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Introduction: Soil organic carbon (SOC) content can vary significantly across a given plot. Therefore, a representative sampling is a prerequisite to obtain meaningful results from analysis and of utter importance when SOC quantification is used to quantify (temporary) carbon dioxide removal (CDR). However, certain management practices aiming to increase SOC further increase the level of heterogeneity and may challenge representative sampling schemes. This includes concentrated root-zone application of biochar, which immediately increases SOC with the input of biochar-C and may promote the local enrichment of non-biochar SOC over time. Methods: Here, we used numerical modelling to quantify the number of single sampling points (soil cores) needed to achieve a representative sample of biochar-C and total SOC on a plot after application of biochar in rows, e.g., for growing vegetables, or in the circumference of trees in agroforestry systems. Results: After row application of 5 t ha-1 biochar in soil with rather low SOC content (26 t ha-1), 140 soil cores (per 0.25 ha plot) where necessary to achieve representative sampling of C-stocks (±5% error) in 90% of the repeated sampling simulation cases. Compared to realistic and cost-effective soil sampling scenarios in agronomic practice, we conclude that concentrated root zone application of biochar makes representative sampling for quantification of SOC in soils with low baseline C-stocks virtually impossible. Discussion: This finding calls into question the soil-sampling and SOC-analysisbased ("result-based") monitoring of SOC as a (temporary) CDR when biochar might have been applied. Considering the rapid scaling of biochar production and use in agriculture, this is a considerable challenge for SOC certification. Instead, action-based incentives, rewarding farmers for carrying out specific practices, could be applied to promote carbon farming practices. [ABSTRACT FROM AUTHOR]

A majority of the world's population resides in urban environments. Findings from prospect refuge theory and broken windows theory suggest that specific environmental aspects and greater urban environment disorder influence how safe the people feel in that environment. In the current investigation, I sought to reevaluate the association between the perceived quality of an urban environment and residents' feelings of safety by utilizing a cross-national, multilevel approach. I obtained nationally representative data from five different surveys conducted by the Eurobarometer (N = 167,212). I estimated multilevel models which suggest that greater urban environment quality relates to increased perceived safety, explaining between 45% and 66% of the variance in perceived safety attributable to cities. Consequently, I provide robust cross-national support for the claim that the environment we live in directly relates to how safe we feel. Ensuring the satisfactory, and high-quality condition of urban environments could be a means of increasing feelings of safety in day-to-day life. Public Significance Statement: Feeling safe in our day-to-day life is a fundamental aspect of our existence. Understanding how different aspects of our life, such as the quality of the urban environment we live in relates to this fundamental need, could shape our comprehension of what it means to feel safe and highlight avenues to increase perceived safety. The current results highlight that higher quality urban environments consistently and modestly relate to increase perceived safety. Ensuring the satisfactory and high-quality condition of urban environments, could potentially also ensure that individuals lead safe lives. [ABSTRACT FROM AUTHOR]

IntroductionSoil organic carbon (SOC) content can vary significantly across a given plot. Therefore, a representative sampling is a prerequisite to obtain meaningful results from analysis and of utter importance when SOC quantification is used to quantify (temporary) carbon dioxide removal (CDR). However, certain management practices aiming to increase SOC further increase the level of heterogeneity and may challenge representative sampling schemes. This includes concentrated root-zone application of biochar, which immediately increases SOC with the input of biochar-C and may promote the local enrichment of non-biochar SOC over time.MethodsHere, we used numerical modelling to quantify the number of single sampling points (soil cores) needed to achieve a representative sample of biochar-C and total SOC on a plot after application of biochar in rows, e.g., for growing vegetables, or in the circumference of trees in agroforestry systems.ResultsAfter row application of 5 t ha−1 biochar in soil with rather low SOC content (26 t ha−1), 140 soil cores (per 0.25 ha plot) where necessary to achieve representative sampling of C-stocks (±5% error) in 90% of the repeated sampling simulation cases. Compared to realistic and cost-effective soil sampling scenarios in agronomic practice, we conclude that concentrated root zone application of biochar makes representative sampling for quantification of SOC in soils with low baseline C-stocks virtually impossible.DiscussionThis finding calls into question the soil-sampling and SOC-analysis-based (“result-based”) monitoring of SOC as a (temporary) CDR when biochar might have been applied. Considering the rapid scaling of biochar production and use in agriculture, this is a considerable challenge for SOC certification. Instead, action-based incentives, rewarding farmers for carrying out specific practices, could be applied to promote carbon farming practices.

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas , an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to 1) quantify SARS-CoV-2 genetic diversity over time, 2) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and 3) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas. [ABSTRACT FROM AUTHOR]

Abstract Background Within the last decades, a substantial number of reports have established bullying behaviours as a severe risk to the health and safety of workers. However, in Hungary, the severity of this issue remains largely unknown. Therefore, the current study aimed to 1) determine the prevalence of offensive workplace behaviours in the Hungarian working population and 2) examine the relationship between exposure to these offensive behaviours and certain mental health indicators. Methods The cross-sectional analyses of the present study are based on a sample of 13,104 active workers being representative of the Hungarian working population according to gender, age, educational level, and 18 occupational sectors. The mid-length version of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) was used to measure workplace offensive behaviours (bullying, sexual harassment, threats of violence, and physical violence) in the 12 months preceding the survey. Examined mental health correlates included depressive symptomatology (Beck Depression Inventory), functional somatic symptoms (PHQ-15), perceived stress (Perceived Stress Scale), and general well-being (WHO Well-being Index). Results Almost half (48.7%) of the sample reported exposure to some form of offensive behaviour; 37.6% of participants reported occasional-, while 11.1% reported weekly or daily exposure. More women than men were exposed to offensive workplace behaviours, and those targeted the most were individuals aged 18–29 and in companies employing 20–49 employees. Top managers reported the lowest amount of bullying, while unskilled labourers reported the most frequent exposure. A moderately strong relationship was discovered between exposure to workplace offensive behaviours and all indicators of mental health. Conclusion Workplace bullying was revealed to be a significant public health concern according to this large, representative data set from Hungary. Strategies to reduce the occurrence and impact of these behaviours on employee health should be a priority for occupational health and safety interventions.

