Search results for: pest forecasting

Authors: Lintle Mohase, Ninikoe Lebusa, Mpho Stephen Mafa

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Wheat is an economically important cereal crop. However, the changing climatic conditions that intensify drought in production areas, and additional pest infestation, such as the Russian wheat aphid (RWA, Diuraphis noxia), severely hamper its production. Drought and pest management require an additional water supply through irrigation and applying inorganic nutrients (including silicon) as alternative strategies to mitigate the stress effects. Therefore, other approaches are needed to enhance wheat productivity during drought stress and aphid abundance. Two wheat cultivars were raised under greenhouse conditions, exposed to drought stress, and treated with silicon before infestation with the South African RWA biotype 2 (RWASA2). The morphological evaluations showed that severe drought or a combination of drought and infestation significantly reduced the plant height of wheat cultivars. Silicon treatment did not alleviate the growth reduction. The biochemical responses were measured using spectrophotometric assays with specific substrates. An evaluation of the enzyme activities associated with oxidative stress and defence responses indicated that drought stress increased NADPH oxidase activity, while silicon treatment significantly reduced it in drought-stressed and infested plants. At 48 and 72 hours sampling periods, a combination of silicon, drought and infestation treatment significantly increased peroxidase activity compared to drought and infestation treatment. The treatment also increased β-1,3-glucanase activity 72 hours after infestation. In addition, silicon and drought treatment increased glucose but reduced sucrose accumulation. Furthermore, silicon, drought, and infestation treatment combinations reduced the sucrose content. Finally, silicon significantly increased the trehalose content under severe drought and infestation, evident at 48 and 72-hour sampling periods. Our findings shed light on silicon’s ability to induce protective biochemical responses during drought and aphid infestation.

Keywords: drought, enzyme activity, silicon, soluble sugars, Russian wheat aphid, wheat

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: Reza Sadeghi

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Carbon dioxide treatment is one of the new methods for storage pest control. It can be used to replace chemical approaches for postharvest. Hazelnut has a considerable share in the annual exports of Iran. In the present study, hazelnut was studied after being exposed to different CO2 pressures (0.1-0.5bar) within 24 hours. Changes in organoleptic properties (colour, firmness, aroma, crispness, and overall acceptability) during fumigation were studied. The results showed that the sensory evaluation showed that carbon dioxide had no effect on the qualitative characteristics of hazelnut.

Keywords: carbon dioxide, hazelnut, qualitative characteristics, organoleptic

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Authors: Nicola Rubino

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This paper presents an analysis of a group of commodity exporting countries' nominal exchange rate movements in relationship to the US dollar. Using a series of Unrestricted Self-exciting Threshold Autoregressive models (SETAR), we model and evaluate sixteen national CPI price differentials relative to the US dollar CPI. Out-of-sample forecast accuracy is evaluated through calculation of mean absolute error measures on the basis of two-hundred and fifty-three months rolling window forecasts and extended to three additional models, namely a logistic smooth transition regression (LSTAR), an additive non linear autoregressive model (AAR) and a simple linear Neural Network model (NNET). Our preliminary results confirm presence of some form of TAR non linearity in the majority of the countries analyzed, with a relatively higher goodness of fit, with respect to the linear AR(1) benchmark, in five countries out of sixteen considered. Although no model appears to statistically prevail over the other, our final out-of-sample forecast exercise shows that SETAR models tend to have quite poor relative forecasting performance, especially when compared to alternative non-linear specifications. Finally, by analyzing the implied half-lives of the > coefficients, our results confirms the presence, in the spirit of arbitrage band adjustment, of band convergence with an inner unit root behaviour in five of the sixteen countries analyzed.

Keywords: transition regression model, real exchange rate, nonlinearities, price differentials, PPP, commodity points

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Authors: Giovanni Masala, Stefania Marica

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Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: E. A. Krasikov

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Dependence of the materials properties on neutron irradiation intensity (flux) is a key problem while usage data of the accelerated materials irradiation in test reactors for forecasting of their capacity for work in realistic (practical) circumstances of operation. Investigations of the reactor pressure vessel steel radiation degradation dependence on fast neutron fluence (embrittlement kinetics) at low flux reveal the instability in the form of the scatter of the experimental data and wave-like sections of embrittlement kinetics appearance. Disclosure of the steel degradation oscillating is a sign of the steel structure cyclic self-recovery transformation as it take place in self-organization processes. This assumption has received support through the discovery of the similar ‘anomalous’ data in scientific publications and by means of own additional experiments. Data obtained stimulate looking-for ways to management of the structural steel radiation stability (for example, by means of nano - structure modification for radiation defects annihilation intensification) for creation of the intelligent self-recovering material. Expected results: - radiation degradation theory and mechanisms development, - more adequate models of the radiation embrittlement elaboration, - surveillance specimen programs improvement, - methods and facility development for usage data of the accelerated materials irradiation for forecasting of their capacity for work in realistic (practical) circumstances of operation, - search of the ways for creating of the radiation stable self-recovery intelligent materials.

Keywords: degradation, radiation, steel, wave-like kinetics

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Authors: Redouane Larbi Boufeniza, Jing-Jia Luo

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This study assesses the suitability of deep learning (DL) for downscaling sea surface temperature (SST) over the Mediterranean Sea in the context of seasonal forecasting. We design a set of experiments that compare different DL configurations and deploy the best-performing architecture to downscale one-month lead forecasts of June–September (JJAS) SST from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for the period of 1982–2020. We have also introduced predictors over a larger area to include information about the main large-scale circulations that drive SST over the Mediterranean Sea region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results showed that the convolutional neural network (CNN)-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme SST spatial patterns. Besides, the CNN-based downscaling yields a much more accurate forecast of extreme SST and spell indicators and reduces the significant relevant biases exhibited by the raw model predictions. Moreover, our results show that the CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of the Mediterranean Sea. The results demonstrate the potential usefulness of CNN in downscaling seasonal SST predictions over the Mediterranean Sea, particularly in providing improved forecast products.

