

Keywords: crop, improve, genetic, agricultural

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Christelle Tchoupé Makougoum

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In the context of agriculture, differences across localities in term of climate change can create systematic variation among farmers technical efficiency. Failure to account for climate variability could lead to wrong conclusions about farmers’ technical efficiency and also it could bias the ranking of farmers according to their managerial performance. The literature on agricultural productivity has given little attention to this issue whereas it is necessary for establishing to what extent climate affects farmers efficiency. This article contributes to the preview literature by two ways. First, it proposed a new econometric model that accounting for the climate change influences on technical efficiency in the specific area of agriculture. Second it estimates the inefficiency due to climate change and the real managerial performance of Malian farmers. Using the Mali’s data from agricultural census and CRU TS3 climatic database we implemented an adjusted stochastic frontier methodology to account for the impact of environmental factors. The results yield three main findings. First, instability in temperatures and rainfall decreases technical efficiency on average. Second, the climate change modifies the classification of the farmers according to their efficiency scores. Thirdly it is noted that, although climate changes are partly responsible for the deviation from the border, the capacity of farmers to combine inputs into the optimal proportion is more to undermine. The study concluded that improving farmer efficiency should include fostering their resilience to climate change.

Keywords: agriculture, climate change, stochastic production function, technical efficiency

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Authors: Fatemehsadat Mortazavizadeh, Amin Dehghani, Majid Mirzaei, Nurulhuda Binti Mohammad Ramli, Adnan Dehghani

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Flood is Malaysia's most common and serious natural disaster. Kelantan River Basin is a tropical basin that experiences a rainy season during North-East Monsoon from November to March. It is also one of the hardest hit areas in Peninsular Malaysia during the heavy monsoon rainfall. Considering the consequences of the flood events, it is essential to develop the flood inundation map as part of the mitigation approach. In this study, the delineation of flood inundation zone in the area of Kelantan River basin using a hydrodynamic model is done by HEC-RAS, QGIS and ArcMap. The streamflow data has been generated with the weather generator based on the observation data. Then, the data is statistically analyzed with the Extreme Value (EV1) method for 2-, 5-, 25-, 50- and 100-year return periods. The minimum depth, maximum depth, mean depth, and the standard deviation of all the scenarios, including the OBS, are observed and analyzed. Based on the results, generally, the value of the data increases with the return period for all the scenarios. However, there are certain scenarios that have different results, which not all the data obtained are increasing with the return period. Besides, OBS data resulted in the middle range within Scenario 1 to Scenario 40.

Keywords: flood inundation, kelantan river basin, hydrodynamic model, extreme value analysis

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Authors: R. Tlili Yaakoubi, H.Nakouri, O. Blanpain, S. Lallahem

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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.

Keywords: automation, optimization, paradigm, RTC

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Authors: S. N. Al-Bahry, Y. M. Al-Wahaibi, S. J. Joshi, E. A. Elshafie, A. S. Al-Bimani

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Oman is one of the oil producing countries in the Arabian Peninsula and the Gulf region. About 30-40 % of oil produced from the Gulf is transported globally along the seacoast of Oman. Oil pollution from normal tanker operations, ballast water, illegal discharges and accidental spills are always serious threats to terrestrial and marine habitats. Due to Oman’s geographical location at arid region where the temperature ranges between high 40s and low 50s Celsius in summers with low annual rainfall, the main source of fresh water is desalinated sea and brackish water. Oil pollution, therefore, pose a major threat to drinking water. Biosurfactants are secondary metabolites produced by microorganisms in hydrophobic environments to release nutrients from solid surfaces, such as oil. In this study, indigenous oil degrading thermophilic spore forming bacteria were isolated from oil fields contaminated soil. The isolates were identified using MALDI-TOF biotyper and 16s RNA. Their growth conditions were optimized for the production of biosurfactant. Surface tension, interfacial tensions and microbial oil biodegradation capabilities were tested. Some thermophilic bacteria degraded either completely or partially heavy crude oil (API 10-15) within 48h suggesting their high potential in oil spill bioremediation and avoiding the commonly used physical and chemical methods which usually lead to other environmental pollution.

