Search results for: pest forecasting

Authors: Anastasia K. Eleftheriadou, Athanasios Sfetsos, Nikolaos Gounaris

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The present work examines the suitability of a seasonal forecasting model downscaled with a very high spatial resolution in order to assess the energy performance and requirements of buildings. The application of the developed model is applied on Greece for a period and with a forecast horizon of 5 months in the future. Greece, as a country in the middle of a financial crisis and facing serious societal challenges, is also very sensitive to climate changes. The commonly used method for the correlation of climate change with the buildings energy consumption is the concept of Degree Days (DD). This method can be applied to heating and cooling systems for a better management of environmental, economic and energy crisis, and can be used as medium (3-6 months) planning tools in order to predict the building needs and country’s requirements for residential energy use.

Keywords: downscaled seasonal models, degree days, energy performance

Procedia PDF Downloads 433

Authors: J-C. Leneveu, A. Chereau, L. Mansart, T. Mesbah, M. Wyka

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Extracting and interpreting information from Big Data represent a stake for years to come in several sectors such as finance. Currently, numerous methods are used (such as Technical Analysis) to try to understand and to anticipate market behavior, with mixed results because it still seems impossible to exactly predict a financial trend. The increase of available data on Internet and their diversity represent a great opportunity for the financial world. Indeed, it is possible, along with these standard financial data, to focus on exogenous data to take into account more macroeconomic factors. Coupling the interpretation of these data with standard methods could allow obtaining more precise trend predictions. In this paper, in order to observe the influence of exogenous data price independent of other usual effects occurring in classical markets, behaviors of Bitcoin users are introduced in a model reconstituting Bitcoin value, which is elaborated and tested for prediction purposes.

Keywords: big data, bitcoin, data mining, social network, financial trends, exogenous data, global economy, behavioral finance

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Authors: Bagireanu Astrid, Bros-Williamson Julio, Duncheva Mila, Currie John

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Digital Twins (DT) are an innovative concept of cyber-physical integration of data between an asset and its virtual replica. They have originated in established industries such as manufacturing and aviation and have garnered increasing attention as a potentially transformative technology within the built environment. With the potential to support decision-making, real-time simulations, forecasting abilities and managing operations, DT do not fall under a singular scope. This makes defining and leveraging the potential uses of DT a potential missed opportunity. Despite its recognised potential in established industries, literature on DT in the built environment remains limited. Inadequate attention has been given to the implementation of DT in construction projects, as opposed to its operational stage applications. Additionally, the absence of a standardised definition has resulted in inconsistent interpretations of DT in both industry and academia. There is a need to consolidate research to foster a unified understanding of the DT. Such consolidation is indispensable to ensure that future research is undertaken with a solid foundation. This paper aims to present a comprehensive systematic literature review on the role of DT in the built environment. To accomplish this objective, a review and thematic analysis was conducted, encompassing relevant papers from the last five years. The identified papers are categorised based on their specific areas of focus, and the content of these papers was translated into a through classification of DT. In characterising DT and the associated data processes identified, this systematic literature review has identified 6 DT opportunities specifically relevant to the built environment: Facilitating collaborative procurement methods, Supporting net-zero and decarbonization goals, Supporting Modern Methods of Construction (MMC) and off-site manufacturing (OSM), Providing increased transparency and stakeholders collaboration, Supporting complex decision making (real-time simulations and forecasting abilities) and Seamless integration with Internet of Things (IoT), data analytics and other DT. Finally, a discussion of each area of research is provided. A table of definitions of DT across the reviewed literature is provided, seeking to delineate the current state of DT implementation in the built environment context. Gaps in knowledge are identified, as well as research challenges and opportunities for further advancements in the implementation of DT within the built environment. This paper critically assesses the existing literature to identify the potential of DT applications, aiming to harness the transformative capabilities of data in the built environment. By fostering a unified comprehension of DT, this paper contributes to advancing the effective adoption and utilisation of this technology, accelerating progress towards the realisation of smart cities, decarbonisation, and other envisioned roles for DT in the construction domain.

