While society benefits from Arctic shipping, it is necessary to recognize that ship operations in Arctic waters pose significant risks to people, the environment, and property. To support the management of those risks, this article presents a comprehensive approach addressing both short-term operational risks, as well as risks related to long-term extreme ice loads. For the management of short-term operational risks, an extended version of the Polar Operational Limit Assessment Risk Indexing System (POLARIS) considering the magnitude of the consequences of potential adverse events is proposed. For the management of risks related to long-term extreme ice loads, guidelines are provided for using existing analytical, numerical, and semi-empirical methods. In addition, to support the design of ice class ship structures, the article proposes a novel approach that can be used in the conceptual design phase for the determination of preliminary scantlings for primary hull structural members.

The fundamental flexural guided wave (FFGW) permits ultrasonic assessment of the wall thickness of solid waveguides, such as tubes or, e.g., long cortical bones. Recently, an optical non-contact method was proposed for ultrasound excitation and detection with the aim of facilitating the FFGW reception by suppressing the interfering modes from the soft coating. This technique suffers from low SNR and requires iterative physical scanning across the source-receiver distance for 2D-FFT analysis. This means that SNR improvement achieved by temporal averaging becomes time-consuming (several minutes) which reduces the applicability of the technique, especially in time-critical applications such as clinical quantitative ultrasound. To achieve sufficient SNR faster, an ultrasonic excitation by a base-sequence-modulated Golay code (BSGC, 64-bit code pair) on coated tube samples (1-5 mm wall thickness and 5 mm soft coating layer) was used. This approach improved SNR by 21 dB and speeded up the measurement by a factor of 100 compared to using a classical pulse excitation with temporal averaging. The measurement now took seconds instead of minutes, while the ability to determine the wall thickness of the phantoms was maintained. The technique thus allows rapid noncontacting assessment of the wall thickness in coated solid tubes, such as the human bone. (C) 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Objective: To study the efficacy of aquatic resistance training on biochemical composition of tibiofemoral cartilage in postmenopausal women with mild knee osteoarthritis (OA). Design: Eighty seven volunteer postmenopausal women, aged 60-68 years, with mild knee OA (Kellgren-Lawrence grades I/II and knee pain) were recruited and randomly assigned to an intervention (n = 43) and control (n = 44) group. The intervention group participated in 48 supervised aquatic resistance training sessions over 16 weeks while the control group maintained usual level of physical activity. The biochemical composition of the medial and lateral tibiofemoral cartilage was estimated using single-slice transverse relaxation time (T2) mapping and delayed gadolinium-enhanced magnetic resonance imaging of cartilage (dGEMRIC index). Secondary outcomes were cardiorespiratory fitness, isometric knee extension and flexion force and knee injury and OA outcome (KOOS) questionnaire. Results: After 4-months aquatic training, there was a significant decrease in both T2 -1.2 ms (95% confidence interval (CI): -2.3 to -0.1, P = 0.021) and dGEMRIC index -23 ms (-43 to -3, P = 0.016) in the training group compared to controls in the full thickness posterior region of interest (ROI) of the medial femoral cartilage. Cardiorespiratory fitness significantly improved in the intervention group by 9.8% (P = 0.010). Conclusions: Our results suggest that, in postmenopausal women with mild knee OA, the integrity of the collagen-interstitial water environment (T2) of the tibiofemoral cartilage may be responsive to low shear and compressive forces during aquatic resistance training. More research is required to understand the exact nature of acute responses in dGEMRIC index to this type of loading. Further, aquatic resistance training improves cardiorespiratory fitness. (C) 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

