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This article outlines a research design for a qualitative comparative study of communication across platforms, media and contexts – in China, the US and Denmark. After addressing the limitations in previous research on digital media in everyday life, we argue in favour of a comparative ethnography of communication that emphasizes the study of intermediality by taking a people-centred approach. The methodological design combines network sampling and maximum variation sampling with communication diaries and elicitation interviews. This design makes it possible to collect small and deep communicative trace data, to cap- ture individuals’ unique linking of all the communication tools and channels available to them and, in turn, to identify the role of the internet as it interacts and intersects with other forms of communication. This article outlines a research design for a qualitative comparative study of communication across platforms, media and contexts – in China, the US and Denmark. After addressing the limitations in previous research on digital media in everyday life, we argue in favour of a comparative ethnography of communication that emphasizes the study of intermediality by taking a people-centred approach. The methodological design combines network sampling and maximum variation sampling with communication diaries and elicitation interviews. This design makes it possible to collect small and deep communicative trace data, to capture individuals’ unique linking of all the communication tools and channels available to them and, in turn, to identify the role of the internet as it interacts and intersects with other forms of communication.

Modern ultrasonic inspections utilize ever-richer data-sets made possible by phased array equipment. A typical inspection may include tens of channels with different refraction angle, that are acquired at high speed. These rich data sets allow highly reliable and efficient inspection in complex cases, such as dissimilar metal or austenitic stainless steel welds. The rich data sets allow human inspectors to detect cracks with low signal-to-noise ratio from the wider signal patterns. There’s a clear trend in the industry to even richer data sets with full matrix capture (FMC) and related techniques. Convolutional neural networks have recently shown capability to detect flaws with human level accuracy in ultrasonic signals at the B-scan level. To enable automated flaw detection at human-level accuracy for critical applications, these neural networks need be developed to take advantage of today’s rich phased array data-sets. In the present paper, we extend previous work and develop convolutional neural networks that perform highly reliable flaw detection on typical multi-channel phased array data on austenitic welds. The results show, that the modern neural networks can accommodate the rich ultrasonic data and display high flaw detection performance.

Background Studies elucidating health-related information and special needs of Syrian migrants living in Germany are urgently required. However, data is scarce and finding appropriate sampling strategies to obtain representative results is challenging. In order to increase survey response in hard-to-reach populations, new methods were developed. One of them is respondent-driven sampling (RDS), a network sampling technique. We aimed to assess if respondent-driven sampling is a better approach to recruit Syrian migrants for health research than classical random sampling via the population registry. Methods A cross-sectional study was conducted in Munich between April and June 2017 inviting adults (18+ years) born in Syria to answer an online questionnaire asking for sociodemographic and health-related information. Recruitment of participants was done using a) random sampling via the population registry (PR) and b) RDS. The two study populations recruited via respondent-driven sampling and the population registry were compared to a sample drawn from the population registry with respect to gender and citizenship. In addition, the two study populations were compared to each other regarding self-reported health status, healthcare utilisation, lifestyle factors, social network size, and acculturation. Results Of 374 persons randomly drawn from the population registry, 49 individuals answered the questionnaire completely (response: 13.1%) while via RDS 195 participants were recruited by 16 seeds. More persons possessed German citizenship in the total sample (20.5, 95% CI: 16.6 to 24.8%) and in the PR study population (28.6, 95% CI: 16.6 to 43.3%) than in the study population (0.5, 95% CI: 0.1 to 1.5%). Participants recruited via the population registry were older, smoked less, reported more often to hold a university degree, and indicated a higher prevalence of chronic diseases, more frequent healthcare utilisation, higher scores of acculturation as well as a larger social network compared to the study population obtained via RDS. Conclusions Response was very low in the PR sample. The number of participants recruited via RDS was larger and led to a study population with substantially different characteristics. Our study thus indicates that RDS is a useful way to gain access to specific subgroups that are hard to reach via traditional random sampling. Electronic supplementary material The online version of this article (10.1186/s12874-018-0652-1) contains supplementary material, which is available to authorized users.