Keywords: Mediterranean Sea, sea surface temperature, seasonal forecasting, downscaling, deep learning

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Authors: Lukas Reznak, Maria Reznakova

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Recession of an economy has a profound negative effect on all involved stakeholders. It follows that timely prediction of recessions has been of utmost interest both in the theoretical research and in practical macroeconomic modelling. Current mainstream of recession prediction is based on standard OLS models of continuous GDP using macroeconomic data. This approach is not suitable for two reasons: the standard continuous models are proving to be obsolete and the macroeconomic data are unreliable, often revised many years retroactively. The aim of the paper is to explore a different branch of recession forecasting research theory and verify the findings on real data of the Czech Republic and Germany. In the paper, the authors present a family of discrete choice probit models with parameters estimated by the method of maximum likelihood. In the basic form, the probits model a univariate series of recessions and expansions in the economic cycle for a given country. The majority of the paper deals with more complex model structures, namely dynamic and bivariate extensions. The dynamic structure models the autoregressive nature of recessions, taking into consideration previous economic activity to predict the development in subsequent periods. Bivariate extensions utilize information from a foreign economy by incorporating correlation of error terms and thus modelling the dependencies of the two countries. Bivariate models predict a bivariate time series of economic states in both economies and thus enhance the predictive performance. A vital enabler of timely and successful recession forecasting are reliable and readily available data. Leading indicators, namely the yield curve and the stock market indices, represent an ideal data base, as the pieces of information is available in advance and do not undergo any retroactive revisions. As importantly, the combination of yield curve and stock market indices reflect a range of macroeconomic and financial market investors’ trends which influence the economic cycle. These theoretical approaches are applied on real data of Czech Republic and Germany. Two models for each country were identified – each for in-sample and out-of-sample predictive purposes. All four followed a bivariate structure, while three contained a dynamic component.

Keywords: bivariate probit, leading indicators, recession forecasting, Czech Republic, Germany

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Authors: Thabo M. Bafitlhile, Adewole Oladele

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Botswana has been affected by floods since long ago and is still experiencing this tragic event. Flooding occurs mostly in the North-West, North-East, and parts of Central district due to heavy rainfalls experienced in these areas. The torrential rains destroyed homes, roads, flooded dams, fields and destroyed livestock and livelihoods. Palapye is one area in the central district that has been experiencing floods ever since 1995 when its greatest flood on record occurred. Heavy storms result in floods and inundation; this has been exacerbated by poor and absence of drainage structures. Since floods are a part of nature, they have existed and will to continue to exist, hence more destruction. Furthermore floods and highway plays major role in erosion and destruction of roads structures. Already today, many culverts, trenches, and other drainage facilities lack the capacity to deal with current frequency for extreme flows. Future changes in the pattern of hydro climatic events will have implications for the design and maintenance costs of roads. Increase in rainfall and severe weather events can affect the demand for emergent responses. Therefore flood forecasting and warning is a prerequisite for successful mitigation of flood damage. In flood prone areas like Palapye, preventive measures should be taken to reduce possible adverse effects of floods on the environment including road structures. Therefore this paper attempts to estimate return periods associated with huge storms of different magnitude from recorded historical rainfall depth using statistical method. The method of annual maxima was used to select data sets for the rainfall analysis. In the statistical method, the Type 1 extreme value (Gumbel), Log Normal, Log Pearson 3 distributions were all applied to the annual maximum series for Palapye area to produce IDF curves. The Kolmogorov-Smirnov test and Chi Squared were used to confirm the appropriateness of fitted distributions for the location and the data do fit the distributions used to predict expected frequencies. This will be a beneficial tool for urgent flood forecasting and water resource administration as proper drainage design will be design based on the estimated flood events and will help to reclaim and protect the road structures from adverse impacts of flood.

Keywords: drainage, estimate, evaluation, floods, flood forecasting

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Authors: Ahmed Ali Samejo, Riffat Sultana

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Pakistan has the sandy Thar Desert in the eastern area, which share border line with India and has exotic fauna and flora, the livelihood of native people rely on livestock and rain fed cultivated fields. The climate of Thar Desert is very harsh and stressful due to frequent drought and very little rainfall, which may occur during monsoon season in the months of July to October and temperature is high, and wind speed also increases in April to June. Schistocerca gregaria is a destructive pest of vegetation from Mauritania to the border line of Pakistan and India. Sometimes they produce swarms which consume all plant where ever they land down and cause the loss in agro-economy of the world. During the recent study, we observed that vegetation was not unique throughout the Thar Desert in the year 2015, because the first spell of rainfall showered over all areas of the Thar Desert in July. However, the second and third spell of rain was confined to village Mahandre jo par and surroundings from August to October. Consequently, vegetation and cultivated crops grew up specially bajra crop (Pennistum glaucum). The climate of Mahandre jo par and surroundings became favorable for S.gregaria, and remaining areas of Thar Desert went hostile. Therefore desert locust attracted to the pleasant area (Mahandre jo par and surroundings) and gradually concentrated, increased reproductive activities, but did not gregarize due to the harvest of bajra crop and the onset of the winter season with an immediate decrease in temperature. An outbreak was near to come into existence, and thereupon conditions become stressful for hoppers to continue further development. Afore mentioned was one reason behind hurdle to the outbreak, another reason might be that migration and concentration of desert locust took place at the end of the season, so climate becomes unfavorable for hoppers, due to dryness of vegetation. Soils also become dry, because rainfall was not showered in end of the season, that’s why eggs that were deposited in late summer were desiccated. This data might be proved fruitful to forecast any outbreak update in future.