Raman spectroscopy has found its way into a wide range of applications and is successfully applied for qualitative and quantitative studies. Despite significant technical progress over the last few decades, there are still some challenges that limit its more widespread usage. This paper presents a holistic approach to addressing simultaneously the problems of fluorescence interference, sample heterogeneity, and laser-induced sample heating. Long wavelength shifted excitation Raman difference spectroscopy (SERDS) at 830 nm excitation combined with wide-area illumination and sample rotation is presented as a suitable approach for the investigation of selected wood species. Wood as a natural specimen represents a well-suited model system for our study as it is fluorescent, heterogeneous, and susceptible to laser-induced modifications. Two different subacquisition times (50 and 100 ms) and two sample rotation speeds (12 and 60 r/min) were exemplarily assessed. Results demonstrate that SERDS can effectively separate the Raman spectroscopic fingerprints of the wood species balsa, beech, birch, hickory, and pine from intense fluorescence interference. Sample rotation in conjunction with 1 mm-diameter wide-area illumination was suitable to obtain representative SERDS spectra of the wood species within 4.6 s. Using partial least squares discriminant analysis, a classification accuracy of 99.4% for the five investigated wood species was realized. This study highlights the large potential of SERDS combined with wide-area illumination and sample rotation for the effective analysis of fluorescent, heterogeneous, and thermally sensitive specimens in a wide range of application areas. Graphical Abstract This is a visual representation of the abstract. [ABSTRACT FROM AUTHOR]

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At the beginning of the 20th century, there was an active debate about random selection of units versus purposive selection of groups of units for survey samples. Neyman's (1934) paper tilted the balance strongly towards varieties of probability sampling combined with design-based inference, and most national statistical offices have adopted this method for their major surveys. However, nonprobability sampling has remained in widespread use in many areas of application, and over time there have been challenges to the Neyman paradigm. In recent years, the balance has tilted towards greater use of nonprobability sampling for several reasons, including: the growing imperfections and costs in applying probability sample designs; the emergence of the internet and other sources for obtaining survey data from very large samples at low cost and at high speed; and the current ability to apply advanced methods for calibrating nonprobability samples to conform to external population controls. This paper presents an overview of the history of the use of probability and nonprobability sampling from the birth of survey sampling at the time of A. N. Kiær (1895) to the present day. [ABSTRACT FROM AUTHOR]

Background: Within the last decades, a substantial number of reports have established bullying behaviours as a severe risk to the health and safety of workers. However, in Hungary, the severity of this issue remains largely unknown. Therefore, the current study aimed to 1) determine the prevalence of offensive workplace behaviours in the Hungarian working population and 2) examine the relationship between exposure to these offensive behaviours and certain mental health indicators. Methods: The cross-sectional analyses of the present study are based on a sample of 13,104 active workers being representative of the Hungarian working population according to gender, age, educational level, and 18 occupational sectors. The mid-length version of the Copenhagen Psychosocial Questionnaire II (COPSOQ II) was used to measure workplace offensive behaviours (bullying, sexual harassment, threats of violence, and physical violence) in the 12 months preceding the survey. Examined mental health correlates included depressive symptomatology (Beck Depression Inventory), functional somatic symptoms (PHQ-15), perceived stress (Perceived Stress Scale), and general well-being (WHO Well-being Index). Results: Almost half (48.7%) of the sample reported exposure to some form of offensive behaviour; 37.6% of participants reported occasional-, while 11.1% reported weekly or daily exposure. More women than men were exposed to offensive workplace behaviours, and those targeted the most were individuals aged 18–29 and in companies employing 20–49 employees. Top managers reported the lowest amount of bullying, while unskilled labourers reported the most frequent exposure. A moderately strong relationship was discovered between exposure to workplace offensive behaviours and all indicators of mental health. Conclusion: Workplace bullying was revealed to be a significant public health concern according to this large, representative data set from Hungary. Strategies to reduce the occurrence and impact of these behaviours on employee health should be a priority for occupational health and safety interventions. [ABSTRACT FROM AUTHOR]

Finding representative samples is important for predicting cancer risk. In particular, it is crucial to identify each representative sample as responsible for the prediction performance. In this article, we present a general framework for finding representative samples by explicitly estimating their inherit contribution levels (or scores). By leveraging explainable models as our score functions such as Shapley value, LIME and influence function, our framework can quantitatively identify the representative level of each sample in cancer risk prediction. Furthermore, a score ensembler is introduced to integrate these scores obtained from various score functions with an additional vector of weight variables optimized by the Fast Iterative Shrinkage-Thresholding Algorithm. Empirical evaluations on four cancer risk datasets with different challenges by using five classifiers suggest that our approach significantly outperforms the state-of-the-art methods. • A general score-based framework is presented to explicitly perform representative sampling tasks. • The framework ensembles score functions to assess representative levels for each sample. • Advanced explainable models are leveraged to find representative samples. • Performances can be improved by weight optimization using FISTA Algorithm on cancer risk prediction. • Our approach outperforms the state-of-the-arts on four cancer risk datasets with different challenges. [ABSTRACT FROM AUTHOR]

