Hourly Basis Research Articles

Assessment of Operational Effectiveness of SynchroGreen Adaptive Signal Control System in South Carolina

An adaptive signal control system (ASCS) can adjust signal timings in real time based on traffic demands. The operational benefits of ASCS vary depending on the type of ASCS, corridor characteristics, and geographical area. This paper evaluates the operational performance of 11 ASCS corridors located throughout South Carolina. These corridors are operated using SynchroGreen, one of several types of ASCS, developed by TrafficWare. Based on the operational analysis, it is found that when SynchroGreen is operational, it reduces the travel time on the corridor by an average of 6.4% and improves travel time reliability by an average of 31.4% compared with when the conventional traffic signal control system (e.g., pre-timed and actuated signal control) is operational. SynchroGreen reduces travel time on a corridor on average 61% of the time during a day and on average 77% of the time during peak periods. Additionally, SynchroGreen improves travel time reliability on average 53% of the time during a day and on average 52% of the time during peak periods. The operational effectiveness of SynchroGreen in reducing travel time and improving travel time reliability is consistent in both directions on an hourly basis for eight corridors and five corridors, respectively. Lastly, SynchroGreen is found to produce greater operational benefits by reducing travel time if the average speed of a corridor is lower than or equal to 35 mph and the number of signals on a corridor is more than 10.

Multi-attraction, hourly tourism demand forecasting

Forecasting tourism demand for multiple tourist attractions on an hourly basis provides important insights for effective and efficient management, such as staffing and resource optimization. However, existing forecasting models are not well equipped to hand the hourly data, which is dynamic and nonlinear. This study develops an improved, artificial intelligent-based model, known as Correlated Time Series oriented Long Short-Term Memory with Attention Mechanism, to solve this problem. The validity of the model is verified through a forecasting exercise for 77 attractions in Beijing, China. The results show that our model significantly outperforms the baseline models. The study advances the tourism demand forecasting literature and offers practical implications for resource optimization while enhancing staff and customer satisfaction.

How to compute long-term averages for air quality assessment at urban areas?

This paper proposes two innovative time-effective approaches to retrieve annual averaged concentrations for air quality assessment in the framework of the AQD. In addition, a traditional method (M1) was applied through numerical simulations for an entire year on an hourly basis to compare the performance of the proposed approaches. The first time-effective approach (M2) is based on the calculation of pollutant concentrations for the full year on an hourly basis through the combination of a set of numerical simulations for 4 typical days weighted by hourly factors obtained from air quality monitoring data. While the second time-effective approach (M3) considers the numerical simulation of pollutant concentrations for a set of typical meteorological conditions. For all the methods, air quality simulations were performed with the second-generation Gaussian model URBAIR. The three methods are applied over two distinct European urban areas, the Aveiro region in Portugal and Bristol in the United Kingdom, for the simulation of NO2 and PM10 annual concentrations.The main results highlight an underestimation of the NO2 annual concentrations by M2 and an overestimation of those concentrations by M3 for the Aveiro region, when compared to M1 as the reference method. While, for Bristol the main differences between methods were found for NO2 concentrations when using M3. M2 underestimates PM10 annual concentrations in the Aveiro Region, while M3 points out underestimation or overestimation of those concentrations for distinct areas of the domain. This study aims to foster the knowledge on air quality assessment under the European policy context, supporting air quality management and urban planning. The innovative nature of this study relies on the proposed time-effective tools, suitable for the fast simulation of complex urban areas applying high spatial resolution. Additionally, these modelling tools may provide key information on air quality to population, particularly where it is not readily available.

