Search results for: 3D plant data

Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

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To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

Procedia PDF Downloads 257

Authors: Grace Omowunmi Soyebi

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A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This data mining application is to be designed using a structured system analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the design and implementation of a computerized medical record system. This computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, data, software development, innovation

Procedia PDF Downloads 70

Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

Procedia PDF Downloads 367

Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

Procedia PDF Downloads 351

Authors: Fatemeh Yousefi, Narges Fallah, Mohsen Kian, Mehrzad Pakzadeh

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The petrochemical complex releases wastewater, which is rich in organic pollutants and could not be treated easily. Treatment of the wastewater from a petrochemical industry has been investigated using a submerged membrane bioreactor (MBR). For this purpose, a pilot-scale submerged MBR with a flat-sheet ultrafiltration membrane was used for treatment of petrochemical wastewater according to Bandar Imam Petrochemical complex (BIPC) Aromatic plant. The testing system ran continuously (24-h) over 6 months. Trials on different membrane fluxes and hydraulic retention time (HRT) were conducted and the performance evaluation of the system was done. During the 167 days operation of the MBR at hydraulic retention time (HRT) of 18, 12, 6, and 3 and at an infinite sludge retention time (SRT), the MBR effluent quality consistently met the requirement for discharge to the environment. A fluxes of 6.51 and 13.02 L m-2 h-1 (LMH) was sustainable and HRT of 6 and 12 h corresponding to these fluxes were applicable. Membrane permeability could be fully recovered after cleaning. In addition, there was no foaming issue in the process. It was concluded that it was feasible to treat the wastewater using submersed MBR technology.

Keywords: membrane bioreactor (MBR), petrochemical wastewater, COD removal, biological treatment

Procedia PDF Downloads 503

Authors: Abdullah Ali Salamai

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Risk management in the construction supply chain (CSC) is vital in construction project risks. CSC has various risks affecting product quality and project timeline, such as operational, social, financial, technical, design, and safety risks. These risks should be mitigated in project construction. So, this paper proposed a set of technologies to overcome risks in CSC, like artificial intelligence (AI), blockchain, data analytics, and IoT, to select the best one. So, the multi-criteria decision-making (MCDM) methodology is used to deal with various risks. The Multi-Attribute Utility Theory (MAUT) method is used to rank technologies. The weights of risks are obtained by the average method by using the decision matrix. The MCDM methodology is integrated with a fuzzy set to overcome uncertainty data. Experts used triangular fuzzy numbers to express their opinions instead of exact numbers. These allow the model to overcome inconsistent and vague data. The MCDM methodology was applied to 18 risks and 5 technologies. The results show that social risks have the highest weight. AI is the best technology for overcoming risks in CSC. AI can integrate with CSC from raw data to final products to deliver to the user.

Keywords: risk management, construction supply chain, fuzzy sets, multi-criteria decision making, supply chain management, artificial intelligence, blockchain

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Authors: Jhovana Silvia Escobar Ortega, Ines Eugenia Garcia De Salamone

