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Background NOAA’s Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires.

AbstractA neutron cross-section evaluation for the n + 103Rh reaction in the resolved resonance region was carried out in the energy range 10−5 eV to 8 keV encompassing thermal energy at 0.0253 eV. The scope of this work is to generate resonance parameters and resonance parameter covariances based on the Reich-Moore reduced R-matrix formalism using the code SAMMY. Some features of the new evaluation are the inclusion of high-resolution capture data in the SAMMY evaluation process and the extension of the resolved resonance range from 4 to 8 keV. Furthermore, the evaluation employs more accurate resonance parameter representation by exploring the use of the LRF = 7 ENDF feature and also the use of the LCOMP = 2 compact format for resonance parameter covariance representation. Included in the SAMMY evaluation are transmission data, capture cross-section data, and neutron scattering length information. Thermal cross-section values listed in the literature, as well as capture resonance integrals, were also incorporated into the evaluation process. [ABSTRACT FROM AUTHOR]

The documentation of performance during functional imaging represents a standard procedure employed to control for compliance, sensorimotor, and cognitive demands. In the case of motor tasks, preciseness, force, and frequency have a significant impact on the magnitude of functional activation. Questionnaires are used in psychological investigations to control for cognitive demand, while psychophysiological documentation is employed to record bodily responses. For longitudinal intervention studies, it is of utmost importance to implement meticulous pre- and post-performance controls and balance to accurately assess changes over time. Any changes in compliance may introduce additional uncontrolled variables, which can hinder the interpretation of functional magnetic resonance imaging (fMRI)-related changes. This narrative review presents strategies for controlling and balancing performance in functional imaging approaches to document neuroplasticity in rehabilitative studies. These strategies include not only motor-related aspects, such as precision, velocity, and force, but also timing aspects, such as the start and stop of movement periods. In addition, it discusses strategies for the modulation and control of movement aspects with visual feedback, as well as for the control of physiological changes during experimental modulation. Although these measures require additional care, which is often more demanding than the neuroimaging part of the study, they are crucial for a relevant interpretation and publication of fMRI studies. [ABSTRACT FROM AUTHOR]

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets. [ABSTRACT FROM AUTHOR]

ABSTRACT Behavioural analysis has been attracting significant attention as a broad indicator of sub‐lethal toxicity and has secured a place as an important subdiscipline in ecotoxicology. Among the most notable characteristics of behavioural research, compared to other established approaches in sub‐lethal ecotoxicology (e.g. reproductive and developmental bioassays), are the wide range of study designs being used and the diversity of endpoints considered. At the same time, environmental hazard and risk assessment, which underpins regulatory decisions to protect the environment from potentially harmful chemicals, often recommends that ecotoxicological data be produced following accepted and validated test guidelines. These guidelines typically do not address behavioural changes, meaning that these, often sensitive, effects are not represented in hazard and risk assessments. Here, we propose a new tool, the EthoCRED evaluation method, for assessing the relevance and reliability of behavioural ecotoxicity data, which considers the unique requirements and challenges encountered in this field. This method and accompanying reporting recommendations are designed to serve as an extension of the “Criteria for Reporting and Evaluating Ecotoxicity Data (CRED)” project. As such, EthoCRED can both accommodate the wide array of experimental design approaches seen in behavioural ecotoxicology, and could be readily implemented into regulatory frameworks as deemed appropriate by policy makers of different jurisdictions to allow better integration of knowledge gained from behavioural testing into environmental protection. Furthermore, through our reporting recommendations, we aim to improve the reporting of behavioural studies in the peer‐reviewed literature, and thereby increase their usefulness to inform chemical regulation. [ABSTRACT FROM AUTHOR]

The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain and Paillier homomorphic cryptosystem, providing unchangeable and traceable data while overcoming privacy concerns. Secondly, based on the uploaded data, we propose a method to determine the approximate correct value region without knowing the exact value. Finally, we offer a data filtering mechanism based on the Paillier cryptosystem using this value region. The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection. [ABSTRACT FROM AUTHOR]