A correlation matrix-based tensor decomposition method for early prediction of sepsis from clinical data

Early detection of sepsis can assist in clinical triage and decision-making, resulting in early intervention with improved outcomes. This study aims to develop a machine learning framework to predict the onset of sepsis through EHR data by applying tensor decomposition on correlation matrices of clinical covariates for every record, arranged on an hourly basis for the length of stay (LOS) in intensive care unit.A third-order tensor θx,y,z representing a clinical correlation among selected 24 covariates for a considered time frame of sepsis onset duration of 6 h, with a stride of 1 h is formed for each record. Such a fused tensor with dimensions θ∈R24×24×LOS for every record undergoes Tucker decomposition with an optimal choice of rank. The factor matrices U1,U2,U3 thus obtained after decomposition are excluded and only the core tensor σ with a dimension σ∈R10×10×LOS has been retained, and used to provide latent features for prediction of sepsis onset. A five-fold cross-validation scheme is employed wherein the obtained 100 latent features from the reshaped core tensor, are fed to Light Gradient Boosting Machine Learning models (LightGBM) for binary classification, further alleviating the involved class imbalance. The machine-learning framework is designed via Bayesian optimization, yielding an average normalized utility score of 0.4314 on publicly available PhysioNet/Computing in Cardiology Challenge 2019 training data.The proposed tensor decomposition deciphers the higher-order interrelations among the considered clinical covariates for early prediction of sepsis and the results obtained are on par with existing state-of-the-art performances.

Prediction of source contributions to urban background PM<sub>10</sub> concentrations in European cities: a case study for an episode in December 2016 using EMEP/MSC-W rv4.15 – Part 2: The city contribution

Abstract. Despite the progress made in the latest decades, air pollution is still the primary environmental cause of premature death in Europe. The urban population risks more likely to suffer to pollution related to high concentrations of air pollutants, such as in particulate matter smaller than 10 µm (PM10). Since the composition of these particulates varies with space and time, the understanding of the origin is essential to determine the most efficient control strategies. A source contribution calculation allows us to provide such information and thus to determine the geographical location of the sources (e.g. city or country) responsible for the air pollution episodes. In this study, the calculations provided by the regional European Monitoring and Evaluation Programme/Meteorological Synthesizing Centre – West (EMEP/MSC-W) rv4.15 model in a forecast mode, with a 0.25∘ longitude × 0.125∘ latitude resolution, and based on a scenario approach, have been explored. To do so, the work has focused on event occurring between 1 and 9 December 2016. This source contribution calculation aims at quantifying over 34 European cities, the “city” contribution of these PM10, i.e. from the city itself, on an hourly basis. Since the methodology used in the model is based on reduced anthropogenic emissions, compared to a reference run, the choice of the percentage in the reductions has been tested by using three different values (5 %, 15 %, and 50 %). The definition of the “city” contribution, and thus the definition of the area defining the cities is also an important parameter. The impact of the definition of these urban areas, for the studied cities, was investigated (i.e. one model grid cell, nine grid cells and the grid cells covering the definition given by the global administrative area – GADM). Using a 15 % reduction in the emission and larger cities for our source contribution calculation (e.g. nine grid cells and GADM) helps to reduce the non-linearity in the concentration changes. This non-linearity is observed in the mismatch between the total concentration and the sum of the concentrations from different calculated sources. When this non-linearity is observed, it impacts the NO3-, NH4+, and H2O concentrations. However, the mean non-linearity represents only less than 2 % of the total modelled PM10 calculated by the system. During the studied episode, it was found that 20 % of the surface predicted PM10 had been from the “city”, essentially composed of primary components. In total, 60 % of the hourly PM10 concentrations predicted by the model came from the countries in the regional domain, and they were essentially composed of NO3- (by ∼ 35 %). The two other secondary inorganic aerosols are also important components of this “rest of Europe” contribution, since SO42- and NH4+ represent together almost 30 % of this contribution. The rest of the PM10 was mainly due to natural sources. It was also shown that the central European cities were mainly impacted by the surrounding countries while the cities located a bit away from the rest of the other European countries (e.g. Oslo and Lisbon) had larger “city” contributions. The usefulness of the forecasting tool has also been illustrated with an example in Paris, since the system has been able to predict the primary sources of a local polluted event on 1–2 December 2016, as documented by local authorities.