Keywords: built environment, design, digital twins, literature review

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Authors: Nomakholwa F. Stokwe, Antoinette P. Malan

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Woolly apple aphid (WAA), Eriosoma lanigerum, is an important pest of apples worldwide. The aphid feeds above ground on buds and leaf axils and the roots of apple trees. Entomopathogenic nematodes (EPNs) of the two families, Steinernematidae and Heterorhabditidae, and their symbiotic bacteria have generated extensive interest as inundative applied biological control agents of insects. With the development of the resistance of WAA to chemicals, export restrictions, and the inability of parasitoids to control the aphid successfully early in the season, considering EPNs as an alternative biocontrol agent is important. Seven EPN species were tested for their pathogenicity against WAA. Laboratory bioassays identified S. yirgalemense and H. zealandica as being the most virulent against the subterranean stage of the WAA, with a mortality rate of 48% and 38%, respectively. Studies on the effect of WAA size showed that the last instar is most susceptible to infection, whereas smaller instars appear to be too small for nematode penetration and infection. Neither increasing the exposure period of the aphids nor increasing the nematode concentration affected the infection rate positively. The haemolymph of WAA showed an inhibitory effect on the development of the symbiotic bacteria, preventing the completion of the nematode’s life cycle.

Keywords: apples, biocontrol, entomopathogenic nematodes, woolly apple aphid

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Authors: Dua K. S. Y. Klaas, Monzur A. Imteaz, Ika Sudiayem, Elkan M. E. Klaas, Eldav C. M. Klaas

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Due to its porous nature, karst terrains – geomorphologically developed from dissolved formations, is vulnerable to water shortage and deteriorated water quality. Therefore, a solid comprehension on sub-surface flow of karst landscape is essential to assess the long-term availability of groundwater resources. In this paper, a single-continuum model using a finite difference model, MODLFOW, was constructed to represent an unconfined carbonate aquifer in a tropical karst island of Rote in Indonesia. The model, spatially discretized in 20 x 20 m grid cells, was calibrated and validated using available groundwater level and atmospheric variables. In the calibration and validation steps, Parameter Estimation (PEST) and geostatistical pilot point methods were employed to estimate hydraulic conductivity and specific yield values. The results show that the model is able to represent the sub-surface flow indicated by good model performances both in calibration and validation steps. The final model can be used as a robust representation of the system for future study on climate and land use scenarios.

Keywords: carbonate aquifer, karst, sub-surface flow, groundwater model

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Authors: Tony Rizk

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Recent material advances and cost efficiencies are transforming the housing industry away from traditional lumber and gypsum material to alternate fiberboard material that is workable and resistant to fire, mold, and pest infestation. The use of these materials may add to the initial cost of construction. However, the life cycle (cradle to grave) cost of houses using these construction materials and methods are lower than the life cycle costs using traditional housing construction materials and methods. This paper will present four (4) case studies of sustainable house projects. Each project was designed and constructed using earthen-based, sustainable fiberboard material that is resistant to fire, mold, and infestation and fabricated at a very low material calorific value. These house projects have a living space ranging from 625 sq. ft. for an accessory dwelling unit and up to 3,200 sq. ft. 1-story and 2-story homes. For each case study, we will present the house engineering design and construction method, the initial construction costs, a summary of the life cycle costs, and a comparison to the life cycle cost of traditional housing available in the literature.

Keywords: residential housing, sustainable housing, life cycle cost, fire resistance, mold, infestation resistance