Author(s): Pogorzelski, David; Nguyen, Uyen; McKay, Paula; Thabane, Lehana; Camara, Megan; Ramsey, Lolita; Seymour, Rachel; Goodman, J Brett; McGee, Sheketha; Fraifogl, Joanne; Hudgins, Andrea; Tanner, Stephanie L; Bhandari, Mohit; Slobogean, Gerard P; Sprague, Sheila; PREP-IT Investigators Executive Committee:; Steering Committee; Adjudication Committee; Data and Safety Monitoring Committee; Research Methodology Core; Patient Centred Outcomes Core; Orthopaedic Surgery Core; Operating Room Core; Infectious Disease Core; Military Core; McMaster University Methods Center; University of Maryland School of Medicine Administrative Center; University of Maryland School of Pharmacy, The PATIENTS Program; PREP-IT Clinical Sites: Lead Clinical Site (Aqueous-PREP and PREPARE); Aqueous-PREP and PREPARE; Aqueous-PREP; PREPARE; PREP-IT Investigators Executive Committee | Abstract: IntroductionPragmatic trials in comparative effectiveness research assess the effects of different treatment, therapeutic, or healthcare options in clinical practice. They are characterized by broad eligibility criteria and large sample sizes, which can lead to an unmanageable number of participants, increasing the risk of bias and affecting the integrity of the trial. We describe the development of a sampling strategy tool and its use in the PREPARE trial to circumvent the challenge of unmanageable work flow.MethodsGiven the broad eligibility criteria and high fracture volume at participating clinical sites in the PREPARE trial, a pragmatic sampling strategy was needed. Using data from PREPARE, descriptive statistics were used to describe the use of the sampling strategy across clinical sites. A Chi-square test was performed to explore whether use of the sampling strategy was associated with a reduction in the number of missed eligible patients.Results7 of 20 clinical sites (35%) elected to adopt a sampling strategy. There were 1539 patients excluded due to the use of the sampling strategy, which represents 30% of all excluded patients and 20% of all patients screened for participation. Use of the sampling strategy was associated with lower odds of missed eligible patients (297/4545 (6.5%) versus 341/3200 (10.7%) p l 0.001).ConclusionsImplementing a sampling strategy in the PREPARE trial has helped to limit the number of missed eligible patients. This sampling strategy represents a simple, easy to use tool for managing work flow at clinical sites and maintaining the integrity of a large trial.

Graphical abstract, A suitable tool for monitoring the spread of SARS-CoV-2 is to identify potential sampling points in the wastewater collection system that can be used to monitor the distribution of COVID-19 disease affected clusters within a city. The applicability of the developed methodology is presented through the description of the 72,837 population equivalent wastewater collection system of the city of Nagykanizsa, Hungary and the results of the analytical and epidemiological measurements of the wastewater samples. The wastewater sampling was conducted during the 3rd wave of the COVID-19 epidemic. It was found that the overlap between the road system and the wastewater network is high, it is 82 %. It was showed that the proposed methodological approach, using the tools of network science, determines confidently the zones of the wastewater collection system and provides the ideal monitoring points in order to provide the best sampling resolution in urban areas. The strength of the presented approach is that it estimates the network based on publicly available information. It was concluded that the number of zones or sampling points can be chosen based on relevant epidemiological intervention and mitigation strategies. The algorithm allows for continuous effective monitoring of the population infected by SARS-CoV-2 in small-sized cities.

Recently, correlation filter-based trackers have received extensive attention due to their simplicity and superior speed. However, such trackers perform poorly when the target undergoes occlusion, viewpoint change or other challenging attributes due to pre-defined sampling strategy. To tackle these issues, in this paper, we propose an adaptive region proposal scheme to facilitate visual tracking. To be more specific, a novel tracking monitoring indicator is advocated to forecast tracking failure. Afterwards, we incorporate detection and scale proposals respectively, to recover from model drift as well as handle aspect ratio variation. We test the proposed algorithm on several challenging sequences, which have demonstrated that the proposed tracker performs favourably against state-of-the-art trackers., 4 pages, 3 figures, IET2018