Keywords: bacteria, bioremediation, biosurfactant, crude-oil-pollution

Procedia PDF Downloads 428

Authors: Shaghayegh Shajari, Mehdi Mahmoodi, Mahmood Rajabian, Uttandaraman Sundararaj, Les J. Sudak

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Although several strain sensors based on carbon nanotubes (CNTs) have been reported, the stretchability and sensitivity of these sensors have remained as a challenge. Highly stretchable and sensitive strain sensors are in great demand for human motion monitoring and human-machine interface. This paper reports the fabrication and characterization of a new type of strain sensors based on a stretchable fluoropolymer / CNT nanocomposite system made via melt-mixing technique. Electrical and mechanical characterizations were obtained. The results showed that this nanocomposite sensor has high stretchability up to 280% of strain at an optimum level of filler concentration. The piezoresistive properties and the strain sensing mechanism of the strain sensor were investigated using Electrochemical Impedance Spectroscopy (EIS). High sensitivity was obtained (gauge factor as large as 12000 under 120% applied strain) in particular at the concentrations above the percolation threshold. Due to the tunneling effect, a non- linear piezoresistivity was observed at high concentrations of CNT loading. The nanocomposites with good conductivity and lightweight could be a promising candidate for strain sensing applications.

Keywords: carbon nanotubes, fluoropolymer, piezoresistive, strain sensor

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Authors: Jalal Sharib@Sarip, Dainee nor Fardzila Ahmad Tugi, Mohd Tarmizi Ishak, Mohd Izwan Abdul Adziz

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This research paper aims to determine the rate of soil erosion and sedimentation by using Cesium-137,137Cs as a medium-term tracer in the Sembrong catchment, Malaysia, over two different study seasons. The results of the analysis show that rates of soil erosion and sedimentation for both seasons were variable. This can be clearly seen where the dry season only gives the value of the rate of soil erosion. Meanwhile, the wet season has given both soil erosion and sedimentation rate values. The dry season had rates of soil erosion between 5.09 t/ha/y to 51.03 t/ha/y. The wet season had soil erosion and sedimentation rates between 8.02 t/ha/y to 39.78 t/ha/y and -4.81 t/ha/y to - 50.81 t/ha/y, each, respectively. rubber and oil palm plantations referring to Station 17 and station 4/6, located near Semberong Lake and Sembrong River, had the highest rates of soil erosion and sedimentation at 51.03 t/ha/y and -50.81 t/ha/y, respectively. Various factors must also be taken into account, such as soil types, the total volume of rainfall received for both seasons, as well as differences in land use at the study stations. In conclusion, 137Cs as a medium-term tracer was successfully used to determine rates of soil erosion and sedimentation in two different seasons for the Sembrong catchment area. The data on soil erosion and sedimentation rates for this study will be very useful for present, and future land and water management in the Sembrong catchment area and may be compared with other similar catchments in Malaysia.

Keywords: soil erosion, sedimentation, cesium-137, catchment management

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Authors: Tolga Aydin, M. Fatih Alaeddinoğlu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatio-temporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newly-formed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: apriori algorithm, association rules, data mining, spatio-temporal data

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Authors: Sam Weber, Deepak Mishra, Susan Wilde, Elizabeth Kramer

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The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing over time, with point and non-point source eutrophication and shifting climate paradigms being blamed as the primary culprits. Excessive nutrients, warm temperatures, quiescent water, and heavy and less regular rainfall create more conducive environments for CyanoHABs. CyanoHABs have the potential to produce a spectrum of toxins that cause gastrointestinal stress, organ failure, and even death in humans and animals. To promote enhanced, proactive CyanoHAB management, risk modeling using geospatial tools can act as predictive mechanisms to supplement current CyanoHAB monitoring, management and mitigation efforts. The risk maps would empower water managers to focus their efforts on high risk water bodies in an attempt to prevent CyanoHABs before they occur, and/or more diligently observe those waterbodies. For this research, exploratory spatial data analysis techniques were used to identify the strongest predicators for CyanoHAB blooms based on remote sensing-derived cyanobacteria cell density values for 771 waterbodies in the Georgia Piedmont and landscape characteristics of their watersheds. In-situ datasets for cyanobacteria cell density, nutrients, temperature, and rainfall patterns are not widely available, so free gridded geospatial datasets were used as proxy variables for assessing CyanoHAB risk. For example, the percent of a watershed that is agriculture was used as a proxy for nutrient loading, and the summer precipitation within a watershed was used as a proxy for water quiescence. Cyanobacteria cell density values were calculated using atmospherically corrected images from the European Space Agency’s Sentinel-2A satellite and multispectral instrument sensor at a 10-meter ground resolution. Seventeen explanatory variables were calculated for each watershed utilizing the multi-petabyte geospatial catalogs available within the Google Earth Engine cloud computing interface. The seventeen variables were then used in a multiple linear regression model, and the strongest predictors of cyanobacteria cell density were selected for the final regression model. The seventeen explanatory variables included land cover composition, winter and summer temperature and precipitation data, topographic derivatives, vegetation index anomalies, and soil characteristics. Watershed maximum summer temperature, percent agriculture, percent forest, percent impervious, and waterbody area emerged as the strongest predictors of cyanobacteria cell density with an adjusted R-squared value of 0.31 and a p-value ~ 0. The final regression equation was used to make a normalized cyanobacteria cell density index, and a Jenks Natural Break classification was used to assign waterbodies designations of low, medium, or high risk. Of the 771 waterbodies, 24.38% were low risk, 37.35% were medium risk, and 38.26% were high risk. This study showed that there are significant relationships between free geospatial datasets representing summer maximum temperatures, nutrient loading associated with land use and land cover, and the area of a waterbody with cyanobacteria cell density. This data analytics approach to CyanoHAB risk assessment corroborated the literature-established environmental triggers for CyanoHABs, and presents a novel approach for CyanoHAB risk mapping in waterbodies across the greater southeastern United States.

