Search results for: statistical model

Authors: Li Hui, Riyadh Hindi

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In the United States, bridge construction often employs overhang brackets to support the deck overhang, the weight of fresh concrete, and loads from construction equipment. This approach, however, can lead to significant torsional moments on the exterior girders, potentially causing excessive girder rotation. Such rotations can result in various safety and maintenance issues, including thinning of the deck, reduced concrete cover, and cracking during service. Traditionally, these issues are addressed by installing temporary lateral bracing systems and conducting comprehensive torsional analysis through detailed finite element analysis for the construction of bridge deck overhang. However, this process is often intricate and time-intensive, with the spacing between temporary lateral bracing systems usually relying on the field engineers’ expertise. In this study, a deep neural network model is introduced to limit exterior girder rotation during bridge deck construction. The model predicts the optimal spacing between temporary bracing systems. To train this model, over 10,000 finite element models were generated in SAP2000, incorporating varying parameters such as girder dimensions, span length, and types and spacing of lateral bracing systems. The findings demonstrate that the deep neural network provides an effective and efficient alternative for limiting the exterior girder rotation for bridge deck construction. By reducing dependence on extensive finite element analyses, this approach stands out as a significant advancement in improving safety and maintenance effectiveness in the construction of bridge decks.

Keywords: bridge deck construction, exterior girder rotation, deep learning, finite element analysis

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Authors: Gordon Newlove Asamoah, Wendy Ama Oti

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This study focused on evaluating access to finance for local oil field companies in Ghana. The study adopted a census survey design in evaluating access to finance for local oil field companies in Ghana. The respondents of this study were 30 management members of three oil field companies in Ghana. The data collected was analysed using Statistical Package for Social Scientists (SPSS) to generate tables and graphs for interpretation. The results show that most companies use equity financing in combination with other forms of financing to finance their business activities. This research has shown the various challenges bordering on the financing of local oil and gas projects, with emphasis on the challenges of raising funds by indigenous oil companies. Financing of the projects by indigenous oil field companies in Ghana is preferably achieved through equity finance mainly because it is the easiest to get compared to all the other forms of financing available. Other sources of financing available are debt financing, joint venture, and retained earnings from the profits generated from their operations. The study made recommendations to local oil field companies as to how they can make good use of the capital market to raise financing.

Keywords: access, financing, oil fields, Ghana

Procedia PDF Downloads 100

Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

Procedia PDF Downloads 311

Authors: Alhaji M. H. Conteh, Xiangbin Yan, Issa Fofana, Brima Gegbe, Tamba I. Isaac

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Rice grain is Sierra Leone’s staple food and the nation imports over 120,000 metric tons annually due to a shortfall in its cultivation. Thus, the insufficient level of the crop's cultivation in Sierra Leone is caused by many problems and this led to the endlessly widening supply and demand for the crop within the country. Consequently, this has instigated the government to spend huge money on the importation of this grain that would have been otherwise cultivated domestically at a cheaper cost. Hence, this research attempts to explore the response of rice supply with respect to its demand in Sierra Leone within the period 1980-2010. The Nerlovian adjustment model to the Sierra Leone rice data set within the period 1980-2010 was used. The estimated trend equations revealed that time had significant effect on output, productivity (yield) and area (acreage) of rice grain within the period 1980-2010 and this occurred generally at the 1% level of significance. The results showed that, almost the entire growth in output had the tendency to increase in the area cultivated to the crop. The time trend variable that was included for government policy intervention showed an insignificant effect on all the variables considered in this research. Therefore, both the short-run and long-run price response was inelastic since all their values were less than one. From the findings above, immediate actions that will lead to productivity growth in rice cultivation are required. To achieve the above, the responsible agencies should provide extension service schemes to farmers as well as motivating them on the adoption of modern rice varieties and technology in their rice cultivation ventures.

Keywords: Nerlovian adjustment model, price elasticities, Sierra Leone, trend equations

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Authors: A. Ghanbari Mardasi, N. Wu, C. Wu

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In this study, a spatial wavelet-based crack localization technique for a thick beam is presented. Wavelet scale in spatial wavelet transformation is optimized to enhance crack detection sensitivity. A windowing function is also employed to erase the edge effect of the wavelet transformation, which enables the method to detect and localize cracks near the beam/measurement boundaries. Theoretical model and vibration analysis considering the crack effect are first proposed and performed in MATLAB based on the Timoshenko beam model. Gabor wavelet family is applied to the beam vibration mode shapes derived from the theoretical beam model to magnify the crack effect so as to locate the crack. Relative wavelet coefficient is obtained for sensitivity analysis by comparing the coefficient values at different positions of the beam with the lowest value in the intact area of the beam. Afterward, the optimal wavelet scale corresponding to the highest relative wavelet coefficient at the crack position is obtained for each vibration mode, through numerical simulations. The same procedure is performed for cracks with different sizes and positions in order to find the optimal scale range for the Gabor wavelet family. Finally, Hanning window is applied to different vibration mode shapes in order to overcome the edge effect problem of wavelet transformation and its effect on the localization of crack close to the measurement boundaries. Comparison of the wavelet coefficients distribution of windowed and initial mode shapes demonstrates that window function eases the identification of the cracks close to the boundaries.