The advancements in artificial intelligence have encouraged the application of deep learning in various fields. However, the accuracy of deep learning algorithms is influenced by the quality of the dataset used. Therefore, a high-quality dataset is critical for deep learning. Data augmentation algorithms can generate large, high-quality datasets. The dataset quality is mainly assessed through qualitative and quantitative evaluations. However, conventional qualitative evaluation methods lack the objective and quantitative parameters necessary for battery synthetic datasets. Therefore, this study proposes the application of the rate of change in linear regression correlation coefficients, Dunn index, and silhouette coefficient as clustering indices for quantitatively evaluating the quality of synthetic time-series datasets of batteries. To verify the reliability of the proposed method, we first applied the TimeGAN algorithm to an open-source battery dataset, generated a synthetic battery dataset, and then compared its similarity to the original dataset using the proposed evaluation method. The silhouette coefficient was confirmed as the most reliable index. Furthermore, the similarity of datasets increased as the silhouette index decreased from 0.1053 to 0.0073 based on the number of learning iterations. The results demonstrate that the insufficient quality of datasets used for deep learning can be overcome and supplemented. Furthermore, data similarity can be efficiently evaluated regardless of the learning environment. In conclusion, we present a new synthetic time-series dataset evaluation method that is more reliable than the conventional representative evaluation method (the training loss rate). [ABSTRACT FROM AUTHOR]

Environmental exposure data are used by decision‐makers to assess environmental risks and implement actions to mitigate risks from contaminants. The first article in this series summarized the available evaluation schemes for environmental exposure data, of which there are few compared to those available for environmental hazard data. The second article covered the assessment of the reliability of environmental exposure data sets under the Criteria for the Reporting and Evaluation of Exposure Data (CREED). The aim of this article is to provide an overview and practical guidance on the relevance assessment in the context of the CREED approach for evaluating exposure monitoring data sets. Systematically considering relevance is critical for both evaluating existing data sets and for optimizing the design of new monitoring studies. Relevance is defined here as the degree of suitability or appropriateness of a data set to address a specific purpose or to answer the questions that have been defined by the assessor or for those generating exposure data. The purpose definition will be the foundation for the relevance assessment, to clarify how the assessor should rate the assessment criteria (fully met, partly met, not met/inappropriate, not reported, not applicable). This will provide transparency for anyone reviewing the outcomes. An explicit gap analysis (i.e., an articulation of the data set limitations for the stated purpose) is an important outcome of the relevance assessment. The relevance evaluation approach is demonstrated with three case studies, all relating to the freshwater aquatic environment, where the data sets are scored as relevant with or without restrictions, not relevant, or not assignable. The case studies represent both organic and inorganic constituents, and have different data characteristics (e.g., percentage of censored data, sampling frequencies, relation to supporting parameters). Integr Environ Assess Manag 2024;20:1004–1018. © 2023 SETAC Key Points: This article provides an overview and guidance on the relevance assessment in the context of the Criteria for the Reporting and Evaluation of Exposure Data approach for evaluating exposure monitoring data sets.Relevance is defined here as the degree of suitability or appropriateness of a data set to address a specific purpose that has been defined by the assessor, or for those generating exposure data.A clear purpose definition is the foundation for the relevance assessment, to clarify how the assessor should rate the assessment criteria (fully met, partly met, not met/inappropriate, not reported, not applicable).This approach provides transparency for anyone reviewing the outcomes, and an explicit gap analysis (i.e., an articulation of the data set limitations for the stated purpose) is an important outcome of the relevance assessment. [ABSTRACT FROM AUTHOR]

Soil moisture plays a crucial role in surface hydrological processes and land–atmosphere interactions. It can influence vegetation growth directly, serving as a significant indicator for monitoring agricultural drought. However, spatially continuous datasets of root zone soil moisture rely on model simulations, introducing numerous uncertainties associated with model parameters and input data. Currently, multiple soil moisture products derived from model simulations exist, but their representation at spatial scales remains unclear. Moreover, their abilities to express soil–atmosphere and soil–vegetation interactions within land–atmosphere coupling are not understood, leading to divergent inclinations toward drought. This study investigates the performance of five soil moisture products, European Centre for Medium-Range Weather Forecasts Reanalysis v5-Land (ERA5-Land), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and SoMo.ml, under drought conditions. The bias, correlation, and difference of standard deviation (STDD) were calculated between these products and the observations from International Soil Moisture Network stations. The causal probability of soil, meteorological, and agricultural drought was calculated using the causal-effect Peter and Clark (PC) Momentary Conditional Independence (MCI) method to evaluate the data propensity of these products. ERA5-Land and SoMo.ml gave a similar performance with the highest accuracy, which was attributed to the use of the same meteorological forcing data. The biases of soil moisture from these two products at surface, middle and deep depths against station observations are below 0.1 m3/m3, and the STDD is within 0.05 m3/m3. The accuracy of GLDAS is comparatively lower, characterized by lower correlations (below 0.2 for deeper layers) and high bias (above 0.15 and 0.2 for middle and deep layers, respectively). This discrepancy could be attributed to substantial biases in the precipitation forcing data. ERA5-Land shows higher spatial resolution and greater spatial heterogeneity, whereas MERRA-2 underperformed in this area. MERRA-2 had the strongest connection to agricultural drought, with a propensity probability of 0.477. Conversely, SoMo.ml demonstrates the strongest connection to meteorological drought, with a propensity probability of 0.234. Due to the errors in simulated and observational data during the MERRA data assimilation, substantial biases in the soil moisture data, and low accuracy in meteorological forcing of GLDAS, there was no clear causal relationship between soil moisture drought and meteorological drought between these two products. These findings provide recommendations for the use of soil moisture products in drought research.

