Online Early Warning System Research Articles

PHENTHAUproc – An early warning and decision support system for hazard assessment and control of oak processionary moth (Thaumetopoea processionea)

The incidence of oak processionary moth (OPM), Thaumetopoea processionea L. (Lep., Notodontidae), in Central Europe has increased since the 1990s. Given potential climatic and land-use changes, increased abundance and range expansion of OPM may occur in the future. These changes could lead to more severe defoliation of oaks and higher threats to humans and animals, caused by the urticating hairs (setae) released by OPM larvae. These negative impacts on oak trees, people and animals urgently require effective hazard mitigation strategies.For OPM hazard assessment and decision support on OPM monitoring and control, the online early warning system “PHENTHAUproc – phenology modelling of Thaumetopoeaprocessionea” was created and validated. PHENTHAUproc implements daily updated temperature-based procedures to calculate the phenological development of OPM and to model the host trees’ phenology, i.e. bud swelling and leaf unfolding of Quercus robur L.The hatching date of the first instar larvae (L1) is calculated using three different already existing models. To determine the onset of L1 feeding, a bud swelling model for Q. robur was generated for the first time. The phenological development of OPM larvae and pupae is predicted by a model which was first created based on laboratory rearing data for the distinct development stages.Moreover, an existing leaf unfolding model for Q. robur was included into PHENTHAUproc. The predicted date of leaf unfolding can be complemented by in situ observations of leaf unfolding by the German Meteorological Service (DWD). The predicted dates of leaf unfolding and OPM forth instar development are used to define the optimal timespan for control measures such as the prophylactic spraying of biological insecticides. In addition, modelling of larval and pupal development can be used to estimate the seasonally increasing hazard resulting from setae dissemination, as well as to calculate the onset of monitoring of flying moths with pheromone traps.PHENTHAUproc provides phenological forecasts on a local and regional level, i.e. for weather station locations and raster maps countrywide. The models can be used anywhere in Europe as soon as air temperature data and information on potential adaptations of OPM to local climate and host tree phenology are available. This enables phenological forecasts for timely and effective protection of oak tree vitality as well as human and animal health against OPM-related hazards.

A Predictive Model for the Bioaccumulation of Okadaic Acid in Mytilus galloprovincialis Farmed in the Northern Adriatic Sea: A Tool to Reduce Product Losses and Improve Mussel Farming Sustainability

Over the last decades, harmful dinoflagellate (Dinophysis spp.) blooms have increased in frequency, duration, and severity in the Mediterranean Sea. Farmed bivalves, by ingesting large amounts of phytoplankton, can become unsafe for human consumption due to the bioaccumulation of okadaic acid (OA), causing Diarrhetic Shellfish Poisoning (DSP). Whenever the OA concentration in shellfish farmed in a specific area exceeds the established legal limit (160 μg·kg−1 of OA equivalents), harvesting activities are compulsorily suspended. This study aimed at developing a machine learning (ML) predictive model for OA bioaccumulation in Mediterranean mussels (Mytilus galloprovincialis) farmed in the coastal area off the Po River Delta (Veneto, Italy), based on oceanographic data measured through remote sensing and data deriving from the monitoring activities performed by official veterinarian authorities to verify the bioaccumulation of OA in the shellfish production sites. LightGBM was used as an ML algorithm. The results of the classification algorithm on the test set showed an accuracy of 82%. Further analyses showed that false negatives were mainly associated with relatively low levels of toxins (<100 μg·kg−1), since the algorithm tended to classify low concentrations of OA as negative samples, while true positives had higher mean values of toxins (139 μg·kg−1). The results of the model could be used to build up an online early warning system made available to shellfish farmers of the study area, aimed at increasing the economic and environmental sustainability of these production activities and reducing the risk of massive product losses.

Genotype × ammonia interaction effects on plasma physiological indexes in turbot (Scophthalmus maximus) cultured under acute ammonia stress for different periods of times

We evaluated six plasma physiological indexes (epinephrine, cortisol, alkaline phosphatase, superoxide dismutase, reduced glutathione, and blood glucose) in turbot (Scophthalmus maximus) cultured in five ammonia concentrations (30.28, 35.90, 42.56, 50.47, and 59.82) at different exposure times (4, 8, 12, 24, and 48 h). Then, we conducted additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction (GGE) biplot analyses to analyze the genotype × ammonia interaction effects on the plasma physiological indexes. The AMMI analysis results revealed that the contributions of ammonia effect first decreased sharply (4–8 h), then decreased slowly (8–24 h), and then increased slowly (24–48 h). The contributions of genotype effect first decreased sharply (4–8 h), then increased slowly (8–24 h), and then decreased slowly (24–48 h). The contributions of genotype × ammonia interactions showed a sharp rise (4–8 h), a sharp decline (8–12 h), a slow decline (12–24 h), and finally a slow increase (24–48 h). The GGE biplot analysis revealed that alkaline phosphatase and reduced glutathione contents/activities are reliable biomarkers that can be used to establish an online early warning system for behavior numerical simulation.