Assessment and statistical analysis of global radiation by decomposing it to its direct and diffse components

In this work, hourly measurements of global solar irradiances obtained from a pyranometer and direct normal irradiances obtained from sunshine duration sensor are assessed through an extensive quality control procedure and statistical analysis on the measured and derived solar parameters for a semimountainous location using data from the last fie years (2016-2020). This information and the method suggested concerning the solar energy capture systems and energy efficiency is useful for engineers who can therefore take knowledge of the local radiation levels. Furthermore, the direct horizontal irradiance canbe easily calculated and the diffuse component can be estimated from the difference of global and direct horizontal irradiances. Monthly mean hourly values of the radiation components are also estimated and shown through isolines diagrams. Representative values of global direct and diffuse irradiances for different times of the year and different hour of the day can be easily read from these diagrams. Simultaneously, a similar analysis is carried out over various solar indices estimated on both an hourly and daily basis. The derived solar indices are the clearness index for global, direct, and diffuse radiation as well as the diffuse and direct fractions from global radiation. The interrelationships between the said indices are also examined.Additionally, the BRL (Boland-Ridley-Lauret) diffuse fraction model which is a multiple predictor logistic model was tested and can be used to estimate the diffuse and later the direct radiation component. The model is a function of clearness index (kt), the apparent solar time (AST), the solar altitude (αs), the daily clearness index (KT) and persistence parameter ψ which is an average of both a lag and lead of the clearness index. The outcome of the model shows that it can be used to estimate successfully the diffuse radiation.

A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network.

Many countries are concerned about high particulate matter (PM) concentrations caused by rapid industrial development, which can harm both human health and the environment. To manage PM, the prediction of PM concentrations based on historical data is actively being conducted. Existing technologies for predicting PM mostly assess the model performance for the prediction of existing PM concentrations; however, PM must be forecast in advance, before it becomes highly concentrated and causes damage to the citizens living in the affected regions. Thus, it is necessary to conduct research on an index that can illustrate whether the PM concentration will increase or decrease. We developed a model that can predict whether the PM concentration might increase or decrease after a certain time, specifically for PM (fine PM) generated by anthropogenic volatile organic compounds. An algorithm that can select a model on an hourly basis, based on the long short-term memory (LSTM) and artificial neural network (ANN) models, was developed. The proposed algorithm exhibited a higher F1-score than the LSTM, ANN, or random forest models alone. The model developed in this study could be used to predict future regional PM concentration levels more effectively.

Optimization of a solar-wind- grid powered desalination system in Saudi Arabia

The utilization of renewable energy to run desalination plants has enormously expanded in the last two decades. In this study, a grid-connected hybrid solar-wind system is proposed to power a small-scale Reverse Osmosis (RO) desalination unit. In a case study, the system's performance has been analyzed under the weather conditions of the Eastern Province, Saudi Arabia. A numerical model has been developed based on a mixed-integer linear programming (MILP) approach to design and size the proposed system. The developed model is solved on an hourly basis to capture hourly variations of weather conditions with the aim to obtain an efficient design to operate the RO plant and supply freshwater to a small community living in a remote area at minimum cost. The developed model allows finding the optimal number of wind turbines, the number of photovoltaic (PV) modules, and the energy purchased from the national grid. Since the desalination energy consumption depends on the feed water conditions, two energy consumption rates are considered, namely, 2 and 4 kWh/m3. The results show that brackish water can be purified for the two different energy requirements at a cost varying between 1.72 and 1.84 $/m3, respectively.

A normative model to estimate the number of persons not social distancing in a 3D complex built space

ABSTRACT In the COVID-19 pandemic, keeping a two-metre physical distance (called social distancing) from others is a critical action to limit the amount of social contact among individuals. Thus, regulating the number of persons in a space is one means to limit social contact and ensure physical distancing among people. In addition to the privacy concerns of individuals’ locations, the lack of elevation information can result in false positive cases for people who are horizontally close but vertically apart beyond the social distance. Therefore, we develop a normative model of the statistical distribution of physical distances to estimate the number of persons not social distancing in a three-dimensional complex built space from the current number of persons in the space (i.e. population density) present on an hourly basis. We conduct an empirical case study in the districts around the central stations of 13 of Japan’s prefectures. To determine the number of people not social distancing in these districts, the proposed model uses open data such as the number of persons before the national emergency declaration, the ratio of the number of persons after the declaration to that before, and the floor area ratio specified by land use regulations in these districts.

Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance

Global Horizontal Irradiation forecasts are necessary for an efficient use of fluctuating energy output from photovoltaic plants. The purpose of this paper is to provide efficient, easy-to-implement Global Horizontal Irradiation forecasts on an hourly basis for a medium-term horizon (longer than 48 h). These forecasts are essential for the strategic deployment of tasks including unit commitment, transmission management, trading, hedging, planning, asset optimization, maintenance scheduling and spinning of power units. Nonetheless, forecasting models for medium term horizons are scarce. This work intends to bridge that gap by proposing a method based on an adjusted combination of moving averages which includes the yearly cycle in addition to the day–night cycle. This method is straightforward to implement and easy to integrate into corporate computing systems. We compare our approach with several well-known alternative methods, such as deep recurrent neural networks or autoregressive integrated moving average models, among others. The results show that recurrent neural networks are 10% more accurate than the second best method, and 90% better than our proposal for very short horizons (up to 5 h ahead). However, this advantage is lost when longer horizons are tested, especially for horizons longer than 30 h ahead, for which our method produces forecasts which are 20% more accurate than those made by recurrent neural networks. The reason behind that improvement at longer horizons is the inclusion of the yearly cycle in the proposed approach.

A systematic approach for data analysis and prediction methods for annual energy profiles: An example for school buildings in Norway

Current research on energy supply systems and building energy demand presents positive impacts from the two sides, with potentials of combining top-down and bottom-up modelling. Mostly, the energy demand input has been employed directly from energy utility companies as a package of information, without considering energy use patterns regarding building type. There lacks a bridge between demand profiles on building stock functions and urban energy supply systems. Accordingly, this article proposes a framework that enables the prediction of annual energy profiles, applied to one educational building type on an hourly basis. The work consists of five steps: (1) getting energy information of 40 district heating (DH) supplied schools in Norway, (2, 3) processing data for getting the modified average hourly demand per m2 and holiday breakpoints through a modified Z-Score, (4) energy forecast of DH and electricity load profiles through temperature moving average, correlation, and linear regression analysis, (5) validation of the predicted yearly profiles by three criteria, and further with the cluster methods for DH profiles. The results showed that the suggested methods for annual energy forecast were satisfying. The defined load profiles might represent the current energy demand of the Nordic school and the methods could be transferred to other building types. With energy analysis of typical building types, the proposed method enables the planners to better understand the energy needs for different building functions.

Eyes Open, Ears On: Supporting Hearing Technology Use in Children with Hearing Loss

Eyes Open, Ears On: Supporting Hearing Technology Use in Children with Hearing Loss