Therapeutic drug monitoring (TDM) of moxifloxacin is recommended to improve the response to tuberculosis treatment and reduce acquired drug resistance. Limited sampling strategies (LSSs) are able to reduce the burden of TDM by using a small number of appropriately timed samples to estimate the parameter of interest, the area under the concentration-time curve. This study aimed to develop LSSs for moxifloxacin alone (MFX) and together with rifampin (MFX+RIF) in tuberculosis (TB) patients. Population pharmacokinetic (popPK) models were developed for MFX (n = 77) and MFX+RIF (n = 24). In addition, LSSs using Bayesian approach and multiple linear regression were developed. Jackknife analysis was used for internal validation of the popPK models and multiple linear regression LSSs. Clinically feasible LSSs (one to three samples, 6-h timespan postdose, and 1-h interval) were tested. Moxifloxacin exposure was slightly underestimated in the one-compartment models of MFX (mean -5.1%, standard error [SE] 0.8%) and MFX+RIF (mean -10%, SE 2.5%). The Bayesian LSSs for MFX and MFX+RIF (both 0 and 6 h) slightly underestimated drug exposure (MFX mean -4.8%, SE 1.3%; MFX+RIF mean -5.5%, SE 3.1%). The multiple linear regression LSS for MFX (0 and 4 h) and MFX+RIF (1 and 6 h), showed mean overestimations of 0.2% (SE 1.3%) and 0.9% (SE 2.1%), respectively. LSSs were successfully developed using the Bayesian approach (MFX and MFX+RIF; 0 and 6 h) and multiple linear regression (MFX, 0 and 4h; MFX+RIF, 1 and 6h). These LSSs can be implemented in clinical practice to facilitate TDM of moxifloxacin in TB patients.

Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.

International audience; This paper describes the methodology we have developed to define a sampling strategy adapted to operational constraints in order to characterize the dihydrogen flow rate of 2714 nuclear waste drums produced by radiolysis reaction of organic mixed with α-emitters. The objective was to perform few but relevant measurements. Thus, a sample of only 38 drums has been selected to be measured. Statistical analysis of drum measurement data of dihydrogen rate provided an estimation of the mean and the upper bound of the physical quantity of interest which gave a good convergence with global measurements from the ventilation system of the facility. Thereafter, performing a factorial data analysis has demonstrated the representativeness of the measurement data set and the sampling strategy assumption validity. Moreover, it provided information that has been used for a regression analysis to develop a linear prediction model of dihydrogen flow rate production for the waste drum characterization.

BackgroundClinical trials utilizing proton density fat fraction (PDFF) as an imaging biomarker for hepatic steatosis have used a laborious region-of-interest (ROI) sampling strategy of placing an ROI in each hepatic segment.PurposeTo identify a strategy with the fewest ROIs that consistently achieves close agreement with the nine-ROI strategy.Study typeRetrospective secondary analysis of prospectively acquired clinical research data.PopulationA total of 391 adults (173 men, 218 women) with known or suspected NAFLD.Field strength/sequenceConfounder-corrected chemical-shift-encoded 3T MRI using a 2D multiecho gradient-recalled echo technique.AssessmentAn ROI was placed in each hepatic segment. Mean nine-ROI PDFF and segmental PDFF standard deviation were computed. Segmental and lobar PDFF were compared. PDFF was estimated using every combinatorial subset of ROIs and compared to the nine-ROI average.Statistical testingMean nine-ROI PDFF and segmental PDFF standard deviation were summarized descriptively. Segmental PDFF was compared using a one-way analysis of variance, and lobar PDFF was compared using a paired t-test and a Bland-Altman analysis. The PDFF estimated by every subset of ROIs was informally compared to the nine-ROI average using median intraclass correlation coefficients (ICCs) and Bland-Altman analyses.ResultsThe study population's mean whole-liver PDFF was 10.1 ± 8.9% (range: 1.1-44.1%). Although there was no significant difference in average segmental (P = 0.452) or lobar (P = 0.154) PDFF, left and right lobe PDFF differed by at least 1.5 percentage points in 25.1% (98/391) of patients. Any strategy with ≥4 ROIs had ICC >0.995. 115 of 126 four-ROI strategies (91%) had limits of agreement (LOA) 0.995, and 2/36 (6%) of two-ROI strategies and 46/84 (55%) of three-ROI strategies had LOA