Keywords: agro-economy, destructive pest, climate, outbreak, vegetation

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Authors: A. H. Nanher, N. P. Singh, Shashidhar Yadav, Sachin Tyagi

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Agricultural production system is an outcome of a complex interaction of seed, soil, water and agro-chemicals (including fertilizers). Therefore, judicious management of all the inputs is essential for the sustainability of such a complex system. Precision agriculture gives farmers the ability to use crop inputs more effectively including fertilizers, pesticides, tillage and irrigation water. More effective use of inputs means greater crop yield and/or quality, without polluting the environment the focus on enhancing the productivity during the Green Revolution coupled with total disregard of proper management of inputs and without considering the ecological impacts, has resulted into environmental degradation. To evaluate a new approach for site-specific nutrient management (SSNM). Large variation in initial soil fertility characteristics and indigenous supply of N, P, and K was observed among Field- and season-specific NPK applications were calculated by accounting for the indigenous nutrient supply, yield targets, and nutrient demand as a function of the interactions between N, P, and K. Nitrogen applications were fine-tuned based on season-specific rules and field-specific monitoring of crop N status. The performance of SSNM did not differ significantly between high-yielding and low-yielding climatic seasons, but improved over time with larger benefits observed in the second year Future, strategies for nutrient management in intensive rice systems must become more site-specific and dynamic to manage spatially and temporally variable resources based on a quantitative understanding of the congruence between nutrient supply and crop demand. The SSNM concept has demonstrated promising agronomic and economic potential. It can be used for managing plant nutrients at any scale, i.e., ranging from a general recommendation for homogenous management of a larger domain to true management of between-field variability. Assessment of pest profiles in FFP and SSNM plots suggests that SSNM may also reduce pest incidence, particularly diseases that are often associated with excessive N use or unbalanced plant nutrition.

Keywords: nutrient, pesticide, crop, yield

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Authors: V. Jagadeesh, K. Venkata Subbaiah, P. Govinda Rao

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This paper explaining about the significance of information technology tools and software packages in supply chain management (SCM) in order to manage the entire supply chain. Managing materials flow and financial flow and information flow effectively and efficiently with the aid of information technology tools and packages in order to deliver right quantity with right quality of goods at right time by using right methods and technology. Information technology plays a vital role in streamlining the sales forecasting and demand planning and Inventory control and transportation in supply networks and finally deals with production planning and scheduling. It achieves the objectives by streamlining the business process and integrates within the enterprise and its extended enterprise. SCM starts with customer and it involves sequence of activities from customer, retailer, distributor, manufacturer and supplier within the supply chain framework. It is the process of integrating demand planning and supply network planning and production planning and control. Forecasting indicates the direction for planning raw materials in order to meet the production planning requirements. Inventory control and transportation planning allocate the optimal or economic order quantity by utilizing shortest possible routes to deliver the goods to the customer. Production planning and control utilize the optimal resources mix in order to meet the capacity requirement planning. The above operations can be achieved by using appropriate information technology tools and software packages for the supply chain management.

Keywords: supply chain management, information technology, business process, extended enterprise

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Authors: M. Zayoud, S. Oueida, S. Ionescu, P. AbiChar

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Background: Wireless sensor network is encompassed of diversified tools of information technology, which is widely applied in a range of domains, including military surveillance, weather forecasting, and earthquake forecasting. Strengthened grounds are always developed for wireless sensor networks, which usually emerges security issues during professional application. Thus, essential technological tools are necessary to be assessed for secure aggregation of data. Moreover, such practices have to be incorporated in the healthcare practices that shall be serving in the best of the mutual interest Objective: Aggregation of encrypted data has been assessed through homomorphic stream cipher to assure its effectiveness along with providing the optimum solutions to the field of healthcare. Methods: An experimental design has been incorporated, which utilized newly developed cipher along with CPU-constrained devices. Modular additions have also been employed to evaluate the nature of aggregated data. The processes of homomorphic stream cipher have been highlighted through different sensors and modular additions. Results: Homomorphic stream cipher has been recognized as simple and secure process, which has allowed efficient aggregation of encrypted data. In addition, the application has led its way to the improvisation of the healthcare practices. Statistical values can be easily computed through the aggregation on the basis of selected cipher. Sensed data in accordance with variance, mean, and standard deviation has also been computed through the selected tool. Conclusion: It can be concluded that homomorphic stream cipher can be an ideal tool for appropriate aggregation of data. Alongside, it shall also provide the best solutions to the healthcare sector.