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Cover crops are an agri-technological alternative to improve all properties of soils. Cover crops such as oats and rye could be used to reduce erosion and favor system sustainability when they are grown in the same agricultural cycle of the soybean crop. This crop is very profitable but its low contribution of easily decomposable residues, due to its low C/N ratio, leaves the soil exposed to erosive action and raises the need to reduce its monoculture. Furthermore, inoculation with the plant growth promoting rhizobacteria contributes to the implementation, development and production of several cereal crops. However, there is little information on its effects on forage crops which are often used as cover crops to improve soil quality. In order to evaluate the effect of combined inoculation with Azospirillum brasilense and Pseudomonas fluorescens on rhizosphere microbial communities, field experiments were conducted in the west of Buenos Aires province, Argentina, with a split-split plot randomized complete block factorial design with three replicates. The factors were: type of cover crop, inoculation and fertilization. In the main plot two levels of fertilization 0 and 7 40-0-5 (NPKS) were established at sowing. Rye (Secale cereale cultivar Quehué) and oats (Avena sativa var Aurora.) were sown in the subplots. In the sub-subplots two inoculation treatments are applied without and with application of a combined inoculant with A. brasilense and P. fluorescens. Due to the growth of cover crops has to be stopped usually with the herbicide glyphosate, rhizosphere soil of 0-20 and 20-40 cm layers was sampled at three sampling times which were: before glyphosate application (BG), a month after glyphosate application (AG) and at soybean harvest (SH). Community level of physiological profiles (CLPP) and Shannon index of microbial diversity (H) were obtained by multivariate analysis of Principal Components. Also, the most probable number (MPN) of nitrifiers and cellulolytics were determined using selective liquid media for each functional group. The CLPP of rhizosphere microbial communities showed significant differences between sampling times. There was not interaction between sampling times and both, types of cover crops and inoculation. Rhizosphere microbial communities of samples obtained BG had different CLPP with respect to the samples obtained in the sampling times AG and SH. Fertilizer and depth of sampling also caused changes in the CLPP. The H diversity index of rhizosphere microbial communities of rye in the sampling time BG were higher than those associated with oats. The MPN of both microbial functional types was lower in the deeper layer since these microorganisms are mostly aerobic. The MPN of nitrifiers decreased in rhizosphere of both cover crops only AG. At the sampling time BG, the NMP of both microbial types were larger than those obtained for AG and SH. This may mean that the glyphosate application could cause fairly permanent changes in these microbial communities which can be considered bio-indicators of soil quality. Inoculation and fertilizer inputs could be included to improve management of these cover crops because they can have a significant positive effect on the sustainability of the agro-ecosystem.

Keywords: community level of physiological profiles, microbial diversity, plant growth promoting rhizobacteria, rhizosphere microbial communities, soil quality, system sustainability

Procedia PDF Downloads 382

Authors: Tayirjan Taylor Isimjan, Essam Jamea, Muien Qaryouti

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The demand for efficient greenhouse production is escalating, necessitating continuous improvements in controlled plant growth environments. Central to maximizing growth are critical light-related factors, including quantity, quality, and geometric distribution of intercepted radiation. This becomes particularly crucial in regions like the Middle East, characterized by high solar radiation and dusty atmospheric conditions. Existing greenhouse technologies often rely on additional expensive equipment to manage light conditions effectively. In this study, we propose a distinct approach employing functional coatings to mitigate dust and block UV and IR radiation, thereby conserving energy and enhancing productivity. By combining UV-IR reflective coatings with dust mitigation strategies, we aim to address both environmental challenges and energy consumption issues faced by greenhouse agriculture in Saudi Arabia.

Keywords: greenhouse, UV-IR reflective coatings, dust mitigation, energy efficiency, productivity

Procedia PDF Downloads 43

Authors: Dawei Xia, Benyan Jiang, Yong Li

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Split-type air conditioners is widely used for indoor temperature regulation in urban residential buildings in summer in China. The energy-related occupant behavior has a great impact on building energy consumption. Obtaining the energy-related occupant behavior data of air conditioners is the research basis for the energy consumption prediction and simulation. Relying on the development of sensing and control technology, this paper selects Zigbee intelligent electronic equipment to monitor the energy-related occupant behavior of 20 households for 3 months in summer. Through analysis of data, it is found that people of different ages in the region have significant difference in the time, duration, frequency, and energy consumption of air conditioners, and form a data model of three basic energy-related occupant behavior patterns to provide an accurate simulation of energy.

Keywords: occupant behavior, Zigbee, split air conditioner, energy simulation

Procedia PDF Downloads 178

Authors: Priya Madhuri P., Kamini J., Jayanthi S. C.