An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings

Due to the availability of smart metering infrastructure, high-resolution electric consumption data is readily available to study the dynamics of residential electric consumption at finely resolved spatial and temporal scales. Analyzing the electric consumption data enables the policymakers and building owners to understand consumer’s demand-consumption behaviors. Furthermore, analysis and accurate forecasting of electric consumption are substantial for consumer involvement in time-of-use tariffs, critical peak pricing, and consumer-specific demand response initiatives. Alongside its vast economic and sustainability implications, such as energy wastage and decarbonization of the energy sector, accurate consumption forecasting facilitates power system planning and stable grid operations. Energy consumption forecasting is an active research area; despite the abundance of devised models, electric consumption forecasting in residential buildings remains challenging due to high occupant energy use behavior variability. Hence the search for an appropriate model for accurate electric consumption forecasting is ever continuing. To this aim, this paper presents a spatial and temporal ensemble forecasting model for short-term electric consumption forecasting. The proposed work involves exploring electric consumption profiles at the apartment level through cluster analysis based on the k-means algorithm. The ensemble forecasting model consists of two deep learning models; Long Short-Term Memory Unit (LSTM) and Gated Recurrent Unit (GRU). First, the apartment-level historical electric consumption data is clustered. Later the clusters are aggregated based on consumption profiles of consumers. At the building and floor level, the ensemble models are trained using aggregated electric consumption data. The proposed ensemble model forecasts the electric consumption at three spatial scales apartment, building, and floor level for hourly, daily, and weekly forecasting horizon. Furthermore, the impact of spatial-temporal granularity and cluster analysis on the prediction accuracy is analyzed. The dataset used in this study comprises high-resolution electric consumption data acquired through smart meters recorded on an hourly basis over the period of one year. The consumption data belongs to four multifamily residential buildings situated in an urban area of South Korea. To prove the effectiveness of our proposed forecasting model, we compared our model with widely known machine learning models and deep learning variants. The results achieved by our proposed ensemble scheme verify that model has learned the sequential behavior of electric consumption by producing superior performance with the lowest MAPE of 4.182 and 4.54 at building and floor level prediction, respectively. The experimental findings suggest that the model has efficiently captured the dynamic electric consumption characteristics to exploit ensemble model diversities and achieved lower forecasting error. The proposed ensemble forecasting scheme is well suited for predictive modeling and short-term load forecasting.

Life Cycle Assessments on Battery Electric Vehicles and Electrolytic Hydrogen: The Need for Calculation Rules and Better Databases on Electricity

LCAs of electric cars and electrolytic hydrogen production are governed by the consumption of electricity. Therefore, LCA benchmarking is prone to choices on electricity data. There are four issues: (1) leading Life Cycle Impact (LCI) databases suffer from inconvenient uncertainties and inaccuracies, (2) electricity mix in countries is rapidly changing, year after year, (3) the electricity mix is strongly fluctuating on an hourly and daily basis, which requires time-based allocation approaches, and (4) how to deal with nuclear power in benchmarking. This analysis shows that: (a) the differences of the GHG emissions of the country production mix in leading databases are rather high (30%), (b) in LCA, a distinction must be made between bundled and unbundled registered electricity certificates (RECs) and guarantees of origin (GOs); the residual mix should not be applied in LCA because of its huge inaccuracy, (c) time-based allocation rules for renewables are required to cope with periods of overproduction, (d) benchmarking of electricity is highly affected by the choice of midpoints and/or endpoint systems, and (e) there is an urgent need for a new LCI database, based on measured emission data, continuously kept up-to-date, transparent, and open access.

Nitrogen dioxide hourly distribution and health risk assessment for winter season in low town of Mohammedia city, Morocco

PurposeAir quality and its assessment in urban areas has become a necessity. This is attributed to the increasing air pollution in urban landscape from anthropogenic activities necessary for economic growth and development. This study investigates air quality and potential health risk posed from nitrogen dioxide (NO2) to the residents of low town of Mohammedia city, Morocco.Design/methodology/approachThe NO2 concentration was measured on an hourly basis for the winter season of the year 2014, 2015 and 2016. The air quality was assessed in terms of Air Quality Index (AQI). Noncarcinogenic risk assessment was done to evaluate possible health risk to the inhabitant of low town from NO2 exposure.FindingsThe maximum concentration reached 85–96 µg/m3 (at 6 p.m., 2014), 96–104 µg/m3 (7–9 p.m., 2015) and 102–117 (8–11 p.m., 2016). The AQI during maximum NO2 levels (peak hours) ranged between 0–50 µg/m3 (good) to 51–100 µg/m3 (unhealthy for sensitive group). The risk quotient (RQ) was calculated for average daily intake and average hourly intake of NO2. RQ was found to be less than 1 (no potential health risk, lifetime and hourly) for all three years. However, increase in RQ value from 0.84 (2014) to 0.98 (2016) indicates increase in potential health risk. Hence, policy and measures should be adopted to reduce the potential health risk.Originality/valueThis study is very first of its kind for the area and hence can serve as reference study for future works. Further studies are required to assess air pollution in other seasons (summer, spring, autumn), impact of climatic condition and parameters on air quality. Also, for direct impact assessment number of cases attributed to air pollution needs to be investigated.

