Threshold Methodology Research Articles

Is economic fitness helpful in achieving environmental sustainability in BRICS economies? A risk exposure perspective

A country’s ability to create complex goods and diversify its lines of products is essential for addressing all types of vulnerabilities. Quantifying a country’s vulnerability to extreme climatic events, such as droughts, superstorms, and other natural disasters, and its capacity for successful adaption, is an essential global need that has been ignored. This study examines the role of economic fitness (EF) in addressing climate change risk exposure in BRICS countries in the context of the environmental Kuznets curve using panel data from 1995 to 2015. Panel threshold methodology is employed to ascertain the nonlinear relationship between EF and climate change risk exposure (i.e., Notre Dame Global Adaptation Initiative Country Index (ND-GAIN)). In addition, empirical associations were estimated using panel-corrected standard errors, Driscoll-Kraay standard errors, and feasible generalized least squares estimation techniques. These findings demonstrated an inverted N-shaped link between EF and ND-GAIN. Moreover, even after controlling for significant ND-GAIN influencing variables such as gross domestic product per capita, financial development, and urbanization, our robustness checks revealed significant and consistent findings.

Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine

Monitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88 ± 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors.

A Comparative Study of Gene Co-Expression Thresholding Algorithms.

The thresholding problem is studied in the context of graph theoretical analysis of gene co-expression data. A number of thresholding methodologies are described, implemented, and tested over a large collection of graphs derived from real high-throughput biological data. Comparative results are presented and discussed.

Abstract PO3-05-11: Enhancing the interpretation of real-world quality of life (QoL) in patients (pts) with hormone receptor positive human epidermal growth factor 2 negative (HR+/HER2−) advanced breast cancer (ABC) enrolled in the POLARIS trial

Abstract Background POLARIS is a prospective, observational real-world study of pts with HR+/HER2− ABC receiving palbociclib in routine clinical practice. We have previously shown that palbociclib treatment did not have any significant adverse impact on pt QoL when assessed by the EORTC-QLQ-C30 questionnaire,1 consistent with published clinical trial findings. Now, to enhance interpretation, we sought to characterize EORTC scores (using updated POLARIS data) into terms that are meaningful to pts, physicians, and other stakeholders, using a similar approach to established clinical problem threshold methodology.2 Methods EORTC data were collected at baseline (BL), monthly for the first 3 months, and every 3 months thereafter until palbociclib discontinuation. Response options for each EORTC question (ordinal scale from 1=very poor to 7=excellent) were grouped into “favorable” (taking a conservative approach of using only responses 5, 6 or 7) and “less favorable” (response of ≤4) for the two items on global health status/QoL. The proportion of pts with favorable or less favorable response was compared between subgroups (line of therapy, age, race, bone metastases, visceral metastases, and dose modification) at timepoints BL, month 6, 12, and 18 using Fisher’s exact test. Results Between January 2017 and December 2018, 1285 pts were enrolled, of whom 1250 received at least one dose of palbociclib and subsequently analyzed as part of the final data cut of 09 Jan 2023. EORTC Q29 ‒ Overall Health completion rates were 1167/1250 (93.4%) at baseline, 732/1250 (58.6%) at month 6, 484/1250 (38.7%) at month 12, and 353/1250 (28.2%) at month 18 (Table). The proportion of pts with a favorable response for EORTC Q29 was consistently higher than with a not favorable response (BL, 56.1% vs 43.9%; month 6, 69.3% vs 30.7%; month 12, 68.6% vs 31.4%; and month 18, 70.0% vs 30.0%; Table). When analyzed by subgroup, no significant differences were observed in the proportion of pts with a favorable (to less favorable) response for any subgroup covariate, except for age at BL; however, this significance was not observed at month 6, 12 or 18 (Table). Similar results were observed for EORTC Q30 – Overall QoL (data not shown). Conclusions The proportion of pts indicating a favorable response in the EORTC questionnaire Overall Health and Overall QoL questions increased or were maintained in pts with HR+/HER2− ABC treated with palbociclib from BL through to month 18 across overall and all subgroups. It is important to note that these data are conditional on the pts who responded to the questions, whose numbers diminished over time. XX XX Citation Format: Gabrielle Rocque, Joanne Blum, Yan Ji, Timothy Pluard, John Migas, Shailendra Lakhanpal, Erin Jepsen, Eric Gauthier, Yao Wang, Monica Montelongo, Joseph C. Cappelleri, Connie Chen, Meghan Karuturi, Debu Tripathy. Enhancing the interpretation of real-world quality of life (QoL) in patients (pts) with hormone receptor positive human epidermal growth factor 2 negative (HR+/HER2−) advanced breast cancer (ABC) enrolled in the POLARIS trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-05-11.