Keywords: aggregation, cipher, homomorphic stream, encryption

Procedia PDF Downloads 241

Authors: Nadeem Munawar, Iftikhar Hussain, Tariq Mahmood

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The lesser bandicoot rat (Bandicota bengalensis) is widely distributed and a serious agricultural pest in Pakistan. It has wide adaptation with rice-wheat-sugarcane cropping systems of Punjab, Sindh and Khyber Pakhtunkhwa and wheat-groundnut cropping system of Pothwar area, thus inflicting heavy losses to these crops. Comparative efficacies of four food baits (onion, guava, potato and peanut butter smeared bread/Chapatti) were tested in multiple feeding tests for kill trapping of this rat species in the Pothwar Plateau between October 2013 to July 2014 at the sowing, tilling, flowering and maturity stages of wheat, groundnut and millet crops. The results revealed that guava was the most preferred bait as compared to the rest of three, presumably due to particular taste and smell of the guava. The relative efficacies of all four tested baits guava also scoring the highest trapping success of 16.94 ± 1.42 percent, followed by peanut butter, potato, and onion with trapping successes of 10.52 ± 1.30, 7.82 ± 1.21 and 4.5 ± 1.10 percent, respectively. In various crop stages and season-wise the highest trapping success was achieved at maturity stages of the crops, presumably due to higher surface activity of the rat because of favorable climatic conditions, good shelter, and food abundance. Moreover, the maturity stage of wheat crop coincided with spring breeding season and maturity stages of millet and groundnut match with monsoon/autumn breeding peak of the lesser bandicoot rat in Pothwar area. The preferred order among four baits tested was guava > peanut butter > potato > onion. The study recommends that the farmers should periodically carry out rodent trapping at the beginning of each crop season and during non-breeding seasons of this rodent pest when the populations are low in numbers and restricted under crop boundary vegetation, particularly during very hot and cold months.

Keywords: Bandicota bengalensis, efficacy, food baits, Pothwar

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Authors: Vijay Kumar, G. K. Grewal, Prasad S. Burange

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India is one of the largest cultivators of cotton in the world. Among the various constraints, insect pests are posing a major hurdle to the success of cotton cultivation. Various bollworms, including the pink bollworm, Pectinophora gossypiella (Saunders), cause serious losses in India, China, Pakistan, Egypt, Brazil, tropical America, and Africa, etc. Bt cotton cultivars having Cry genes were introduced in India in 2002 (Cry1Ac) and 2006 (Cry1Ac+ Cry2Ab) for control of American, spotted, and pink bollworms. Pink bollworm (PBW) larvae infest flowers, squares, and bolls. Larva burrows into flowers and bolls to feed on pollen and seeds, respectively. It has a shorter lifecycle and more generations per year, so it develops resistance more quickly than other bollworms. Further, it has cryptic feeding sites, i.e., flowers and bolls/seeds, so it is not exposed to harsh environmental fluctuations and insecticidal applications. The cry toxin concentration is low in its feeding sites, i.e., seeds and flowers of cotton. The use of insecticide and Bt cotton is the primary control measure that has been successful in limiting the damage of PBW. But with the passage of time, it has developed resistance against insecticides and Bt cotton. However, the use of insecticides increases chemical control costs while causing secondary pest problems and environmental pollution. Extensive research has indicated that monitoring and control measures such as biological, cultural, chemical, and host plant resistance methods can be integrated for effective PBW management. The potential of various biological control organisms needs to be explored. The impact of transgenic cotton on non-target organisms, particularly natural enemies, which play an important role in pest control, is still being debated. According to some authors, Bt crops have a negative impact on natural enemies, particularly parasitoids. An experiment was carried out in the Integrated Pest Management Laboratory of the Department of Entomology, Punjab Agricultural University, Ludhiana, Punjab, India, to study the natural parasitization of PBW on Bt cotton in 2022. A large population of larvae of PBW were kept individually in plastic containers and fed with cotton bolls until the emergence of a parasitoid cocoon. The first cocoon of the parasitoid was observed on October 25, 2022. Symptoms of parasitization were never seen on larvae. Larvae stopped feeding and became inactive before the emergence of parasitoids for pupation. Grub makes its way out of larvae by making a hole in the integument, and immediately after coming out, it spins the cocoon. The adult parasitoid emerged from the cocoon after eight days. The parasitoids that emerged from the cocoon were identified as Cotesia (Braconidae: Hymenoptera) based on the features of the adult. Out of 475 larvae of PBW, 87 were parasitized, with 18.31% of parasitization. Out of these, 6.73% were first instar, 10.52% were second instar, and 1.05% were third instar larvae of PBW. No parasitization was observed in fourth instar larvae. Parasitoids were observed during the fag end of cropping season and mostly on the earlier instars. It is concluded that the potential of Cotesia may be explored as a biological control agent against PBW, which is safer to human beings, environment and non-taraltoget organisms.

Keywords: biocontrol, Bt cotton, Cotesia, Pectinophora gossypiella

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Authors: Imen Blibech, Mohieddine Ksantini, Manohar Shete

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Bactrocera oleae is one of the most economically damaging insects of olive in Tunisia and other producing countries of olive trees. As a reliable alternative to synthetic chemical insecticides, botanical insecticides are considered natural control methods safe for the environment and human health. The certified botanical insecticide ULTRA-ACT® effectively on large scale of insects is approved per Indian and International organic standards certified organic pesticides. Olives with signs of olive fly infestation were collected from productive olive trees in three Sahel localities of Tunisia. Infested fruits were separated daily for larval stage control purposes, into new rearing boxes under microclimatic conditions at 75% R.H, 25 ± 3°C and 8 L-16D. Treatment with ULTRA-ACT® extract solutions was made by dipping methods; each fruit was pipetted in 5 mL of extract for 10 seconds then air- dried. Five doses of ULTRA-ACT® were used for a bioassay, plus a water-only control. A total of 200 infested olive fruits were treated in separate dishes with a proportion of 10 olives per dish. A total of 20 dishes were used for each concentration treatment as well as 20 dished utilized as control. The bioassay was conducted with 3 replicates. The development of the larval and pupal stages was recorded since the egg hatching until emergence of adults. It was determined that ULTRA-ACT® extracts on succeeding concentrations; 0.25, 0.5, 1 and 2% show significant effect on the biology of the pest. Increased concentration decreased significantly adult emergence from pupae and affect the egg hatchability percentage. Therefore, larval mortality increased insignificantly with the increase of the product concentration. The 2nd instar larvae were more susceptible to the product and after 72 hours the maximum mortality (75%) was observed with ULTRA-ACT® 2%. The present work aimed to give a possible and efficient alternative solution for B. oleae biological control with a promising botanical insecticide.

