Reliable Dataset Research Articles

Viscoelastic characterisation of carbon black reinforced rubber using the torsion pendulum: Guidelines and temperature sweep results

A comprehensive set of guidelines on the use of the torsion pendulum for the viscoelastic characterisation of carbon black reinforced rubber is presented, including the set up selection, the post processing steps and the final evaluation of the method using temperature sweep results at small strains. The torsion pendulum is based on the free vibration principle, in which the sample is instantaneously perturbed to initiate a torsional movement and then left to freely vibrate. From the frequency and damping of this impulse response function, the properties of the material can be obtained against temperature and time. However, despite the extensive use for many years of the torsion pendulum, the difficulty of activating only the desired torsional mode hinders the acquisition of accurate results. The method presented aims to improve the torsion pendulum performance and can be divided in two stages. The pre-testing phase covers the influence of the test set up and sample geometry on the measured response. The test set up will determine the frequency at which the dynamic properties are obtained, while the sample geometry will influence the torsional strain and bending deflection. The post-testing phase includes the steps to post-process the curve and the criteria to identify and select the most reliable data sets from all of the measured data. The quality of the curve is assessed in the frequency domain by evaluating the frequency components of its Fast Fourier Transform and in the time domain by evaluating its fit to an ideal exponentially decreasing sinusoidal curve. The final selection of the response functions that can be used to measure the viscoelastic properties of the rubber samples is made based on the maximum strain level and the minimum variation of the dynamic properties within one curve. The validity of the method is then tested by comparing temperature sweep results before and after the application of the presented guidelines, where a clear decrease on the results deviation can be observed. Finally, the results obtained with this method are compared to Dynamic Mechanical Analysis results over the same temperature range, where a good fit is obtained. Thus, this investigation presents a comprehensive method, useful to all torsional pendulum users aiming to measure and characterise the mechanical behaviour of viscoelastic materials.

A data-driven multicriteria decision making tool for assessing investments in energy efficiency

Mainstreaming energy efficiency financing has been considered a key priority during the last decade among several stakeholders. The capability offered by Multicriteria Decision Analysis to integrate cross-domain financial and energy consumption data, combined with statistical analysis techniques and data abundance, contributes to building the necessary market confidence in energy efficiency projects and make them an attractive investment asset class. In this context, the aim of this paper is to propose a solid methodological framework in order to support the financing procedure of energy efficiency investments, and to identify improved grant financing plans, considering a series of factors which are of vital importance for the sustainability of such actions and the limitation of investment risk. A decision support tool, developed in Python, is presented which implements the suggested methodology, improving the decision making for the investor in terms of the percentage of grant financing per project. The developed methodology has been applied on a reliable dataset of energy efficiency projects from several cities in Latvia, where the actual performance of the investments is exploited. The application of the methodology has resulted in a financing plan which achieves about the same energy savings, while bringing 15% reduction of the energy efficiency investments’ cost.

Using a surrogate-assisted Bayesian framework to calibrate the runoff-generation scheme in the Energy Exascale Earth System Model (E3SM) v1

Abstract. Runoff is a critical component of the terrestrial water cycle, and Earth system models (ESMs) are essential tools to study its spatiotemporal variability. Runoff schemes in ESMs typically include many parameters so that model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at a global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using a surrogate-assisted Bayesian framework. First, the polynomial chaos expansion machinery with Bayesian compressed sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5∘ × 0.5∘ for 1991–2010. The error metric between the ELM simulations and the benchmark data is selected to construct the surrogates, which facilitates efficient calibration and avoids the more conventional, but challenging, construction of high-dimensional surrogates for the ELM simulated runoff. Second, the Sobol' index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that, in most regions, ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multimodel ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

A data model for integrating BIM and blockchain to enable a single source of truth for the construction supply chain data delivery

