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The real-time provision of high-quality estimates of the ocean wave parameters at appropriate spatial resolutions are essential for the sustainable operations of marine structures. Machine learning affords considerable opportunity for providing additional value from sensor networks, fusing metocean data collected by various platforms. Exploiting the ship-as-a-wave-buoy concept, this article proposes the integration of vessel-based observations into a wave-nowcasting framework. Surrogate models are trained using a high-fidelity physics-based nearshore wave model to learn the spatial correlations between grid points within a computational domain. The performance of these different models are evaluated in a case study to assess how well wave parameters estimated through the spectral analysis of ship motions can perform as inputs to the surrogate system, to replace or complement traditional wave buoy measurements. The benchmark study identifies the advantages and limitations inherent in the methodology incorporating ship-based wave estimates to improve the reliability and availability of regional sea state information.

Performance data from ships is subject to distributional shifts, sometimes referred to as concept drift. In this study, synthetic monitoring data is simulated for the KVLCC2, considering publicly available reference data and a semi-empirical simulation framework. Neural networks are trained to predict the required shaft power and to overcome the deterioration in model accuracy due to concept drift, three methods of incremental learning are applied and compared: (1) Layer freezing, (2) regularization, and (3) elastic weight consolidation. Furthermore, an implicit methodology for quantifying the changing hull and propeller performance is presented. In addition, a generic feature engineering framework is used for eliminating insignificant features. In two investigations, sudden and incremental concept drift scenarios are examined, and the effect of different uncertainty categories on model performance is studied in parallel based on three different datasets. As a main finding, it is confirmed that data quality is of great importance for accurate machine learning-driven performance monitoring — even in simulated environments. Furthermore, the study shows that freezing layers during incremental learning proves to be most robust and accurate, but it will be part of future work to examine this on actual sensor data.

The analysis of wave-ship interactions is highly relevant for the safety – as well as the energy efficiency – of the maritime operations. One application is the onboard estimation of the ship’s responses in incoming seaways, which requires regular and accurate updates of the vessel’s seakeeping model, accounting for possible changes in the operational conditions. This paper presents a simple approach for fast estimation of the wave-to-motion transfer functions of vessels. Prior information of the wave spectrum, characterizing the sea state, and ship motion measurements, i.e. time series sequences from onboard sensors, are supposed to be available. Semi-empirical closed-form expressions derived for abox-shaped vessel define Parameterized Response Amplitude Operators (P-RAOs) for the heave and pitch motions. The five input parameters, namely the ship speed, length, breadth, draught and block coefficient, are regarded as optimization variables. An optimization problem is established to minimize the spectral discrepancy between, on one hand, the measured responses, and, on the other hand, the theoretical responses computed with the wave spectrum and P-RAOs. Numerical simulations of measured motions in a predefined long-crested sea state are carried out in the frequency domain for two different ships, a small research vessel and a container ship, using a set of RAOs obtained by a commercial potential flow code. The simulated measurements are considered as the ground truth. Tuning of the P-RAOs is carried out, and the results show a fairly good agreement between the tuned P-RAOs and the true RAOs over a wide portion of the frequency range. Moreover, the normalized error between the true and estimated response spectra is significantly decreased after tuning the P-RAOs.

The aim of this study was to predict genomic breeding values for metabolic body weight (MBW) by a single-step model and to compare obtained breeding values to those from a multistep genomic prediction model and a pedigree-based BLUP model. Data contained over 3 million observations from Danish, Finnish and Swedish Red dairy cattle born in the years 1990 to 2017. The applied multiple-trait animal models included six traits: 1st, 2nd, and 3rd parity MBW, and first parity stature, chest width and body depth. Breeding values from the singlestep model were in good agreement with those from the multi-step model (correlations were 0.98-0.99), but BLUP breeding values differed from genomic breeding values slightly for bulls and clearly for cows. The single-step model gave higher validation reliabilities for bulls and cows than the multi-step model. The results support using a single-step approach for the genomic evaluation of MBW.