Scene classification, which consists of assigning images with semantic labels by exploiting the local spatial arrangements and structural patterns inside tiled regions, is a key problem in the automatic interpretation of optical high-spatial resolution remote sensing imagery. Many state-of-the-art methods, e.g., the bag-of-visual-words model and its variants, the topic models and unsupervised feature learning-based approaches, share similar procedures: patch sampling, feature learning and classification. Patch sampling is the first and a key procedure, and it has a considerable influence on the results. In the literature, many different sampling strategies have been used, e.g., random sampling and saliency-based sampling. However, the sampling strategy that is most suitable for the scene classification of optical high-spatial resolution remote sensing images remains unclear. In this paper, we comparatively study the effects of different sampling strategies under the scenario of scene classification of optical high-spatial resolution remote sensing images. We divide the existing sampling methods into two types: random sampling and saliency-based sampling. Here, we consider the commonly-used grid sampling to be a specific type of random sampling method, and the saliency-based sampling consists of keypoint-based sampling and salient region-based sampling. To compare their performances, we rely on a standard bag-of-visual-words model to learn the global features for testing because of its simplicity, robustness and efficiency. In addition, we conduct experiments using a Fisher kernel framework to validate our conclusions. The experimental results obtained on two commonly-used datasets using different feature learning methods show that random sampling can provide comparable and even better performance than all of the saliency-based strategies.

In March 2013 a large shipment of maize, intended for feed was subject of an alert in the Rapid Alert System for Food and Feed of the European Commission (EC) because the aflatoxin B1 (AFB1) level in the load exceeded the EC regulated maximum level of 20 μg/kg. Since the shipment had passed import controls and was already distributed (mainly to German farms), a massive recall followed. The aim of the current study was to investigate questions, raised by authorities and industry, related to the effectivity of EU sampling procedures, the influence of sample homogenisation procedures and sample storage conditions on the test results, and fungal identification as unexpected mycotoxins were identified during this study. The Netherlands Food and Consumer Product Safety Authority seized a shipload of maize in July 2013, suspected to be contaminated with AFB1. The shipload was sampled according to the 2009 and 2013 EC Sampling Regulations to compare the outcomes of both sampling protocols. Mycotoxin analysis of the incremental samples showed high mean levels of AFB1, aflatoxin G1 (AFG1), and ochratoxin A (OTA). Also an extreme inhomogeneous distribution of aflatoxins and OTA was proven. Analysis of samples homogenised according to the slurry method showed improved performance as compared to samples homogenised through dry homogenisation. Sampling and sample homogenisation according to the Regulation from 2013 showed a closer estimate of the ‘true’ AFB1 content as compared to sampling according to the Regulation from 2009. No influence of laboratory storage conditions on AFB1 concentration could be determined. Fungal identification revealed Aspergillus flavus as the main source of AFB1 in this shipment. Infrequent occurrence of Aspergillus parasiticus might have been the source of AFG1. The occurrence of sometimes large amounts of OTA could not be explained, however it was suggested that Aspergillus welwitschiae might have played a role.

The French government is planning the construction of offshore wind farms (OWF) in the next decade (around 2900 MW). Following the European Environmental Impact Assessment Directive 85/337/EEC, several studies have been undertaken to identify the environmental conditions and ecosystem functioning at selected sites prior to OWF construction. However, these studies are generally focused on the conservation of some species and there is no holistic approach for analysing the effects arising from OWF construction and operation. The objective of this article is to promote a sampling strategy to collect data on the different ecosystem compartments of the future Dieppe-Le Tréport (DLT) wind farm site, adopting an ecosystem approach, which could be applied to other OWFs for the implementation of a trophic network analysis. For that purpose, an Ecopath model is used here to derive indices from Ecological Network Analysis (ENA) to investigate the ecosystem structure and functioning. The results show that the ecosystem is most likely detritus-based, associated with a biomass dominated by bivalves, which could act as a dead end for a classic trophic food web since their consumption by top predators is low in comparison to their biomass. The systemic approach developed for DLT OWF site should be applied for other French and European installations of Offshore Wind Farm.