Keywords: Bactrocera oleae, olive insect pest, Ultra Act®, larval mortality, pupal emergency, biological control

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: Mehdi Piri Ouchtapeh, Fariba Mehrkhou, Maryam Fourouzan

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The cabbage aphid, Bravicoryne brassicae (Hemiptera: Aphididae), is known as an economically important and oligophagous pest of different cole crops. The polyvolitine characteristics of B. brassicae resulted in resistance to insecticides. For this purpose, in this study, the sub-lethal concentration (LC25) of two insecticides, clothianidin and summer oil, on the life table parameters and population projection of cabbage aphid were studied at controlled condition (20±1 ℃, R.H. 60 ±5 % and a photoperiod of 16:8 h (L:D). The dipping method was used in bioassay and life table studies. Briefly, the leaves of cabbage containing 15 the same-aged (24h) adults of cabbage aphid (four replicates) were dipped into the related concentrations of insecticides for 10 s. The sub-lethal (LC25) obtained concentration were used 5.822 and 108.741 p.p.m for clothianidin and summer oil, respectively. The biological and life table studies were done using at least 100, 93 and 82 the same age of eggs for control, summer oil and clothianidin treatments respectively. The life history data of the greenhouse whitefly cohorts exposed to sublethal concentration of the aforementioned insecticides were analyzed using the computer program TWOSEX–MSChart based on the age-stage, two-sex life table theory. The results of this study showed that the used insecticides affected the developmental time, survival rate, adult longevity, and fecundity of the F1 generation. The developmental time on control, clothianidin and summer oil treatments was obtained (5.91 ± 0.10 days), (7.64 ± 0.12 days) and (6.66 ± 0.10 days), respectively. The sublethal concentration of clothianidin resulted in decreasing of adult longevity (8.63 ± 0.30 days), fecundity (14.14 ± 87 nymphs), survival rate (71%) and the life expectancy (10.26 days) of B. brassicae, as well. Additionally, usage of LC25 insecticides led to decreasing of the net reproductive rate (R0) of the cabbage aphid compared to summer oil and control treatments. The intrinsic rate of increase (r) (day-1) was decreased in F1 adults of cabbage aphid compared with other treatments. Additionally, the population projection results were accordance with the population growth rate of cabbage aphid. Therefore, the findings of this research showed that, however, both of the insecticides were effective on cabbage aphid population, but clothianidin was more effective and could be consider in the management of aforementioned pest.

Keywords: the cabbage aphid, sublethal effects, survival rate, population projection, life expectancy

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Authors: Mwafaq Ibdah, Mossab, Yahyaa, Dor Rachmany, Yoram Gerchman, Doron Holland, Liora Shaltiel-Harpaz

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Pear Psylla is the most important pest of pear in all pear-growing regions, in Asian, European, and the USA. Pear psylla damages pears in several ways: high-density populations of these insects can cause premature leaf and fruit drop, diminish plant growth, and reduce fruit size. In addition, their honeydew promotes sooty mold on leaves and russeting on fruit. Pear psyllas are also considered vectors of pear pathogens such as Candidatus Phytoplasma pyri causing pear decline that can lead to loss of crop and tree vigor, and sometimes loss of trees. Psylla control is a major obstacle to efficient integrated pest management. Recently we have identified two naturally resistance pear accessions (Py.760-261 and Py.701-202) in the Newe Ya’ar live collection. GC-MS volatile metabolic profiling identified several volatile compounds common in these accessions but lacking, or much less common, in a sensitive accession, the commercial Spadona variety. Among these volatiles were styrene and its derivatives. When the resistant accessions were used as inter-stock, the volatile compounds appear in commercial Spadona scion leaves, and it showed reduced susceptibility to pear psylla. Laboratory experiments and applications of some of these volatile compounds were very effective against psylla eggs, nymphs, and adults. The genes and enzymes involved in the specific reactions that lead to the biosynthesis of styrene in plant are unknown. We have identified a phenolic acid decarboxylase that catalyzes the formation of p-hydroxystyrene, which occurs as a styrene analog in resistant pear genotypes. The His-tagged and affinity chromatography purified E. coli-expressed pear PyPAD1 protein could decarboxylate p-coumaric acid and ferulic acid to p-hydroxystyrene and 3-methoxy-4-hydroxystyrene. In addition, PyPAD1 had the highest activity toward p-coumaric acid. Expression analysis of the PyPAD gene revealed that its expressed as expected, i.e., high when styrene levels and psylla resistance were high.