Systematic review of the uncertainty of coral reef futures under climate change

Climate change impact syntheses, such as those by the Intergovernmental Panel on Climate Change, consistently assert that limiting global warming to 1.5 °C is unlikely to safeguard most of the world’s coral reefs. This prognosis is primarily based on a small subset of available models that apply similar ‘excess heat’ threshold methodologies. Our systematic review of 79 articles projecting coral reef responses to climate change revealed five main methods. ‘Excess heat’ models constituted one third (32%) of all studies but attracted a disproportionate share (68%) of citations in the field. Most methods relied on deterministic cause-and-effect rules rather than probabilistic relationships, impeding the field’s ability to estimate uncertainty. To synthesize the available projections, we aimed to identify models with comparable outputs. However, divergent choices in model outputs and scenarios limited the analysis to a fraction of available studies. We found substantial discrepancies in the projected impacts, indicating that the subset of articles serving as a basis for climate change syntheses may project more severe consequences than other studies and methodologies. Drawing on insights from other fields, we propose methods to incorporate uncertainty into deterministic modeling approaches and propose a multi-model ensemble approach to generating probabilistic projections for coral reef futures.

The impact of FDI on economic growth in developing countries: the role of FDI inflow and trade openness

This study examines the relationship between foreign direct investment (FDI) inflow, trade openness, and the influence of FDI on economic growth. The threshold methodology and GMM estimation are employed to analyze panel data from 60 developing countries in the period between 1995 and 2019. This study demonstrates the positive impact of FDI on economic growth in developing countries. However, the study also finds a significant threshold of FDI inflow relative to GDP that changes the impact of inward FDI on GDP growth. Regarding the role of trade openness, a significant threshold is found, which also indicates the absorptive capacity of the host countries. Moving from below to above this threshold, an increase in FDI inflow leads to a lower increase in economic growth. An increase in FDI relative to GDP stimulates growth only when the host country has sufficient absorptive capacity with regard to trade openness above the threshold. We suggest developing countries tighten the coherence of trade liberalization and FDI attraction policies to obtain the benefits of FDI and make changes to attract new investors when they succeed in attracting massive FDI inflows.

Uncovering spatiotemporal pattern of floods with Sentinel-1 synthetic aperture radar in major rice-growing river basins of Tanzania