Keywords: cyanobacteria, land use/land cover, remote sensing, risk mapping

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Authors: Saman Ghaffarian, Ilgin Gökaşar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

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Authors: Xiaoheng Tan, Xianfang Li, Tan Guo, Yuchuan Liu, Zhijun Yang, Hongye Li, Kai Fu, Yufang Wu, Heling Gong

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The image data collected in reality often have high dimensions, and it contains noise and redundant information. Therefore, it is necessary to extract the compact feature expression of the original perceived image. In this process, effective use of prior knowledge such as data structure distribution and sample label is the key to enhance image feature discrimination and robustness. Based on the above considerations, this paper proposes a local preserving discriminant feature learning model based on graph optimization. The model has the following characteristics: (1) Locality preserving constraint can effectively excavate and preserve the local structural relationship between data. (2) The flexibility of graph learning can be improved by constructing a new local geometric structure graph using label information and the nearest neighbor threshold. (3) The L₂,₁ norm is used to redefine LDA, and the diagonal matrix is introduced as the scale factor of LDA, and the samples are selected, which improves the robustness of feature learning. The validity and robustness of the proposed algorithm are verified by experiments in two public image datasets.

Keywords: feature extraction, graph optimization local preserving projection, linear discriminant analysis, L₂, ₁ norm

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Authors: Jiahui Song, Ravindra Joshi

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High-intensity, nanosecond, pulsed electric fields have been shown to be useful non-thermal tools capable of producing a variety of specific cellular responses. While reversible and temporary changes are often desired based on electromanipulation, irreversible effects can also be important objectives. These include elimination of tumor cells and bacterial decontamination. A simple model-based rate-equation treatment of the various cellular biochemical processes was used to qualitatively predict the pulse number-dependent caspase activation and cell survival trends. The model incorporated the caspase-8 associated extrinsic pathway, the delay inherent in its activation, cytochrome c release, and the internal feedback mechanism between caspase-3 and Bid. Results were roughly in keeping with the experimental cell-survival data. A pulse-number threshold was predicted followed by a near-exponential fall-off. The intrinsic pathway was shown to be much weaker as compared to the extrinsic mechanism for electric pulse induced cell apoptosis. Also, delays of about an hour are predicted for detectable molecular concentration increases following electrical pulsing.

Keywords: apoptosis, cell survival, model, pathway

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Authors: Muhammad Naeem Mushtaq, Muhammad Arshad, Shahid Majeed

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Maize is known as queen of cereals because of its highest genetic yield potential and multipurpose characteristics in human being and animal diet. Maize crop visited by many major, minor, visitors and sporadic insect pests. This study was conducted during 2014 to evaluate the richness and evenness of these insect pests and their interaction with metrological conditions at University of Agriculture, Faisalabad. In this experiment, two localities were selected; one was treated with pesticide and second was untreated. Maize field visited by many insect pests. Those insect pests were collected by using three collection method: sweep net, pitfall trap and plant inspection. The data was collected weekly interval from August to October and statistically analyzed by using Shannon Index which showed the results of insect pest richness and evenness. The value of Shannon Index was higher with the increase in number of species and abundance of insects. Camponotus nearcticus was most abundant in sweep net and pitfall trap method while Rhopalosiphum maidis was abundant in plant inspection method. Temperature was negatively co-relate with the insect population in all three collection methods while the relative humidity and rainfall had varying results.

Keywords: abundance, evenness, maize, richness

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Authors: Ren Junn Hwang, Chung-Chien Lu, Jain-Shing Wu

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Cloud outsource storage is one of important services in cloud computing. Cloud users upload data to cloud servers to reduce the cost of managing data and maintaining hardware and software. To ensure data confidentiality, users can encrypt their files before uploading them to a cloud system. However, retrieving the target file from the encrypted files exactly is difficult for cloud server. This study proposes a protocol for performing multikeyword searches for encrypted cloud data by applying k-nearest neighbor technology. The protocol ranks the relevance scores of encrypted files and keywords, and prevents cloud servers from learning search keywords submitted by a cloud user. To reduce the costs of file transfer communication, the cloud server returns encrypted files in order of relevance. Moreover, when a cloud user inputs an incorrect keyword and the number of wrong alphabet does not exceed a given threshold; the user still can retrieve the target files from cloud server. In addition, the proposed scheme satisfies security requirements for outsourced data storage.

