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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]

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]

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]

Raman spectroscopy was compared with near infrared (NIR) hyperspectral imaging for determination of fat composition (%EPA + DHA) in salmon fillets at short exposure times. Fillets were measured in movement for both methods. Salmon were acquired from several different farming locations in Norway with different feeding regimes, representing a realistic variation of salmon in the market. For Raman, we investigated three manual scanning strategies; i) line scan of loin, ii) line scan of belly and iii) sinusoidal scan of belly at exposure times of 2s and 4s. NIR images were acquired while the fillets moved on a conveyor belt at 40 cm/s, which corresponds to an acquisition time of 1s for a 40 cm long fillet. For NIR images, three different regions of interest (ROI) were investigated including the i) whole fillet, ii) belly segment, and iii) loin segment. For both Raman and NIR measurements, we investigated an untrimmed and trimmed version of the fillets, both relevant for industrial in-line evaluation. For the trimmed fillets, a fat rich deposition layer in the belly was removed. The %EPA + DHA models were validated by cross validation (N = 51) and using an independent test set (N = 20) which was acquired in a different season. Both Raman and NIR showed promising results and high performances in the cross validation, with R2 CV = 0.96 for Raman at 2s exposure and R2 CV = 0.97 for NIR. High performances were obtained also for the test set, but while Raman had low and stable biases for the test set, the biases were high and varied for the NIR measurements. Analysis of variance on the squared test set residuals showed that performance for Raman measurements were significantly higher than NIR at 1% significance level (p = 0.000013) when slope-and-bias errors were not corrected, but not significant when residuals were slope-and-bias corrected (p = 0.28). This indicated that NIR was more sensitive to matrix effects. For Raman, signal-to-noise ratio was the main limitation and there were indications that Raman was close to a critical sample exposure time at the 2s signal accumulation. [Display omitted] • Feasibility of in-line estimation of fatty acid features (%EPA + DHA) in salmon fillets. • Raman spectroscopy was compared with near infrared hyperspectral imaging (NIR). • Promising results for both methods. • Indications that shorter exposure times than 2s might be challenging for Raman. • Raman gave low and stable biases (test set). NIR gave high and varied biases. [ABSTRACT FROM AUTHOR]

Sensors are an increasingly widespread tool for monitoring utility usage (e.g., electricity) and environmental data (e.g., temperature). In large-scale projects, it is often impractical and sometimes impossible to place sensors at all sites of interest, for example due to limited sensor numbers or access. We test whether cluster analysis can be used to address this problem. We create clusters of potential sensor sites using factors that may influence sensor measurements. The clusters provide groups of sites that are similar to each other, and that differ between groups. Sampling a few sites from each group provides a subset that captures the diversity of sites. We test the approach with two types of sensors: utility usage (gas and water) and outdoor environment. Using a separate analysis for each sensor type, we create clusters using characteristics from up to 298 potential sites. We sample across these clusters to provide representative coverage for sensor installations. We verify the approach using data from the sensors installed as a result of the sampling, as well as using other sensor measures from all available sites over one year. Results show that sensor data vary across clusters, and vary with the factors used to create the clusters, thereby providing evidence that this cluster-based approach captures differences across sensor sites. This novel methodology provides representative sampling across potential sensor sites. It is generalisable to other sensor types and to any situation in which influencing factors at potential sites are known. We also discuss recommendations for future sensor-based large-scale projects. • A novel generalisable methodology for deployment of a limited number of sensors. • Cluster analysis allows data-driven unbiased sampling across possible sensor sites. • Clusters of similar homes are based on factors that influence the sensor measures. • Chosen sensor sites are verified using the resulting sensor data. • The methodology is applied and tested using gas, water and environmental sensors. [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., Competing Interests: Author MF was employed by Neadvance Machine Vision SA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Moreira da Silva, Gonçalves, Franco-Gonçalo, Colaço, Alves-Pimenta, Ginja, Ferreira and Filipe.)

• The transport behaviors of airborne radioactive substances in the stack of HTR-PM have been investigated. • The well-mixed effluent location which can obtain a representative sample in the HTR-PM stack has been determined. • Methods for the improvement of flow mixing in the stack have been discussed and presented. Monitoring the airborne radioactive effluent from the stacks of nuclear power plants is very important. In order to obtain reliable and accurate measurements, the samples acquired from the stack must be representative of the whole. Thus, sampling should be performed at a location where the airflow is well mixed. We studied the transport behavior of airborne radioactive substances in the stack of a high-temperature gas-cooled reactor pebble-bed module (HTR-PM) demonstration power plant using a computational fluid dynamics method. The distributions of effluent flow velocity and concentration have been determined and compared to the mixing uniformity criteria of the ISO-2889 standard, then the source of uniformity is analyzed from vorticity distribution, particle trajectory, cyclone angle and coefficient of variation. We have determined the well-mixed location within the HTR-PM stack where a representative sample can be obtained. We discussed methods for the improvement of flow mixing and analyze the key factors involved. [ABSTRACT FROM AUTHOR]

• Logistic model is competitive in statistical effectiveness as allometric model. • Logistic model outperforms allometric model in ecological significances. • Model performances have been verified with the measured biomass and published data. • Biased sampling partly explains the repeated verifications of allometric model. 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. [ABSTRACT FROM AUTHOR]