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

The |$k$| th min threshold is to judge whether the |$k$| th smallest element of an attribute set along with a confidential file is greater than a predefined threshold, which is a fundamental, primitive operation in data evaluation, such as risk evaluation in business investment. However, it will compromise the privacy of the confidential files when proceeding with such a data evaluation because there is often a large amount of sensitive information involved in them, which the organizations/individuals are reluctant to expose due to the risk of losing a competitive advantage. Motivated by the issue how to preserve the privacy of the confidential files during data evaluation, in this research, we first present a new encryption notion called |$k$| th min threshold encryption (KTE) for serving privacy-preserving data evaluation. In this notion, the confidential file will be encrypted under an attribute set for its privacy protection prior to being sent to a receiver, and a decryption key is generated from a threshold |$d$| and a rank |$k$| of element, both selected by the receiver. The decryption will be successful if and only if the |$k$| th smallest element of the attribute set is greater than |$d$|⁠. We then describe a concrete construction of KTE in the public-key setting. In particular, our construction features optimally short private keys, which only consists of one group element. By virtue of this advantage, it is quite practical because of only two pairing operations for decryption computation. [ABSTRACT FROM AUTHOR]

A new equation of state for argon was developed with the view of extending the range of validity of the equation of state previously proposed by Tegeler et al. and obtaining a better physical description of the experimental thermodynamic data for the whole fluid region (single-phase, metastable, and saturation states). As proposed by Tegeler et al., this equation is also based on a functional form of the residual part of the reduced Helmholtz free energy. However, in this work, the fundamental equation for Helmholtz free energy was derived from the measured quantities CV(ρ, T) and P(ρ, T). The empirical description of the isochoric heat capacity CV(ρ, T) was based on an original empirical description explicitly containing the metastable states. The thermodynamic properties (internal energy, entropy, and free energy) were then obtained by combining the integration of CV(ρ, T). The arbitrary functions introduced by the integration process were deduced from a comparison between calculated and experimental pressure P(ρ, T) data. The new formulation is valid for the whole fluid region from the melting line to 2300 K and for pressures up to 50 GPa. It also predicts the existence of a maximum of the isochoric heat capacity CV along isochors, as experimentally observed in several other fluids. For many applications, an approximate form of the equation of state for the liquid phase may be sufficient. A Tait–Tammann equation is therefore proposed between the triple-point temperature and 148 K. [ABSTRACT FROM AUTHOR]

According to the “adequacy-for-purpose” view of data evaluation, data should be evaluated as better or worse relative to a given research purpose and corresponding research context. In this paper, I apply the adequacy-for-purpose view to a novel case study—concerning the use of paleoecological data to make predictions about coral reef response to contemporary climate change—and then use the case study to suggest two extensions to the adequacy-for-purpose view. First, I argue that we can evaluate research purposes according to their productivity (how well these research purposes serve other, more ultimate purposes). Second, I argue that we can also evaluate research purposes according to their plausibility (including whether we have access to data that are adequate for these research purposes). The relationship between data evaluation and purpose setting should be seen as an iterative one, in which both data and purposes are refined in concert over time. [ABSTRACT FROM AUTHOR]