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Authors: Hanife Genç

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The olive fruit fly, Bactrocera oleae (Rossi), is an economically important and endemic pest in olive (Oleae europae) orchards in Turkey. The aim of this study was to determine olive fruit fly infestation in different olive cultivars in the laboratory. Olive fly infested fruits were collected in Çanakkale province to establish wild fly population. After having reproductive olive fly colonies, 14 olive cultivars were tested in the controlled laboratory conditions, at 23±2 °C, 65% RH and 16:8 h (light: dark) photoperiod. The olive samples from 14 different olive cultivars were collected in October 2015, in Campus of Dardanos, Çanakkale Onsekiz Mart University. Observations were carried out detecting some biological parameters such as the number of oviposition stings, active infestation, total infestation, the number of pupae and the adult emergence. The results indicated that oviposition stings were not associated with pupal yield. A few pupae were found within olive fruits which were not able to exit. Screening of the varieties suggested that less susceptible cultivar to olive fruit fly attacks was Arbequin while Gemlik-2M 2/3 showed significant susceptibility. Ovipositional preference of olive fly females and the success of larval development in different olive varieties are crucial for establishing new olive orchards to prevent high olive fruit fly infestation.

Keywords: infestation, olive fruit fly, olive cultivars, oviposition sting

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Authors: Mohammad Mahdi Mousavi

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Bankruptcy prediction models have been implemented for continuous evaluation and monitoring of firms. With the huge number of bankruptcy models, an extensive number of studies have focused on answering the question that which of these models are superior in performance. In practice, one of the drawbacks of existing comparative studies is that the relative assessment of alternative bankruptcy models remains an exercise that is mono-criterion in nature. Further, a very restricted number of criteria and measure have been applied to compare the performance of competing bankruptcy prediction models. In this research, we overcome these methodological gaps through implementing an extensive range of criteria and measures for comparison between dynamic and static bankruptcy models, and through proposing a multi-criteria framework to compare the relative performance of bankruptcy models in forecasting firm distress for UK firms.

Keywords: bankruptcy prediction, data envelopment analysis, performance criteria, performance measures

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Authors: Mehdi Basirat, Mohammad Rouhani, Shahla Borzouei, Majid Zarangi, Asma Abolghasemi, Mohammad Fazel Soltani, Mohammad Gorji, Mohammad Amin Samih

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The common pistachio psylla, Agonoscena pistaciae Burckhardt and Lauterer (Hemiptera: Aphalaridae), as one of the most detrimental pests in all pistachio producing regions, causes great economic damages to pistachio trees. Nowadays, various pesticides are used to control the common pistachio psylla and robust pesticide exposure has occurred in orchards. In this study, field experiments were conducted during 2018–2021 to assess the effects of sulfur on A. pistaciae. This study compared sulfur with asafoetida extract and pesticide (acetamiprid) on A. pistaciae based on complete randomized blocks with three replications. The analysis results of variance showed that the effect of treatments on egg (F2,24 = 17.61, P = 0.00) and nymphs (F2,24 = 18.29, P = 0.00) had a significant difference at a 1% level. The results demonstrated that sulfur had the highest measure of control on eggs and nymphs significantly compared to the plant extract and pesticide (negative control). These results provide support to the potential use of sulfur as an alternative pest management tool against A. pistaciae. The results clearly indicated that sulfur could control the common pistachio psylla population for six weeks at least.

Keywords: Agonoscena pistaciae, pesticide exposure, pistachio, sulfur

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Authors: Debraj Adhikari, Resham Bahadur Thapa, Samudra Lal Joshi, Jason Jinping Du, Sundar Tiwari

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The purpose of this study was to keep a record of fruit fly species (Tephritidae: Diptera) in the sweet orange orchards of Sindhuli district, Nepal. Male fruit fly species were trapped and collected fortnightly using para-pheromone lures (methyl eugenol and cue lure) in Steiner traps at 25 orchards starting in March 2019 and continuing until February 2021. During the monitoring period, there was a significant variation in the occurrence of the fruit fly species. Fruit flies were captured in greater numbers during warm and rainy months (June, July, August, September) than during dry and winter months (December, January, February). Higher numbers of fruit flies were trapped in methyl eugenol than cue lure traps. Bactrocera dorsalis, B. zonata were major fruit fly species trapped in the methyl eugenol trap. Whereas, Zeugodacus tau, Z. cucurbitae, Z. scutellaris, and Dacus longicornis were major fruit fly species trapped in the cue lure trap. The findings of this study could be used to develop a long-term pest management strategy for the agro-ecological system.