X-ray bone semantic segmentation is one crucial task in medical imaging. Due to deep learning's emergence, it was possible to build high-precision models. However, these models require a large quantity of annotated data. Furthermore, semantic segmentation requires pixel-wise labeling, thus being a highly time-consuming task. In the case of hip joints, there is still a need for increased anatomic knowledge due to the intrinsic nature of the femur and acetabulum. Active learning aims to maximize the model's performance with the least possible amount of data. In this work, we propose and compare the use of different queries, including uncertainty and diversity-based queries. Our results show that the proposed methods permit state-of-the-art performance using only 81.02% of the data, with O(1) time complexity.

Background and Aims: Previous research has identified numerous risk and protective factors of adolescent problematic gaming (PG) at the individual and social levels; however, the influence of socio‐economic indicators on PG is less known. This study aimed to measure the contribution of individual and socio‐economic factors involved in PG risk among adolescents from 30 European countries. Design Multi‐level logistic regression analysis of survey data from the 2019 European School Survey Project on Alcohol and Other Drugs (ESPAD) cross‐sectional study using self‐administered anonymous questionnaires. Setting: Thirty European countries. Participants: A representative cohort of 15–16‐year‐old students (n = 88 998 students; males = 49.2%). Measurements The primary outcome measure was adolescents' (low and high) risk of PG. Individual key predictors included self‐report assessments of socio‐demographic characteristics, time spent gaming and family variables (parental regulation and monitoring, family support). Main country‐level predictors comprised Gini coefficient for economic inequalities and benefits for families and children (% gross domestic product), retrieved from international public data sets and national thematic reports. The data analysis plan involved multi‐level logistic regression. Findings Participants who reported stronger parental regulation [odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.79–0.83] and higher family support (OR = 0.93, 95% CI = 0.91–0.95) reported lower risk of PG. At the country‐level, economic inequalities (OR = 1.05, 95% CI = 1.03–1.07) were positively associated with the risk of PG, while benefits for families and children (OR = 0.78, 95% CI = 0.70–0.89) were negatively correlated with the risk of PG. Conclusions: Supportive family environments, lower country‐level economic inequalities and higher government expenditures on benefits for families and children appear to be associated with a lower risk of problematic gaming among European adolescents. [ABSTRACT FROM AUTHOR]

The problem of representative selection amounts to sampling few informative exemplars from large datasets. Existing approaches to data selection often fall short of simultaneously handling non-linear data structures, sampling concise and non-redundant subsets, rejecting outliers, and yielding interpretable outcomes. This paper presents a novel representative selection approach, dubbed MOSAIC, for drawing descriptive sketches of arbitrary manifold structures. Resting upon a novel quadratic formulation, MOSAIC advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures novelty of the samples by minimizing redundancy, and rejects disruptive information by effectively detecting outliers. Theoretical analyses shed light on geometrical characterization of the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC’s superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Raman spectroscopy is a viable tool within process analytical technologies due to recent technological advances. In this article, we evaluate the feasibility of Raman spectroscopy for in-line applications in the food industry by estimating the concentration of the fatty acids EPA + DHA in ground salmon samples (n = 63) and residual bone concentration in samples of mechanically recovered ground chicken (n = 66). The samples were measured under industry like conditions: They moved on a conveyor belt through a dark cabinet where they were scanned with a wide area illumination standoff Raman probe. Such a setup should be able to handle relevant industrial conveyor belt speeds, and it was studied how different speeds (i.e., exposure times) influenced the signal-to-noise ratio (SNR) of the Raman spectra as well as the corresponding model performance. For all samples we applied speeds that resulted in 1 s, 2 s, 4 s, and 10 s exposure times. Samples were scanned in both heterogenous and homogenous state. The slowest speed (10 s exposure) yielded prediction errors (RMSECV) of 0.41%EPA + DHA and 0.59% ash for the salmon and chicken data sets, respectively. The more in-line relevant exposure time of 1 s resulted in increased RMSECV values, 0.84% EPA + DHA and 0.84% ash, respectively. The increase in prediction error correlated closely with the decrease in SNR. Further improvements of model performance were possible through different noise reduction strategies. Model performance for homogenous and heterogenous samples was similar, suggesting that the presented Raman scanning approach has the potential to work well also on intact heterogenous foods. The estimation errors obtained at these high speeds are likely acceptable for industrial use, but successful strategies to increase SNR will be key for widespread in-line use in the food industry. [ABSTRACT FROM AUTHOR]

Stigmatization of water and food products can constrain markets and prevent the implementation of scientifically safe solutions to environmental problems, such as water scarcity. Recycled water can be a cost‐effective, dependable, and safe solution to water shortages. However, consumers generally either require a large reduction in price to purchase products made with recycled water or reject such products outright. If emerging sustainable agricultural technologies, such as recycled water, are to be used to address growing water shortages worldwide, policymakers, water managers, and industry stakeholders must identify effective strategies for mitigating the stigma associated with recycled water. Using field experiments involving 1420 adult participants, we test the effectiveness of two stigma‐mitigating techniques. We also demonstrate a novel twist to the collection of representative samples in non‐hypothetical field experimental settings and then compare the results to a more traditional field experiment that recruited participants at large public gatherings. The analysis of these two different samples suggests a common finding: passing recycled water through a natural barrier, such as an aquifer, removes the stigma consumers would otherwise attach to it. We also find that the trophic level an organism occupies in the food chain influences stigmatizing behavior. The greater the steps in the food chain between an organism and the use of recycled water, the less it is stigmatized by consumers. These results have important implications for efforts to promote large‐scale potable and non‐potable water recycling projects and the use of recycled water in the agricultural industry. [ABSTRACT FROM AUTHOR]