Implementation of an early warning system with hyperspectral imaging combined with deep learning model for chlorine in refuse derived fuels

Chlorine content is one of the most important parameters in Refuse Derived Fuels (RDFs) used as a fuel in cement kilns. The main problem with the use of RDF is that chlorine in the waste weakens the cement, increases the risk of corrosion in the kiln and forms toxic gas emissions. Alternative fuels containing high amounts of chlorine, such as plastic waste should be used in limited quantities with the quality of the kiln used and the cement being should be preserved by preparing the appropriate RDF mixture. Analyses conducted on the samples taken before the RDF is given to the furnace are time consuming and costly. Therefore, in this study, the aim is to present a more efficient solution to classify by using chlorine analysis results with hyperspectral imaging and a deep learning model study. For this purpose, a model was created using validated laboratory results and spectral data from samples, the model was tested on a prototype conveyor belt, and was implemented using an online early warning system for high chlorine concentrations. The chlorine content of the RDF samples used in the study ranged from 0.10% to 1.41%, with an average of 0.27%. According to the results, the accuracy, precision, Recall and F1 Score related to the early warning system were found to be 0.8909, 0.8889, 0.8889, 0.8889, respectively. In addition, chlorine measurements were performed at 200, 500 and 1000 mm/s belt speeds and accuracy values of 78.39%, 76.35% and 69.94 %, respectively were obtained.

The role of parameters in Bayesian Online Changepoint Detection: detecting early warning of mount Merapi eruptions

Indonesia is a country that is surrounded by active volcanoes, which may erupt at any time; therefore, an online early warning system of volcanic eruption is crucial. In this paper, an online early warning system is constructed based on the changepoints detection on earthquake magnitude time series. This online early warning system is built using a Bayesian Online Changepoint Detection (BOCPD) method. One of the method's advantages is that one can customize the parameters (initial hyper-parameters and hazard-rate parameter) of BOCPD to follow a chosen constraint. These parameters determine the time and number of changepoints. An algorithm, called Appropriate Parameters of Bayesian Online Changepoint Detection for Early Warning (APBOCPD-EW), is proposed to get the parameters that lead the detection to the early warning points before eruption. We apply the algorithm for online early warning of mount Merapi eruptions. The results show that the proposed method produces parameters that give good estimation time for early warnings of mount Merapi's eruptions.

HABreports: Online Early Warning of Harmful Algal and Biotoxin Risk for the Scottish Shellfish and Finfish Aquaculture Industries

We present an on-line early warning system that is operational in Scottish coastal waters to minimize the risk to humans and aquaculture businesses in terms of the human health and economic impacts of harmful algal blooms (HABs) and their associated biotoxins. The system includes both map and time-series based visualization tools. A “traffic light” index approach is used to highlight locations at elevated HAB/biotoxin risk. High resolution mathematical modelling of cell advection, in combination with satellite remote sensing, provides early warning of HABs that advect from offshore waters to the coast. Expert interpretation of HAB, biotoxin and environmental data in light of recent and historical trends is used to provide, on a weekly basis, a forecast of the risk from HABs and their biotoxins to allow mitigation measures to be put in place by aquaculture businesses, should a HAB event be imminent.