Keywords: bactrocera, cue lure, methyl eugenol, monitoring, zeugodacus

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Authors: S. O. Ekpo

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Environmental science deals with everyday challenges such as a cell for call for good and safe quality air, water, food and healthy leaving condition which include destruction of biodiversity and how to conserve these natural resources for sustainable development. Biodiversity or species richness is the sum of all the different species of animals, plants, fungi and microorganisms leaving on earth and variety of habitats in which they leave. Human beings leave on plants and animals on daily basis for food, clothing, medicine, housing, research and trade or commerce; besides this, biodiversity serves to purify the air, water and land of contaminant, and recycle useful materials for continual use of man. However, man continual incessant exploitation and exploration has affected biodiversity negatively in many ways such habitant fragmentation and destruction, introduction of invasive species, pollution, overharvesting, prediction and pest control amongst others. Measures such as recycling material, establishing natural parks, sperm bank, limiting the exploitation of renewable resources to sustainable yield and urban and industrial development as well as prohibiting hunting endangered species and release of non native live forms into an area will go a long way towards conserving biodiversity for continues profitable yield.

Keywords: biodiversity, conservation, exploitation and exploration sustainable yield, recycling of materials

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Authors: Yuzhi Cai

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In this paper, we introduce a newly developed quantile function model that can be used for estimating conditional distributions of financial returns and for obtaining multi-step ahead out-of-sample predictive distributions of financial returns. Since we forecast the whole conditional distributions, any predictive quantity of interest about the future financial returns can be obtained simply as a by-product of the method. We also show an application of the model to the daily closing prices of Dow Jones Industrial Average (DJIA) series over the period from 2 January 2004 - 8 October 2010. We obtained the predictive distributions up to 15 days ahead for the DJIA returns, which were further compared with the actually observed returns and those predicted from an AR-GARCH model. The results show that the new model can capture the main features of financial returns and provide a better fitted model together with improved mean forecasts compared with conventional methods. We hope this talk will help audience to see that this new model has the potential to be very useful in practice.

Keywords: DJIA, financial returns, predictive distribution, quantile function model

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Authors: Y. Atibi, A. Boutaleb Joutei, T. Slimani

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Insecticidal properties of Alliaceae are widely known, they are plant with varied biological properties. Garlic and onion are known for their positive effect on health, including the prevention of cardiovascular disease and some digestive cancers. These health benefits molecules are also responsible for pest potential control of Alliaceae. With these properties, we can consider using Alliaceae as acaricides. The purpose of this study was to compare the effect of chemical and biopesticides on citrus mites, especially Tetranychus urticae, Panonychus citri and Eutetranychus orientalis. Chemical treatment (Dicofol) and biopesticides (Garlic juice + Alcohol) applied on this study to control the various stages of mites, have reduced the proliferation of mobile forms and reducing the number of eggs to acceptable levels. Garlic juice + alcohol revealed efficiency from 50 to 57.69 % against the mobile forms of T. urticae, however, it was effective against the mobile forms of P. citri and E. orientalis with an efficiency of 85.71 % and 100 % respectively, its action has also reduced the number of eggs of T. urticae and E. orientalis at low levels. Therefore, this biopesticide is conceivable viewpoint technical and economic as the infestation by mite is low.

Keywords: Garlic juice, acaricide, biopesticide, mites, alcohol, Tetranychus urticae, Panonychus citri, Eutetranychus orientalis.