Keywords: edge effect, scale optimization, small crack locating, spatial wavelet

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Authors: Y. Laidani, G. Henini, S. Hanini, A. Labbaci, F. Souahi

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In this study, pomegranate skin, a material suitable for the conditions in Algeria, was chosen as adsorbent material for removal of lead in an aqueous solution. Biosorption studies were carried out under various parameters such as mass adsorbent particle, pH, contact time, the initial concentration of metal, and temperature. The experimental results show that the percentage of biosorption increases with an increase in the biosorbent mass (0.25 g, 0.035 mg/g; 1.25 g, 0.096 mg/g). The maximum biosorption occurred at pH value of 8 for the lead. The equilibrium uptake was increased with an increase in the initial concentration of metal in solution (Co = 4 mg/L, q_t = 1.2 mg/g). Biosorption kinetic data were properly fitted with the pseudo-second-order kinetic model. The best fit was obtained by the Langmuir model with high correlation coefficients (R² > 0.995) and a maximum monolayer adsorption capacity of 0.85 mg/g for lead. The adsorption of the lead was exothermic in nature (ΔH° = -17.833 kJ/mol for Pb (II). The reaction was accompanied by a decrease in entropy (ΔS° = -0.056 kJ/K. mol). The Gibbs energy (ΔG°) increased from -1.458 to -0.305 kJ/mol, respectively for Pb (II) when the temperature was increased from 293 to 313 K.

Keywords: biosorption, Pb (+II), pomegranate skin, wastewater

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Authors: Ibrahim Halil Korkmaz, Eren Özceylan, Cihan Çetinkaya

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Logistics function has great potential for increasing sustainable competitive advantage, profitability, productivity, customer satisfaction and decreasing costs in all sectors. The performance of logistics sector, which has such great influence on the overall performance of the economy, attracts more attention of both researchers and sector representatives day by day. The purpose of this study is to determine the effects of research and development expenditures which spent by enterprises operating in the transportation and storage sectors on Turkey’s logistic performance index (LPI). To do so, research and development investment expenditure among the years 2009-2015 of Turkish transportation and storage firms data from the Turkish Statistical Institute and Turkeys country points in the World Bank logistics performance index in the same years data were examined. As the result of the parametric evaluation, it is seen that the research and development expenditures made have a positive effect on the logistic performance of Turkey.

Keywords: logistics performance index, R&D investments, transportation, storage, Turkey

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Authors: Gyoomi Kim, Jiyoung Bae

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In the field of language education research, there are not many tries to empirically examine learners’ acceptance behavior and related factors of mobile-assisted language learning (MALL). This study is one of the few attempts to propose an integrated theoretical framework that explains MALL users’ acceptance behavior and potential factors. Constructs from technology acceptance model (TAM) and MALL research are tested in the integrated framework. Based on previous studies, a hypothetical model was developed. Four external variables related to the MALL user’s acceptance behavior were selected: subjective norm, content reliability, interactivity, self-regulation. The model was also composed of four other constructs: two latent variables, perceived ease of use and perceived usefulness, were considered as cognitive constructs; attitude toward MALL as an affective construct; behavioral intention to use MALL as a behavioral construct. The participants were 438 undergraduate students who enrolled in an intensive English program at one university in Korea. This particular program was held in January 2018 using the vacation period. The students were given eight hours of English classes each day from Monday to Friday for four weeks and asked to complete MALL courses for practice outside the classroom. Therefore, all participants experienced blended MALL environment. The instrument was a self-response questionnaire, and each construct was measured by five questions. Once the questionnaire was developed, it was distributed to the participants at the final ceremony of the intensive program in order to collect the data from a large number of the participants at a time. The data showed significant evidence to support the hypothetical model. The results confirmed through structural equation modeling analysis are as follows: First, four external variables such as subjective norm, content reliability, interactivity, and self-regulation significantly affected perceived ease of use. Second, subjective norm, content reliability, self-regulation, perceived ease of use significantly affected perceived usefulness. Third, perceived usefulness and perceived ease of use significantly affected attitude toward MALL. Fourth, attitude toward MALL and perceived usefulness significantly affected behavioral intention to use MALL. These results implied that the integrated framework from TAM and MALL could be useful when adopting MALL environment to university students or adult English learners. Key constructs except interactivity showed significant relationships with one another and had direct and indirect impacts on MALL user’s acceptance behavior. Therefore, the constructs and validated metrics is valuable for language researchers and educators who are interested in MALL.