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Rooftop rainwater harvesting is a crucial practice to address water scarcity, pollution, and flooding. This study aims to estimate the rooftop rainwater harvesting potential (RRWHP) for Suryapet, India, using building footprint data and average rainfall data. The study uses rainfall grids from the India Meteorological Department and Very High Resolution Satellite data to capture building footprints and calculate the RRWHP for a five-year period (2015-2020). Buildings with an area of more than 20 square meters are considered. A conservative figure of 60% efficiency for the catchment area is considered. The study chose 31,770 buildings with an effective rooftop area of around 1.56 sq. km. The city experiences annual rainfall values ranging from 791 mm to 987 mm, with August being the wettest month. The projected annual rooftop rainwater harvesting potential is 1.3 billion litres.

Keywords: buildings, rooftop rainwater harvesting, sustainable water management, urban

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Authors: S. Jeyarajan

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Concept of knowledge is discovered from conducted study for successive Competition in Sri Lankan Non-State Higher Educational Institutes. The Concept discovered out of collected Knowledge Management Practices from Emerald inside likewise reputed literatures and of Non-State Higher Educational sector. A test is conducted to reveal existences and its reason behind of these collected practices in Sri Lankan Non-State Higher Education Institutes. Further, unavailability of such study and uncertain on number of participants for data collection in the Sri Lankan context contributed selection of research method as qualitative method, which used attributes of Delphi Method to manage those likewise uncertainty. Data are collected under Dramaturgical Method, which contributes efficient usage of the Delphi method. Grounded theory is selected as data analysis techniques, which is conducted in intermixed discourse to manage different perspectives of data that are collected systematically through perspective and modified snowball sampling techniques. Data are then analysed using Grounded Theory Development Techniques in Intermix discourses to manage differences in Data. Consequently, Agreement in the results of Grounded theories and of finding in the Foreign Study is discovered in the analysis whereas present study conducted as Qualitative Research and The Foreign Study conducted as Quantitative Research. As such, the Present study widens the discovery in the Foreign Study. Further, having discovered reason behind of the existences, the Present result shows Concept for Knowledge from Sri Lankan Non-State sector to manage higher educational Institutes in successful manner.

Keywords: adherence of snowball sampling into perspective sampling, Delphi method in qualitative method, grounded theory development in intermix discourses of analysis, knowledge management for success of higher educational institutes

Procedia PDF Downloads 157

Authors: Jiangxia Han, Liang Xue, Keda Chen

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Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

Procedia PDF Downloads 119

Authors: Won Il Jung, Gene Lee, Luis Rabelo

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Since many weapon systems are getting more complex and diverse, various problems occur in terms of the acquisition cost, time, and performance limitation. As a matter of fact, the experiment execution in real world is costly, dangerous, and time-consuming to obtain Required Operational Characteristics (ROC) for a new weapon acquisition although enhancing the fidelity of experiment results. Also, until presently most of the research contained a large amount of assumptions so therefore a bias is present in the experiment results. At this moment, the new methodology is proposed to solve these problems without a variety of assumptions. ROC of the new weapon system is developed through the new methodology, which is a way to analyze big data generated by simulating various scenarios based on virtual and constructive models which are involving 6 Degrees of Freedom (6DoF). The new methodology enables us to identify unbiased ROC on new weapons by reducing assumptions and provide support in terms of the optimal weapon systems acquisition.

Keywords: big data, required operational characteristics (ROC), virtual and constructive models, weapon acquisition

Procedia PDF Downloads 278

Authors: T. Nouri, Y. H. A. Reguieg, A. Latigui, A. Ouaini

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Salinity is considered as the most important abiotic factor. It limits growth and productivity of plants and degrades agricultural soils and ecosystem in arid and semi arid area. The study was conducted on Vicia faba L.’Semilla violeta’. Sowing was realized in plastic pots containing sandy substrates of bentonite 0, 3, 5, 7, and 10% associated with abiotic stresses of salinity corresponding to doses of NaCl, MgCl2 and MgSO4 20, 40, and 60 mmol/l respectively. The purpose of this work is to study the combined effect of salinity and of bentonite on a plant commonly cultivated in Algeria the broad bean Vicia faba has through the chemical and hydrous parameter. The results show that the combined action of strong concentration salt (40 and 60 mmol/l) and of bentonite a reduction of the relative content water reveals, against an increase in the content of hydrous deficit and of sodium. The growth of broad bean is significant in the substrate amended to 5 % of bentonite.