Dynamic Prospective Average and Marginal GHG Emission Factors—Scenario-Based Method for the German Power System until 2050

Due to the continuous diurnal, seasonal, and annual changes in the German power supply, prospective dynamic emission factors are needed to determine greenhouse gas (GHG) emissions from hybrid and flexible electrification measures. For the calculation of average emission factors (AEF) and marginal emission factors (MEF), detailed electricity market data are required to represent electricity trading, energy storage, and the partial load behavior of the power plant park on a unit-by-unit, hourly basis. Using two normative scenarios up to 2050, different emission factors of electricity supply with regard to the degree of decarbonization of power production were developed in a linear optimization model through different GHG emission caps (Business-As-Usual, BAU: −74%; Climate-Action-Plan, CAP: −95%). The mean hourly German AEF drops to 182 gCO2eq/kWhel (2018: 468 gCO2eq/kWhel) in the BAU scenario by the year 2050 and even to 29 gCO2eq/kWhel in the CAP scenario with 3700 almost emission-free hours from power supply per year. The overall higher MEF decreases to 475 and 368 gCO2eq/kWhel, with a stricter emissions cap initially leading to a higher MEF through more gas-fired power plants providing base load. If the emission intensity of the imported electricity differs substantially and a storage factor is implemented, the AEF is significantly affected. Hence, it is not sufficient to use the share of RES in net electricity generation as an indicator of emission intensity. With these emission factors it is possible to calculate lifetime GHG emissions and determine operating times of sector coupling technologies to mitigate GHG emissions in a future flexible energy system. This is because it is decisive when lower-emission electricity can be used to replace fossil energy sources.

One-year operational modal analysis of a steel bridge from high-resolution macrostrain monitoring: Influence of temperature vs. retrofitting

Vibration monitoring from strain data is a promising alternative to the more conventional acceleration-based monitoring because a dense measurement grid can be achieved at a relatively low cost and because strain mode shapes are more sensitive to local stiffness changes than displacement mode shapes. However, the feasibility of monitoring strain mode shapes of full-scale civil structures, where the operational dynamic strain levels are of very low amplitude and temperature changes can influence the modal characteristics, has remained an open question. The present work provides a proof of concept in which the deck of a steel tied arch railway bridge is instrumented with eighty Fiber-optic Bragg Grating strain sensors, multiplexed in four fibers, that are interrogated with a technique that achieves high accuracy and precision. For more than a year, the natural frequencies and strain mode shapes of ten modes have been automatically identified from operational strain time histories, with typical root-mean-square values of 0.01 microstrain, on an hourly basis. Furthermore, using these modal data, the influence of temperature fluctuations and that of a retrofitting of the hangers connecting the bridge deck and the arches, which took place during the monitoring period, are extensively investigated. Both have an influence on the overall stiffness of the bridge and therefore they result in clear changes in the natural frequencies. They do not have an influence on the local stiffness and therefore they do not influence the strain mode shapes, except when the retrofitting induces an interaction between previously well-separated modes.

Different circadian rest-active rhythms in Kleine-Levin syndrome: a prospective and case-control study.