Keywords: blended MALL, learner factors/variables, mobile-assisted language learning, MALL, technology acceptance model, TAM, theoretical framework

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Authors: Hadi Zarafshani, Shidvash Vakilipour, Shahin Teimori, Sara Barati

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In the present study, the aerodynamic performance of a rigid two-dimensional pitching bio-inspired corrugate airfoil was numerically investigated at Reynolds number of 14000. The Open Field Operations And Manipulations (OpenFOAM) computational fluid dynamic tool is used to solve flow governing equations numerically. The k-ω SST turbulence model with low Reynolds correction (k-ω SST LRC) and the pimpleDyMFOAM solver are utilized to simulate the flow field around pitching bio-airfoil. The lift and drag coefficients of the airfoil are calculated at reduced frequencies k=1.24-4.96 and the angular amplitude of A=5^°-20^°. Results show that in a fixed reduced frequency, the absolute value of the sectional lift and drag coefficients increase with increasing pitching amplitude. In a fixed angular amplitude, the absolute value of the lift and drag coefficients increase as the pitching reduced frequency increases.

Keywords: bio-inspired pitching airfoils, OpenFOAM, low Reynolds k-ω SST model, lift and drag coefficients

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Authors: Ahmed N. Shmroukh, Gamal Tag Abdel-Jaber, Rashed D. Aldughpassi

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The feasibility of improving the performance of the proposed solar still unit which operated in very hot climate is investigated numerically and verified with experimental data. This solar desalination unit with proposed auxiliary device as seawater preheating system using petrol based textherm oil was used to produce pure fresh water from seawater. The effective evaporation area of basin is about 1 m². The unit was tested in two main operation modes which are normal and with seawater preheating system. The results showed that, there is good agreement between the theoretical data and the experimental data; this means that the numerical model can be accurately dependable for predicting the proposed solar still performance and design parameters. The results also showed that the fresh water productivity of the solar still in the modified preheating case which is higher than normal case, leads to an increase in productivity of 42%.

Keywords: improving productivity, seawater desalination, solar stills, theoretical model

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Authors: Maor Farid, Oleg Gendelman

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The following study deals with fluid vibration of a liquid in a partially filled vessel under periodic ground excitation. This external excitation might lead to hidraulic impact applied on the vessel inner walls. In order to model these sloshing dynamic regimes, several equivalent mechanical models were suggested in the literature, such as series of pendula or mass-spring systems that are able to impact the inner tank walls. In the following study, we use the latter methodology, use parameter values documented in literature corresponding to cylindrical tanks and consider structural elasticity of the tank. The hydraulic impulses are modeled by the high-exponent potential function. Additional system parameters are found with the help of Finite-Element (FE) analysis. Model-driven stress assessment method is developed. Finally, vibration mitigation performances of both tuned mass damper (TMD) and nonlinear energy sink (NES) are examined.

Keywords: nonlinear energy sink (NES), reduced-order modelling, liquid sloshing, vibration mitigation, vibro-impact dynamics

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Authors: T. Nalowa, L. Foncha, S. Eposi

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Prostate enlargement, prostate cancer are major global health problems affecting many men as they advance in age. It is highly recommended to encourage older men to get Prostate Specific Antigen test screening frequently. Conventional treatments like radiation, chemotherapy are associated with many side effects. And this situation is a call for concern. Traditional medicine is affordable, easily prepared with little or no side effects and it contains many phytochemicals. The study aims to find the cure for prostate cancer and prostate enlargement by extracting products from plant tissues of specific herbs to determine anti-inflammatory, anti-cancer, and anti-hematuria properties. Descriptive statistical analysis was applied to describe the data process. The commonly used method of preparation was extraction. Overall, 40 patients were classified based on their medical conditions on their underlying user report. Rural communities in Fako are rich sources of plants with medicinal properties. The used plants consequently provide basic information and aid to investigate the cure of prostate cancer and prostate enlargement, with great significance.

Keywords: cancer, enlargement, metastases, prostate

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Authors: F. Sarkinfada, Dabo N. Tukur, Abbas A. Muaz, Adamu A. Yahuza