Keywords: salinity, bentonite, Vicia faba L, sodium content, hydrous parameters

Procedia PDF Downloads 352

Authors: Muhammad Tayyab, Saba Saira

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It is an established fact that organizations across the globe consider employees as their assets and try to advance their well-being. However, the local firms of developing countries are mostly profit oriented and do not have much concern about their employees’ engagement or commitment. Like other developing countries, the local organizations of Pakistan are also less concerned about the well-being of their employees. Especially public sector organizations lack concern regarding engagement, satisfaction or commitment of the employees. Therefore, this study aimed at investigating the impact of job recognition and job motivation on organizational commitment in the mediation role of employee engagement. The data were collected from land record officers of board of revenue, Punjab, Pakistan. Structured questionnaire was used to collect data through physically visiting land record officers and also through the internet. A total of 318 land record officers’ responses were finalized to perform data analysis. The data were analyzed through confirmatory factor analysis and structural equation modeling technique. The findings revealed that job recognition and job motivation have direct as well as indirect positive and significant impact on organizational commitment. The limitations, practical implications and future research indications are also explained.

Keywords: job motivation, job recognition, employee engagement, employee commitment, public sector, land record officers

Procedia PDF Downloads 114

Authors: Martin Georgiev, Milena Nanova, Damyan Damov

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Architects, engineers, and developers need to analyse and implement a wide spectrum of data in different formats, if they want to produce viable residential developments. Usually, this data comes from a number of different sources and is not well structured. The main objective of this research project is to provide parametric tools working with real geodesic data that can generate residential solutions. Various codes, regulations and design constraints are described by variables and prioritized. In this way, we establish a common workflow for architects, geodesists, and other professionals involved in the building and investment process. This collaborative medium ensures that the generated design variants conform to various requirements, contributing to a more streamlined and informed decision-making process. The quantification of distinctive characteristics inherent to typical residential structures allows a systematic evaluation of the generated variants, focusing on factors crucial to designers, such as daylight simulation, circulation analysis, space utilization, view orientation, etc. Integrating real geodesic data offers a holistic view of the built environment, enhancing the accuracy and relevance of the design solutions. The use of generative algorithms and parametric models offers high productivity and flexibility of the design variants. It can be implemented in more conventional CAD and BIM workflow. Experts from different specialties can join their efforts, sharing a common digital workspace. In conclusion, our research demonstrates that a generative parametric approach based on real geodesic data and collaborative decision-making could be introduced in the early phases of the design process. This gives the designers powerful tools to explore diverse design possibilities, significantly improving the qualities of the building investment during its entire lifecycle.

Keywords: architectural design, residential buildings, urban development, geodesic data, generative design, parametric models, workflow optimization

Procedia PDF Downloads 33

Authors: Martin Pellon Consunji

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Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