Kleine-Levin syndrome (KLS) is a rare recurrent hypersomnia. Our study aimed at monitoring the movements of patients with KLS using actigraphy and evaluating their circadian rhythm. Twenty young patients with KLS and 14 age-matched controls were recruited. Each individual wore an actigraphy for more than 6 months to monitor at least two attacks. Controls kept wearing the device for at least 7 days. The activity counts were averaged in hourly basis and the day-to-night amplitude was quantified by the differences of the averaged activity counts during daytime and nighttime. The hourly activities of different days were aligned and averaged to construct the circadian profile. Parametric and nonparametric estimation of circadian rhythm was calculated. We applied detrended fluctuation analysis to evaluate the temporal correlations beneath the activity fluctuations at multiple time scales. Circadian rhythm in asymptomatic period showed no significant difference compared to the controls. During hypersomnia attack, the amplitude of the circadian rest-active rhythms drastically decreased and decreased interdaily stability (IS) was found, as well as significant decreased M10 and short-time fractal correlation (α1). Drastically decreased mean and standard deviation of activity were noted, compared to the pre-attack phase and recovery phase. α1 and M10 increased during the late attack phase, and overcompensated IS was noted in the recovery phase. This study confirmed that circadian rest-active rhythms was affected when KLS hypersomnia attack. Several parameters including M10, IS, and α1 may be physiological markers of KLS, which can help to predict the end of hypersomnia episodes.

Not only big bulls — Correlation between morphometry, reproductive success, and testosterone level in a flooded savannah population of the Spectacled Caiman (Caiman crocodilus)

Testosterone is a steroid hormone involved in the expression of many morphological, physiological, and behavioral traits that arguably affect reproductive success. The evidence for that link is, however, incomplete or absent in the research on crocodilian species. Testosterone levels are also known to change throughout the breeding season, often on an hourly basis, which may further complicate studying their relationship with breeding success. We tested here whether baseline testosterone levels, measured out of the breeding season, are correlated with morphometry and reproductive success in Spectacled Caiman (Caiman crocodilus (Linnaeus, 1758)). Paternity tests, based on the amplification and genotyping of eight fluorochrome labeled microsatellites, failed to support a continuous relationship between these variables. Although adult males of all sizes contribute to reproduction, paternity was overrepresented in a few males with high values of maleness index (bigger males), supporting a despotic or pyramidal hierarchy among males. Maternity assignments supported the existence of multiple paternity, a phenomenon previously attributed in this species to the lack of large males caused by human hunting. The idea of larger males having more offspring is widespread in crocodilians, but to our knowledge, this is the first investigation that prove this dogma.

Investigation into the performance of dose rate measurement instruments used in non-governmental networks

In the aftermath of a nuclear or radiological accident, an extended mapping of reliable dose rate values is of key importance for any governmental decision and countermeasures. Presently, numerous dosimetry network stations, operated by the national governments of the member states in Europe, provide such dose rate data on an hourly basis. Nevertheless, there are large areas in Europe that are not covered at all by these early warning networks and other areas that show only a low density of governmental network stations. Hence, there may be a significant lack of information in case of a nuclear or radiological emergency. As a consequence of the Fukushima Daiichi nuclear power plant accidents in 2011, a number of non-governmental radiation monitoring networks (NRMN) appeared on the internet, providing dose rate data based on stationary as well as on mobile measurements of ionizing radiation by laypersons. Especially the mobile detectors are able to cover large areas in short time. Therefore, it is of considerable importance to investigate the feasibility of using dose rate data from non-governmental networks as a complementary input to the European Radiological Data Exchange Platform (EURDEP). Within the European Metrology Program for Innovation and Research (EMPIR), the project 16ENV04 “Preparedness” has studied the metrological relevance of such non-governmental dose rate data (also called crowd-sourced radiological monitoring) in the most comprehensive way so far. Sixteen different dose rate detector systems (in general 4 of each type, plus 2 types with 2 detectors, i.e. 68 detectors in total) used in NRMN have been investigated for the reliability of their data and the corresponding networks, and their data provision to the public were analyzed. The most relevant performance parameters of dosimetry systems (detector's inherent background, energy dependence and linearity of the response as well as the response to secondary cosmic radiation, the sensitivity to small increases of the dose rate and finally the stability of the detector's indication at various climatic conditions - temperature and humidity) have been investigated for fourteen representative types of non-governmental dose rate measuring instruments. Results of this comprehensive performance study of the simple, light-weighted and cheap dose rate meters used in NRMN, and conclusions on the feasibility of using their data for governmental monitoring in case of a nuclear or radiological emergency are presented.