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Authors: Banaz Sdiq Abdulla

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Four local species (Allium sativum, Capsicum annum, Anethum graveolens, and Ocimum basilicum) were evaluated in the laboratory of Biolog Department, College of Education, for their ability to protect stored rice from the infection by weevil Sitophilus oryzae. Aqueous extracts of the plant species were applied as direct admixture of three concentrations levels of 1%, 2.5%, and 5% (W/V) to assess for mortality, adult emergence, and repellency and weight losses. The results showed that Al. sativum extracts was the most effective as it gave the highest mortality (90%)at 5% concentration followed by Capsicum annum (80%) on the 4th day post treatment, the result showed that the plant extract of different concentrations exhibited different level of reduction in adult emergence and different repellency of adults of Sitophilus oryzae. Allium sativum recorded the lowest mean number of adult emergence (8) followed by Capsicum annum (10) at 5% concentration, while Capsicum annum was found to be revealed complete repellent agent (100%) repellency on the 6th hours against Sitophilus oryzae followed by Allium sativum and Anethum graveolens (81.8%). There was a significant (P>0.05) reduction in the weight lossed by the weevils with less damaged recorded on grain treated with Allium sativum and Capsicum annum (1.6%) and (2.3%) respectively.

Keywords: plant extraction, rice, protectant, pest

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Authors: Mehrnoosh Abouzari

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Artificial intelligence has covered all areas of human life and has helped or replaced many jobs. One of the areas of application of artificial intelligence in the police is to detect crime, identify the accused or victim and prove the crime. It will play an effective role in implementing preventive justice and creating security in the community, and improving judicial decisions. This will help improve the performance of the police, increase the accuracy of criminal investigations, and play an effective role in preventing crime and high-risk behaviors in society. This article presents and analyzes the capabilities and capacities of artificial intelligence in police and similar examples used worldwide to prove the necessity of using artificial intelligence in the police. The main topics discussed include the performance of artificial intelligence in crime detection and prediction, the risk capacity of criminals and the ability to apply arbitray institutions, and the introduction of artificial intelligence programs implemented worldwide in the field of criminal investigation for police.

Keywords: police, artificial intelligence, forecasting, prevention, software

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Authors: Abd Alati Ali Abushnaq, Malek Essnni, Abduraouf Eteer

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The present study proposes a comprehensive approach to effectively utilize associated gas from the KK remote station, eliminating the practice of flaring and mitigating greenhouse gas (GHG) emissions. The proposed integrated system involves diverting the associated gas via a newly designed pipeline, seamlessly connecting to the existing 12-inch pipeline at the tie-in point. The proposed destination is the low-pressure system at A-100 or 3rd stage, where the associated gas will be channeled towards the NGL (natural gas liquid) plant for processing. To ensure the system's efficacy under varying gas production scenarios, the study employs two industry-standard simulation software packages, Aspen HYSYS and PIPSIM. The simulated results demonstrate the system's ability to handle the projected increase in gas production, reaching up to 38 MMSCFD. This comprehensive analysis ensures the system's robustness and adaptability to future production demands.

Keywords: associated gas, flaring mitigation, GHG emissions, pipeline diversion, NGL plant, KK remote station, production forecasting, Aspen HYSYS, PIPSIM

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Authors: Rochelyn Dona, Mohamed F. Nur, Serdar Satar

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The biological response of Aphis gossypii Glover (Hom. Aphididae) was investigated on the effects of seven cucumber varieties (Cucumis sativus L.) such as Kitir, Muhika, Ayda, Beit, 14-F1, Ruzgar, and Ptk in the laboratory condition at 24±1°C, 65±5% relative humidity (RH) and a photoperiod of 16:8 (L:D) hour. The results were related that the developmental time of A. gossypii at the nymphal stages was presented a significant difference only on the first instar stage. From the lowest to the highest respectively, 0.98 days on ruzgar to 1.18 days on Kitir, the second nymphal stage 0.98 days to Beit alfa, 1.08 days on Muhika, the third from 0.94 days to Kitir, from 1.16 days to 14-F1, and the last instar 1.22 days on Ptk, 1.48 days on Kitir were investigated. The total development time was evaluated at 4.46 days Beit on alfa 4.72 days on Kitir. The offspring number was 60.42 aphids on ayda and 83.72 aphids on muhika, the significant differences between varieties were based on one-way ANOVA (Tukey test). The lifetime of A. gossypii was recorded 19.10 days on Kitir, 27.64 days on Ptk. The results showed that cucumber cultivars were affected by the biological life of A. gossypii. The combination of this study with the other methods of the IPM tactics can serve as the best strategy for controlling this pest on cucumber varieties into the greenhouse.