Renewable energy sources are increasing globally, mainly due to efforts to achieve net zero emissions. In Brazil, solar photovoltaic electricity generation has grown substantially in recent years, with the installed capacity rising from 2455 MW in 2018 to 47,033 MW in August 2024. However, the intermittency of solar energy increases the challenges of forecasting solar generation, making it more difficult for decision-makers to plan flexible and efficient distribution systems. In addition, to forecast power generation to support grid expansion, it is essential to have adequate data sources, but measured climate data in Brazil is limited and does not cover the entire country. To address this problem, this study evaluates the global horizontal irradiance (GHI) of four global reanalysis datasets—MERRA-2, ERA5, ERA5-Land, and CFSv2—at 35 locations across Brazil. The GHI time series from reanalysis was compared with ground-based measurements to assess its ability to represent hourly GHI in Brazil. Results indicate that MERRA-2 performed best in 90% of the locations studied, considering the root mean squared error. These findings will help advance solar forecasting by offering an alternative in regions with limited observational time series measurements through the use of reanalysis datasets.

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one "real" smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics. [ABSTRACT FROM AUTHOR]

High-resolution mass spectrometry (HRMS) has become an important tool for monitoring chemical residues in food, but the time and effort required to evaluate the large amount of data generated by HRMS can be a limiting factor in the widespread application of this tool. Suspect screening, i.e., searching HRMS data against large compound databases or mass lists, represents a practical compromise between using HRMS data to only look for target compounds and performing full non-target analysis. Several different approaches for suspect screening using HRMS data were tested using data from shrimp and eel spiked with veterinary drugs and pesticides as well as from imported aquaculture samples. Most of the analytes (>70%) in the spiked samples were detected and identified by searching against compound databases. To query larger databases and on-line resources such as mzCloud, it was necessary to use software capable of differential analysis and selective filtering, such as Compound Discoverer. Using selective filtering, the number of compounds detected in fish sample extracts could be reduced from tens of thousands to a few hundred by subtracting method blanks and comparing to matrix blank extracts. This smaller list of potential compounds could be further evaluated and compared to available databases and libraries. Analysis of imported aquaculture samples resulted in detection of unexpected contaminants including the dewormer levamisole, the insecticide buprofezin, and potentially the plant alkaloid ricinine. [ABSTRACT FROM AUTHOR]

Design plays a major role in online advertising. While specific aspects such as color or animation have been extensively studied, there is a surprising lack of comprehensive research on the overall impact of visual design and aesthetics. This study delved into the effects of varying levels of visual complexity in banner ads using the eye-tracking method. Out of 108 participants browsing test webpages with specially designed banners, data from 90 adequately measured data sets were used in the study. Notably, ads with a low level of visual complexity outperformed ads with a high level of visual complexity. While users noticed complex ads slightly faster (by 0.84%), they fixated on them significantly less (by 9.09%) and looked at them less frequently (by 4.79%). An implemented survey questionnaire examining the user perception of the banners reinforced the superiority of simple ads, as they were perceived as 4.40% more appealing in comparison. The study further delved into the correlation between objectively and subjectively evaluated data, exploring the credibility of subjective methods in the process. Considering our results and findings from other studies, it was evident that visually complex ads demanded more cognitive effort, could be more distracting, negatively impacted attention, could contribute to banner blindness and were perceived as less appealing. [ABSTRACT FROM AUTHOR]

The advancements in artificial intelligence have encouraged the application of deep learning in various fields. However, the accuracy of deep learning algorithms is influenced by the quality of the dataset used. Therefore, a high-quality dataset is critical for deep learning. Data augmentation algorithms can generate large, high-quality datasets. The dataset quality is mainly assessed through qualitative and quantitative evaluations. However, conventional qualitative evaluation methods lack the objective and quantitative parameters necessary for battery synthetic datasets. Therefore, this study proposes the application of the rate of change in linear regression correlation coefficients, Dunn index, and silhouette coefficient as clustering indices for quantitatively evaluating the quality of synthetic time-series datasets of batteries. To verify the reliability of the proposed method, we first applied the TimeGAN algorithm to an open-source battery dataset, generated a synthetic battery dataset, and then compared its similarity to the original dataset using the proposed evaluation method. The silhouette coefficient was confirmed as the most reliable index. Furthermore, the similarity of datasets increased as the silhouette index decreased from 0.1053 to 0.0073 based on the number of learning iterations. The results demonstrate that the insufficient quality of datasets used for deep learning can be overcome and supplemented. Furthermore, data similarity can be efficiently evaluated regardless of the learning environment. In conclusion, we present a new synthetic time-series dataset evaluation method that is more reliable than the conventional representative evaluation method (the training loss rate).