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Appropriate Quality Assurance (QA) of parasite-based diagnosis of malaria to justify Artemisinin-based Combination Therapy (ACT) is essential for Malaria Programmes. In Low and Middle Income Countries (LMIC), resource constrain appears to be a major challenge in implementing the conventional QA system. We designed and implemented a modified LQAS model for QA of malaria parasite (MP) microscopy and RDT in a State Specialist Hospital (SSH) and a University Health Clinic (UHC) in Kano, Nigeria. The capacities of both facilities for MP microscopy and RDT were assessed before implementing a modified LQAS over a period of 3 months. Quality indicators comprising the qualities of blood film and staining, MP positivity rates, concordance rates, error rates (in terms of false positives and false negatives), sensitivity and specificity were monitored and evaluated. Seventy one percent (71%) of the basic requirements for malaria microscopy was available in both facilities, with the absence of certifies microscopists, SOPs and Quality Assurance mechanisms. A daily average of 16 to 32 blood samples were tested with a blood film staining quality of >70% recorded in both facilities. Using microscopy, the MP positivity rates were 50.46% and 19.44% in SSH and UHS respectively, while the MP positivity rates were 45.83% and 22.78% in SSH and UHS when RDT was used. Higher concordance rates of 88.90% and 93.98% were recorded in SSH and UHC respectively using microscopy, while lower rates of 74.07% and 80.58% in SSH and UHC were recorded when RDT was used. In both facilities, error rates were higher when RDT was used than with microscopy. Sensitivity and specificity were higher when microscopy was used (95% and 84% in SSH; 94% in UHC) than when RDT was used (72% and 76% in SSH; 78% and 81% in UHC). It could be feasible to implement an integrated QA model for MP microscopy and RDT using modified LQAS in Malaria Control Programmes in Low and Middle Income Countries that might have resource constrain for parasite-base diagnosis of malaria to justify ACT treatment.

Keywords: malaria, microscopy, quality assurance, RDT

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Authors: Selinda England, Ben Bodnaryk

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With an unprecedented increase in provincial immigration and government support for greater international and culturally diverse learners, a trade/applied learning-focused polytechnic with four campuses within one Canadian province saw the need for intercultural awareness and an intercultural teaching competence strategy for faculty training. An institution-wide pre-assessment needs survey was conducted in 2018, in which 87% of faculty professed to have some/no training when working with international and/or culturally diverse learners. After researching fellow Polytechnics in Canada and seeing very little in the way of faculty support for intercultural competence, an institutional project team comprised of members from all facets of the Polytechnic was created and included: Indigenous experts, Academic Chairs, Directors, Human Resource Managers, and international/settlement subject matter experts. The project team was organized to develop and implement a new academic model focused on enriching intercultural competence among faculty. Utilizing a competency based model, the project team incorporated inclusive terminology into competency indicators and devised a four-phase proposal for implementing intercultural teacher training: a series of workshops focused on the needs of international and culturally diverse learners, including teaching strategies based on current TESOL methodologies, literature and online resources for quick access when planning lessons, faculty assessment examples and models of interculturally proficient instructors, and future job descriptions - all which promote and encourage development of specific intercultural skills. Results from a post-assessment survey (to be conducted in Spring 2020) and caveats regarding improvements and next steps will be shared. The project team believes its intercultural and inclusive teaching competency-based model is one of the first, institution-wide faculty supported initiatives within the Canadian college and Polytechnic post-secondary educational environment; it aims to become a leader in both the province and nation regarding intercultural competency training for trades, industry, and business minded community colleges and applied learning institutions.

Keywords: cultural diversity and education, diversity training teacher training, teaching and learning, teacher training

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Authors: Pius Babuna

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Sustainable water use is important for the existence of the human race. Water resources carrying capacity (WRCC) measures the sustainability of water use; however, the calculation and optimization of WRCC remain challenging. This study used a mathematical model (the Logistics Growth of Water Resources -LGWR) and a linear objective function to model water sustainability. We tested the validity of the models using data from Ghana. Total freshwater resources, water withdrawal, and population data were used in MATLAB. The results show that the WRCC remains sustainable until the year 2132 ±18, when half of the total annual water resources will be used. The optimized water treatment cost suggests that Ghana currently wastes GHȼ 1115.782± 50 cedis (~$182.21± 50) per water treatment plant per month or ~ 0.67 million gallons of water in an avoidable loss. Adopting an optimized water treatment scheme and a watershed management approach will help sustain the WRCC.

Keywords: water resources carrying capacity, smart water management, optimization, sustainable water use, water withdrawal

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Authors: Serkan Tezgel

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Effectively managing threats and power that transcend national boundaries requires a reformulation from the traditional post-modern navy to an adaptive and institutional transnational navy. By analyzing existing soft power concept, post-modern navy, and sea power, this study proposes the transnational navy, founded on the triangle of main attributes of transnational companies, 'Global Competitiveness, Local Responsiveness, Worldwide Learning and Innovation Sharing', a new model which will lead to a positive peace with an institutional network. This transnational model necessitates 'Transnational Navies' to help establish peace with collective and transnational understanding during a transition period 'Reactive Postmodern Navy' has been experiencing. In this regard, it is fairly claimed that a new paradigm shift will revolve around sea power to establish good order at sea with collective and collaborative initiatives and bound to breed new theories and ideas in the forthcoming years. However, there are obstacles to overcome. Postmodern navies, currently shaped by 'Collective Maritime Security' and 'Collective Defense' concepts, can not abandon reactive applications and acts. States deploying postmodern navies to realize their policies on international platforms and seapower structures shaped by the axis of countries’ absolute interests resulted in multipolar alliances and coalitions, but the establishment of the peace. These obstacles can be categorized into three tiers in establishing a unique transnational model navy: Strategic, Organizational and Management challenges. To overcome these obstacles and challenges, postmodern navies should transform into cooperative, collective and independent soft transnational navies with the transnational mentality, global commons, and institutional network. Such an adaptive institution can help the world navigate to a positive peace.