Extreme value statistics can often be based on the assumption that exceedance events of a high threshold level are statistically independent and identically distributed (i.i.d. process), which further implies the Poisson assumption to be valid. This makes it possible to express the extreme response statistics through the mean up-crossing rate. For non-linear processes, analytic expressions of the mean up-crossing rate do not in general exist. Reliable statistics of mean up-crossing rate based on the brute-force approach, e.g. Monte Carlo simulation (MCS) require long time domain simulations considering a number of different ensemble input. The associated computations can be very time consuming especially when a detailed physical (e.g. hydrodynamic) model is applied. The First Order Reliability Method (FORM) has previously been found efficient for estimation of extreme value prediction of stationary stochastic time domain processes, However, if the non-linearity in a response is significant, the accuracy of the FORM linearized mean up-crossing rate can be limited. The present work attempts to improve the extreme value prediction for non-linear parametric roll motions of ships based on applications of the FORM approach and suggests a model for the mean up-crossing rate for strong non-linear response, validated by comparing with MCS results.

Landets statsminister sørger offentligt over de dødsfald, som skyldes corona. Og ja, det er sørgeligt. Men kan man gradbøje værdien af dødsfald? Et offer for en trafikulykke forårsaget af for høj fart eller uagtsomhed er også et tab, der efterlader sorg.

This paper investigates a semi-empirical model used to estimate added-wave resistance on a ship sailing in waves. The model relies on measurements from a continuous monitoring system, and produces an estimate - the indirect measurement - of added-wave resistance, based on the difference between, on the one side, the measured power and, on the other side, a summation of theoretically calculated resistance contributions but neglecting the component because of seaway. Hence, the added-wave power is obtained as the surplus. The model has been applied to more than three months of full-scale data recorded on an in-service operating container ship. The results indicate reasonable levels of the added-wave power, emphasising that both positive and negative values are obtained, since the model is not restricted to relative wave directions forward of beam. It is found that following waves often lead to 'negative resistance', in fact creating a thrust, but, importantly, following waves can also increase the total resistance significantly.

Extreme value statistics can often be based on the assumption that exceedance events of a high threshold level are statistically independent and identically distributed (i.i.d. process), which further implies the Poisson assumption to be valid. This makes it possible to express the extreme response statistics through the mean up-crossing rate. For non-linear processes, analytic expressions of the mean up-crossing rate do not in general exist. Reliable statistics of mean up-crossing rate based on the brute-force approach, e.g. Monte Carlo simulation (MCS) require long time domain simulations considering a number of different ensemble input. The associated computations can be very time consuming especially when a detailed physical (e.g. hydrodynamic) model is applied. The First Order Reliability Method (FORM) has previously been found efficient for estimation of extreme value prediction of stationary stochastic time domain processes, However, if the non-linearity in a response is significant, the accuracy of the FORM linearized mean up-crossing rate can be limited. The present work attempts to improve the extreme value prediction for nonlinear parametric roll motions of ships based on applications of the FORM approach and suggests a model for the mean up-crossing rate for strong non-linear response, validated by comparing with MCS results.

Emerging Markets has since the end of the 1980’s received substantial attention by both practitioners and scholars. The notion of BRIC and MINT has often guided the discussion and acted as empirical point of departure. However, the term “emerging markets” suggest that countries “emerge” from less-developed status and join the group of developed countries. The notion of “emerging markets” per se should therefore predict that over time some of the countries characterized by this category would either retro- or progress beyond their status as emerging market. The purpose of this paper is to revisit the emerging market category to identify and reflect on the potential development of countries previously categorized as emerging markets. Using concurrent economic data from the World Bank, we aim to provide an answer to the question: which countries can currently be classified as emerging markets, and to which extent does existing notions of BRIC and MINT stilly apply for future research? We find that 38 countries currently can be identified as emerging markets and that the notions of BRIC and MINT to some degree still apply for future research.

In this paper, a computationally efficient online sea state estimation algorithm isproposed for estimation of the on site sea state. The algorithm finds the wave spectrum estimate from motion measurements in heave, roll and pitch by iteratively solving a set of linear equations. The main vessel parameters and motion transfer functions are required as input. Apart from this the method is signal-based, with no assumptions on the wave spectrum shape, and as a result it is computationally efficient. The algorithm is implemented in a dynamic positioning (DP)control system, and tested through simulations in different sea states, with heading changes. Discrete stability analysis is performed to find iteration gains in the algorithm.