Procedia PDF Downloads 106

Authors: Donald Wright

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The ultimate goal of this study is to reinvigorate the philosophical underpinnings the study of urbanization with scientific data with the goal of circumventing what seems an inevitable future clash between rural and urban populations. To that end urban infrastructure must be sustainable economically, politically and ecologically over the course of several generations as cities continue to grow with the incorporation of climate refugees. Our research will provide data concerning the projected increase in population over the coming two decades in Morocco, and the population will shift from rural areas to urban centers during that period of time. As a result, urban infrastructure will need to be adapted, developed or built to fit the demand of future internal migrations from rural to urban centers in Morocco. This paper will also examine how past experiences of internally displaced people give insight into the challenges faced by future migrants and, beyond the gathering of data, how people react to internal migration. This study employs four different sets of research tools. First, a large part of this study is archival, which involves compiling the relevant literature on the topic and its complex history. This step also includes gathering data bout migrations in Morocco from public data sources. Once the datasets are collected, the next part of the project involves populating the attribute fields and preprocessing the data to make it understandable and usable by machine learning algorithms. In tandem with the mathematical interpretation of data and projected migrations, this study benefits from a theoretical understanding of the critical apparatus existing around urban development of the 20th and 21st centuries that give us insight into past infrastructure development and the rationale behind it. Once the data is ready to be analyzed, different machine learning algorithms will be experimented (k-clustering, support vector regression, random forest analysis) and the results compared for visualization of the data. The final computational part of this study involves analyzing the data and determining what we can learn from it. This paper helps us to understand future trends of population movements within and between regions of North Africa, which will have an impact on various sectors such as urban development, food distribution and water purification, not to mention the creation of public policy in the countries of this region. One of the strengths of this project is the multi-pronged and cross-disciplinary methodology to the research question, which enables an interchange of knowledge and experiences to facilitate innovative solutions to this complex problem. Multiple and diverse intersecting viewpoints allow an exchange of methodological models that provide fresh and informed interpretations of otherwise objective data.

Keywords: climate change, machine learning, migration, Morocco, urban development

Procedia PDF Downloads 128

Authors: Kavyadevi N., Kaviya G., Gowsalya P., Janani M., Mohanraj S.

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Hevea brasiliensis, also known as the rubber tree, is one of the foremost assets of crops in the world. One of the most significant advantages of the Rubber Plant in terms of air oxygenation is its capacity to reduce the likelihood of an individual developing respiratory allergies like asthma. To construct such a system that can properly identify crop diseases and pests and then create a database of insecticides for each pest and disease, we must first give treatment for the illness that has been detected. We shall primarily examine three major leaf diseases since they are economically deficient in this article, which is Bird's eye spot, algal spot and powdery mildew. And the recommended work focuses on disease identification on rubber tree leaves. It will be accomplished by employing one of the superior algorithms. Input, Preprocessing, Image Segmentation, Extraction Feature, and Classification will be followed by the processing technique. We will use time-consuming procedures that they use to detect the sickness. As a consequence, the main ailments, underlying causes, and signs and symptoms of diseases that harm the rubber tree are covered in this study.

Keywords: image processing, python, convolution neural network (CNN), machine learning

Procedia PDF Downloads 59

Authors: K. Allaw, J. Adjizian Gerard, M. Chehayeb, A. Raad, W. Fahs, A. Badran, A. Fakherdin, H. Madi, N. Badaro Saliba

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Population estimation using Geographic Information System (GIS) and remote sensing faces many obstacles such as the determination of permanent residents. A permanent resident is an individual who stays and works during all four seasons in his village. So, all those who move towards other cities or villages are excluded from this category. The aim of this study is to identify the factors affecting the percentage of permanent residents in a village and to determine the attributed weight to each factor. To do so, six factors have been chosen (slope, precipitation, temperature, number of services, time to Central Business District (CBD) and the proximity to conflict zones) and each one of those factors has been evaluated using one of the following data: the contour lines map of 50 m, the precipitation map, four temperature maps and data collected through surveys. The weighting procedure has been done using decision tree method. As a result of this procedure, temperature (50.8%) and percentage of precipitation (46.5%) are the most influencing factors.