After an oil spill, collecting reliable and legally defensible oil samples is critical for determining oil sources and assessing environmental impacts. This review of guidance documents and the scientific literature identifies accepted practices for collecting oil samples in the marine environment for chemical analysis. Recommended sampling techniques differ depending on the form of the oil being sampled (e.g., oil sheens/slicks versus tar balls). Preventing sample contamination and collecting representative oil samples are crucial. Collecting suspected source and background samples is recommended for source identification. Guidance on quality control samples varies, but at minimum, blanks are required to meet study data quality objectives. [ABSTRACT FROM AUTHOR]

There are historically established approaches for solving various issues, in particular, to determining the change of guard on duty time in the context of operative activities of fire service divisions. The use of new mathematical methods and information technologies allows considering in detail the advisability of choosing the time of changing of guard, as well as its impact on other aspects of activities of fire service divisions. The article describes the main methodological approaches for solving the problem, in particular, using the tools of the R software environment.

Online social networks (OSNs) have become important platforms for efficiently connecting people and promoting information dissemination, which is of great importance to our social life and society. However, due to privacy concerns, and access limitations, it is difficult to obtain the whole network of OSNs for analysis, so it is critical to have a representative subgraph. Yet due to the same reasons, we are in lack of the original network as the ground truth which poses great challenges on evaluating sampling methods on the performance on unbiasedness, let alone representativeness. Thus uniform sampling (UNI) [Gjoka et al. 2010] was proposed to obtain an unbiased nodal property distribution as of the original network to evaluate the degree of bias of other methods. Yet UNI sampling suffers from its low efficiency, and the representativeness and connectivity of the obtained subgraph, which is formed by the sampled nodes and connections between them, are rarely studied. We propose an adaptive UNI sampling (adpUNI) method to overcome previously mentioned disadvantages of UNI by dividing the userID space into several intervals, whose sampling probability adaptively changes based on its target rate. By adding its neighbors of the targeted node into the sample set (adpUNI+N), we can further improve the performance on sampling efficiency and obtain a more connective and representative subgraph. When applied to Sina Weibo and Twitter, our methods over-perform other classical methods on sampling efficiency, and always have a better performance on connectivity and representativeness than UNI sampling. And we also find that an unbiased sample doesn't guarantee a more representative subgraph.

Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R2CV) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R2CV from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses. Graphical Abstract [ABSTRACT FROM AUTHOR]

Allometric model has been applied worldwide to estimate vegetation biomass for decades. However, this model fails to restrict the accelerating increase of biomass as body size grows. That contradicts to the size-related resource limits and intra-specific competitions. Thus, we tested logistic model with limiting factor of the carrying capacity at the 30-year dominant shrub species in Loess Plateau of China, including Caragana korshinskii (51 branches), Salix psammophila (44 branches) and Vitex negundo (28 branches). Our results indicated that logistic model was statistically effective as allometric model indicated by the adjusted code of determination, p-value, Akaike’s Information Criterion and Root Mean Square Error. It was also of more ecological significances by providing the equilibrium growth rate, equilibrium biomass (the asymptotic biomass as branch grew), and point-of-inflections (thresholds for different trends of biomass increase). That had been double-checked with our measured biomass and the published data in previous studies. The unrepresentative samples with the tendency favoring the small- and middle-sized branches, and the consequently biased tendency and overfitting in models from random quirks of samples, might partly explain the repeated validation of allometric model for estimating biomass in previous studies. In general, logistic model outperformed allometric model to estimate shrub biomass of allometric scaling with body size. An appropriate model for biomass estimation benefited to precisely compute carbon sequencing and to assess climate change impact on ecosystems functioning.

In the field of multi-style textile defect detection, a common challenge is the difficulty of adapting the inherent detection model to different styles of textile defects. Changes in the color or style of the textile often result in a decrease in the accuracy of defect detection. Relying solely on the model for fine-tuning inspections can lead to catastrophic forgetting, which significantly impacts the performance of the textile defect detector. To address these challenges, a multi-task correlation distillation (MTCD) anomaly detection method based on knowledge distillation and representative sampling is proposed to detect multi-style textile defects. To enable MTCD to detect defects of new-style textiles while maintaining the detection of old-style textiles, two main modules are introduced. The distillation adaptation module (DAM) explores the intra-feature correlation in the feature space of the target detector, allowing the student model to acquire knowledge of new-style textile defect detection while inheriting the teacher model's detection ability for old-style textile defects. The representative sampling module (RSM) stores representative knowledge of textile defect detection for old-style textiles, facilitating the transfer of knowledge learned from detecting new-style textile defect styles and maintaining the ability to detect defects in old-style textiles. This increases the detection accuracy of the student model for new-style textile defects. The results show that the proposed MTCD method can adapt to the new textile defect detection while maintaining the accuracy of the old textile defect detection and avoiding the problem of catastrophic forgetting. Furthermore, it offers a better balance between stability and plasticity, making it a promising solution for defect detection of multi-style textiles in industrial production environments. [ABSTRACT FROM AUTHOR]