Проблемні питання оподаткування цифрових послуг у сфері електронної комерції

Метою статті є аналіз сучасного стану ринку електронної комерції, з’ясування доцільності запровадження оподаткування цифрових послуг, а також розробка практичних заходів щодо підтримки суб’єктів ринку електронної комерції в умовах поглиблення економічної кризи.
 Наукова новизна. У статті здійснено аналіз сучасного стану ринку електронної комерції в умовах поглиблення економічної кризи в Україні та світі. Проаналізовано пропозиції Організації економічного співробітництва та розвитку, а також рекомендації Організації Об’єднаних Націй для урядів країн щодо посилення уваги до податкової та фіскальної політик в умовах кризи з метою підтримки їхнього стабільного розвитку та оподаткування у майбутньому. Розглянуто доцільність запровадження податку на цифрові послуги (Digital Services Tax) у сфері електронної комерції на прикладі окремих країн-членів Європейського Союзу. Запропоновано заходи щодо стимулювання та підтримки суб’єктів цієї діяльності в Україні.
 Висновки. За результатами дослідження з’ясовано, що поглиблення економічної кризи внаслідок пандемії, спричиненої коронавірусом COVID-19, призвело до суттєвого падіння економіки. Багато компаній зазнали значних фінансових втрат та потребують державної підтримки. Внаслідок введення жорстких карантинних заходів споживачі та представники бізнесу переорієнтувалися на купівлю (продаж) товарів (робіт, послуг) он-лайн, тож сфера електронної комерції змогла гідно витримати кризові явища. Відмічено, що країни-члени Європейського Союзу формують ґрунтовні підходи до правового регулювання та оподаткування цього сегменту ринку. Запровадження оподаткування цифрових послуг у сфері електронної комерції необхідно здійснювати на основі єдиного підходу, який має бути розроблено на основі міжнародного діалогу. З’ясовано, що запровадження Digital Services Tax окремо взятими державами є недоцільним, оскільки спричинятиме викривлення конкуренції між країнами. З метою врегулювання проблемних питань в частині оподаткування цифрових послуг у сфері електронної комерції в Україні запропоновано зосередитися на наступних напрямах: 1) оподаткування ПДВ електронних послуг, які надаються нерезидентами; 2) продовження та розширення імплементованих заходів анти-BEPS; 3) розробка та упровадження он-лайн системи раннього попередження банкрутства.

Research on Multi- node Frame Early Warning System of Power Grid Based on Abnormal Data Extraction

Nowadays, the collection of electrical energy measurement data is mainly completed by smart meters, electrical energy data monitoring equipment and electrical energy data management equipment. Due to system defects, equipment failures and human factors, it is prone to abnormal data collection. The design concept of online early warning system for extracting abnormal features of electrical energy data based on multi- node real-time computing framework is proposed in the paper to analyze the hardware and software of the online early warning system for extracting abnormal features of electrical energy data.

Monitoring the Activated Sludge Activities Affected by Industrial Toxins via an Early-Warning System Based on the Relative Oxygen Uptake Rate (ROUR) Index

Shock load from industrial wastewater is known to harm the microbial activities of the activated sludge in wastewater treatment plants (WWTPs) and disturb their performance. This study developed a system monitoring the activated sludge activities based on the relative oxygen uptake rate (ROUR) and explored the influential factors with wastewater and the activated sludge samples collected from a typical WWTP in the Taihu Lake of southern Jiangsu province, China. The ROUR was affected by the concentration of toxic substances, mixed liquid suspended solids (MLSS), hydraulic retention time (HRT) and pH. Higher toxin contents significantly decreased the ROUR and the EC50 value of Zn2+, Ni2+, Cr(VI), Cu2+, and Cd2+ was 13.40, 15.54, 97.56, 12.01, and 14.65 mg/L, respectively. The ROUR declined with the increasing HRT and MLSS above 2000 mg/L had buffering capacities for the impacts of toxic substances to some extent. The ROUR remained stable within a broad range pH (6–10), covering most of the operational pH in WWTPs and behaving as an appropriate indicator for monitoring the shock load. A toxicity model assessing and predicting the ROUR was developed and fitted well with experimental data. Coupling the ROUR monitoring system and toxicity model, an online early-warning system was assembled and successfully used for predicting the toxicity of different potential toxic metals. This study provides a new universal toxicity model and an online early-warning system for monitoring the shock load from industrial wastewater, which is useful for improving the performance of WWTPs.