Keywords: remote sensing, GIS, permanent residence, decision tree, Lebanon

Procedia PDF Downloads 116

Authors: Mahboubeh Davoudi Pahnekolayi

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Begonia rex cv. DS-EYWA is a rare, unique cultivar of begonia rex with curly colorful leaves. Optimization of an in vitro efficient regeneration protocol by focusing on transverse Thin Cell Layer (tTCL) petiole explants for high-scale production of such a beautiful cultivar was considered as our main purpose in this experiment. Thus, various concentrations of Plant Growth Regulators (PGRs) including 6-Benzylaminopurine (BAP), Thidiazuron (TDY), and –Naphthaleneacetic Acid (NAA), were selected in a Completely Randomized Design (CRD) to establish and optimize the direct organogenesis efficiency of this cultivar. Cultivation of 1 mm tTCL petiole explants in noted treatments showed that 1.5 mgl-1 BAP + 0.5 mgl-1 NAA can induce the highest number of direct regenerated shoots and lower concentration of BAP (0.5 mgl-1) can be suggested for shoot elongation before rooting stage. Elongated shoots were successfully rooted in MS free basal medium and acclimatized in 1:1 peat moss: perlite sterilized pot mixture.

Keywords: begonia rare cultivar, direct organogenesis, explant type, regeneration, thin cell layering (TCL)

Procedia PDF Downloads 53

Authors: Ploysai Ohama, Nattida Tumpat

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Natural dye extracted from Caesalpinia sappan Linn. was applied to a cotton fabric and silk yarn by dyeing process. The dyestuff component of Caesalpinia sappan Linn. was extracted using water and ethanol. Analytical studies such as UV–VIS spectrophotometry and gravimetric analysis were performed on the extracts. Brazilein, the major dyestuff component of Caesalpinia sappan Linn. was confirmed in both aqueous and ethanolic extracts by UV–VIS spectrum. The color of each dyed material was investigated in terms of the CIELAB (L*, a* and b*) and K/S values. Cotton fabric dyed without mordant had a shade of reddish-brown, while those post-mordanted with aluminum potassium sulfate, ferrous sulfate and copper sulfate produced a variety of wine red to dark purple color shades. Cotton fabric and silk yarn dyeing was studied using aluminum potassium sulfate as a mordant. The observed color strength was enhanced with increase in mordant concentration.

Keywords: natural dyes, plant materials, dyeing, mordant

Procedia PDF Downloads 278

Authors: N. K. Bhute, B. B. Bhosle

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Field experiment was conducted during kharif 2005, at the experimental farm of the Department of Genetics and Plant Breeding, College of Agriculture, Marathwada Agricultural University, Parbhani, Maharashtra. F1 and F2 hybrids of 23 Bt and 5 non-Bt hybrids were grown in a randomized block design with two replications. The results showed that among F1 hybrids, open boll damage due to bollworm complex was not noticed in 4233 Bt and 4247 Bt cotton hybrids which were found significantly superior over MECH 6301 Bt (3.2 %), 4255 Bt (3.28 %) and it was at par with rest of the hybrids. Among F2 hybrids minimum open boll damage (3.10 %) was noticed in Proagro 144 Bt, which was found significantly superior over rest of the hybrids except 4234 Bt (4.17 %) and 4254 Bt (4.98 %) which were at par with each other. In respect of seed cotton yield, among F1 hybrids maximum yield (15.51 q/ha) was recorded in 4233 Bt which was found significantly superior over rest of the hybrids except 4237 Bt (15.24 q/ha). Among F2 maximum yield (15.44 q/ha) was recorded in 4233 Bt which was found significantly superior over rest of the hybrids except 4258 Bt (15.41 q/ha), 4239 Bt (15.098 q/ha) which were at par with each other. Thus F2 Bt cotton express Bt protein in segregated pattern in which bollworm attack was more as compared to F1 which affects yield as well as quality of lint.