Circle detection is a critical issue in image analysis and object detection. Although Hough transform based solvers are largely used, randomized approaches, based on the iterative sampling of the edge pixels, are object of research in order to provide solutions less computationally expensive. This work presents a randomized iterative work-flow, which exploits geometrical properties of isophotes in the image to select the most meaningful edge pixels and to classify them in subsets of equal isophote curvature. The analysis of candidate circles is then performed with a kernel density estimation based voting strategy, followed by a refinement algorithm based on linear error compensation. The method has been applied to a set of real images on which it has also been compared with two leading state of the art approaches and Hough transform based solutions. The achieved results show how, discarding up to 57% of unnecessary edge pixels, it is able to accurately detect circles within a limited number of iterations, maintaining a sub-pixel accuracy even in the presence of high level of noise. HighlightsAn iterative randomized circle detection algorithm is proposed.Curvature of isophotes is used to reduce the number of necessary iterations.Edges on circumference(s) are selected by a Kernel density estimation strategy.Sub-pixel accuracy was maintained on real images even in presence of high noise levels.Isophotes analysis reduces the influence of the used edge map on the results.

International audience; This work aims at describing the diversity of osteomyelitis of the jaw (OJ) and at assessing the relevance of a new method designed to avoid salivary contamination during bone sampling in order to improve microbiological analysis and clinical decision-making. We reviewed medical and microbiological data of patients with a suspected OJ based on clinical and/or CT-scan signs and at least one bone sample made for microbiological analysis. During the study period, a new procedure for intraoral bone sampling was elaborated by surgeons and infectious diseases specialists authoring this article (based on stratified samples, cleaning of the surgical site and change of instruments between each sample). A comparison of the microbiological analyses between the two procedures was performed. From 2012 to 2017, 56 patients were included. Median age was 58 years (11-90), sex ratio: 1.24. Main risk factors were having a dental disease (n = 24) or cancer (n = 21). Nineteen patients with the new sample procedure were compared to 37 patients with standard procedure, especially non-cancer patients (n = 16 and 19, respectively). With the new procedure, a median of 3 (1-7) microorganisms per sample was recovered, vs. 7 (1-14) with the former (p

Recent years have seen an increase in the adoption of geometric tolerances. It is often possible to find several geometric tolerances defined on a single part. However, this poses inspection issues: the values of the geometric error may be interrelated; therefore, the presence of multiple tolerances should be considered in inspection design. In this work, a methodology is proposed for planning CMM sampling strategies for multiple tolerances based on the minimization of inspection costs. A model for inspection costs is proposed, which takes into account the influence of the inspection strategy on measurement and inspection errors costs, both directly and through its impact on measurement uncertainty. The cost is then minimized by means of a suitable optimization algorithm, thus defining an optimal sampling strategy. The approach can be adopted both to optimize generic, uniform, sampling strategies, and to generate manufacturing specific strategies, which consider the manufacturing signature, i.e., the part shape deviation from design nominal inherent to a specific manufacturing process. The latter kind of strategies is shown to be the most effective to minimize costs. A case study which illustrates the methodology is presented.

Background A ubiquitous issue in research is that of selecting a representative sample from the study population. While random sampling strategies are the gold standard, in practice, random sampling of participants is not always feasible nor necessarily the optimal choice. In our case, a selection must be made of 12 hospitals (out of 89 Dutch hospitals in total). With this selection of 12 hospitals, it should be possible to estimate blood use in the remaining hospitals as well. In this paper, we evaluate both random and purposive strategies for the case of estimating blood use in Dutch hospitals. Methods Available population-wide data on hospital blood use and number of hospital beds are used to simulate five sampling strategies: (1) select only the largest hospitals, (2) select the largest and the smallest hospitals (‘maximum variation’), (3) select hospitals randomly, (4) select hospitals from as many different geographic regions as possible, (5) select hospitals from only two regions. Simulations of each strategy result in different selections of hospitals, that are each used to estimate blood use in the remaining hospitals. The estimates are compared to the actual population values; the subsequent prediction errors are used to indicate the quality of the sampling strategy. Results The strategy leading to the lowest prediction error in the case study was maximum variation sampling, followed by random, regional variation and two-region sampling, with sampling the largest hospitals resulting in the worst performance. Maximum variation sampling led to a hospital level prediction error of 15 %, whereas random sampling led to a prediction error of 19 % (95 % CI 17 %-26 %). While lowering the sample size reduced the differences between maximum variation and the random strategies, increasing sample size to n = 18 did not change the ranking of the strategies and led to only slightly better predictions. Conclusions The optimal strategy for estimating blood use was maximum variation sampling. When proxy data are available, it is possible to evaluate random and purposive sampling strategies using simulations before the start of the study. The results enable researchers to make a more educated choice of an appropriate sampling strategy. Electronic supplementary material The online version of this article (doi:10.1186/s12874-015-0089-8) contains supplementary material, which is available to authorized users.