Keywords: fault-tolerance search, multi-keywords search, outsource storage, ranked search, searchable encryption

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

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Authors: Myoung-Jin Lee, Sung-Jin Lee, Young-Kon Park, Jin-Wook Kim, Bo-Kyoung Kim, Song-Hun Chong, Sun-Ii Kim

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The ability to resist the shear strength arises mainly from the web panel of steel girders and as such, the shear buckling strength of these girders has been extensively investigated. For example, Blaser’s reported that when buckling occurs, the tension field has an effect after the buckling strength of the steel is reached. The findings of these studies have been applied by AASHTO, AISC, and to the European Code that provides guidelines for designs aimed at preventing shear buckling. Steel girders are susceptible to corrosion resulting from exposure to natural elements such as rainfall, humidity, and temperature. This corrosion leads to a reduction in the size of the web panel section, thereby resulting in a decrease in the shear strength. The decrease in the panel section has a significant effect on the maintenance section of the bridge. However, in most conventional designs, the influence of corrosion is overlooked during the calculation of the shear buckling strength and hence over-design is common. Therefore, in this study, a steel girder with an A/D of 1:1, as well as a 6-mm-, 16-mm-, and 12-mm-thick web panel, flange, and intermediate reinforcing material, respectively, were used. The total length was set to that (3200 mm) of the default model. The effect of corrosion shear buckling was investigated by determining the volume amount of corrosion, shape of the erosion patterns, and the angular change in the tensile field of the shear buckling strength. This study provides the basic data that will enable designs that incorporate values closer (than those used in most conventional designs) to the actual shear buckling strength.

Keywords: corrosion, shear buckling strength, steel girder, shear strength

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Authors: Kunihisa Kakumoto

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This technical paper is for the prototype of solar energy utilization for finding sustainable conditions. This model has been simulated under the climate conditions in Japan. At the beginning of the study, the solar model house was built up on site. And the concerned data was collected in this model house for several years. On the basis of these collected data, the concept on the solar community was built up. For the finding sustainable conditions, the amount of the solar energy generation and its reduction of carbon dioxide and the reduction of carbon dioxide by the green planting and the amount of carbon dioxide according to the normal daily life in the solar community and the amount of the necessary water for the daily life in the solar community and the amount of the water supply by the rainfall on-site were calculated. These all values were taken into consideration. The relations between each calculated result are shown in the expression of inequality. This solar community and its consideration for finding sustainable conditions can be one prototype to do the feasibility study for our life in the future

Keywords: carbon dioxide, green planting, smart city, solar community, sustainable condition, water activity

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Authors: Rabaa Tlili Yaakoubi, Hind Nakouri, Olivier Blanpain

Abstract:

The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of the CARDIO project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 40 to 100%. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 60% of total volume rejected to the natural environment and of 80 % in the number of discharges.

Keywords: RTC, paradigm, optimization, automation

Procedia PDF Downloads 284

Authors: Dedah Ahmed Babou, Nicolas Bez

Abstract:

The Min-Max autocorrelation factor (MAF) approach makes it possible to express in a space formed by spatially independent factors, spatiotemporal observations. These factors are ordered in decreasing order of spatial autocorrelation. The starting observations are thus expressed in the space formed by these factors according to temporal coordinates. Each vector of temporal coefficients expresses the temporal evolution of the weight of the corresponding factor. Applying this approach has enabled us to achieve the following results: (i) Define a spatially orthogonal space in which the projections of the raw data are determined; (ii) Define a limit threshold for the factors with the strongest structures in order to analyze the weight, and the temporal evolution of these different structures (iii) Study the correlation between the temporal evolution of the persistent spatial structures and that of the observed average abundance (iv) Propose prototypes of campaigns reflecting a high vs. low abundance (v) Propose a classification of campaigns that highlights seasonal and/or temporal similarities. These results were obtained by analyzing the octopus yield during the scientific campaigns of the oceanographic vessel Al Awam during the period 1989-2017 in the Mauritanian exclusive economic zone.

Keywords: spatiotemporal , autocorrelation, kriging, variogram, Octopus vulgaris

Procedia PDF Downloads 147

Authors: Reena Murali, David Peter S.

Abstract:

The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies shows that up regulation of mRNA cause serious diseases like Cancer. So designing effective siRNA with good knockdown effects play an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (ΔG), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: artificial neural network, double stranded RNA, RNA interference, short interfering RNA

Procedia PDF Downloads 526

Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

Abstract:

The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

Procedia PDF Downloads 325