Keywords: Bt cotton, bollworms, F1 and F2 generations, unprotected condition

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Authors: Zohreh Moghaddas, Morteza Yazdani, Farhad Hosseinzadeh

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Nowadays, data envelopment analysis (DEA) models featuring network structures have gained widespread usage for evaluating the performance of production systems and activities (Decision-Making Units (DMUs)) across diverse fields. By examining the relationships between the internal stages of the network, these models offer valuable insights to managers and decision-makers regarding the performance of each stage and its impact on the overall network. To further empower system decision-makers, the inverse data envelopment analysis (IDEA) model has been introduced. This model allows the estimation of crucial information for estimating parameters while keeping the efficiency score unchanged or improved, enabling analysis of the sensitivity of system inputs or outputs according to managers' preferences. This empowers managers to apply their preferences and policies on resources, such as inputs and outputs, and analyze various aspects like production, resource allocation processes, and resource efficiency enhancement within the system. The results obtained can be instrumental in making informed decisions in the future. The top result of this study is an analysis of infeasibility and incorrect estimation that may arise in the theory and application of the inverse model of data envelopment analysis with network structures. By addressing these pitfalls, novel protocols are proposed to circumvent these shortcomings effectively. Subsequently, several theoretical and applied problems are examined and resolved through insightful case studies.

Keywords: inverse models of data envelopment analysis, series network, estimation of inputs and outputs, efficiency, resource allocation, sensitivity analysis, infeasibility

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Authors: Osei Yaw Atakora

Abstract:

A brief study involving the use of natural plant product in trapping of Bactrocera dorsalis was conducted in selected mango orchards in two agro ecological zone of Ghana for the major mango season. The main objective of the study was to compare the attractiveness of different concentrations of aqueous leaf extract of Pimenta racemosa with a commercial methyl eugenol (Stop Mating Block). A total number of 174,388 organisms were captured with 171,412 identified as B. dorsalis and 2,976 identified as non-target (other insects and spiders). Significant differences (P < 0.05) were observed in the performance of the different treatments across the selected experimental farms. Stop Mating Block performed better than the different concentrations with a significant margin. The result suggests that Stop Mating Block performed better than the extract but it is economically preferable since most farmers in Ghana are small-holder farmers.

Keywords: bactrocera dorsalis, methyl eugenol, Pimenta racemosa, stop mating block

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Authors: Yumei Wu, Xiaoyan Dang, Shenglong Yang, Shengmao Zhang

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The East China Sea is one of four main seas in China, where there are many fishery resources. Some important fishing grounds, such as Zhousan fishing ground important to society. But the eco-environment is destroyed seriously due to the rapid developing of industry and economy these years. In this paper, about twenty-year satellite data from MODIS and the statistical information of marine environment from the China marine environmental quality bulletin were applied to do the research. The chlorophyll-a concentration data from MODIS were dealt with in the East China Sea and then used to analyze the features and variations of plankton biomass in recent years. The statistics method was used to obtain their spatial and temporal features. The plankton biomass in the Yangtze River estuary and the Taizhou region were highest. The high phytoplankton biomass usually appeared between the 88th day to the 240th day (end-March - August). In the peak time of phytoplankton blooms, the Taizhou islands was the earliest, and the South China Sea was the latest. The intensity and period of phytoplankton blooms were connected with the global climate change. This work give us confidence to use satellite data to do more researches about the China Sea, and it also provides some help for us to know about the eco-environmental variation of the East China Sea and regional effect from global climate change.

Keywords: the East China Sea, phytoplankton biomass, temporal and spatial variation, phytoplankton bloom

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Authors: Monireh Movahedi

Abstract:

Considering undesirable effects of chemical insecticides on environment and human health, most studies focused on insecticidal effects of plant materials. Here, the insecticidal effects of methanol, ethylacetat and n-Hexan extracts of Rosmarinus officinalis L. on larval stage of the cotton bollworm were studied. From each extract, six concentrations, including 5, 10, 20, 40 and 60 mg/ml were prepared and added on larval artificial diet. Moreover, solution of distilled water and tween 2% considered as check treatment. All experiments were done in laboratory temperature of 25±3 ºC, RH =50±10% and natural photoperiod during growing season. Each treatment had four replications and each replication carried out on 10 first instar larva with <24h age. Larval mortality was recorded 3 and 7 days after treat. Based on results, LC50 of methanol, ethylacetat and n-Hexan extracts of R. officinalis were 2.78, 15.87 and 15.70 ml/mg, respectively. On the other hand, antifeedant effect of methanol, ethylacetat and n-Hexan for R. officinalis estimated as 43.13%, 55.11% and 9.19%, respectively. All the obtained results revealed that methanol and ethylacetat extracts of R. officinalis are effective extracts for controlling the cotton bollworm population.