Active learning is an important technique to alleviate the problem when there is abundant unlabeled data but scarce labeled data. It aims to choose the most valuable samples to label in order to build powerful predictive models with minimal supervision. However, under the setup of active learning, when the data are characterized by high-dimensional features, it will be difficult to get a reliable estimate on the model parameters, as the labeled data are limited. Most existing works tackle this problem by learning a low-dimensional representation of data before active learning, but it cannot guarantee to obtain promising results, as traditional feature extraction techniques and active learning algorithms are designed independently. In this paper, we propose an efficient hybrid active learning algorithm, called Recursive Maximum Margin Active Learning. We optimize the active learning and semi-supervised feature extraction under a unified framework to tackle the high-dimensional features' problems and select the most representative samples in the low-dimensional space. By introducing the semi-supervised maximum margin criterion into active learning, we can conduct sample selection and feature extraction recursively at each iteration of active learning to learn more accurate models. The extensive experimental results show that the proposed method outperforms several state-of-the-art active learning methods on publicly available datasets.

Swedish legislation stipulates the precision required for estimates of parameters used to determine the value of various forest fuels. The net energy value of fuel, as it is received, is often used to set the trade price. The estimate of energy content is based on the moisture content of samples taken from each truckload and the weight of the biomass; the ash content and net calorific value are measured a few times each year. Hence, it is necessary to know the variation in moisture content to ensure that a sufficient number of samples are taken, a number based on the allowed variation and precision of estimates, as defined in the legalization. In this study, the variation in moisture content was measured by taking samples from 18 truckloads of comminuted forest fuels during the winter. The results showed that the current sampling regime, i.e., manually taking four samples from each truckload, is sufficient for deliveries with 10 truckloads for logging residue chips and 4 for stem wood chips. The number of samples should be increased to 12–43, 8–21, and, 17–82 depending on assortment for what the measuring act defines as large deliveries (>=50 tonnes; >=3 truckloads), medium-sized deliveries (<50 – >25 tonnes; 2 truckloads) and single truck deliveries (<= 25 tonnes; 1 truckload), respectively. Current research into fast online sampling and analysis methods could resolve this issue for small deliveries. [ABSTRACT FROM AUTHOR]

In the recent decade, numerous scientific studies investigated the utility of GPS data to bridge the existing data gap in cycling. Almost all studies faced the lack of representativeness of data because the participation in data collection was influenced by self-selection of participants. Thus, the GPS data were biased by dominant user groups. However, the difference between biased samples and representative data has not been quantified, yet, as there was no data from representative selected samples that could be used for comparison. The present work investigates whether and how cycling behaviour of different groups differs. It furthermore examines the parameters cycling behaviour depends on. For this purpose, nearly 200 cyclists were selected according to representativeness regarding age, gender and type of cyclist. The study participants recorded their ways using a GPS smartphone app during a two-week field study. Data analysis revealed little influence of the user group on cycling behaviour, only. In contrast, the variables age, gender and trip purpose show a strong influence on cycling speed, distance, acceleration and frequency. The study results point out the need for representative data collection in GPS-based studies on cycling.

Summary: Although thousands of solid tumors have been sequenced to date, a fundamental under-sampling bias is inherent in current methodologies. This is caused by a tissue sample input of fixed dimensions (e.g., 6 mm biopsy), which becomes grossly under-powered as tumor volume scales. Here, we demonstrate representative sequencing (Rep-Seq) as a new method to achieve unbiased tumor tissue sampling. Rep-Seq uses fixed residual tumor material, which is homogenized and subjected to next-generation sequencing. Analysis of intratumor tumor mutation burden (TMB) variability shows a high level of misclassification using current single-biopsy methods, with 20% of lung and 52% of bladder tumors having at least one biopsy with high TMB but low clonal TMB overall. Misclassification rates by contrast are reduced to 2% (lung) and 4% (bladder) when a more representative sampling methodology is used. Rep-Seq offers an improved sampling protocol for tumor profiling, with significant potential for improved clinical utility and more accurate deconvolution of clonal structure.

The chemical analysis of an urolith is often interpreted as "stone's composition". However, it must be taken into consideration, that in most cases, only a fragment of the stone has been sent to the laboratory. In some recurrent patients, stone compositions either vary considerably between episodes or the analytical result obtained from the stone fragment does not fit with the data of e.g. current 24 h-urinalysis or urinary pH-records. The question arises, whether this outcome may be the result of an improper stone sampling scheme. On a simple layered 2D-stone model composed of two mineral phases it is shown, how the choice of a stone fragment process may influence the result of "stone composition". Depending on the initial position of fragment within the whole stone, the respective calculated analyses can relevantly differ from the whole stone composition as well as strongly between two fragments. Even under the simplified conditions of a 2D-2-component-model "grown" under defined conditions, the differences between the analyses of the different specimens taken from a stone are in part remarkable. The more it can be argued that these differences increase if a real 3D-urolith is investigated. Further sampling biases may evolve and increase the problem of proper sampling:, e.g., if an urolith's more resistant parts remain intact while ESWL or laser-based stone fragmentation ("dusting"), the weak parts became fully disintegrated and removed from the body as fine-grained sludge—the stone's fine fraction is lost although its composition may carry important information on the stone's pathogenesis. Consequently, a "stone analysis" only obtained from the harder remains reveals an incomplete result, a fact that in principle limits its clinical interpretation. Choice of stone fragment is crucial. The extent of the uncertainty of an analysis resulting from potential selection biases should not be underestimated. Thus, sampling should be considered as an important part of the processes of quality assurance and management. Errors made at this early stage of diagnosis finding will affect the analytical result and thus influence the clarification of the underlying pathomechanism. This can lead to an improper metaphylactic strategy potentially causing recurrent stone formation which otherwise would have been prevented. A decision scheme for analysis of urinary stones removed using endoscopic methods is suggested. [ABSTRACT FROM AUTHOR]