PurposeDespite a large amount of BIM data at the handover stage, it is still difficult to identify and effectively isolate valuable construction supply chain (CSC) data that need to be reliably handed over for operation. Moreover, the role of reconciling disparate data is usually played by one party. The integration of blockchain and BIM is a plausible framework for building a reliable digital asset lifecycle. This paper proposes a BIM single source of truth (BIMSSoT) data model using blockchain for ensuring a reliable CSC data delivery.Design/methodology/approachThis paper utilises a blended methodology, the foundation of which is ingrained in business and management research with elements of information and communication technology (ICT) research wherever required. Knowledge elicitation case studies utilising novel interventions such as a data flow diagram (DFD), taxonomy and entity-relationship diagram (ERD) were used in this paper to develop the BIMSSoT data model. The model was validated using an expert forum, and its technological feasibility was established by developing a proof of concept.FindingsThe practical contribution of this research leads to the progression of BIM towards digital engineering to go beyond object-based 3D modelling by building structured and reliable datasets, transitioning from project-centric records to a digital ecosystem of linked databases by utilizing blockchain's potential for ensuring trusted data.Originality/valueTo the best of the author's knowledge, prior to this paper, no research had investigated a detailed data model development leveraging blockchain and BIM to integrate an immutable and complete record of CSC data as another dimension of BIM for operations.

High-fidelity positive-unlabeled deep learning for semi-supervised fault detection of chemical processes

With the rapid development of the modern chemical process industry, process monitoring techniques have been investigated to enhance the loss prevention capability. Fault detection which attempts to detect whether an abnormal condition has happened is an essential step of process monitoring. Supervised learning based fault detection methods usually are not feasible in practical industry situations, because their most important prerequisite that adequate data labels are accessible is extremely hard to meet. In real industrial situations, a common scenario is that a few normal state data are labeled while no fault state data are labeled, and this is a natural fit for the positive-unlabeled (PU) learning. In this work, a three-step high-fidelity PU (THPU) approach based on deep learning is proposed for semi-supervised fault detection of chemical processes. A self-training nearest neighbors (STNN) algorithm is designed in Step 1 to conservatively generate a small reliable positive dataset by extracting data from the unlabeled training dataset. A positive and negative data recognition (PNDR) algorithm is designed in Step 2 to augment the reliable positive dataset and generate a reliable negative dataset based on the remaining unlabeled dataset. A convolutional neural network is trained in Step 3 based on the above reliable datasets as the binary classifier for online fault detection. The benchmark simulated Tennessee Eastman process dataset and a real industrial hydro-cracking process dataset are utilized to illustrate the effectiveness and robustness of the proposed THPU approach. The experimental results demonstrate the superiority of the proposed approach compared to the other competing PU learning approaches and supervised fault detection models.

Exploring automatic inconsistency detection for literature-based gene ontology annotation.

MotivationLiterature-based gene ontology annotations (GOA) are biological database records that use controlled vocabulary to uniformly represent gene function information that is described in the primary literature. Assurance of the quality of GOA is crucial for supporting biological research. However, a range of different kinds of inconsistencies in between literature as evidence and annotated GO terms can be identified; these have not been systematically studied at record level. The existing manual-curation approach to GOA consistency assurance is inefficient and is unable to keep pace with the rate of updates to gene function knowledge. Automatic tools are therefore needed to assist with GOA consistency assurance. This article presents an exploration of different GOA inconsistencies and an early feasibility study of automatic inconsistency detection.ResultsWe have created a reliable synthetic dataset to simulate four realistic types of GOA inconsistency in biological databases. Three automatic approaches are proposed. They provide reasonable performance on the task of distinguishing the four types of inconsistency and are directly applicable to detect inconsistencies in real-world GOA database records. Major challenges resulting from such inconsistencies in the context of several specific application settings are reported. This is the first study to introduce automatic approaches that are designed to address the challenges in current GOA quality assurance workflows. The data underlying this article are available in Github at https://github.com/jiyuc/AutoGOAConsistency.

Estimating Maize Yield in the Black Soil Region of Northeast China Using Land Surface Data Assimilation: Integrating a Crop Model and Remote Sensing.