This work focuses on the effective utilisation of varying data sources in injection moulding for process improvement through a close collaboration with an industrial partner. The aim is to improve productivity in an injection moulding process consisting of more than 100 injection moulding machines. It has been identified that predicting quality through Machine Process Data is the key to increase productivity by reducing scrap. The scope of this work is to investigate whether a sufficient prediction accuracy (less than 10% of the specification spread) can be achieved by using readily available Machine Process Data or additional sensor signals obtained at a higher cost are needed. The latter comprises Machine Profile and Cavity Profile Data. One of the conclusions is that the available Machine Process Data does not capture the variation in the raw material that impacts element quality and therefore fails to meet the required prediction accuracy. Utilising Machine Profiles or Cavity Profiles have shown similar results in reducing the prediction error. Since the cost of implementing cavity sensors in the entire production is higher than utilising the Machine Profiles, further exploration around improving the utilisation of Machine Profile Data in a setting where process variation and labelled data are limited is proposed. [ABSTRACT FROM AUTHOR]

In-depth analysis of the non-metallic inclusion population in solidified metals is essential considering the high cleanliness requirements for metallic components. For this purpose, automated feature analysis (AFA) is a powerful inclusion analysis tool, providing statistical supported information about quantity, elemental composition, morphology and size of inclusions. However, thorough data evaluation is imperative to ensure accurate and reliable results. The paper introduces the key principles of evaluating AFA data, emphasizing its capabilities and constraints by utilizing four large datasets obtained from solidified steel blocks, each containing a minimum of 4000 features. Additionally, methodologies for identifying characteristic inclusion species within a dataset, constructing suitable rulefiles for inclusion classification through statistical analyzes, and interpreting the resulting outcomes are presented. The size distribution of inclusions is analyzed using population density functions, focusing on the absolute inclusion count within specific size ranges. Cluster analysis is presented considering the inclusions‘ positional parameters.

The big data sampling method for real-time and high-speed streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate evaluation of the value characteristics of streaming data. The SDSLA sampling method based on mineral drilling exploration can evaluate the valuable information of streaming data containing many discrete data in real-time, but when the range of discrete data is irregular, it has low sampling accuracy for discrete data. Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS, which takes well as the analysis unit, dynamically changes the size and position of the well, and accurately locates the position and range of discrete data. A new model SDVEM is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions. Experiments show that compared with the SDSLA algorithm, the sample sampled by the SDDS algorithm has higher evaluation accuracy, and the probability distribution of the sample is closer to the original streaming data, with the AOCV indicator being nearly 10% higher. In addition, the SDDS algorithm can achieve over 90% accuracy, recall, and F1 score for training and testing neural networks with small sampling rates, all of which are higher than the SDSLA algorithm. In summary, the SDDS algorithm not only accurately evaluates the value characteristics of streaming data but also facilitates the training of neural network models, which has important research significance in big data estimation. [ABSTRACT FROM AUTHOR]

National Computer Security Incident Response Teams (CSIRTs) have been established worldwide to coordinate responses to computer security incidents at the national level. While it is known that national CSIRTs routinely use different types of tools and data from various sources in their cyber incident investigations, limited studies are available about how national CSIRTs evaluate and choose which tools and data to use for incident response. Such an evaluation is important to ensure that these tools and data are of good quality and, consequently, help to increase the effectiveness of the incident response process and the quality of incident response investigations. Seven online focus group discussions with 20 participants (all staff members) from 15 national CSIRTs across Africa, Asia Pacific, Europe, and North and South America were carried out to address this gap. Results from the focus groups led to four significant findings: (1) there is a confirmed need for a systematic evaluation of tools and data used in national CSIRTs, (2) there is a lack of a generally accepted standard procedure for evaluating tools and data in national CSIRTs, (3) there is a general agreement among all focus group participants regarding the challenges that impinge a systematic evaluation of tools and data by national CSIRTs, and (4) we identified a list of candidate criteria that can help inform the design of a standard procedure for evaluating tools and data by national CSIRTs. Based on our findings, we call on the cyber security community and national CSIRTs to develop standard procedures and criteria for evaluating tools and data that CSIRTs, in general, can use. [ABSTRACT FROM AUTHOR]