Keywords: cucumber cultivars, fecundity, intrinsic rate, mortality, resistance

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

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In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

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Authors: Obe Olumide Olayinka, Victor Balanica, Eugen Neagoe

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The effects of hypertension are often lethal thus its early detection and prevention is very important for everybody. In this paper, a neural network (NN) model was developed and trained based on a dataset of hypertension causative parameters in order to forecast the likelihood of occurrence of hypertension in patients. Our research goal was to analyze the potential of the presented NN to predict, for a period of time, the risk of hypertension or the risk of developing this disease for patients that are or not currently hypertensive. The results of the analysis for a given patient can support doctors in taking pro-active measures for averting the occurrence of hypertension such as recommendations regarding the patient behavior in order to lower his hypertension risk. Moreover, the paper envisages a set of three example scenarios in order to determine the age when the patient becomes hypertensive, i.e. determine the threshold for hypertensive age, to analyze what happens if the threshold hypertensive age is set to a certain age and the weight of the patient if being varied, and, to set the ideal weight for the patient and analyze what happens with the threshold of hypertensive age.

Keywords: neural network, hypertension, data set, training set, supervised learning

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Authors: Vinayaka Gude Divya Sampath

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The research introduces an integrated prediction model to assess the susceptibility of roads in a future flooding event. The model consists of deep learning algorithm for forecasting gauge height data and Flood Inundation Mapper (FIM) for spatial flooding. An optimal architecture for Long short-term memory network (LSTM) was identified for the gauge located on Tangipahoa River at Robert, LA. Dropout was applied to the model to evaluate the uncertainty associated with the predictions. The estimates are then used along with FIM to identify the spatial flooding. Further geoprocessing in ArcGIS provides the susceptibility values for different roads. The model was validated based on the devastating flood of August 2016. The paper discusses the challenges for generalization the methodology for other locations and also for various types of flooding. The developed model can be used by the transportation department and other emergency response organizations for effective disaster management.

Keywords: deep learning, disaster management, flood prediction, urban flooding

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Authors: Tianyu Wang, Nikita Karandikar

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The uncertainty around cost, safety, and feasibility of the decarbonized shipping fuels has made it increasingly complex for the shipping companies to set pricing strategies and forecast their freight revenues going forward. The increase in the green fuel surcharges will ultimately influence the automobile’s consumer prices. The auto shipping demand (ton-miles) has been gradually shifting from long-haul to short-sea trade over the past years following the relocation of the original equipment manufacturer (OEM) manufacturing to regions such as South America and Southeast Asia. The objective of this paper is twofold: 1) to investigate the car-carriers freight revenue development over the years when the trade pattern is gradually shifting towards short-sea exports 2) to empirically identify the quantitative impact of such trade pattern shifting to mainly freight rate, but also vessel size, fleet size as well as Green House Gas (GHG) emission in Roll on-Roll Off (Ro-Ro) shipping. In this paper, a model of analyzing and forecasting ton-miles and freight revenues for the trade routes of AS-NA (Asia to North America), EU-NA (Europe to North America), and SA-NA (South America to North America) is established by deploying Automatic Identification System (AIS) data and the financial results of a selected car carrier company. More specifically, Wallenius Wilhelmsen Logistics (WALWIL), the Norwegian Ro-Ro carrier listed on Oslo Stock Exchange, is selected as the case study company in this paper. AIS-based ton-mile datasets of WALWIL vessels that are sailing into North America region from three different origins (Asia, Europe, and South America), together with WALWIL’s quarterly freight revenues as reported in trade segments, will be investigated and compared for the past five years (2018-2022). Furthermore, ordinary‐least‐square (OLS) regression is utilized to construct the ton-mile demand and freight revenue forecasting. The determinants of trade pattern shifting, such as import tariffs following the China-US trade war and fuel prices following the 0.1% Emission Control Areas (ECA) zone requirement after IMO2020 will be set as key variable inputs to the machine learning model. The model will be tested on another newly listed Norwegian Car Carrier, Hoegh Autoliner, to forecast its 2022 financial results and to validate the accuracy based on its actual results. GHG emissions on the three routes will be compared and discussed based on a constant emission per mile assumption and voyage distances. Our findings will provide important insights about 1) the trade-off evaluation between revenue reduction and energy saving with the new ton-mile pattern and 2) how the trade flow shifting would influence the future need for the vessel and fleet size.