In Tanzania, 71% of rice is grown in a rainfed lowland rice production ecosystem, primarily in river basins where extreme weather events like floods are frequent. For a six-year period (2017–2022), flood mapping was conducted using Sentinel-1 data in the Google Earth Engine (GEE) platform, utilizing change detection and thresholding methodology. In addition to flood mapping, land use and land cover (LULC) were also analyzed using Sentinel-2 data in GEE, employing the Random Forest (RF) algorithm for classification. The aim was to understand the spatiotemporal extent of floods in two study locations. The resulting flood maps achieved an overall accuracy (OA) greater than 90% for all sites and study years. The findings revealed that agricultural land was the predominant land use/cover in both sub-basins, and floods were widespread in both regions. The study highlighted the interannual variability in flood extent, both spatially and temporally. Specifically, at the Ikwiriri site, floods were more extensive in 2020, covering 54.95% of the cultivated area, while in 2017, the minimum flood extent occurred, affecting 14% of the cultivated area. Similarly, at the Mngeta site, extensive floods were observed in 2020, with floods impacting 5.53% of the cultivated areas, while lower flood extents were observed in 2017, affecting 1.49% of the cultivated areas. Furthermore, the study demonstrated distinct spatiotemporal patterns of floods in both locations, with areas in proximity to rivers and wetlands experiencing more frequent floods. The research showcased the capabilities of the GEE cloud computation platform for flood inundation mapping, emphasizing its potential for enhancing our understanding of rice-producing environments. The generated flood maps can be utilized to guide the selection of areas for trials of flood-tolerant rice varieties and the dissemination of technologies such as flood-tolerant rice varieties, contributing to the resilience of rice farmers in these two floodplains.

DITAN: A deep-learning domain agnostic framework for detection and interpretation of temporally-based multivariate ANomalies

We present DITAN, a novel unsupervised domain-agnostic framework for detecting and interpreting temporal-based anomalies. It is based on an encoder-decoder architecture with both implicit/explicit attention and adjustable layers/units for predicting normality as regular patterns in sequential data. A two-stage thresholding methodology with built-in pruning is used to detect anomalies, while root cause and similarities are interpreted in data and units space. Our approach is designed to intersect the 9 fundamental characteristics extracted from the union of related works. We demonstrate the DITAN modules on real-world datasets of 6 multivariate time series contaminated by point and contextual temporal-based anomalies at a varying duration. Experiments show a dominant predictability power of DITAN against the originally proposed models. DITAN is able to determine critical regions and thus identify anomalous events similarly well. Informative similarities between anomalous records are interpreted, since almost all similarities in units space are also verified in data space.

When Does Milk Spoil? The Use of Rejection Threshold Methodology to Investigate the Influence of Total Microbial Numbers on the Acceptability of Fresh Chilled Pasteurised Milk

The consumer rejection threshold (RjT) method was applied to determine the total microbial numbers (TMNs) where consumers find that the quality of whole fresh chilled pasteurised milk (WFCPM) and skim milk (Trim) stored at 4.5 ± 0.5 °C is no longer acceptable. Food spoilage progression was supported by measurements of VOCs and the terms consumers used to describe the ageing fresh chilled pasteurised milk (FCPM). RjTs for TMN of 7.43 and 7.34 log10 CFU.mL−1 for WFCPM and Trim, respectively were derived using Hill’s equation from a series of paired preference tests comparing fresh and aged milks (3–26 days) assessed by consumers (WFCPM, n = 55; Trim, n = 52). A poor relationship between storage time and TMN was found, owing mainly to batch-to-batch and within-batch variation in the milk’s post-pasteurization contamination (PPC) levels. At the RjT, there was a significant change in the signal intensities for a number of spoilage-related VOCs that occurred in the FCPM headspace (p ≤ 0.05), which were measured using proton transfer reaction–mass spectrometry (PTR-MS), including m/z 33, 45, 47, 61, 63, 69, 71, 87, and 89, tentatively identified as methanol; acetaldehyde; ethanol; acetate (acetic acid and acetate esters); dimethyl sulphide (DMS); isoprene, furan, and aldehydes; 2-butanone; and pentanal and butyrates (butyric acid and butyrate esters), respectively. Consumers described the milks at TMN greater than the RjTs using terms like off, expired, sour, spoilt or rancid. This multidisciplinary study has provided data on the importance of PPC and subsequent increases in TMN on VOCs associated with FCPM and consumer’s preferences and highlighted the value of measuring a range of variables when investigating consumer’s perception of food quality and shelf-life.

Gradient-based Volumetric PET Parameters on Immediate Pre-ablation FDG-PETPredict Local Tumor Progression in Patients with Colorectal Liver Metastasis Treated by Microwave Ablation.