Characterizing and studying oil spills in marine environments for forensic purposes and Natural Resource Damage Assessment often requires timely sampling of multiple, diverse matrices in a range of field conditions. The diversity of such investigations has heretofore precluded using a focused and well-defined set of sampling practices. Defining appropriate data quality objectives and utilizing quality assurance/quality control practices throughout the sampling and analysis process can help ensure sample representativeness and study data's accuracy, reliability, and usability. These are particularly important considerations when using the non-standard oil spill sampling practices sometimes required to achieve Natural Resource Damage Assessment objectives. [ABSTRACT FROM AUTHOR]

This review summarizes sampling and quality control practices recommended by regulatory agencies and scientific organizations for acquiring reliable, defensible sediment samples in marine environments. Selecting an appropriate sediment sampling method can depend on water depth, sediment type, depth of sediment penetration required, and the study's data quality objectives, among other factors. The primary concern of most sediment sampling methods is preserving sediment stratigraphy and chemical gradients, particularly at the sediment-water interface, while avoiding contamination. Quality control samples and ancillary observations and measurements are also critical to sediment sample interpretation and reliability as part of Natural Resource Damage Assessment. [ABSTRACT FROM AUTHOR]

This review summarizes government agency and scientific organization guidance for collecting representative, reliable, and defensible water samples for trace metal analysis, and provides a framework for choosing appropriate sampling techniques to meet a study's data quality objectives, with respect to Natural Resource Damage Assessment in the marine environment. Preventing or limiting contamination is a primary concern during the sampling process, with the goal of constraining sampling-related contamination to below the targeted screening levels. Best practices should include clean-hands/dirty-hands sampling techniques and acid-washed plastic sample bottles. Quality control samples should be collected to detect and quantify contamination and to monitor the precision of the measurement process. [ABSTRACT FROM AUTHOR]

Does any existing single actor have, or could any existing single actor come to have, the authority to geoengineer? In this paper, I will focus on Solar Radiation Management strategies (leaving at least some Carbon Dioxide Removal strategies on the table). I'll argue that global democratic authorization is possible in principle, and could be obtained on the basis of large-scale representative sampling. I present experimental findings from the Australian context showing that democratic authorization would not be granted, and conclude that if we can expect this result to generalize, then the deployment of SRM by a single actor is impermissible. [ABSTRACT FROM AUTHOR]

With the International Maritime Organization's (IMO) International Convention for the Control and Management of Ships' Ballast Water and Sediments now in force, determining abundance and distribution of phytoplankton inside ballast tanks is critical for successful ballast water management, particularly when assessing compliance. The relationship between the abundance and distribution of cells was examined to obtain the best representative sample of the entire phytoplankton community in ballast tanks, comparing three ballast water sampling techniques including in-line, in-tank, and Van Dorn bottle methods. Lloyd's index, Dy, and Gini index were applied to compare methods of sample collection and determine representativeness of samples and performance of sampling methods. Phytoplankton abundance trends from live microscopy counts using fluorescein diacetate (FDA) were also compared to those using a FlowCAM on preserved samples. The phytoplankton community showed a patchy distribution inside the ballast tank and this trend was observed across all voyages. The estimated marginal mean analysis showed that in hypothetical conditions (e.g., 702 m3 of water in ballast tank and phytoplankton whole-tank abundance of 19,522 cells), the difference among the three methods was small. Conversely, statistical analysis performed on empiric abundances using a negative binomial regression model determined that the volume discharged during sampling of ballast water has an effect on the number of cells collected on a given voyage. Results of this study also confirmed that the in-line method may be a better method at collecting phytoplankton samples from ballast tanks than the in-tank or Van Dorn method, regardless of the time at which samples are collected. Finally, the number of living cells and the number of preserved cells showed similar trends for most of the voyages, despite fewer samples analyzed using FDA. [ABSTRACT FROM AUTHOR]

For decades, social psychologists have collected data primarily from college undergraduates and, recently, from haphazard samples of adults. Yet researchers have routinely presumed that thus observed treatment effects characterize "people" in general. Tests of seven highly cited social psychological phenomena (two involving opinion change resulting from social influence and five involving the use of heuristics in social judgments) using data collected from randomly sampled, representative groups of American adults documented generalizability of the six phenomena that have been replicated previously with undergraduate samples. The 1 phenomenon (a cross-over interaction revealing an ease of retrieval effect) that has not been replicated successfully previously in undergraduate samples was also not observed here. However, the observed effect sizes for the replicated phenomena were notably smaller on average than the meta-analytic effect sizes documented by past studies of college students. Furthermore, the phenomena were strongest among participants with the demographic characteristics of the college students who typically provided data for past published studies, even after correcting for publication bias in past studies using a new method, called the behaviorally-informed file-drawer adjustment. The six successful replications suggest that phenomena identified in traditional laboratory research also appear as expected in representative samples but more weakly, so observed effect sizes should be generalized with caution. The evidence of demographic moderators suggests interesting opportunities for future research to better understand the mechanisms of the effects and their limiting conditions. [ABSTRACT FROM AUTHOR]