Accurate yield estimation at the regional scale has always been a persistent challenge in the agricultural sector. With the vigorous emergence of remote sensing land surface observations in recent decades, data assimilation methodology has become an effective means to promote the accuracy and efficiency of yield estimation by integrating regional data and point-scale crop models. This paper focuses on the black soil area of Northeast China, a national strategic grain production base, applying the AquaCrop crop growth model to simulate the fractional vegetation cover (FVC) and maize yield from 2000 to 2020 and then forming a reliable FVC optimization dataset based on an ensemble Kalman filter (EnKF) assimilation algorithm with remote sensing products. Using the random forest model, the regression relationship between FVC and yield was established from the long-term time series data, which is crucial to achieve better yield estimation through the optimized FVC. The major findings include the following: (1) The R2 of the assimilated FVC and maize yield can reach 0.557. (2) When compared with the local statistical yield, our method reduced the mean absolute error (MAE) from 1.164 ton/ha (based on GLASS FVC products) to 1.004 ton/ha (based on the calibrated AquaCrop model) and then to 0.888 ton/ha (the result after assimilation). The above results show that we have proposed a yield estimation method to provide accurate yield estimations by combining data assimilation and machine learning. This study provided deep insights into understanding the variations in FVC and revealed the spatially explicit yield prediction ability from the time series land surface parameters, which has significant potential for optimizing water and soil resource management.

Recognizing irrelevant faces in short-form videos based on feature fusion and active learning

In recent years, short-form videos spread rapidly around the world and became a popular way of entertainment for people to share their daily lives. However, many videos record behaviors of other people without their awareness and are uploaded onto the short-form video platforms. Such behavior severely invades personal privacy and can even bring risks of personal information leakage. At present, few studies focus on detecting privacy violations in short-form videos. Meanwhile, due to the difficulty in transferring existing models to the scenario of short-form videos and the lack of reliable datasets, it is very challenging to recognize irrelevant faces in short-form videos. To deal with this problem, we constructed and published an irrelevant faces dataset (IF-Dataset) with 43,965 irrelevant face images and 89,924 relevant face images based on the videos collected from Douyin (the Chinese version of TikTok). In addition, we constructed a framework that implemented our proposed deep learning model Multi-features Multi-head Fusion Network (MMFNet) to recognize irrelevant faces from short-form videos. The experimental results show that the F1 score of the MMFNet can reach 87.03%. We also proposed a novel loss function as well as an active learning system to improve the generalization ability of models, which can reach the Relative Error Reduction (RER) up to 29.58%. Our work provides both theoretical and practical support for face protection in short-form videos.

Integrating weather observations and local-climate-zone-based landscape patterns for regional hourly air temperature mapping using machine learning

Air temperature is a crucial variable of urban meteorology and is essential to many urban environments, urban climate and climate-change-related studies. However, due to the limited observational records of air temperature and the complex urban morphology and environment, it might not be easy to map the hourly air temperature with a fine resolution at the surface level within and around cities via conventional methods. Thus, this study employed machine learning (ML) algorithms and meteorological and landscape data to develop hourly air temperature mapping techniques and methods at the 1-km resolution over a multi-year warm seasons period. Guangdong Province, China was selected for the case study. Random forest algorithm was employed for the hourly air temperature mapping. The validation results showed that the hourly air temperature maps exhibit good accuracy from 2008 to 2019, with mean R2, root mean square error (RMSE) and mean absolute error (MAE) values of 0.8001, 1.4821 °C and 1.0872 °C, respectively. The importance assessment of the driving factors showed that meteorological factors, especially relative humidity, contributed the most to the air temperature mapping. Simultaneously, landscape factors also played a non-negligible role. Further analysis revealed that the maps steadily maintained high accuracy at nighttime (20:00–7:00), which is essential for investigating nighttime urban climate conditions, especially the urban heat island effect. Moreover, a correlation existed between the nighttime air temperature changes and urban morphology represented by the local climate zones. Air temperatures tended to fall more slowly in the core of metropolitan areas than in the urban fringe. Using ML, this study reliably improves the spatial refinement of hourly air temperature mapping and reveals the spatially explicit air temperature patterns in and around cities at different times in a day during the warm seasons. Moreover, it provides a novel valuable and reliable dataset for air-temperature-related implementation and studies.

Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving.

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.