This study aimed to evaluate the optimal method of segmentation of colorectal liver metastasis (CLM) on immediate pre-ablation PET scans and assess the prognostic value of quantitative pre-ablation PET parameters with regards to local tumor control. A secondary objective was to correlate the target tumor size estimation by PET methods with the tumor measurements on anatomical imaging. A prospectively accrued cohort of 55 CLMs (46 patients) treated withreal-time 18F-FDG-PET/CT-guided percutaneous microwave ablation was followed-up for a median of 10.8months (interquartile: 5.5-20.2). Total lesion glycolysis (TLG) and metabolic tumor volume (MTV) values of each CLM were derived from pre-ablation 18F-FDG-PET with gradient and threshold PET segmentation methodologies. The event was defined as local tumor progression (LTP). Time-dependent receiver operating characteristic (ROC) curve analyses were used to assess area under the curves (AUCs). Intraclass correlation (ICC) and 95.0% confidence interval (CI) were performed to measure the linear relationships between the continuous variables. AUCs for prediction of LTP obtained from time-dependent ROC analysis for the gradient technique were higher in comparison to the threshold methodologies (AUCs for TLG and volume were: 0.790 and 0.807, respectively). ICC between PET gradient-based and anatomical measurements were higher in comparison to threshold methodologies (ICC for the longest diameter: 733 (95.0% CI 0.538-0.846), ICC for the shortest diameter: .747 (95.0% CI 0.546-0.859), p-values < 0.001). The gradient-based technique had a higher AUC for prediction of LTP after microwave ablation of CLM and showed the highest correlation with anatomical imaging tumor measurements.

A Novel Approach for Quaternion Algebra Based JSEG Color Texture Segmentation

In this work, a novel colour quantization approach has been applied to the JSEG colour texture segmentation using quaternion algebra. As a rule, the fundamental vectors of the colour space are derived by inverting the three RGB colour directions in the complex hyperplanes. In the proposed system, colour is represented as a quaternion because quaternion algebra provides a very intuitive means of working with homogeneous coordinates. This representation views a colour pixel as a point in the three-dimensional space. A novel quantization approach that makes use of projective geometry and level set methods has been produced as a consequence of the suggested model. The JSEG colour texture segmentation will use this technique. The new colour quantization approach utilises the binary quaternion moment preserving thresholding methodology, and is therefore a splintering clustering method. This method is used to segment the colour clusters found inside the RGB cube and the colour consistency throughout the spectrum and in the space are both considered. The results of the segmentation are compared with JSEG as well as with the most recent standard segmentation techniques. These comparisons show that the suggested quantization technique makes JSEG segmentation more robust.

Blind Curvelet-Based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

Distributed acoustic sensing (DAS) is a new seismic monitoring technology. DAS generates a large amount of data, necessitating the development of new technologies to allow for cost-effective processing and handling. The raw seismic data is noisy and must be processed. The curvelet transform is an excellent choice for processing seismic data due to its localized nature, as well as its frequency and dip characteristics. However, its capabilities are limited in case of noise other than white. This paper proposes a denoising method based on a combination of the curvelet transform and a whitening filter, as well as a procedure for estimating noise variance. The whitening filter is included to improve the performance of the curvelet transform in both coherent and incoherent noise cases, as well as to simplify the noise variance estimation method and make it easier to use standard threshold methodology without delving into the curvelet domain. Two data sets are used to validate the suggested technique. Pseudo-synthetic data set created by adding noise to the actual noise-free data collection from the Netherlands offshore F3 block and the on-site data set (with ground roll noise) from east Texas, USA. Experimental results demonstrate that the proposed algorithm achieves the best results under various types of noise.