Most commercially available auto-sampling devices do not support a continuous flow-proportional sampling mode, which would conceptually be the best for collecting representative composite samples. Instead different discrete sampling modes are available. Household chemicals can show considerable random short-term variations. With the anticorrosive benzotriazole, relating to a middle-frequent household activity, we show that, besides an accurate flow meter, mainly three factors are decisive for the representativeness of a substance's average load: the substance's load pattern, the sampling frequency and the length of the composite sample. When the sampling intervals are 10 minutes or longer, errors in the order of ±40% (standard deviation) or more have to be accepted, if the substance of interest is contained in a low number of wastewater pulses (i.e. the level of household activity). This particularly holds true for specific pharmaceuticals e.g. carbamazepine. Ammonium would be less critical, because it relates to a larger number of sources in the same catchment.

Determining ammonia emission factors (EF) for fattening pig facilities is important from both a regulatory and a research point of view. However, measurements to determine an EF can be time consuming and costly. Several reduced sampling strategies were developed in the past to reduce the costs and measuring time, by taking into account parameters that influence NH3 emissions. A methodology to evaluate the precision and accuracy of estimated EFs solely as a function of the sampling frequency and strategy is demonstrated. This evaluation was done by using two long-term, high frequency datasets which both contained measurements during two consecutive pig fattening periods. These datasets were subjected to simulated sampling strategies. Long-term, low-frequency grab sampling proved to be more accurate than short-term monitoring. Repetitive short-term sampling events result in increased precision, but as this entails higher investment in time and money it is imperative to strike the balance between desired precision and available resources. A method to help as set guidelines to decide upon the number of short-term sampling events or the length of a long-term, low-frequency monitoring strategy is presented.

Two features characterize a good inspection system: it is accurate, and compared to the manufacturing cost, it is not expensive. Unfortunately, few measuring systems posses both these characteristics, i.e. low uncertainty comes with a cost. But also high uncertainty comes with a cost, because measuring systems with high uncertainty tend to generate more inspection errors, which come with a cost. In the case of geometric inspection, the geometric deviation is evaluated from a cloud of points sampled on a part. Therefore, not only the measuring device has to be selected, but also the sampling strategy has to be planned, i.e. the sampling point cloud size and where points should be located on the feature to inspect have to be decided. When the measuring device is already available, as it often happens in geometric measurement, where most instruments are flexible, an unwise strategy planning can be the largest uncertainty contributor. In this work, a model for the evaluation of the overall inspection cost is proposed. The optimization of the model can lead to an optimal inspection strategy in economic sense. However, the model itself is based on uncertainty evaluation, in order to assess the impact of measurement error on inspection cost. Therefore, two methodologies for evaluating the uncertainty will be proposed. These methodologies will be focused on the evaluation of the contribution of the sampling strategy to the uncertainty. Finally, few case studies dealing with the inspection planning for a Coordinate Measuring Machine will be proposed.