Keywords: postmodern navy, transnational navy, transnational mentality, institutional network

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Authors: A. A. Akharayi, B. E. Anjola

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The economic development of a country depends greatly on internally built revenue. Small and Medium-scale Enterprise (SMEs) contributes to the economic buoyancy as it provides employment for the teeming population, encourages job creation by youths who believes in themselves and also by others who have gathered finance enough to invest in growable investment. SMEs is faced with several challenges. The study investigates the role and challenges of SMEs Somolu Local Government Area. Simple random sampling techniques were used to select entrepreneurs (SMEs owners and managers). One hundred and fifty (150) registered SMEs were selected across the LGA data collection with the use of well-structured questionnaire. The data collected were analysed using Statistical Package for Social Science (SPSS) version 21. The result of the analysis indicated that marketing, finance, social facilities and indiscriminate taxes among other high level of fund available significantly (p <0 .05) increase firm capacity while marketing showed a significant (p < 0.05) relationship with profit level.

Keywords: challenge, development, economic, small and medium scale enterprise

Procedia PDF Downloads 234

Authors: Rim Bouhaouel, Naoufel Kraïem, Zuhoor Al Khanjari

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Unified Modeling Language (UML) is considered as one of the widespread modeling language standardized by the Object Management Group (OMG). Therefore, the model driving engineering (MDE) community attempts to provide reuse of UML diagrams, and do not construct it from scratch. The UML model appears according to a specific software development process. The existing method generation models focused on the different techniques of transformation without considering the development process. Our work aims to construct an UML component from fragments of UML diagram basing on an agile method. We define UML fragment as a portion of a UML diagram, which express a business target. To guide the generation of fragments of UML models using an agile process, we need a flexible approach, which adapts to the agile changes and covers all its activities. We use the software product line (SPL) to derive a fragment of process agile method. This paper explains our approach, named RECUP, to generate UML fragments following an agile process, and overviews the different aspects. In this paper, we present the approach and we define the different phases and artifacts.

Keywords: UML, component, fragment, agile, SPL

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Authors: Vangelis Papadimitropoulos

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This paper is part of the research program “Techno-Social Innovation in the Collaborative Economy”, funded by the Hellenic Foundation for Research and Innovation (H.F.R.I.) for the years 2022-2024. The paper showcases the Open Food Network (OFN) as an open-sourced digital platform supporting short food supply chains in local agricultural production and consumption. The paper outlines the research hypothesis, the theoretical framework, and the methodology of research as well as the findings and conclusions. Research hypothesis: The model of open cooperativism as a vehicle for systemic change in the agricultural sector. Theoretical framework: The research reviews the OFN as an illustrative case study of the three-zoned model of open cooperativism. The OFN is considered a paradigmatic case of the model of open cooperativism inasmuch as it produces commons, it consists of multiple stakeholders including ethical market entities, and it is variously supported by local authorities across the globe, the latter prefiguring the mini role of a partner state. Methodology: Research employs Ernesto Laclau and Chantal Mouffe’s discourse analysis -elements, floating signifiers, nodal points, discourses, logics of equivalence and difference- to analyse the breadth of empirical data gathered through literature review, digital ethnography, a survey, and in-depth interviews with core OFN members. Discourse analysis classifies OFN floating signifiers, nodal points, and discourses into four themes: value proposition, governance, economic policy, and legal policy. Findings: OFN floating signifiers align around the following nodal points and discourses: “digital commons”, “short food supply chains”, “sustainability”, “local”, “the elimination of intermediaries” and “systemic change”. The current research identifies a lack of common ground of what the discourse of “systemic change” signifies on the premises of the OFN’s value proposition. The lack of a common mission may be detrimental to the formation of a common strategy that would be perhaps deemed necessary to bring about systemic change in agriculture. Conclusions: Drawing on Laclau and Mouffe’s discourse theory of hegemony, research introduces a chain of equivalence by aligning discourses such as “agro-ecology”, “commons-based peer production”, “partner state” and “ethical market entities” under the model of open cooperativism, juxtaposed against the current hegemony of neoliberalism, which articulates discourses such as “market fundamentalism”, “privatization”, “green growth” and “the capitalist state” to promote corporatism and entrepreneurship. Research makes the case that for OFN to further agroecology and challenge the current hegemony of industrial agriculture, it is vital that it opens up its supply chains into equivalent sectors of the economy, civil society, and politics to form a chain of equivalence linking together ethical market entities, the commons and a partner state around the model of open cooperativism.