The most mineralized tissue of the mammalian body is tooth enamel. Especially in species with thick enamel, three-dimensional (3D) tomography data has shown that the distribution of enamel varies across the occlusal surface of the tooth crown. Differences in enamel thickness among species and within the tooth crown have been used to examine taxonomic affiliations, life history, and functional properties of teeth. Before becoming fully mineralized, enamel matrix is secreted on the top of a dentine template, and it remains to be explored how matrix thickness is spatially regulated. To provide a predictive framework to examine enamel distribution, we introduce a computational model of enamel matrix secretion that maps the dentine topography to the enamel surface topography. Starting from empirical enamel-dentine junctions, enamel matrix deposition is modeled as a diffusion-limited free boundary problem. Using laboratory microCT and synchrotron tomographic data of pig molars that have markedly different dentine and enamel surface topographies, we show how diffusion-limited matrix deposition accounts for both the process of matrix secretion and the final enamel distribution. Simulations reveal how concave and convex dentine features have distinct effects on enamel surface, thereby explaining why the enamel surface is not a straightforward extrapolation of the dentine template. Human and orangutan molar simulations show that even subtle variation in dentine topography can be mapped to the enamel surface features. Mechanistic models of extracellular matrix deposition can be used to predict occlusal morphologies of teeth. Author summary Teeth of most mammals are covered by a layer of highly mineralized enamel that cannot be replaced or repaired. The enamel layer is not uniform over the underlying dentine, and spatial regulation of enamel formation is critical for making a functional tooth. To explore which kind of mechanisms could underlie the complex patterns of enamel distribution, we present a computational model. Starting from tomography-imaged teeth from which enamel has been digitally removed, enamel is restored on dentine surfaces by simulating diffusion-limited secretion of enamel matrix. Our simulations show how the combination of subtle features of dentine and diffusion-limited secretion of the enamel matrix can substantially increase the complexity of the enamel surface. We propose that the strength of the diffusion-limited process is a key factor in determining enamel distribution among mammalian species.

Satellite remote sensing data, such as moderate resolution imaging spectroradiometers (MODIS) and advanced very high-resolution radiometers (AVHRR), are being widely used to monitor sea ice conditions and their variability in the Bohai Sea, the southernmost frozen sea in the Northern Hemisphere. Monitoring the characteristics of the Bohai Sea ice can provide crucial information for ice disaster prevention for marine transportation, oil field operation, and regional climate change studies. Although these satellite data cover the study area with fairly high spatial resolution, their typically limited cloudless images pose serious restrictions for continuous observation of short-term dynamics, such as sub-seasonal changes. In this study, high spatiotemporal resolution (500 m and eight images per day) geostationary ocean color imager (GOCI) data with a high proportion of cloud-free images were used to monitor the characteristics of the Bohai Sea ice, including area and thickness. An object-based feature extraction method and an albedo-based thickness inversion model were used for estimating sea ice area and thickness, respectively. To demonstrate the efficacy of the new dataset, a total of 68 GOCI images were selected to analyze the evolution of sea ice area and thickness during the winter of 2012-2013 with severe sea ice conditions. The extracted sea ice area was validated using Landsat Thematic Mapper (TM) data with higher spatial resolution, and the estimated sea ice thickness was found to be consistent with in situ observation results. The entire sea ice freezing-melting processes, including the key events such as the day with the maximum ice area and the first and last days of the frozen season, were better resolved by the high temporal-resolution GOCI data compared with MODIS or AVHRR data. Both characteristics were found to be closely correlated with cumulative freezing/melting degree days. Our study demonstrates the applicability of the GOCI data as an improved dataset for studying the Bohai Sea ice, particularly for purposes that require high temporal resolution data, such as sea ice disaster monitoring.