Keywords: pear Psylla, volatile, GC-MS, resistance

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Authors: Huseyin Duran, Yesim Benal Oztekin, Kazim Kubilay Vursavus, Ilker Huseyin Celen

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There are two main approaches to manufacturing, market and usage of plant protection machinery in Turkey. The first approach is called as ‘Product Safety Approach’ and could be summarized as minimum health and safety requirements of consumer needs on plant protection equipment and machinery products. The second approach is the practices related to the Plant Protection Equipment and Machinery Directive. Product safety approach covers the plant protection machinery product groups within the framework of a new approach directive, Machinery Safety Directive (2006/42 / AT). The new directive is in practice in our country by 03.03.2009, parallel to the revision of the EU Regulation on the Directive (03.03.2009 dated and numbered 27158 published in the Official Gazette). ‘Pesticide Application for Machines’ paragraph is added to the 2006/42 / EC Machinery Safety Directive, which is, in particular, reveals the importance of primary health care and product safety issue, explaining the safety requirements for machines used in the application of plant protection products. The Ministry of Science, Industry and Technology is the authorized organizations in our country for the publication and implementation of this regulation. There is a special regulation, carried out by Ministry of Food, Agriculture and Livestock General Directorate of Food and Control, on the manufacture and sale of plant protection machinery. This regulation, prepared based on 5996 Veterinary Services, Plant Health, Food and Feed Law, is ‘Regulation on Plant Protection Equipment and Machinery’ (published on 02.04.2011 whit number 27893 in the Official Gazette). The purposes of this regulation are practicing healthy and reliable crop production, the preparation, implementation and dissemination of the integrated pest management programs and projects for the development of human health and environmentally friendly pest control methods. This second regulation covers: approval, manufacturing, licensing of Plant Protection Equipment and Machinery; duties and responsibilities of the dealers; principles and procedures related to supply and control of the market. There are no inspection procedures for the application of currently used plant protection machinery in Turkey. In this study, content and application principles of all regulation approaches currently used in Turkey are summarized.

Keywords: plant protection equipment and machinery, product safety, market surveillance, inspection procedures

Procedia PDF Downloads 243

Authors: Crysovaladou Kefaloudi, Kassiani Mellou, Eirini Saranti-Papasaranti, Athanasios Koustenis, Chrysoula Botsi, Agapios Terzidis

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Recent migration crisis led to a vast migrant – refugees movement to Greece which created an urgent need for hosting settlements. Taken into account the protection of public health from possible pathogens related to water and food supply as well as waste and sewage accumulation, a 'Living Conditions Recording Form' was created in the context of 'PHILOS' European Program funded by the Asylum Migration and Integration Fund (AMIF) of EU’s DG Migration and Home Affairs, in order to assess a number of environmental sanitation parameters, in refugees – migrants camps in mainland. The assessment will be completed until the end of July. From March to June 2017, mobile unit teams comprised of health inspectors of sub-action 2 of “PHILOS” proceeded with the assessment of living conditions in twenty-two out of thirty-one camps and 'Stata' was used for the statistical analysis of obtained information. Variables were grouped into the following categories: 1) Camp administration, 2) hosted population number, 3) accommodation, 4) heating installations, 5) personal hygiene, 6) sewage collection and disposal, 7) water supply, 8) waste collection and management, 9) pest control, 10) fire safety, 11) food handling and safety. Preliminary analysis of the results showed that camp administration was performed in 90% of the camps by a public authority with the coordination of various NGOs. The median number of hosted population was 222 ranging from 62 to 3200, and the median value of hosted population per accommodation type was 4 in 19 camps. Heating facilities were provided in 86.1% of camps. In 18.2 % of the camps, one personal hygiene facility was available per 6 people ranging in the rest of the camps from 1 per 3 to 1 per 20 hosted refugees-migrants. Waste and sewage collection was performed depending on populations demand in an adequate way in all recorded camps. In 90% of camps, water was supplied through the central water supply system. In 85% of camps quantity and quality of water supply inside camps was regularly monitored for microbial and chemical indices. Pest control was implemented in 86.4% of the camps as well as fire safety measures. Food was supplied by catering companies in 50% of the camps, and the quality and quantity food was monitored at a regular basis. In 77% of camps, food was prepared by the hosted population with the availability of proper storage conditions. Furthermore, in all camps, hosted population was provided with personal hygiene items and health sanitary educational programs were implemented in 77.3% of camps. In conclusion, in the majority of the camps, environmental sanitation parameters were satisfactory. However, waste and sewage accumulation, as well as inadequate pest control measures were recorded in some camps. The obtained data have led to a number of recommendations for the improvement of sanitary conditions, disseminated to all relevant stakeholders. Special emphasis was given to hygiene measures implementation during food handling by migrants – refugees, as well as to waste and sewage accumulation taking in to account the population’s cultural background.

Keywords: environmental sanitation parameters, food borne diseases risk assessment, refugee – migrants camps, water borne diseases risk assessment

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Authors: Hayriye Anıl, Görkem Kar

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In addition to being rich in renewable energy resources, Turkey is one of the countries that promise potential in geothermal energy production with its high installed power, cheapness, and sustainability. Increasing imbalance penalties become an economic burden for organizations since geothermal generation plants cannot maintain the balance of supply and demand due to the inadequacy of the production forecasts given in the day-ahead market. A better production forecast reduces the imbalance penalties of market participants and provides a better imbalance in the day ahead market. In this study, using machine learning, deep learning, and, time series methods, the total generation of the power plants belonging to Zorlu Natural Electricity Generation, which has a high installed capacity in terms of geothermal, was estimated for the first one and two weeks of March, then the imbalance penalties were calculated with these estimates and compared with the real values. These modeling operations were carried out on two datasets, the basic dataset and the dataset created by extracting new features from this dataset with the feature engineering method. According to the results, Support Vector Regression from traditional machine learning models outperformed other models and exhibited the best performance. In addition, the estimation results in the feature engineering dataset showed lower error rates than the basic dataset. It has been concluded that the estimated imbalance penalty calculated for the selected organization is lower than the actual imbalance penalty, optimum and profitable accounts.