An appropriate sampling method that provides for the representation of the collected material and the reliability of results plays a crucial role in environmental monitoring. This is especially important in soil quality investigations on sites with a differentiated surface microrelief, as in the case of afforested post-arable soils that have a specific, deep furrow-and-ridge microrelief. The present research was carried out on three sites afforested with pine (4-, 8-, and 15-year-old stands) located near a large tailings pond collecting the wastes from copper ore enrichment. Soils were sampled at depths of 0–10 and 0–30 cm, separately in the furrows and ridges. The “wide-furrow plow” contributed to the spatial variation in soil properties, including higher pH, organic carbon, and Cu content in soils of the ridges. The difference in Cu content in the ridges and furrows initially reached 300 %, and decreased with the decline of the furrow-and-ridge microrelief to 60 % at 15 years after the plowing. Observed rate of the furrow shallowing allows for an estimation of the time necessary for the complete disappearance of the furrow-and-ridge microrelief and associated variability in soil properties to at least 30–40 years after the plowing. Afforestation plowing had little impact on the Zn variability which was not influenced by the emissions from the tailings pond. Soil sampling in contaminated sites with furrow-and-ridge microrelief must collect equal quantities of soil samples from both furrows and ridges to allow a reliable estimation of the mean trace elements’ concentration.

When working with 3D scanner devices, one of the most critical problems is usually the low quality of the point cloud provided by the scanning device. This problem mainly consists of the following two aspects. The first one is surely the choice of the strategy used to acquire the object shape. Most of the times, the selected strategy is based on selective sampling. This choice proved to be valid, especially when working with Free-Form surfaces: by using a selective sampling strategy is in fact possible to limit point density increase to those regions showing high morphological complexity. The second aspect is the difficulty of identifying which 3D scanner device is the one that better fulfils the specific application needs, which vary depending on the specific scenario in which the costumer/user works (resolution, accuracy, …). As far as this last issue is concerned, the presence of many different acquisition technologies and devices on the market is a source of confusion for the users, who sometimes choose the wrong solution instead of finding the most efficient one. Hence, in order to support the potential users in their selection, this paper aims to propose a solution able to integrate the morphological analysis of the object acquired with the costumer needs (resolution, accuracy, …) and with the 3D scanner performances in order to help users to identify the optimal solution.

The reverse engineering process represents one of the best known methodologies for creating three-dimensional (3D) virtual models starting from physical ones. Even if in the last few years its usage has significantly increased, the remarkable involvement of the operator has until now represented a significant constraint for its growth. Having regard to the fact that this process, and in particular its first step (that is the acquisition phase), strongly depends on the operator's ability and expertise, this paper aims at proposing a strategy for automatically supporting an ''optimal'' acquisition phase. Moreover, the acquisition phase represents the only moment in which there is a direct contact between the virtual model and the physical model. For this reason, designing an ''optimal'' acquisition phase will provide as output an efficient set of morphological data, which will turn out to be extremely useful for the following reverse engineering passages (pre-processing, segmentation, fitting, ...). This scenario drives the researcher to use a selective sampling plan, whose grid dimensions are correlated with the complexity of the local surface region analyzed, instead of a constant one. As a consequence, this work proposes a complete operative strategy which, starting from a first raw preliminary acquisition, will provide a new selective sampling plan during the acquisition phase, in order to allow a deeper and more efficient new scansion. The proposed solution does not require the creation of any intermediate model and relies exclusively on the analysis of the metrological performances of the 3D scanner device and of the morphological behaviour of the surface acquired.

Bianca Maria Colosimo, Ester Gutierrez Moya, Giovanni Moroni andStefano Petr`oAbstract: More and more often, procedures for profile/surface monitoring and inspectionrefer to geometric specifications. In fact, the number of form tolerance requirements (such asstraightness, roundness, flatness, cylindricity, free-form profiles and surfaces) are increasinglyappearing in technical drawings. Usually, Coordinate Measuring Machines (CMMs) are usedto measure points on the specific feature under study in order to decide whether the machineditem is conforming to requirements. In fact, measured points are used to compute the formerror which has to be smaller than the tolerance value to avoid scrapping or reworking. Theestimated form error is usually affected by uncertainty, which depends on both the sample size(number of points measured on the profile/surface) and the sampling strategy (location ofthe points on the profile/surface). The present research work focuses on the second issue andinvestigates the performance of several existing sampling strategies with the ones achievedby two newly presented approaches. These new approaches explore advantages arising byincluding a statistical analysis of a set of machined features in the sampling strategy design.Throughout the paper, a real case study concerning flatness tolerance is used as reference.Keywords: Sampling strategy, geometric tolerance, measuring, CMM, variable selection, PCA.