Estimating Path Choice Models through Floating Car Data

The path choice models play a key role in transportation engineering, especially when coupled with an assignment procedure allowing link flows to be obtained. Their implementation could be complex and resource-consuming. In particular, such a task consists of several stages, including (1) the collection of a large set of data from surveys to infer users’ path choices and (2) the definition of a model able to reproduce users’ choice behaviors. Nowadays, stage (1) can be improved using floating car data (FCD), which allow one to obtain a reliable dataset of paths. In relation to stage (2), different structures of models are available; however, a compromise has to be found between the model’s ability to reproduce the observed paths (including the ability to forecast the future path choices) and its applicability in real contexts (in addition to guaranteeing the robustness of the assignment procedure). Therefore, the aim of this paper is to explore the opportunities offered by FCD to calibrate a path/route choice model to be included in a general procedure for scenario assessment. The proposed methodology is applied to passenger and freight transport case studies. Significant results are obtained showing the opportunities offered by FCD in supporting path choice simulation. Moreover, the characteristics of the model make it easily applicable and exportable to other contexts.

Statistical Calibration of Long-Term Reanalysis Data for Australian Fire Weather Conditions

Abstract Reconstructed weather datasets, such as reanalyses based on model output with data assimilation, often show systematic biases in magnitude when compared with observations. Postprocessing approaches can help adjust the distribution so that the reconstructed data resemble the observed data as closely as possible. In this study, we have compared various statistical bias-correction approaches based on quantile–quantile matching to correct the data from the Twentieth Century Reanalysis, version 2c (20CRv2c), with observation-based data. Methods included in the comparison utilize a suite of different approaches: a linear model, a median-based approach, a nonparametric linear method, a spline-based method, and approaches that are based on the lognormal and Weibull distributions. These methods were applied to daily data in the Australian region for rainfall, maximum temperature, relative humidity, and wind speed. Note that these are the variables required to compute the forest fire danger index (FFDI), widely used in Australia to examine dangerous fire weather conditions. We have compared the relative errors and performances of each method across various locations in Australia and applied the approach with the lowest mean-absolute error across multiple variables to produce a reliable long-term bias-corrected FFDI dataset across Australia. The spline-based data correction was found to have some benefits relative to the other methods in better representing the mean FFDI values and the extremes from the observed records for many of the cases examined here. It is intended that this statistical bias-correction approach applied to long-term reanalysis data will help enable new insight on climatological variations in hazardous phenomena, including dangerous wildfires in Australia extending over the past century.

A Minimum Quantum Chemistry CCSD(T)/CBS Data Set of Dimeric Interaction Energies for Small Organic Functional Groups: Heterodimers.

We extend our previous quantum chemistry calculations of interaction energies for 31 homodimers of small organic functional groups (the SOFG-31 data set) by including 239 heterodimers with monomers selected within the SOFG-31 data set, thus resulting in the SOFG-31+239 data set. The minimum-level theoretical scheme contains (1) the basis set superposition error corrected supermolecule (BSSE-SM) approach for intermolecular interactions; (2) the second-order Møller–Plesset perturbation theory (MP2) with the Dunning’s aug-cc-pVXZ (X = D, T, Q) basis sets for the geometry optimization and correlation energy calculations; and (3) the single-point energy calculations with the coupled cluster with single, double, and perturbative triple excitations method at the complete basis set limit [CCSD(T)/CBS] using the well-tested extrapolation methods for the MP2 energy calibrations. In addition, we have performed a parallel series of energy decomposition calculations based on the symmetry adapted perturbation theory (SAPT) in order to gain chemical insights. That the above procedure cannot be further reduced has been proven to be very crucial for constructing reliable data sets of interaction energies. The calculated CCSD(T)/CBS interaction energy data can serve as a benchmark for testing or training less accurate but more efficient calculation methods, such as the electronic density functional theory. As an application, we employ a segmental SAPT model previously developed for the SOFG-31 data set to predict binding energies of large heterodimer complexes. These model energy “quanta” can be used in coarse-grained molecular dynamics simulations by avoiding large-scale calculations.