A new equation of state for argon was developed with the view of extending the range of validity of the equation of state previously proposed by Tegeler et al. and obtaining a better physical description of the experimental thermodynamic data for the whole fluid region (single-phase, metastable, and saturation states). As proposed by Tegeler et al., this equation is also based on a functional form of the residual part of the reduced Helmholtz free energy. However, in this work, the fundamental equation for Helmholtz free energy was derived from the measured quantities CV(ρ, T) and P(ρ, T). The empirical description of the isochoric heat capacity CV(ρ, T) was based on an original empirical description explicitly containing the metastable states. The thermodynamic properties (internal energy, entropy, and free energy) were then obtained by combining the integration of CV(ρ, T). The arbitrary functions introduced by the integration process were deduced from a comparison between calculated and experimental pressure P(ρ, T) data. The new formulation is valid for the whole fluid region from the melting line to 2300 K and for pressures up to 50 GPa. It also predicts the existence of a maximum of the isochoric heat capacity CV along isochors, as experimentally observed in several other fluids. For many applications, an approximate form of the equation of state for the liquid phase may be sufficient. A Tait–Tammann equation is therefore proposed between the triple-point temperature and 148 K.

In this work, an approach towards automated extraction and evaluation of static and kinematic coefficients of friction is presented. By replacing manual evaluation with an automated process, this approach yields promising results, simplifies data handling, and saves significant time. The methodology shows potential for application to a wide range of experimental data and provides advanced processing capabilities using the extracted and mathematically evaluated data points. However, for data with high signal-to-noise ratios, automatic detection still requires further optimization to improve accuracy. [ABSTRACT FROM AUTHOR]

Smart data selection can quickly sieve valuable information from initial data. Doing so improves the efficiency of analyzing situations to aid in better decision-making. Past methods have mostly been based on expert experience, which may be subjective and inefficient when dealing with large, complex datasets. Recently, the system analysis method has been exploited to find the key data. However, few studies address the indirect effects and heterogeneity of time series data. In this study, a data selection method, the modified Decision-Making Trial and Evaluation Laboratory (DEMATEL) method based on the objective data grey relational analysis (GRA), is used to enhance the ability to analyze time-series data. GRA was first applied to assess the direct impact in the raw data indicators. Then, a modified DEMATEL was adopted to find the overall impact by including the indirect impact and data heterogeneity. We applied the method to analyze the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset and perform the remaining useful life (RUL) prediction of aircraft engines. The results suggest that our method predicts well. Our work offers a nuanced approach of identifying key information in time series data and has potential applications. [ABSTRACT FROM AUTHOR]

The analysis of samples from unsequenced and/or understudied species as well as samples where the proteome is derived from multiple organisms poses two key questions. The first is whether the proteomic data obtained from an unusual sample type even contains peptide tandem mass spectra. The second question is whether an appropriate protein sequence database is available for proteomic searches. We describe the use of automated de novo sequencing for evaluating both the quality of a collection of tandem mass spectra and the suitability of a given protein sequence database for searching that data. Applications of this method include the proteome analysis of closely related species, metaproteomics, and proteomics of extinct organisms.

Amide hydrogen-deuterium exchange mass spectrometry (HDX-MS) has become widely popular for mapping protein-ligand interfaces, for understanding protein-protein interactions, and for discovering dynamic allostery. Several platforms are now available which provide large data sets of amide hydrogen/deuterium exchange mass spectrometry (HDX-MS) data. Although many of these platforms provide some down-stream processing, a comprehensive software that provides the most commonly used down-stream processing tools such as automatic back-exchange correction options, analysis of overlapping peptides, calculations of relative deuterium uptake into regions of the protein after such corrections, rigorous statistical analysis of the significance of uptake differences, and generation of high quality figures for data presentation is not yet available. Here we describe the Deuterium Exchange Correction and Analysis (DECA) software package, which provides all these downstream processing options for data from the most popular mass spectrometry platforms. The major functions of the software are demonstrated on sample data.

t test, a significant test in statistics, shows wide applicability, and plays essential roles in method validation, data evaluation, and quality control. For example, you can compare the mean of a data set with a known value to see if there is a bias in a method. For comparison of two data sets, you need to first make a distinction between unpaired data and paired data. However, in our analytical chemistry textbooks, only unpaired t test is briefly introduced. In contrast, through various examples, both paired t test and unpaired t test are comprehensively introduced in English analytical chemistry textbooks. It is suggested that more systematic statistical contents be integrated into our analytical chemistry teaching. It should be a beneficial attempt for more scientific textbook construction. [ABSTRACT FROM AUTHOR]