Keywords: AIS, automobile exports, maritime big data, trade flows

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Authors: Muhammad Naeem Mushtaq, Muhammad Arshad, Shahid Majeed

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

Keywords: abundance, evenness, maize, richness

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Authors: Marlene Perez Villalpando, Kelly Joel Gurubel Tun

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In this paper, we propose an optimal control strategy for a hydroelectric power plant subject to input disturbances like meteorological phenomena. The engineering characteristics of the system are described by a nonlinear model. The random availability of renewable sources is predicted by a high-order neural network trained with an extended Kalman filter, whereas the power generation is regulated by the optimal control law. The main advantage of the system is the stabilization of the amount of power generated in the plant. A control supervisor maintains stability and availability in hydropower reservoirs water levels for power generation. The proposed approach demonstrated a good performance to stabilize the reservoir level and the power generation along their desired trajectories in the presence of disturbances.

Keywords: hydropower, high order neural network, Kalman filter, optimal control

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Authors: M. M. Wagh, V. V. Kulkarni

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The rising economic activities, growing population and improving living standards of world have led to a steady growth in its appetite for quality and quantity of energy services. As the economy expands the electricity demand is going to grow further, increasing the challenges of the more generation and stresses on the utility grids. Appropriate energy model will help in proper utilization of the locally available renewable energy sources such as solar, wind, biomass, small hydro etc. to integrate in the available grid, reducing the investments in energy infrastructure. Further to these new technologies like smart grids, decentralized energy planning, energy management practices, energy efficiency are emerging. In this paper, the attempt has been made to study and review the recent energy planning models, energy forecasting models, and renewable energy integration models. In addition, various modeling techniques and tools are reviewed and discussed.

Keywords: energy modeling, integration of renewable energy, energy modeling tools, energy modeling techniques

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Authors: Gleda Kutrolli, Maksi Kutrolli, Etjon Meco

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SARS-CoV-2 virus is currently one of the most infectious pathogens for humans. It started in China at the end of 2019 and now it is spread in all over the world. The origin and diffusion of the SARS-CoV-2 epidemic, is analysed based on the discussion of viral phylogeny theory. With the aim of understanding the spread of infection in the affected countries, it is crucial to modelize the spread of the virus and simulate its activity. In this paper, the prediction of coronavirus outbreak is done by using SIR model without vital dynamics, applying different numerical technique solving ordinary differential equations (ODEs). We find out that ABM and MRT methods perform better than other techniques and that the activity of the virus will decrease in April but it never cease (for some time the activity will remain low) and the next cycle will start in the middle July 2020 for Norway and Denmark, and October 2020 for Sweden, and September for Finland.

Keywords: forecasting, ordinary differential equations, SARS-COV-2 epidemic, SIR model

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Authors: Yuri Katz, Kun Liu, Arunram Atmacharan

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Due to illiquidity, mispricing on Credit Markets is inevitable. This creates huge challenges to banks and investors as they seek to find new ways of risk valuation and portfolio management in a post-credit crisis world. Here, we analyze the difference in behavior of the spread-to-maturity in investment and high-yield categories of US corporate bonds between 2014 and 2023. Deviation from the theoretical dependency of this measure in the universe under study allows to identify multiple cases of mispriced credit risk. Remarkably, we observe mispriced bonds in both categories of credit ratings. This identification is supported by the application of the state-of-the-art machine learning model in more than 90% of cases. Noticeably, the ML-driven model-based forecasting of a category of bond’s credit ratings demonstrate an excellent out-of-sample accuracy (AUC = 98%). We believe that these results can augment conventional valuations of credit portfolios.