Using Twitter to Detect Hate Crimes and Their Motivations: The HateMotiv Corpus

With the rapidly increasing use of social media platforms, much of our lives is spent online. Despite the great advantages of using social media, unfortunately, the spread of hate, cyberbullying, harassment, and trolling can be very common online. Many extremists use social media platforms to communicate their messages of hatred and spread violence, which may result in serious psychological consequences and even contribute to real-world violence. Thus, the aim of this research was to build the HateMotiv corpus, a freely available dataset that is annotated for types of hate crimes and the motivation behind committing them. The dataset was developed using Twitter as an example of social media platforms and could provide the research community with a very unique, novel, and reliable dataset. The dataset is unique as a consequence of its topic-specific nature and its detailed annotation. The corpus was annotated by two annotators who are experts in annotation based on unified guidelines, so they were able to produce an annotation of a high standard with F-scores for the agreement rate as high as 0.66 and 0.71 for type and motivation labels of hate crimes, respectively.

Review of Thermal and Physiological Properties of Human Breast Tissue.

Electromagnetic thermal therapies for cancer treatment, such as microwave hyperthermia, aim to heat up a targeted tumour site to temperatures within 40 and 44 °C. Computational simulations used to investigate such heating systems employ the Pennes’ bioheat equation to model the heat exchange within the tissue, which accounts for several tissue properties: density, specific heat capacity, thermal conductivity, metabolic heat generation rate, and blood perfusion rate. We present a review of these thermal and physiological properties relevant for hyperthermia treatments of breast including fibroglandular breast, fatty breast, and breast tumours. The data included in this review were obtained from both experimental measurement studies and estimated properties of human breast tissues. The latter were used in computational studies of breast thermal treatments. The measurement methods, where available, are discussed together with the estimations and approximations considered for values where measurements were unavailable. The review concludes that measurement data for the thermal and physiological properties of breast and tumour tissue are limited. Fibroglandular and fatty breast tissue properties are often approximated from those of generic muscle or fat tissue. Tumour tissue properties are mostly obtained from approximating equations or assumed to be the same as those of glandular tissue. We also present a set of reliable data, which can be used for more accurate modelling and simulation studies to better treat breast cancer using thermal therapies.

High-resolution weather network reveals a high spatial variability in air temperature in the Central valley of California with implications for crop and pest management.

Weather is the most important driver of crop development. However, spatial variability in weather makes it hard to obtain reliable high resolution datasets across large areas. Most growers rely on data from a single station that can be up to 50km away to make decisions about irrigation, pest management and penology-associated cultural practices at the block level. In this regard, we hypothesize that kriging a large network of weather stations can improve thermal time data quality compared to using the closest station. This study aims to explore the spatial variability in California’s Central Valley and what is the relationship between the density of weather stations used and the error in the measurement of temperature related metrics and derived models. For this purpose, we used temperature records from January 1st 2020 to March 1st 2021 collected by the California Irrigation Management Information System (CIMIS) and a system of 731 weather stations placed above the canopy of trees in commercial orchards (in-orchard). We observed large discrepancies (>300 GDDTb0) in thermal time accumulation between using an interpolation of all stations available and just using the closest CIMIS station. Our data suggests these differences are not systematic bias but true differences in mesoclimate. Similar results were observed for chill accumulation in areas especially prone to not meeting pistachio chill requirements where the discrepancies between using the site-specific in-orchard weather station network and not using them were up to 10 CP. The use of this high resolution network of weather stations revealed spatial patterns in grape, almond, pistachio and pests phenology not reported before. Whereas previous studies have been focused on predictions at the county or state or regional level, our data suggests that a finer resolution can result in major improvements in the quality of data crucial for crop decision making.