When developing a process flowsheet, the risks in achieving positive financial outcomes are minimised by ensuring representative metallurgical samples and quality testwork. A case study is presented based on an underground gold operation, where poor metallurgical sampling led to grade and recovery underperformance. Sampling-related issues included: poor liaison between geologists and metallurgists; poor domaining; too few metallurgical samples collected and tested; unrepresentative sample composites and sub-samples; poor laboratory practice; and a lack of documentation and QAQC. These issues led to disruption over four years and are estimated to have cost around US$115M in lost revenue and US$7.5M in corrective expenditure. After an initial characterisation programme, a variability mini-bulk sampling and testwork phase was undertaken. This was followed by a pilot programme, progressing to trial mining and production. The paper emphasises the need for fit for purpose metallurgical sampling and testwork, and the early application of variability sampling. [ABSTRACT FROM AUTHOR]

Abstract Representative groundwater sampling is critical for establishing contaminant distributions, evaluating the effectiveness of remediation operations, monitoring the protection of human health and the environment, and sustainably managing groundwater resources. However, traditional low flow (low stress) or three-well-volume purge well sampling techniques can render high labor costs or high wastewater volumes. Newly developed passive samplers can only deal with limited analytical needs. A new High Stress Low Flow (HSLF) method is proposed involving dual pumping rates, which may significantly reduce purge time and wastewater production, while maintaining analytical needs, thus offering a new tool to promote green and sustainable remediation. A three-dimension numerical model was used to evaluate the potential benefits of the proposed HSLF approach. Compared to low flow sampling and three-well-volume purge methods, it was calculated that HSLF can reduce sampling time by up to 81.0% and 81.3%, respectively, and reduce wastewater production by up to 12.5% and 91.2%, respectively. The improvement achieved was affected by formation characteristics (e.g. hydraulic conductivity) and operation parameters (e.g. pumping rates and drawdown control). Further optimization and field testing is required to recognize the full potential of this newly proposed method. Graphical abstract Unlabelled Image Highlights • Proposed a new method for representative groundwater sampling • Dramatically reduce sampling time thus saving labor cost • Significantly reduce wastewater production and lower environmental footprint • Aligns well with green and sustainable remediation [ABSTRACT FROM AUTHOR]

Abstract Random samples of individual fish for the cause of estimating body size average and variation in fish groups is a widespread practice in scientific experiment as well as in fish production. Sample accuracy and precision naturally increases with sample size, but may very well depend on other factors such as fish size dependent success rate of capture method and spatial-behavioural size segregation. In 4 case studies with Atlantic salmon (Salmo salar) where the entire population was crowded and measured for length and weight, we show how the estimated mean weight and condition factor, and their deviation from the true population mean, evolve with number of sampled fish: 1) sampling of 3400 pre-smolts (77 g) netted by hand from a 5 m tank, 2) 466 post-smolts (711 g) netted by hand from a 5 m sea cage, 3) 5569 post-smolts (394 g) netted by hand from a 12 m sea cage, 4) the same fish as in 3, but 9 months later (3367 g, n = 5288) and netted out by using a crane mounted tarpaulin container. Sampling of cases 1, 3 and 4 took place over consecutive days. Cumulative mean, i.e. the mean for each new fish sampled, of weight and condition factor were calculated for each sampling day. To simulate a perfectly random sampling to compare with that observed, the order in which the individuals in the entire population were sampled were randomized, and cumulative means calculated in the same manner. With the randomized sampling order the cumulative mean values rapidly approached true population mean and remained at that level for both weight and condition factor in all days on all sampling events. In contrast, the cumulative mean weight and/or condition factor of the observed sampling order generally deviated from the true mean even after a large number of fish had been sampled, and the patterns in how cumulative mean values developed was inconsistent between days. Highlights • Accuracy of estimation of the population mean size in a rearing unit is dependent of sampling method and sample size. • In 4 case studies, we calculated how mean weight and condition factor evolved with number of fish sampled. • Mean weight and/or condition factor deviated from the true mean even after hundreds of fish had been sampled. • The study shows that estimation of the population mean is easily biased even when a large number of fish is sampled. [ABSTRACT FROM AUTHOR]

Abstract A harmonization of sampling, sample preparation and detection is pivotal in order to obtain comparable data on microplastics (MP) in the environment. This paper develops and proposes a suitable sampling concept for waterbodies that considers different plastic specific properties and influencing factors in the environment. Both artificial water including defined MP fractions and the discharge of a wastewater treatment plant were used to verify the derived sampling procedure, sample preparation and the subsequent analysis of MP using thermal extraction-desorption gas chromatography - mass spectrometry (TED-GC-MS). A major finding of this paper is that an application of various particle size classes greatly improves the practical handling of the sampling equipment. Size classes also enable the TED-GC-MS to provide any data on the MP size distribution, a substantial sampling property affecting both the necessary sampling volume and the optimal sampling depth. In the artificial water with defined MP fractions, the recovery rates ranged from 80 to 110%, depending on the different MP types and MP size classes. In the treated wastewater, we found both polyethylene and polystyrene in different size classes and quantities. Graphical abstract Image 1 Highlights • General physical and practical aspects for sampling water to detect microplastics. • Presentation of a filtration cascade procedure with defined size classes. • Validation experiments for the method's results in recovery rates of 80–110%. • Exemplary results of sampling from wastewater treatment plant. [ABSTRACT FROM AUTHOR]