Keywords: credit risk, credit ratings, bond pricing, spread-to-maturity, machine learning

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Authors: Ekapon Thienthaworn

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This research aims to study the reliability of the forecasting factor in social media by using survey research methods with questionnaires. The sampling is the group of undergraduate students in Bangkok. A multiple-step random number of 400 persons, data analysis are descriptive statistics with multivariate regression analysis. The research found the average of the overall trust at the intermediate level for reading the news in social media and the results of the multivariate regression analysis to find out the factors that forecast credibility of the media found the only content that has the power to forecast reliability of undergraduate students in Bangkok to reading the news on social media at the significance level.at 0.05.These can be factors with forecasts reliability of news in social media by a variable that has the highest influence factor of the media content and the speed is also important for reliability of the news.

Keywords: credibility of news, behaviors and attitudes, social media, web board

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Authors: Karim Hamidi Machekposhti, Hossein Sedghi, Abdolrasoul Telvari, Hossein Babazadeh

Abstract:

Rainfall and runoff phenomenon is a chaotic and complex outcome of nature which requires sophisticated modelling and simulation methods for explanation and use. Time Series modelling allows runoff data analysis and can be used as forecasting tool. In the paper attempt is made to model river runoff data and predict the future behavioural pattern of river based on annual past observations of annual river runoff. The river runoff analysis and predict are done using ARIMA model. For evaluating the efficiency of prediction to hydrological events such as rainfall, runoff and etc., we use the statistical formulae applicable. The good agreement between predicted and observation river runoff coefficient of determination (R²) display that the ARIMA (4,1,1) is the suitable model for predicting Karkheh River runoff at Iran.

Keywords: time series modelling, ARIMA model, river runoff, Karkheh River, CLS method

Procedia PDF Downloads 316

Authors: Muhammad Tariq, Bushra Siddique, Muhammad Naeem, Asim Gulzar

Abstract:

The chemical ecology of natural enemies can play a pivotal role in any Integrated Pest Management (IPM) program. Different chemical cues help to correspond in the diversity of associations between prey and host plant species. Coccinellaseptempunctata and Diaeretiellarapae have the abilities to explore several chemical cues released by plants under herbivore attack that may enhance their efficiency of foraging. In this study, the behavioral responses of Coccinellaseptempunctata and Diaeretiellarapae were examined under the application of two semiochemicals and a plant extract and their combinations using four-arm olfactometer. The bioassay was consists of a pairwise treatment comparison. Data pertaining to the preference of C. septempunctata and D. rapae after treatment application were recorded and analyzed statistically. The mean number of entries and time spent of Coccinellaseptempunctata and D. rapaewere greater in arms treated with E-β-Farnesene. However, the efficacy of E-β-Farnesene was enhanced when combined with β-pinene. Thus, the mean number of entries and time spent of C. septempunctata and D. rapaewere highest in arms treated with the combination of E-β-Farnesene x β-pinene as compared with other treatments. The current work has demonstrated that the insect-derived semiochemicals may enhance the efficacy of natural enemies when applied in combination.

Keywords: olfectometer, parasitoid, predator, preference

Procedia PDF Downloads 114

Authors: Hidir S. Nogay

Abstract:

In this study, two systems were created to predict interior temperature in induction motor. One of them consisted of a simple ANN model which has two layers, ten input parameters and one output parameter. The other one consisted of eight ANN models connected each other as cascaded. Cascaded ANN system has 17 inputs. Main reason of cascaded system being used in this study is to accomplish more accurate estimation by increasing inputs in the ANN system. Cascaded ANN system is compared with simple conventional ANN model to prove mentioned advantages. Dataset was obtained from experimental applications. Small part of the dataset was used to obtain more understandable graphs. Number of data is 329. 30% of the data was used for testing and validation. Test data and validation data were determined for each ANN model separately and reliability of each model was tested. As a result of this study, it has been understood that the cascaded ANN system produced more accurate estimates than conventional ANN model.

Keywords: cascaded neural network, internal temperature, inverter, three-phase induction motor

Procedia PDF Downloads 323