Cloud misclassification is a serious problem in the retrieval of aerosol optical depth (AOD), which might considerably bias the AOD results. On the one hand, residual cloud contamination leads to AOD overestimation, whereas the removal of high-AOD pixels (due to their misclassification as clouds) leads to underestimation. To remove cloudcontaminated areas in AOD retrieved from reflectances measured with the (Advanced) Along Track Scanning Radiometers (ATSR-2 and AATSR), using the ATSR dual-view algorithm (ADV) over land or the ATSR single-view algorithm (ASV) over ocean, a cloud post-processing (CPP) scheme has been developed at the Finnish Meteorological Institute (FMI) as described in Kolmonen et al. (2016). The application of this scheme results in the removal of cloudcontaminated areas, providing spatially smoother AOD maps and favourable comparison with AOD obtained from the ground-based reference measurements from the AERONET sun photometer network. However, closer inspection shows that the CPP also removes areas with elevated AOD not due to cloud contamination, as shown in this paper. We present an improved CPP scheme which better discriminates between cloud-free and cloud-contaminated areas. The CPP thresholds have been further evaluated and adjusted according to the findings. The thresholds for the detection of high-AOD regions (> 60% of the retrieved pixels should be high-AOD (> 0.6) pixels), and cloud contamination criteria for lowAOD regions have been accepted as the default for AOD global post-processing in the improved CPP. Retaining elevated AOD while effectively removing cloud-contaminated pixels affects the resulting global and regional mean AOD values as well as coverage. Effects of the CPP scheme on both spatial and temporal variation for the period 2002-2012 are discussed. With the improved CPP, the AOD coverage increases by 10-15% with respect to the existing scheme. The validation versus AERONET shows an improvement of the correlation coefficient from 0.84 to 0.86 for the global data set for the period 2002-2012. The global aggregated AOD over land for the period 2003-2011 is 0.163 with the improved CPP compared to 0.144 with the existing scheme. The aggregated AOD over ocean and globally (land and ocean together) is 0.164 with the improved CPP scheme (compared to 0.152 and 0.150 with the existing scheme, for ocean and globally respectively). Effects of the improved CPP scheme on the 10-year time series are illustrated and seasonal and temporal changes are discussed. The improved CPP method introduced here is applicable to other aerosol retrieval algorithms. However, the thresholds for detecting the high-AOD regions, which were developed for AATSR, might have to be adjusted to the actual features of the instruments.

In this study, sea ice concentration (SIC) budgets were calculated for five ocean-sea ice reanalyses (CFSR, C-GLORSv7, GLORYS12v1, NEMO-EnKF and ORAS5), in the Southern Ocean and compared with observations. Benefiting from the assimilation of SIC, the reanalysis products display a realistic representation of sea ice extent as well as sea ice area. However, when applying the SIC budget diagnostics to decompose the changes in SIC into contributions from advection, divergence, thermodynamics, deformation and data assimilation, we find that both atmospheric and oceanic forcings and model configurations are significant contributors on the budget differences. For the CFSR, the primary source of deviation compared to other reanalyses is the stronger northward component of ice velocity, which results in stronger sea ice advection and divergence. Anomalous surface currents in the CFSR are proposed to be the main cause of the ice velocity anomaly. Furthermore, twice the mean ice thickness in the CFSR compared to other reanalyses makes it more susceptible to wind and oceanic stresses under Coriolis forces, exacerbating the northward drift of sea ice. The C-GLORSv7, GLORYS12v1 and NEMO-EnKF have some underestimation of the contribution of advection and divergence to changes in SIC in autumn, winter and spring compared to observations, but are more reasonable in summer. ORAS5, although using the same coupled model and atmospheric forcing as C-GLORSv7 and GLORYS12v1, has a more significant underestimation of advection and divergence to changes in SIC compared to these two reanalyses. The results of the SIC budgets of five ocean-sea ice reanalyses in the Southern Ocean suggest that future reanalyses should focus on improving the modelling of sea ice velocities, for example through assimilation of sea ice drift observations.

Arctic landfast sea ice (LFSI) represents an important quasi-stationary coastal zone. Its evolution is determined by the regional climate and bathymetry. This study investigated the seasonal cycle and interannual variations of LFSI along the northwest coast of Kotelny Island. Initial freezing, rapid ice formation, stable and decay stages were identified in the seasonal cycle based on application of the visual inspection approach (VIA) to MODIS/Envisat imagery and results from a thermodynamic snow/ice model. The modeled annual maximum ice thickness in 1995-2014 was 2.02 +/- 0.12 m showing a trend of -0.13 m decade(-1). Shortened ice season length (-22 d decade(-1)) from model results associated with substantial spring (2.3 degrees C decade(-1)) and fall (1.9 degrees C decade(-1)) warming. LFSI break-up resulted from combined fracturing and melting, and the local spatiotemporal patterns of break-up were associated with the irregular bathymetry. Melting dominated the LFSI break-up in the nearshore sheltered area, and the ice thickness decreased to an average of 0.50 m before the LFSI disappeared. For the LFSI adjacent to drift ice, fracturing was the dominant process and the average ice thickness was 1.56 m at the occurrence of the fracturing. The LFSI stages detected by VIA were supported by the model results.