Keywords: sustainability, the digital commons, open cooperativism, innovation

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Authors: Medjadj Tarek, Ghribi Hayet

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This study aims to develop a flood sensitivity assessment tool using machine learning (ML) techniques and geographic information system (GIS). The importance of this study is integrating the geographic information systems (GIS) and machine learning (ML) techniques for mapping flood risks, which help decision-makers to identify the most vulnerable areas and take the necessary precautions to face this type of natural disaster. To reach this goal, we will study the case of the city of M'sila, which is among the areas most vulnerable to floods. This study drew a map of flood-prone areas based on the methodology where we have made a comparison between 3 machine learning algorithms: the xGboost model, the Random Forest algorithm and the K Nearest Neighbour algorithm. Each of them gave an accuracy respectively of 97.92 - 95 - 93.75. In the process of mapping flood-prone areas, the first model was relied upon, which gave the greatest accuracy (xGboost).

Keywords: Geographic information systems (GIS), machine learning (ML), emergency mapping, flood disaster management

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Authors: Gustavo Funes, Eduardo Peters, Jose Delpiano

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It is widely known that photovoltaic technology has been massively distributed over the last decade despite its low-efficiency ratio. Dust deposition reduces this efficiency even more, lowering the energy production and module lifespan. In this work, we developed an artificial vision algorithm based on CIELAB color space to identify dust over panels in an autonomous way. We performed several experiments photographing three different types of panels, 30W, 340W and 410W. Those panels were soiled artificially with uniform and non-uniform distributed dust. The algorithm proposed uses statistical tools to provide a simulation with a 100% soiled panel and then performs a comparison to get the percentage of dirt in the experimental data set. The simulation uses a seed that is obtained by taking a dust sample from the maximum amount of dust from the dataset. The final result is the dirt percentage and the possible distribution of dust over the panel. Dust deposition is a key factor for plant owners to determine cleaning cycles or identify nonuniform depositions that could lead to module failure and hot spots.

Keywords: dust detection, photovoltaic, artificial vision, soiling

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Authors: Jia Li, Xing Wei, Yuchen Hong, Yang Lu

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Detecting vehicle behavior has always been the focus of intelligent transportation, but with the explosive growth of the number of vehicles and the complexity of the road environment, the vehicle behavior videos captured by traditional surveillance have been unable to satisfy the study of vehicle behavior. The traditional method of manually labeling vehicle behavior is too time-consuming and labor-intensive, but the existing object detection and tracking algorithms have poor practicability and low behavioral location detection rate. This paper proposes a vehicle behavior detection algorithm based on the dual-stream convolution network and the multi-dimensional video dynamic detection network. In the videos, the straight-line behavior of the vehicle will default to the background behavior. The Changing lanes, turning and turning around are set as target behaviors. The purpose of this model is to automatically mark the target behavior of the vehicle from the untrimmed videos. First, the target behavior proposals in the long video are extracted through the dual-stream convolution network. The model uses a dual-stream convolutional network to generate a one-dimensional action score waveform, and then extract segments with scores above a given threshold M into preliminary vehicle behavior proposals. Second, the preliminary proposals are pruned and identified using the multi-dimensional video dynamic detection network. Referring to the hierarchical reinforcement learning, the multi-dimensional network includes a Timer module and a Spacer module, where the Timer module mines time information in the video stream and the Spacer module extracts spatial information in the video frame. The Timer and Spacer module are implemented by Long Short-Term Memory (LSTM) and start from an all-zero hidden state. The Timer module uses the Transformer mechanism to extract timing information from the video stream and extract features by linear mapping and other methods. Finally, the model fuses time information and spatial information and obtains the location and category of the behavior through the softmax layer. This paper uses recall and precision to measure the performance of the model. Extensive experiments show that based on the dataset of this paper, the proposed model has obvious advantages compared with the existing state-of-the-art behavior detection algorithms. When the Time Intersection over Union (TIoU) threshold is 0.5, the Average-Precision (MP) reaches 36.3% (the MP of baselines is 21.5%). In summary, this paper proposes a vehicle behavior detection model based on multi-dimensional dynamic detection network. This paper introduces spatial information and temporal information to extract vehicle behaviors in long videos. Experiments show that the proposed algorithm is advanced and accurate in-vehicle timing behavior detection. In the future, the focus will be on simultaneously detecting the timing behavior of multiple vehicles in complex traffic scenes (such as a busy street) while ensuring accuracy.