Aflatoxin B1 (AFB1), a toxic fungal metabolite associated with human and animal diseases, is a natural contaminant encountered in agricultural commodities, food and feed. Heterogeneity of AFB1 makes risk estimation a challenge. To overcome this, novel sample selection, preparation and extraction steps were designed for representative sampling of chicken feed. Accuracy, precision, limits of detection and quantification, linearity, robustness and ruggedness were used as performance criteria to validate this modification and Horwitz function for evaluating precision. A modified sampling protocol that ensured representativeness is documented, including sample selection, sampling tools, random procedures, minimum size of field-collected aggregate samples (primary sampling), procedures for mass reduction to 2 kg laboratory (secondary sampling), 25 g test portion (tertiary sampling) and 1.3 g analytical samples (quaternary sampling). The improved coning and quartering procedure described herein (for secondary and tertiary sampling) has acceptable precision, with a Horwitz ratio (HorRat = 0.3) suitable for splitting of 25 g feed aliquots from laboratory samples (tertiary sampling). The water slurring innovation (quaternary sampling) increased aflatoxin extraction efficiency to 95.1% through reduction of both bias (−4.95) and variability of recovery (1.2–1.4) and improved both intra-laboratory precision (HorRat = 1.2–1.5) and within-laboratory reproducibility (HorRat = 0.9–1.3). Optimal extraction conditions are documented. The improved procedure showed satisfactory performance, good field applicability and reduced sample analysis turnaround time.

Accurate assessment of ballast water's community composition and organism concentrations is crucial for effective management. Yet, the lack of standardized global sampling methods presents challenges to achieving this objective. Inconsistencies hinder data comparison across regions and vessels, impeding efficient ballast water treatment and discharge regulation. This study conducted land-based tests to establish suitable methodologies. For organisms ≥50 μm and ≥10 μm to <50 μm, the recommended max flow rate is 50 L/min; for <10 μm organisms, 25 L/min is advised to prevent cell damage. Sampling should cover substantial discharge durations. To ensure representation, ≥50 μm organisms require ≥1m3, while ≥10 μm to <50 μm and <10 μm organisms need 20 L. The ultimate aim is standardized methods for assessing ballast water across regions and vessel types, facilitating effective management to curb invasive species and protect aquatic ecosystems. • Standardized sampling methods are vital for evaluating ballast water management effectiveness. • This study proposes specific parameters for sampling flow rates, timing, and volumes. • The sampling flow rate significantly affects the community composition and concentrations of organisms ≥50 μm. • The sampling process insignificantly influences community composition of organisms ≥10 μm to <50 μm. [ABSTRACT FROM AUTHOR]

Suspended particulate matter (SPM) plays a major role in nutrient cycles and for the transport of pollutants within local and transboundary water catchments. Obtaining representative SPM samples from rivers, lakes, inland and coastal waters is crucial for quantitative and qualitative chemical analyses to correctly describe the chemical status of a water body. However, a representative sampling of SPM over time is challenging due to the heterogeneity of SPM particles sizes, their non-uniform distribution in rivers, and a variety of sampling devices being in use. Therefore, we investigated the efficiencies of five different sampling devices commonly used in national and international monitoring programs to collect representative SPM samples. We tested three passive sedimentation-based samplers (SBSs: sedimentation box, SB; sedimentation tank, ST; Raetz Sampler, RS), and two active separation techniques (continuous flow centrifuge, CFC; vacuum filtration, VF) in an experimental laboratory setup using in-house SPM standard suspensions (mineral, organic, and microplastic particles) with defined particle sizes. The mass-based efficiencies of the three examined SBSs were 0–66% for the mineral and organic particles <75 μm, where the mean particle sizes of collected samples were always shifted to bigger sizes compared to the initial suspensions. The efficiencies of the three SBSs to collect microplastic particles <80 μm were <20% due to the lower densities of microplastic compared to organic and mineral particles. In contrast to the SBSs, VF and CFC units showed excellent efficiencies >86% for all tested materials, with similar particle size distributions of the sampled material compared to those of the inlet suspensions. In conclusion, SPM sampling efficiencies of sampling units have to be carefully considered and compared to the respective aims of the monitoring approaches, especially when statements are derived from quantitative results on SPM. [Display omitted] • Laboratory experiments reveal biases in suspended particulate matter sampling. • Passive, sedimentation-based samplers underestimate small particles. • Centrifuge and filtration units sample quali- and quantitatively representative. • Continuous-flow centrifuges remain best practice for quantitatively correct samples. • Low-tech sedimentation-based samplers serve for qualitative sampling. [ABSTRACT FROM AUTHOR]

Active learning aims at obtaining high-accuracy models with as a few labeled data as possible, by iteratively and elaborately selecting most valuable data to query labels during the learning process, thereby the cost of labeling data can be reduced. Most previous active learning approaches consider the situation of centralized processing, where all the unlabeled data are supposed to be gathered together in one place. Due to the development of distributed applications, distributed processing has attracted a lot of interests given the situation that data are distributed at different nodes over network. In this paper, we focus on the issue of distributed active learning (DAL) for the classification problem. We propose a fully decentralized active learning approach, which consists of two parts, namely, a distributed sample selection strategy and a distributed classification algorithm. The former helps nodes to cooperatively select data based on uncertainty, diversity, and representativeness of data. Due to the introducing of a randomized preselection method in the strategy, we can achieve diversity of the selected data without any information exchange among nodes. The latter helps each node to train its local multi-class classification model in a global sense without transmitting original data among nodes. We demonstrate the effectiveness of the proposed DAL approach on several real data sets. Simulation results show that the proposed approach can significantly reduce the number of labeled data needed for obtaining a high-accuracy classifier in distributed case.