The importance of data assets as intangible corporate assets is being emphasized as more business activities based on digital technology are being carried out. This study proposes the development of a data valuation model that can enable companies to use data assets as collateral for loans in financial transactions. To this end, a model was designed with a focus on the cost approach, which is less likely to involve arbitrariness and error among other valuation model approaches. Furthermore, a model simulation was conducted after securing transaction data of a Korean secondhand marketplace provider. Among the total costs of this marketplace provider, the cost of using data reflecting the ratio of data activities was derived, focusing on financial statements and tangible and intangible assets for the last five years. The data asset acquisition costs were derived, and the data replacement costs were calculated by reflecting the past price and wage growth rates. The results revealed that simulation companies could use a total of KRW 26.8 billion worth of data as collateral for a loan. Accordingly, the data valuation model developed in this study will contribute to reinforcing the value of corporate data assets and proposing a new means of corporate financing. [ABSTRACT FROM AUTHOR]

Whereas many independent methods are used to estimate snow water equivalent (SWE) and its spatial distribution and seasonal variability, a need exists for a systematic characterization of inter-model differences at annual, seasonal, and regional scales necessary to quantify the associated uncertainty in these datasets. This study conducts a multi-scale validation and comparison, based on Airborne Snow Observatory data, of five state-of-the-art SWE datasets in the Sierra Nevada, California, including three SWE datasets from retrospective models: an INiTial REConstruction model (REC-INT), an improved REConstruction model based on the ParBal energy balance model (REC-ParBal), and a Sierra Nevada SWE REConstruction with Data Assimilation (REC-DA), and two operational SWE datasets from the U.S. National Weather Service, including the Snow Data Assimilation System (SNODAS) and the National Water Model (NWM-SWE). The results show that REC-DA and REC-ParBal provide the two most accurate estimates of SWE in the snowmelt season, both with small positive biases. REC-DA provides the most accurate spatial distribution of SWE (R2 = 0.87, MAE = 66 mm, PBIAS = 8.3%) at the pixel scale, while REC-ParBal has the least basin-wide PBIAS (R2 = 0.79, MAE = 73 mm, PBIAS = 4.1%) in the snowmelt season. Moreover, REC-DA underestimates peak SWE by −5.8%, while REC-ParBal overestimates it by 7.5%, when compared with the measured peak SWE at snow pillow stations across the Sierra Nevada. The two operational SWE products—SNODAS and NWM-SWE—are less accurate. Furthermore, the inter-model comparison reveals a certain amount of disagreement in snow water storage across time and space between SWE datasets. This study advances our understanding of regional SWE uncertainties and provides critical insights to support future applications of these SWE data products and therefore has broad implications for water resources management and hydrological process studies.

This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one “real” smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics.

Purpose: Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system. Design/methodology/approach: In the present study, Kano's integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities. Findings: The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization. Research limitations/implications: The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources. Originality/value: The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives. [ABSTRACT FROM AUTHOR]

A reference density database of aqueous alkali halide solutions is presented. The solutes are the 20 alkali halides consisting of the cations Li+, Na+, K+, Rb+, Cs+, and anions F−, Cl−, Br−, I−. Experimental density data of these aqueous electrolyte solutions are extensively collected and critically evaluated. A data evaluation procedure is proposed, utilizing support vector regression (SVR) to compare the experimental datasets against each other. Data evaluation is based on agreement with data from other sources rather than accuracy claimed in the literature. Datasets with large deviation from others are identified and removed. The proposed reference database consists of 11 081 data points of 586 datasets from 309 references. Maximum deviations between the selected datasets do not exceed 1%, and are smaller than 0.5% for most of the aqueous alkali halide solutions. SVR models are also trained based on the reference database. Data distribution is visualized using a Gaussian mixture model. Applicability domains of the SVR models are analyzed using Williams plots. An executable program is provided for calculations based on the SVR models. [ABSTRACT FROM AUTHOR]

The present study compares the spatial and temporal characteristics of the Madden-Julian Oscillation (MJO) in Fengyun-3B (FY-3B) polar-orbiting satellite reprocessed outgoing longwave radiation (OLR) data and NOAA OLR data during 2011–2020. The spatial distributions of climatological mean and intraseasonal standard deviation of FY-3B OLR during boreal winter (November–April) and boreal summer (May–October) are highly consistent with those of NOAA OLR. The FY-3B and NOAA OLRs display highly consistent features in the wavenumber-frequency spectra, the occurrence frequency of MJO active days, the eastward propagation of MJO along the equator, and the interannual variability of MJO according to diagnoses using the all-season multivariate EOF analysis. These results indicate that the FY-3B OLR produced by the polar-orbiting satellites is of high quality and worthy of global application. [ABSTRACT FROM AUTHOR]