WALKSHOPS – TESTING A LOW THRESHOLD METHODOLOGY FOR PARTICIPATORY CITY MAKING

This research is about an inclusive, participatory methodology for smart city research: walking in the ‘datafied’ city. So-called walkshops facilitate experiences of and discussion about the environment in which social challenges arise, together with urban dwellers who may perceive them in different ways than decision-makers. We are investigating if walkshops provide a low-threshold methodology for meaningful involvement of all urban dwellers in technological decision-making, with a specific interest in the involvement of societal groups that are vulnerable. The General Data Protection Regulation (GDPR), for example, highlights the importance of transparency, accountability, and control of all technology users over the processing and use of their personal data. This includes so-called vulnerable data subjects. In smart, datafied cities, personal data protection provisions and rights could contribute to empowering urban dwellers. However, taking self-determined decisions regarding legal provisions and complex technical systems is often difficult, and nigh impossible for some. Despite the legal efforts to empower all data subjects vis-à-vis the processing of their personal data, inherent power imbalances remain, and the provisions hardly reach some groups. An important aim of the walkshop methodology is therefore to be inclusive and attractive for anyone. Against this backdrop, we describe how the methodology was tested, and evaluated during eighteen walks in the cities of Brussels, Gent, and Leuven. Evaluations by researchers, participants, and partner organisations, showed very promising results despite some methodological challenges (e.g. participant recruitment). Walkshops show the potential for socially relevant empirical work that creates win-win situations for researchers, public partners, and citizenries.

Defining the South African Acute Respiratory Infectious Disease Season.

The acute respiratory infectious disease season, or colloquially the "flu season", is defined as the annually recurring period characterized by the prevalence of an outbreak of acute respiratory infectious diseases. It has been widely agreed that this season spans the winter period globally, but the precise timing or intensity of the season onset in South Africa is not well defined. This limits the efficacy of the public health sector to vaccinate for influenza timeously and for health facilities to synchronize efficiently for an increase in cases. This study explores the statistical intensity thresholds in defining this season to determine the start and finish date of the acute respiratory infectious disease season in South Africa. Two sets of data were utilized: public-sector hospitalization data that included laboratory-tested RSV and influenza cases and private-sector medical insurance claims under ICD 10 codes J111, J118, J110, and J00. Using the intensity threshold methodology proposed by the US CDC in 2017, various thresholds were tested for alignment with the nineteen-week flu season as proposed by the South African NICD. This resulted in varying thresholds for each province. The respiratory disease season commences in May and ends in September. These findings were seen in hospitalization cases and medical insurance claim cases, particularly with influenza-positive cases in Baragwanath hospital for the year 2019. These statistically determined intensity thresholds and timing of the acute respiratory infectious disease season allow for improved surveillance and preparedness among the public and private healthcare.

Exploring the response to a new range of ethanol reductions in Chardonnay and Syrah wines using a Consumer Rejection Threshold approach

Ethanol is one of the major components of wine, which has a substantial impact on its sensory characteristics. However, data concerning consumer response to ethanol or changes in ethanol remains limited. The aim of this study was to determine the threshold ethanol concentrations beyond which ethanol lowering becomes undesirable in Chardonnay and Syrah wines using the consumer rejection threshold (CRT) methodology. Base wines from these two cultivars were first dearomatised and fully dealcoholised using spinning cone column technology. Then, control wines with a similar ethanol content to the base wines (13.8 and 13.5 % v/v for Chardonnay and Syrah respectively) and wines with lower ethanol contents were reconstituted by mixing the final beverage, the aroma fraction, food grade ethanol and distilled water. CRT values were determined as 2.8 % v/v for Chardonnay and at 7.0 % v/v for Syrah. These particularly low and unexpected concentrations indicate that consumer sensory liking might not be necessarily driven by ethanol concentration, especially for white wines. However, the post-evaluation questionnaire showed that consumers were expecting a high minimal ethanol content for quality wines (10.9 % v/v ± 1.2 and 11.7 % v/v ± 1.5 for white and red wines respectively) and had a limited experience with low and dealcoholised wines. Overall, our data, which are still preliminary and deserve to be validated using different base wines with a larger number of consumers, show that consumers would not necessarily be refractory from a sensory standpoint to the consumption of low ethanol beverages made from wine. Our findings strongly encourage professionals from the wine industry and public authorities to raise awareness about the increase in quality of such products and their benefits for human health.