To better understand where, when, and how to control occupational exposures, it is vital that hygienists understand the factors that contribute to elevated or reduced exposure levels. Over the last two decades a burgeoning literature examining the determinants of exposure has developed, yet to date the methods used in this regard have rarely been summarized in texts or elsewhere. The purpose of this article is to provide an overview of the techniques used to study the predictors of workplace exposures. Both experimental and observational studies are examined, and the advantages and limitations of each are discussed. Fundamental study design features are reviewed. These include the selection and measurement of factors potentially related to exposure, as well as the measurement of exposure itself. Decisions reached by investigators in selecting the number of sites and workers, the number of repeated observations per worker, and the duration of sampling are discussed. Also examined are issues that commonly arise in the course of data analysis of exposure determinants. These include transformation of exposure variables, correlation of predictor variables, empirical model building, and interpretation of results. Finally, methods employed to evaluate the validity of findings are summarized.

The Bayesian method (BM) can use previous information for the optimization of dosage regimen. However, Bayes' law remains true when the parameters are obtained from the infinite population. Therefore a bias might exist in the previous information and affect BM predictive performance. To overcome this shortcoming, the blood drug concentration of a patient can be used to individualize his pharmacokinetic parameters. Until now, at least two sampling strategies, i.e. steady-state and non-steady-state sampling strategies, have been developed to individualize and predict blood drug concentration. In the present study we used five sampling strategies: (1) all samples; (2) post-infusion samples; (3) during-infusion samples; (4) samples within 95% confidence interval/interquartile range of a steady-state concentration; (5) the sample of the mean/median at the mid-time-point of a steady-state to individualize and predict blood cyclosporine concentrations in haematological patients with multidrug resistance. We investigated the effects of different sampling strategies on BM and the nonlinear least squared method (NLLSM) predictive performances. The results showed that BM predictive performance was better than NLLSM. But the results did not prove that the steady-state sampling strategies were superior to the non-steady-state ones. © 1995 The Italian Pharmacological Society.

Caves and other subterranean habitats fulfill the requirements of experimental model systems to address general questions in ecology and evolution. Yet, the harsh working conditions of these environments and the uniqueness of the subterranean organisms have challenged most attempts to pursuit standardized research.Two main obstacles have synergistically hampered previous attempts. First, there is a habitat impediment related to the objective difficulties of exploring subterranean habitats and our inability to access the network of fissures that represents the elective habitat for the so‐called “cave species.” Second, there is a biological impediment illustrated by the rarity of most subterranean species and their low physiological tolerance, often limiting sample size and complicating laboratory experiments.We explore the advantages and disadvantages of four general experimental setups (in situ, quasi in situ, ex situ, and in silico) in the light of habitat and biological impediments. We also discuss the potential of indirect approaches to research. Furthermore, using bibliometric data, we provide a quantitative overview of the model organisms that scientists have exploited in the study of subterranean life.Our over‐arching goal is to promote caves as model systems where one can perform standardized scientific research. This is important not only to achieve an in‐depth understanding of the functioning of subterranean ecosystems but also to fully exploit their long‐discussed potential in addressing general scientific questions with implications beyond the boundaries of this discipline., We discuss best practices and novel ideas for performing standardized research in subterranean ecosystems, by focusing on key impediments, experimental ideas, and model systems.

In the context of environmental monitoring, outdoor wireless cameras are vulnerable to natural hazards. To benefit from the inexpensive imaging sensors, we introduce a multi-camera monitoring system to share the physical risk. With multiple cameras focusing at a common scenery of interest, we propose an interleaved sampling strategy to minimize per-camera consumption by distributing sampling tasks among cameras. To overcome the uncertainties in the sensor network, we propose a robust adaptive synchronization scheme to build optimal sampling configuration by exploiting the broadcast nature of wireless communication. The theory as well as simulation results verify the fast convergence and robustness of the algorithm. Under the interleaved sampling configuration, we propose three video coding methods to compress correlated video streams from disjoint cameras, namely, distributed/independent/joint coding schemes. The energy profiling on a two-camera system shows that independent and joint coding perform substantially better. The comparison between two-camera and single-camera system shows 30%–50% per-camera consumption reduction. On top of these, we point out that MIMO technology can be potentially utilized to push the communication consumption even lower.