A method for the evaluation of experimental data from spectroscopic ellipsometry is proposed which combines the global-search optimization algorithm with statistical model selection criteria. The hybrid genetic-gradient search algorithm (HGGA) is applied to find the optical parameters and thickness of a diamond-like carbon (DLC) coating deposited on SW7M stainless steel. Akaike and Bayesian information criteria are used to evaluate the different dielectric function models. The method is able to find optical model parameters even in case of a limited initial knowledge about the material optical constants. At the same time, the optimal dielectric function model for the description of the material optical properties can be selected unambiguously from the set of candidate models. [ABSTRACT FROM AUTHOR]

Gene transcription is largely regulated by cis-regulatory elements. Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) is an emerging technology that can accurately map cis-regulatory elements in animals and plants. However, the presence of cell walls and chloroplasts in plants hinders the extraction of high-quality nuclei, thereby affects the quality of ATAC-seq data. Meanwhile, it is tricky to perform ATAC-seq with different tissue types, especially for those with limited size and amount. Moreover, with rapid growth of ATAC-seq datasets from plants, powerful and easy-to-use data analysis pipelines for ATAC-seq, especially for wheat is lacking. Here, we provided an all-in-one solution for mapping open chromatin in wheat including both experimental and data analysis procedure. We efficiently obtained nuclei with less cell debris from various wheat tissues. High-quality ATAC-seq data from young spike and ovary, which are hard to harvest were generated. We determined that the saturation sequencing depth of wheat ATAC-seq is about 16 Gb. Particularly, we developed a powerful and easy-touse online pipeline to analyze the wheat ATAC-seq data and this pipeline can be easily extended to other plant species. The method developed here will facilitate plant regulatory genome study not only for wheat but also for other plant species. [ABSTRACT FROM AUTHOR]

Fengyun-3E, which is equipped with MicroWave Humidity Sounder 2 (MWHS-2) for detecting both temperature and humidity, is China's latest polar orbiting meteorological satellite and China's first satellite in early-morning orbit. The observation bias and observation error characteristics of MWHS-2 are evaluated by using ERA-5 as the background field. The results show that the biases range from −4 to 2 K, and the observation errors are within 1.5 K except for the window channels. A further analysis of the dependence on the scanning angles indicates that observation errors for the window channels and the 118-GHz channels increase with decreasing scanning angles. Observation errors of the window channels and water vapor channels are also latitudinally dependent, and the maximum errors in the high latitudes reach 2.0 K, while those in the tropical regions are approximately 0.8 K. In addition, the observed brightness temperature of FY-3E MWHS-2 is accompanied by striping noises along the track and high-frequency oscillation noises along the scanlines. The noise mitigation results show that both noises are approximately 0.2 K. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a novel distributed learning framework where multiple participants collaboratively train a global model without sharing any raw data to preserve privacy. However, data quality may vary among the participants, the most typical of which is label noise. The incorrect label would significantly damage the performance of the global model. In FL, the inaccessibility of raw data makes this issue more challenging. Previously published studies are limited to using a task-specific benchmark-trained model to evaluate the relevance between the benchmark dataset in the server and the local one on the participants' side. However, such approaches have failed to exploit the cooperative nature of FL itself and are not practical. This paper proposes a Consensus-based Label Correction approach (CLC) in FL, which tries to correct the noisy labels using the developed consensus method among the FL participants. The consensus-defined class-wise information is used to identify the noisy labels and correct them with pseudo-labels. Extensive experiments are conducted on several public datasets in various settings. The experimental results prove the advantage over the state-of-art methods. The link to the source code is https://github.com/bixiao-zeng/CLC.git. [ABSTRACT FROM AUTHOR]

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. The qualitative and quantitative reproducibility was also assessed based on 18 measurements of one sample across three batches. A total of 570 peptides were detected. While 89 peptides were identified in all measurements, the majority of peptides (161) were detected only once and mostly based on nonindicative spectra. The reproducibility of label-free quantification (LFQ) in six measurements of the same sample was similar after processing the data by either the PTM algorithm or the LFQ algorithm. In both cases, about one-third of the peptides showed a coefficient of variation of above 20%. However, the LFQ algorithm increased the number of quantified peptides from 75 to 179. Data are available at the PRIDE Archive with the data set identifier PXD050363.