Keywords: machine learning, deep learning, time series models, feature engineering, geothermal energy production forecasting

Procedia PDF Downloads 91

Authors: Hui Hsin Huang

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The downstream manufactures will order their materials from different upstream suppliers to maintain a certain level of the demand. This paper proposes a bivariate model to portray this phenomenon of material demand. We use empirical data to estimate the parameters of model and evaluate the RMSD of model calibration. The results show that the model has better fitness.

Keywords: recency, ordering time, materials demand quantity, multi-source ordering

Procedia PDF Downloads 515

Authors: N. Tarai, S. Seighi, S. Doumandji

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The date palm trees Phoenix dactylifera L. are subject to infestation with a variety of insect pests and mite associated, the Carob moth Ectomyelois ceatoniae (Zeller)(Lepidoptera, Pyralidae) is a key pest. Survey of the insect and mite pests associated with date palm trees in the seven stations at Biskra Oasis, throughout two successive years, from October 2011 until September 2012 revealed twelve insect pests belonging to ten families and six orders in addition to one mite belonging to one family from order Acari.

Keywords: date palm, insect, pests, infestation, mit, Biskra, Oasis

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: V. Churkin, M. Lopatin

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The purpose of the paper is to estimate the US small wind turbines market potential and forecast the small wind turbines sales in the US. The forecasting method is based on the application of the Bass model and the generalized Bass model of innovations diffusion under replacement purchases. In the work an exponential distribution is used for modeling of replacement purchases. Only one parameter of such distribution is determined by average lifetime of small wind turbines. The identification of the model parameters is based on nonlinear regression analysis on the basis of the annual sales statistics which has been published by the American Wind Energy Association (AWEA) since 2001 up to 2012. The estimation of the US average market potential of small wind turbines (for adoption purchases) without account of price changes is 57080 (confidence interval from 49294 to 64866 at P = 0.95) under average lifetime of wind turbines 15 years, and 62402 (confidence interval from 54154 to 70648 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 90,7%, while in the second - 91,8%. The effect of the wind turbines price changes on their sales was estimated using generalized Bass model. This required a price forecast. To do this, the polynomial regression function, which is based on the Berkeley Lab statistics, was used. The estimation of the US average market potential of small wind turbines (for adoption purchases) in that case is 42542 (confidence interval from 32863 to 52221 at P = 0.95) under average lifetime of wind turbines 15 years, and 47426 (confidence interval from 36092 to 58760 at P = 0.95) under average lifetime of wind turbines 20 years. In the first case the explained variance is 95,3%, while in the second –95,3%.

Keywords: bass model, generalized bass model, replacement purchases, sales forecasting of innovations, statistics of sales of small wind turbines in the United States

Procedia PDF Downloads 334

Authors: Shailendra Kumar, Kamal Kumar Gupta

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Dysdercus koenigii is an economically important pest of cotton worldwide. The pest damages the crop by sucking sap, staining lint, reducing the oil content of the seeds and deteriorating the quality of cotton. Plant possesses a plethora of secondary metabolites which are used as defense mechanism against herbivores. One of the important categories of such chemicals is insect growth regulators and the intermediates in their biosynthesis. Farnesol belongs to sesquiterpenoid. It is an intermediate in Juvenile hormone biosynthetic pathway in insects has been widely reported in the variety of plants. This chemical can disrupt the normal metabolic function and therefore, affects various life processes of the insects. Present study tested the efficacy of farnesol against Dysdercus koenigii. 2μl of 5% (100µg) and 10% (200µg) of the farnesol was applied topically on the dorsum of thoracic region of the newly emerged fifth instar nymphs of Dysdercus. The treated insects were observed daily for their survival, weight gain, and developmental anomalies for a period of ten days. The results indicated that treatment with 200µg farnesol decreased survival of the insects to 70% after 24h of exposure. At lower doses, no significant decrease in the survival was observed. However, the surviving nymphs showed alteration in growth, development, and metamorphosis. The weight gain in the treated nymphs showed deviation from control. The treated nymphs showed an increase in mortality during subsequent days and increase in the nymphal duration. The number of nymphs undergoing metamorphosis decreased to 46% and 88% in the treatments with the dose of 200µg and 100µg respectively. Severe developmental anomalies were also observed in the treated nymphs. The treated nymphs moulted into supernumerary nymphs, adultoids, adults with exuviae attached and adults with wing deformities. On treatment with 200µg; 26% adultoid, 4% adults with exuviae attached and 12% adults with wing deformed were produced. Treatment with 100µg resulted in production of 34% adultoid, 26% adults with deformed wing and 4% adults with exuviae attached. Many of the treated nymphs did not metamorphose into adults, remained in nymphal stage and died. Our results indicated potential application plant-derived secondary metabolites like farnesol in the management of Dysdercus population.