Soil Reflectance Composites—Improved Thresholding and Performance Evaluation

Reflectance composites that capture bare soil pixels from multispectral image data are increasingly being analysed to model soil constituents such as soil organic carbon. These temporal composites are used instead of single-date multispectral images to account for the frequent vegetation cover of soils and, thus, to get broader spatial coverage of bare soil pixels. Most soil compositing techniques require thresholds derived from spectral indices such as the Normalised Difference Vegetation Index (NDVI) and the Normalised Burn Ratio 2 (NBR2) to separate bare soils from all other land cover types. However, the threshold derivation is handled based on expert knowledge of a specific area, statistical percentile definitions or in situ data. For operational processors, such site-specific and partly manual strategies are not applicable. There is a need for a more generic solution to derive thresholds for large-scale processing without manual intervention. This study presents a novel HIstogram SEparation Threshold (HISET) methodology deriving spectral index thresholds and testing them for a Sentinel-2 temporal data stack. The technique is spectral index-independent, data-driven and can be evaluated based on a quality score. We tested HISET for building six soil reflectance composites (SRC) using NDVI, NBR2 and a new index combining the NDVI and a short-wave infrared (SWIR) band (PV+IR2). A comprehensive analysis of the spectral and spatial performance and accuracy of the resulting SRCs proves the flexibility and validity of HISET. Disturbance effects such as spectral confusion of bare soils with non-photosynthetic-active vegetation (NPV) could be reduced by choosing grassland and crops as input LC for HISET. The NBR2-based SRC spectra showed the highest similarity with LUCAS spectra, the broadest spatial coverage of bare soil pixels and the least number of valid observations per pixel. The spatial coverage of bare soil pixels is validated against the database of the Integrated Administration and Control System (IACS) of the European Commission. Validation results show that PV+IR2-based SRCs outperform the other two indices, especially in spectrally mixed areas of bare soil, photosynthetic-active vegetation and NPV. The NDVI-based SRCs showed the lowest confidence values (95%) in all bands. In the future, HISET shall be tested in other areas with different environmental conditions and LC characteristics to evaluate if the findings of this study are also valid.

Climate change and windstorm losses in Poland in the twenty-first century

ABSTRACT Although windstorms are not as spectacular as large, fluvial floods, they cause significant losses every year. Due to the difficulties in measuring this damage, the literature on the impact of climate change on it is relatively scarce. The aim of this paper is to estimate the impact of climate change on losses caused by windstorms in Poland over the twenty-first century. To do that, damage over threshold methodology is applied, in which losses depend on how much a given threshold is exceeded. In line with existing literature, the 98th percentile threshold and cubic damage function are applied to the data on the wind speed of gust from EURO-CORDEX simulations. As a result, it turns out that the losses caused by windstorms in Poland will increase in relative terms until 2100 by 29% in the RCP4.5 scenario and 32% in the RCP8.5 scenario. By 2050, these changes will be less significant, but still increases by 6% in RCP4.5 and 16% in RCP8.5 can be expected.