Keywords: vehicle behavior detection, convolutional neural network, long short-term memory, deep learning

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Authors: A. Bouzabata, A. Boukhari

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Curcuma longa L. (Zingiberaceae), commonly known as turmeric, has a long history of traditional uses for culinary purposes as a spice and a food colorant. The present study aimed to document the ethnobotanical knowledge about Curcuma longa and to assess the variation in the herbalists’ experience in Northeastern Algeria. Data were collected by semi-structured questionnaires and direct interviews with 30 herbalists. Ethnobotanical indices, including the fidelity level (FL%), the relative frequency citation (RFC) and use value (UV) were determined by quantitative methods. Diversity in the knowledge was analyzed using univariate, non-parametric and multivariate statistical methods. Three main categories of uses were recorded for C. longa: for food, for medicine and for cosmetic purposes. As a medicine, turmeric was used for the treatment of gastrointestinal, dermatological and hepatic diseases. Medicinal and food uses were correlated with both forms of use (rhizome and powder). The age group did not influence the use. Multivariate analyses showed a significant variation in traditional knowledge, associated with the use value, origin, quality and efficacy of the drug. These findings suggested that the geographical origin of C. longa affected the use in Algeria.

Keywords: curcuma, indices, knowledge, variation

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Authors: Mohamed Shaban, Adel Yahya

Abstract:

The goal of this paper study is to schedule the power generation units to minimize fuel consumption cost based on a model that solves unit commitment problems. This can be done by utilizing forward dynamic programming method to determine the most economic scheduling of generating units. The model was applied to a power station, which consists of four generating units. The obtained results show that the applications of forward dynamic programming method offer a substantial reduction in fuel consumption cost. The fuel consumption cost has been reduced from $116,326 to $102,181 within a 24-hour period. This means saving about 12.16 % of fuel consumption cost. The study emphasizes the importance of applying modeling schedule programs to the operation of power generation units. As a consequence less consumption of fuel, less loss of power and less pollution

Keywords: unit commitment, forward dynamic, fuel cost, programming, generation scheduling, operation cost, power system, generating units

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Authors: Piyangkun Kukutapan, Siridech Boonsang

Abstract:

The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.

Keywords: maximum power point tracking, multilayer perceptron netural network, optimal duty cycle, DC generator

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

Abstract:

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: Ximena Castillo, Jaime Pizarro

Abstract:

This research describes the development of a very cheap mesoporous silica material similar to hexagonal mesoporous silica (HMS) and using a silicate extract as precursor. This precursor is obtained from cheap fly ash by an easy calcination process at 850 °C and a green extraction with water. The obtained mesoporous fly ash material had a surface area of 282 m2 g-1 and a pore size of 5.7 nm. It was functionalized with ethylene diamino moieties via the well-known SAMMS method, followed by a DRIFT analysis that clearly showed the successful functionalization. An excellent adsorbent was obtained for the adsorption of sulfate anions by the solid’s modification with copper forming a copper-ethylenediamine complex. The adsorption of sulfates was studied in a batch system ( experimental conditions: pH=8.0; 5 min). The kinetics data were adjusted according to a pseudo-second order model with a high coefficient of linear regression at different initial concentrations. The adsorption isotherm that best fitted the experimental data was the Freundlich model. The maximum sulfate adsorption capacity of this very cheap fly ash based adsorbent was 146.1 mg g-1, 3 times greater than the values reported in literature and commercial adsorbent materials.

Keywords: fly ash, mesoporous materials, SAMMS, sulfate

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Authors: Anishur Rahman, Maria Areias, Diogo Simões, Ana Figeuiredo, Filipa Figueiredo, João Nunes

Abstract:

The circular economy (CE) has gained increased awareness among academics, businesses, and decision-makers as it stimulates resource circularity in the production and consumption systems. A large epistemological study has explored the principles of CE, but scant attention eagerly focused on analysing how CE is evaluated, consented to, and enforced using economic metabolism data and business intelligent framework. Economic metabolism involves the ongoing exchange of materials and energy within and across socio-economic systems and requires the assessment of vast amounts of data to provide quantitative analysis related to effective resource management. Limited concern, the present work has focused on the regional flows pilot region from Portugal. By addressing this gap, this study aims to promote eco-innovation and sustainability in the regions of Intermunicipal Communities Região de Coimbra, Viseu Dão Lafões and Beiras e Serra da Estrela, using this data to find precise synergies in terms of material flows and give companies a competitive advantage in form of valuable waste destinations, access to new resources and new markets, cost reduction and risk sharing benefits. In our work, emphasis on applying artificial intelligence (AI) and, more specifically, on implementing state-of-the-art deep learning algorithms is placed, contributing to construction a business intelligent approach. With the emergence of new approaches generally highlighted under the sub-heading of AI and machine learning (ML), the methods for statistical analysis of complex and uncertain production systems are facing significant changes. Therefore, various definitions of AI and its differences from traditional statistics are presented, and furthermore, ML is introduced to identify its place in data science and the differences in topics such as big data analytics and in production problems that using AI and ML are identified. A lifecycle-based approach is then taken to analyse the use of different methods in each phase to identify the most useful technologies and unifying attributes of AI in manufacturing. Most of macroeconomic metabolisms models are mainly direct to contexts of large metropolis, neglecting rural territories, so within this project, a dynamic decision support model coupled with artificial intelligence tools and information platforms will be developed, focused on the reality of these transition zones between the rural and urban. Thus, a real decision support tool is under development, which will surpass the scientific developments carried out to date and will allow to overcome imitations related to the availability and reliability of data.