Optimal Feature Selection with Weight Optimised Deep Neural Network for Incremental Learning-Based Intrusion Detection in Fog Environment

Fog computing acts as an intermediate component to reduce the delays in communication among end-users and the cloud that offer local processing of requests among end-users through fog devices. Thus, the primary aim of fog devices is to ensure the authenticity of incoming network traffic. Anyhow, these fog devices are susceptible to malicious attacks. An efficient Intrusion Detection System (IDS) or Intrusion Prevention System (IPS) is necessary to offer secure functioning of fog for improving efficiency. IDSs are a fundamental component for any security system like the Internet of things (IoT) and fog networks for ensuring the Quality of Service (QoS). Even though different machine learning and deep learning models have shown their efficiency in intrusion detection, the deep insight of managing the incremental data is a complex part. Therefore, the main intent of this paper is to implement an effective model for intrusion detection in a fog computing platform. Initially, the data dealing with intrusion are collected from diverse benchmark sources. Further, data cleaning is performed, which is to identify and remove errors and duplicate data, to create a reliable dataset. This improves the quality of the training data for analytics and enables accurate decision making. The conceptual and temporal features are extracted. Concerning reducing the data length for reducing the training complexity, optimal feature selection is performed based on an improved meta-heuristic concept termed Modified Active Electrolocation-based Electric Fish Optimization (MAE-EFO). With the optimally selected features or data, incremental learning-based detection is accomplished by Incremental Deep Neural Network (I-DNN). This deep learning model optimises the testing weight using the proposed MAE-EFO by concerning the objective as to minimise the error difference between the predicted and actual results, thus enhancing the performance of new incremental data. The validation of the proposed model on the benchmark datasets and other datasets achieves an attractive performance when compared over other state-of-the-art IDSs.

Potato Plant Disease Detection using Deep Learning

Agriculture is one of the essential sectors for the survival of humankind. Farmers who grow potatoes are facing a lot of economical losses every year because of diseases that happened to a potato plant and if a farmer can detect the disease early then it can save lots of waste and can prevent the ecnomic loss. At the same time, digitalization touching across all the fields that became easier to handle various difficult tasks. Adapting technology as well as digitalization is very crucial for the field of agriculture to benefit the farmer as well as the consumer. Due to adopting technology and regular monitoring, one can able to identify the diseases at the very initial stages and those can be eradicated to obtain a better yield of the crop. In this document, a methodology was proposed for the detection as well as the classification of diseases that occur for the potato plants. For this scenario, the openly accessible, standard, and reliable data set was considered which was popularly known as Plant Village Dataset. There are basically two types of Diseases in potato plant. 1) Early Blight (caused by fungus). 2) Late Blight (caused by small microorganism). For Image Processing and training of model CNN (Convolutional Neural networks) is used. For data cleaning and preprocessing tf dataset and augmentation is used etc.

Using High-Frequency PAR Measurements to Assess the Quality of the SIF Derived from Continuous Field Observations

Fluctuations in illumination are one of the major sources for SIF retrieval errors during temporal continuous field measurements. In this study, we propose a method for evaluating the quality of SIF based on simultaneous measurements of photosynthetically active radiation (PAR), which are acquired using a quantum sensor at a sampling frequency higher than that obtained using spectral measurements. The proposed method is based on the coefficient of variation (known as relative standard deviation) of the high-frequency PAR during a SIF measurement to determine the quality of the SIF value. To evaluate the method, spectral and PAR data of a healthy maize canopy were collected under various illumination conditions, including clear, cloudy, and rapidly fluctuating illumination. The SIF values were retrieved by 3FLD, SFM, and SVD. The results showed that SFM and 3FLD were sensitive to illumination stability. The determination coefficients (R2) between PAR and SIF extracted by SFM and 3FLD were higher than 0.8 on clear and cloudy days and only approximately 0.48 on the day with rapidly fluctuating illumination. By removing the unqualified data using the proposed method, the R2 values of SFM and 3FLD on the day of rapidly fluctuating illumination significantly increased to 0.72. SVD was insensitive to illumination stability. The R2 values of SVD on days with clear, cloudy, and rapidly fluctuating illumination were 0.73, 0.76, and 0.61, respectively. By removing the unqualified data, the R2 values of SVD were increased to 0.66 on the day with rapidly fluctuating illumination. The results indicated that the quality assessment method based on high-frequency PAR data can eliminate the incorrect SIFs due to unstable illumination. The method can be used to extract more accurate and reliable SIF datasets from long-term field observations for the study of the relationship between SIF and vegetation photosynthesis.

Bias‐adjustment of high‐resolution temperature CORDEX data over the Carpathian region: Expected changes including the number of summer and frost days

AbstractClimate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region.