PDF Based Icing Image Recognition Applied to Online Early Warning System for Transmission Lines

This paper proposes an online early warning technique and the probability distribution function based icing image recogition for overhead power transmission lines. The main functionality of the online early warning of icing, forest fire,lighting, insulator flashover, conductor gallopiong and invasion which is based on a scheme of the online inspection system for the transmission lines with optical fibre communication technology. As a case study, the early warning of icing is discussed in this paper and a comprehensive icing early warning scheme has been proposed: the local meteorological conditions and mechanical data are applied for the initial criteria of the icing early warning system, and the PDF based image recognition technology is then used to finally decide the icing condition of transmission lines, which can be mor effectively dealt with than complex pictures acquired at towers. DOI: http://dx.doi.org/10.11591/telkomnika.v13i3.7210

CROP MONITORING BY MEANS OF AN ON-LINE EARLY WARNING SYSTEM

ISHS III International Symposium on Models for Plant Growth, Environmental Control and Farm Management in Protected Cultivation (HortiModel 2006) CROP MONITORING BY MEANS OF AN ON-LINE EARLY WARNING SYSTEM

Measurement of phycocyanin fluorescenceas an online early warning system for cyanobacteria in reservoir intake water

Cyanobacterial blooms in drinking water reservoirs may cause a variety of water quality problems, including those of taste and odor, and can compromise the water supply destined for human consumption. In response to this problem an online monitoring tool for analyzing the cyanobacterial concentration in intake water is of practical value. This study demonstrated a positive correlation between phycocyanin fluorescence and cyanobacterial biomass during Microcystis aeruginosa blooms in a lowland drinking water reservoir, using online detection. The highest correlation coefficients were found for a cyanobacterial biomass concentration below 15 mg freshweight/L, indicating that this method can be an effective early warning system. Rapid changes in fluorescence were observed when wind drift moved higher cyanobacterial concentrations into the water intake, indicating that fluorescence could be employed as a quick warning for changed requirements for plant operations.

FUZZY MODELING OF CYANOBACTERIAL SURFACE WATERBLOOMS: VALIDATION WITH NOAA‐AVHRR SATELLITE IMAGES

Surface waterblooms of toxic cyanobacteria (scums) interfere with the use of lakes, for instance in the production of drinking water or for recreation. Routine monitoring data are not sufficient for early warning due to the large temporal and spatial variability in the occurrence of surface waterblooms, and the time lag between the formation of the scum and the availability of relevant information for risk management. We combined a “traditional” dynamic simulation model based upon differential equations with fuzzy logic to describe the three main conditions governing surface waterbloom formation: (1) a preexisting population of cyanobacteria, (2) buoyancy of the cells, and (3) stability of the water column. The attributes and membership functions of the fuzzy model were based on earlier field studies of diel changes in buoyancy and vertical distribution of cyanobacteria. The model was applied without further calibration to the large lake IJsselmeer (1200 km2) in the Netherlands, and we validated the model output using 12 years of NOAA‐AVHRR (National Oceanic and Atmospheric Administration–Advanced Very High Resolution Radiometers) satellite images on which surface blooms are discernible as an enhanced vegetation index or increased surface water temperature. Existing surface blooms were predicted with high accuracy, but additional blooms were also predicted. A statistical test (Cohen's Kappa) showed that correct predictions of the absence or presence of surface blooms were highly unlikely to have occurred by chance only. The model can be used to predict the occurrence of surface waterblooms in advance on the basis of the long‐term weather forecast, leaving time for appropriate management of the problem. The lake management has expressed interest in converting the present model into a fully operational–online–early warning system.

Rapid Screening of a Large Group of Polar Pesticides in River Water by On-Line Trace Enrichment and Column Liquid Chromatography

Abstract Column liquid chromatography on a reversed-phase (C-18) analytical column using a linear acetonitrile-water gradient and diode-array detection is used in an on-line early-warning system of over 50 pesticides in surface water. It allows the separation of most compounds with detection limits of about 1–5 μg/1 after preconcentration of 30 ml of sample. Problems encountered for co-eluting pesticides with similar spectra and pesticides with weak ultraviolet absorption over 230 nm, are discussed. If the system is combined with on-line trace enrichment on a styrene-divinylbenzene copolymer (PLRP-S) precolumn, the overall resolution is only slightly affected, and the qualitative information obtained remains about the same. Chromatograms of spiked (2–10 μg/1) surface (river Rhine) water containing 54 test compounds are shown. Preliminary studies show the stability of several pesticides in water to be rather low. Some degradation products have been detected in 1-month-old methanolic and aqueous solutions.

An on-line early warning system for ice jams and stoppages on the upper Niagara River

Ice stoppages and/or ice jams that occur on the upper Niagara River can cause reductions in hydroelectric power generation at the plants operating on the river. A method for monitoring and predicting the potential occurrence of ice stoppages and/or jams on the river was developed. The method was incorporated into a computerized, on-line early warning information system for use by the operators of the generating facilities. The information is used by the operators in making decisions that may help reduce the impacts of the ice.