Keywords: circular economy, artificial intelligence, economic metabolisms, machine learning

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Authors: F. Longo, G. Granello, G. Tecchio, F. Da Porto

Abstract:

Unreinforced masonry (URM) infill walls are widely used throughout the world, also in seismic prone regions, as partitions in reinforced concrete building frames. Even if they do not represent structural elements, they can dramatically affect both strength and stiffness of RC structures by acting as a diagonal strut, modifying shear and displacements distribution along the building height, with uncertain consequences on structural safety. In the last decades, many refined models have been developed to describe infill walls effect on frame structural behaviour, but generally restricted to in-plane actions. Only very recently some new approaches were implemented to consider in-plane/out-of-plane interaction of URM infill walls in progressive collapse simulations. In the present work, a particularly promising macro-model was adopted for the progressive collapse analysis of infilled RC frames. The model allows to consider the bi-directional interaction in terms of displacement and strength capacity for URM infills, and to remove the infill contribution when the URM wall is supposed to fail during the analysis process. The model was calibrated on experimental data regarding two different URM panels thickness, modelling with particular care the post-critic softening branch. A frame specimen set representing the most common Italian structures was built considering two main normative approaches: a traditional design philosophy, corresponding to structures erected between 50’s-80’s basically designed to support vertical loads, and a seismic design philosophy, corresponding to current criteria that take into account horizontal actions. Non-Linear Static analyses were carried out on the specimen set and some preliminary evaluations were drawn in terms of different performance exhibited by the RC frame when the contemporary effect of the out-of-plane damage is considered for the URM infill.

Keywords: infill Panels macromodels, in plane-out of plane interaction, RC frames, URM infills

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Siti Khairunniza Bejo

Abstract:

Conventional methods for nutrient soil mapping are based on laboratory tests of samples that are obtained from surveys. The time and cost involved in gathering and analyzing soil samples are the reasons that researchers use Predictive Soil Mapping (PSM). PSM can be defined as the development of a numerical or statistical model of the relationship among environmental variables and soil properties, which is then applied to a geographic database to create a predictive map. Kriging is a group of geostatistical techniques to spatially interpolate point values at an unobserved location from observations of values at nearby locations. The main problem with using kriging as an interpolator is that it is excessively data-dependent and requires a large number of closely spaced data points. Hence, there is a need to minimize the number of data points without sacrificing the accuracy of the results. In this paper, an Artificial Neural Networks (ANN) scheme was used to predict macronutrient values at un-sampled points. ANN has become a popular tool for prediction as it eliminates certain difficulties in soil property prediction, such as non-linear relationships and non-normality. Back-propagation multilayer feed-forward network structures were used to predict nitrogen, phosphorous and potassium values in the soil of the study area. A limited number of samples were used in the training, validation and testing phases of ANN (pattern reconstruction structures) to classify soil properties and the trained network was used for prediction. The soil analysis results of samples collected from the soil survey of block C of Sawah Sempadan, Tanjung Karang rice irrigation project at Selangor of Malaysia were used. Soil maps were produced by the Kriging method using 236 samples (or values) that were a combination of actual values (obtained from real samples) and virtual values (neural network predicted values). For each macronutrient element, three types of maps were generated with 118 actual and 118 virtual values, 59 actual and 177 virtual values, and 30 actual and 206 virtual values, respectively. To evaluate the performance of the proposed method, for each macronutrient element, a base map using 236 actual samples and test maps using 118, 59 and 30 actual samples respectively produced by the Kriging method. A set of parameters was defined to measure the similarity of the maps that were generated with the proposed method, termed the sample reduction method. The results show that the maps that were generated through the sample reduction method were more accurate than the corresponding base maps produced through a smaller number of real samples. For example, nitrogen maps that were produced from 118, 59 and 30 real samples have 78%, 62%, 41% similarity, respectively with the base map (236 samples) and the sample reduction method increased similarity to 87%, 77%, 71%, respectively. Hence, this method can reduce the number of real samples and substitute ANN predictive samples to achieve the specified level of accuracy.

Keywords: artificial neural network, kriging, macro nutrient, pattern recognition, precision farming, soil mapping

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