Keywords: development, Dysdercus koenigii, farnesol, survival

Procedia PDF Downloads 339

Authors: M. Mulumpwa, W. W. L. Jere, M. Lazaro, A. H. N. Mtethiwa

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The study considered Seasonal Autoregressive Integrated Moving Average (SARIMA) processes to select an appropriate stochastic model to forecast the monthly data from the Lake Malawi water levels for the period 1986 through 2015. The appropriate model was chosen based on SARIMA (p, d, q) (P, D, Q)S. The Autocorrelation function (ACF), Partial autocorrelation (PACF), Akaike Information Criteria (AIC), Bayesian Information Criterion (BIC), Box–Ljung statistics, correlogram and distribution of residual errors were estimated. The SARIMA (1, 1, 0) (1, 1, 1)12 was selected to forecast the monthly data of the Lake Malawi water levels from August, 2015 to December, 2021. The plotted time series showed that the Lake Malawi water levels are decreasing since 2010 to date but not as much as was the case in 1995 through 1997. The future forecast of the Lake Malawi water levels until 2021 showed a mean of 474.47 m ranging from 473.93 to 475.02 meters with a confidence interval of 80% and 90% against registered mean of 473.398 m in 1997 and 475.475 m in 1989 which was the lowest and highest water levels in the lake respectively since 1986. The forecast also showed that the water levels of Lake Malawi will drop by 0.57 meters as compared to the mean water levels recorded in the previous years. These results suggest that the Lake Malawi water level may not likely go lower than that recorded in 1997. Therefore, utilisation and management of water-related activities and programs among others on the lake should provide room for such scenarios. The findings suggest a need to manage the Lake Malawi jointly and prudently with other stakeholders starting from the catchment area. This will reduce impacts of anthropogenic activities on the lake’s water quality, water level, aquatic and adjacent terrestrial ecosystems thereby ensuring its resilience to climate change impacts.

Keywords: forecasting, Lake Malawi, water levels, water level fluctuation, climate change, anthropogenic activities

Procedia PDF Downloads 209

Authors: Pawan Kumar, Pitamber Singh Negi

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Many of the regeneration failures in conifers are often being attributed to heavy insect attack and pathogens during the period of seed formation and under storage conditions. Conifer berries and seed insects occur throughout the known range of the hosts and also limit the production of seed for nursery stock. On occasion, even entire seed crops are lost due to insect attacks. The berry and seeds of both the species have been found to be infected with insects. Recently, heavy damage to the berry and seeds of Juniper and Chilgoza Pine was observed in the field as well as in stored conditions, leading to reduction in the viability of seeds to germinate. Both the species are under great threat and regeneration of the species is very low. Due to lack of adequate literature, the study on the damage potential of seed insects was urgently required to know the exact status of the insect-pests attacking seeds/berries of both the pine species so as to develop pest management practices against the insect pests attack. As both the species are also under threat and are fighting for survival, so the study is important to develop management practices for the insect-pests of seeds/berries of Juniper and Chilgoza pine so as to evaluate in the nursery, as these species form major vegetation of their distribution zones. A six-year study on the management of insect pests of seeds of Chilgoza revealed that seeds of this species are prone to insect pests mainly borers. During present investigations, it was recorded that cones of are heavily attacked only by Dioryctria abietella (Lepidoptera: Pyralidae) in natural conditions, but seeds which are economically important are heavily infected, (sometimes up to 100% damage was also recorded) by insect borer, Plodia interpunctella (Lepidoptera: Pyralidae) and is recorded for the first time ‘to author’s best knowledge’ infesting the stored Chilgoza seeds. Similarly, Juniper berries and seeds were heavily attacked only by a single borer, Homaloxestis cholopis (Lepidoptera: Lecithoceridae) recorded as a new report in natural habitat as well as in stored conditions. During the present investigation details of insect pest attack on Juniper and Chilgoza pine seeds and berries was observed and suitable management practices were also developed to contain the insect-pests attack.

Keywords: borer, chilgozapine, cones, conifer, Lepidoptera, juniper, management, seed

Procedia PDF Downloads 131

Authors: S. Chuenchooklin, S. Taweepong, U. Pangnakorn

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This research was conducted in the Mae Sot Watershed whereas located in the Moei River Basin at the Upper Salween River Basin in Tak Province, Thailand. The Mae Sot Municipality is the largest urbanized in Tak Province and situated in the midstream of the Mae Sot Watershed. It usually faces flash flood problem after heavy rain due to poor flood management has been reported since economic rapidly bloom up in recently years. Its catchment can be classified as ungauged basin with lack of rainfall data and no any stream gaging station was reported. It was attached by most severely flood event in 2013 as the worst studied case for those all communities in this municipality. Moreover, other problems are also faced in this watershed such shortage water supply for domestic consumption and agriculture utilizations including deterioration of water quality and landslide as well. The research aimed to increase capability building and strengthening the participation of those local community leaders and related agencies to conduct better water management in urban area was started by mean of the data collection and illustration of appropriated application of some short period rainfall forecasting model as the aim for better flood relief plan and management through the hydrologic model system and river analysis system programs. The authors intended to apply the global rainfall data via the integrated data viewer (IDV) program from the Unidata with the aim for rainfall forecasting in short period of 7 - 10 days in advance during rainy season instead of real time record. The IDV product can be present in advance period of rainfall with time step of 3 - 6 hours was introduced to the communities. The result can be used to input to either the hydrologic modeling system model (HEC-HMS) or the soil water assessment tool model (SWAT) for synthesizing flood hydrographs and use for flood forecasting as well. The authors applied the river analysis system model (HEC-RAS) to present flood flow behaviors in the reach of the Mae Sot stream via the downtown of the Mae Sot City as flood extents as water surface level at every cross-sectional profiles of the stream. Both models of HMS and RAS were tested in 2013 with observed rainfall and inflow-outflow data from the Mae Sot Dam. The result of HMS showed fit to the observed data at dam and applied at upstream boundary discharge to RAS in order to simulate flood extents and tested in the field, and the result found satisfied. The result of IDV’s rainfall forecast data was compared to observed data and found fair. However, it is an appropriate tool to use in the ungauged catchment to use with flood hydrograph and river analysis models for future efficient flood relief plan and management.

Keywords: global rainfall, flood forecast, hydrologic modeling system, river analysis system

Procedia PDF Downloads 338