Keywords: Helocoverpa armigera, Rosemarinus officinalis, extract, methanol, ethylacetat, n-Hexan

Procedia PDF Downloads 152

Authors: M. Elanchezhian, C. Gunasekaran, A. Agnes Deepa, M. Salahudeen

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In recent years climate change is one of the most emerging threats facing by biodiversity both the animals and plants species. Elevated carbon dioxide and ozone concentrations, extreme temperature, changes in rainfall patterns, insects-plant interaction are the main criteria that affect biodiversity. In the present study, which emphasis the climate change and its effects on terrestrial insect diversity in Mukuruthi National Park a protected areas of Western Ghats in India. Sampling was done seasonally at the three areas using pitfall traps, over the period of January to December 2013. The statistical findings were done by Shannon wiener diversity index (H). A significant seasonal variation pattern was detected for total insect’s diversity at the different study areas. Totally nine orders of insects were recorded. Diversity and abundance of terrestrial insects shows much difference between the Natural, Shoal forest and the Grasslands.

Keywords: biodiversity, climate change, mukuruthi national park, terrestrial invertebrates

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Authors: Aijuka Nicholas

Abstract:

A research study was undertaken to characterize the aquifers and identify the potential groundwater recharge zones in the Kagandi district. Quantitative characterization of hydraulic conductivities of aquifers is of fundamental importance to the study of groundwater flow and contaminant transport in aquifers. A conditional approach is used to represent the spatial variability of hydraulic conductivity. Briefly, it involves using qualitative and quantitative geologic borehole-log data to generate a three-dimensional (3D) hydraulic conductivity distribution, which is then adjusted through calibration of a 3D groundwater flow model using pumping-test data and historic hydraulic data. The approach consists of several steps. The study area was divided into five sub-watersheds on the basis of artificial drainage divides. A digital terrain model (DTM) was developed using Arc GIS to determine the general drainage pattern of Kagandi watershed. Hydrologic characterization involved the determination of the various hydraulic properties of the aquifers. Potential groundwater recharge zones were identified by integrating various thematic maps pertaining to the digital elevation model, land use, and drainage pattern in Arc GIS and Sufer golden software. The study demonstrates the potential of GIS in delineating groundwater recharge zones and that the developed methodology will be applicable to other watersheds in Uganda.

Keywords: aquifers, Arc GIS, groundwater recharge, recharge zones

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Authors: Mohamed Ramdani, Hassen Abdellaoui, Abdenour Boudrassen

Abstract:

Mobile platform’s attitude estimation approaches mainly based on combined positioning techniques and developed algorithms; which aim to reach a fast and accurate solution. In this work, we describe the design and the implementation of an attitude determination (AD) process, using only measurements from GPS sensors. The major issue is based on smoothed GPS code data using Hatch filter and raw carrier-phase measurements integrated into attitude algorithm based on vectors measurement using least squares (LSQ) estimation method. GPS dataset from a static experiment is used to investigate the effectiveness of the presented approach and consequently to check the accuracy of the attitude estimation algorithm. Attitude results from GPS multi-antenna over short baselines are introduced and analyzed. The 3D accuracy of estimated attitude parameters using smoothed measurements is over 0.27°.

Keywords: attitude determination, GPS code data smoothing, hatch filter, carrier-phase measurements, least-squares attitude estimation

Procedia PDF Downloads 142