Determination of hedonic thresholds by varying three stimuli

The Hedonic Threshold Methodology (HTM), through the determination of compromised acceptance threshold (CAT) and hedonic rejection threshold (HRT), has several applications by the food industry. In order to further increase the field of application of HTM, the objective was to use a mixture design in the Hedonic Threshold Methodology, to enable the determination of hedonic thresholds (CAT and HRT) by varying the intensity of three stimuli simultaneously. It was investigated how much it is possible to replace the NaCl content (1.8% flour weight basis) with two other ingredients (KCL/yeast extract or KCL/enzyme preparation), without compromising the acceptance (CAT) and without resulting in sensory rejection (HRT) of crackers. Reduction in the acceptance started to occur by reducing the NaCl content by around 64% (from 1.81% to 0.651%), with the addition of 1.149% KCl. Two combinations of KCl and yeast extract (1.783% and 0.017%, or 0.693% and 1.107%, respectively) allowed producing a cracker with no NaCl without sensory rejection. The reduction in acceptance also started to occur by reducing the NaCl content by around 40% (from 1.81% to 1.086%), with adding of 0.358% KCl and 0.356% enzyme preparation. A sensory rejection begins to occur by reducing the NaCl content by around 99% (1.81–0.012%), with the addition of 1.215% KCl and 0.573% enzyme preparation (transglutaminase). The use of the mixture design in HTM allowed the unprecedented determination of hedonic thresholds varying three stimuli. This expands the possibilities for applications of sensory thresholds.

A risk model to compute the volatility and the need for collateral margins in energy futures contracts in Brazil

PurposeBrazil is characterized by the inexistence of a more robust system of guarantees and rules to minimize risks and protect agents in energy futures contracts. In this sense, this study aims to answer the question of how a centralized clearing agent can compute safety margin requirements to help reduce the systemic risk of the energy futures contracts market in Brazil.Design/methodology/approachThe intermediate steps and specific objectives are to analyze the volatility behavior, identify the autoregressive conditional heteroscedasticity effects and model the variance of the return series. Based on this, the authors calculate the value-at-risk and conditional value-at-risk metrics for the energy futures contracts. As a robustness test, the authors added a peak over threshold methodology from extreme values theory.FindingsIn general, monthly products require margins because of their higher variance. With the asymmetrical distribution of returns, the authors needed to consider different maintenance margins for the long and short positions. It was also shown that two guarantee margins were required to secure the contracts as follows: the initial margin and the maintenance margin. The three factors that defined the size of the maintenance margin the volatility, skewness and kurtosis of the return series.Originality/valueThe contribution of this study lies in promoting the understanding of the risk dimensions of the energy derivatives market in Brazil and it offers concrete recommendations for how to mitigate this risk through market mechanisms and structures. Similar arrangements can be applied to other emerging markets.

FLOOD MONITORING USING SENTINEL-1 SAR DATA: A CASE STUDY BASED ON AN EVENT OF 2018 AND 2019 SOUTHERN PART OF KERALA

Abstract. Flood is a natural hazard influenced by rainfall and dam collapse, which propels release of enormous amount of water. In the last two decades flood is the second largest natural hazards occurred worldwide, which caused serious damage to life properties, settlements and economic activities. Flood mapping is a process that is useful for assessment and reduces the risk factor during the flood. An effective monitoring of flood prone area is necessary to handle GIS techniques and without remote sensing data it is difficult to identify the flooded area in this study Microwave remote sensing plays a lead role in natural hazards, here Synthetic Aperture Radar (SAR) data is the best way for monitoring flood hazards. In this study Southern part of the Kerala is chosen as the study area, In August 2018, during the south west monsoon due to heavy rainfall a severe flood affected the southern part of Kerala which saw a 37% increase in the rate of normal rainfall. The objective of the study is to find the flood zone area using SAR data and estimate the flood occurrence over a period of time. However a satellite imagery of optical data is used to analyse the pre and post event of flood, but during a heavy rainfall, cloud may interrupt the data acquisition. SAR satellite imagery fromSentinel-1A is a cloud penetrating data available in all kind of weather conditions during day and night time, which provides a good source of high resolution data sets. To identify the flood affected area an adapted technology of threshold methodology developed by using SAR data and change detection for the year 2018 and 2019, will illustrate flood extended part in southern part of Kerala. The result shows the estimation of flood extended part of the study area and the damages occurred during a flooded time period of post and pre event, vulnerability assessing of crop and agriculture is to obtain an intensity of the damaged areas which is closely associated with the river channel, the Polarization displays a similar sequence for amount of flooding. The study helps to find the reason of flood extent and to equip with better planning for risk reduction and management during a flooding period.