This paper presents a novel method for estimating the sea state parameters based on the heave, roll and pitch response of a vessel conducting station keeping automatically by a dynamic positioning (DP) system, i.e., without forward speed. The proposed algorithm finds the wave spectrum estimate from the response measurements by iteratively solving a set of linear equations, and it is computationally efficient. The main vessel parameters are required as input. Apart from this the method is signal-based, with no assumptions on the wave spectrum shape. Performance of the proposed algorithm is demonstrated on full-scale experimental DP data of a vessel in three different sea states at head, bow quartering, beam, stern quartering and following sea waves, respectively.

The purpose of the Ph.D. dissertation is to examine how do joint learning influence management of international joint ventures in emerging markets? Entering and developing presence in emerging markets often require foreign firms to set up International Joint Ventures (IJVs) with local partners, especially when the prime motive of the operation is to capture opportunities in the local markets. However, the performance of IJVs generally turns out to be quite poor whether measured in economic value added, parent organization’s satisfaction or survival. For instance, termination rates of IJVs are depicted as high in the literature, and some scholars have even demonstrated termination rates of IJVs varying between 30 – 70 %. Thus, management of IJVs, in general, appears to be a difficult issue, especially in emerging markets where firms experience that their IJVs often does not meet their expectations. Inter-partner learning has been proposed in the literature as a major cause of IJV instability. However, current research indicates that when IJV partners engage in joint learning to create new IJV-specific knowledge that benefits both partners it stabilizes and sustains the IJV. Yet, joint learning in IJVs have rarely been examined in the context of IJVs in emerging markets. This paucity of studies concerning joint learning and knowledge creation in IJVs have been noted at a critical research gap in the literature.

Entering and developing presence in emerging economies often require foreign firms to set up International Joint Ventures (IJVs) with local partners, especially when the prime motive of the operation is to capture opportunities in the local markets (Mohr & Puck, 2005). IJVs have therefore become an important way for firms to enter these markets and a significant force in shaping a firm’s global strategy in expanding international activities abroad. However, research has shown that IJVs in emerging economies are unstable, and firms have experienced that performance of their IJVs in these emerging economies often does not meet their expectations (Fang & Zou, 2010). Extant research has shown that, in general, management of IJVs appears to be a difficult issue. 60-70% of all partnerships are not successful (Lambe, Spekman, & Hunt, 2002). Inter-partner learning has been proposed in the literature as a major cause of IJV instability (S. H. Park & Ungson, 1997). Yet current research indicates that when IJV partners engage in joint learning to create new IJV-specific knowledge that benefits both partners it stabilizes and sustains the IJV (Fang & Zou, 2010). For instance, Fang and Zou (2010) have demonstrated that creation of new IJV-specific knowledge can be imperative to performance while Pak, Ra, and Lee (2015) demonstrated that IJV parents can become beneficiaries of collaborative sensing and seize the market opportunities (Johanson & Vahlne, 2006). However, inter-organizational knowledge creation processes in IJVs have rarely been examined (Pak et al., 2015) especially not in the context of IJVs in emerging economies. This paucity of studies concerning knowledge creation and inter-organizational learning in IJVs have been noted at a critical research gap in the literature (J.-Y. Park, Ha, & Lew, 2015). The purpose of the paper is thus to investigate the antecedents to the superior performance of IJVs in emerging economies by identifying conditions that enable knowledge creation processes.

Additional file 6: Figure S5. Plots of association signals for single marker analysis (gray), single marker analysis with the 8-SNP haplotype (located within the MOCS1 gene) as cofactor (blue) for calf survival (latter calvings). The position of the MOCS1 gene is marked by a red rectangle. The haplotype could not explain the QTL variance for the targeted region.

Additional file 5: Table S2. SNPs included in a custom-made low-density SNP chip to identify carriers for the 500 kb deletion. SNPs located within the putative chromosomal deletion on bovine chromosome 23 were included in a custom-made low-density SNP chip to identify carriers based on genotype intensity from SNP array binding.

Additional file 1: Table S1. Top associated SNPs on bovine autosomes, if genome-wide significant in the genome scan for birth index in Nordic red cattle. The most significantly associated SNPs with birth index on each chromosome from the genome are listed.

Additional file 3: Figure S3. Test for Hardy-Weinberg proportions for the SNPs within the targeted region on bovine chromosome 23. Red circle indicates an excess of homozygotes and green circle indicates a deficit of homozygotes.

Additional file 4: Figure S4. Sequence coverage from a non-carrier and a carrier bull of HAPQTL. The red rectangle shows the end region of the breaking point. The coverage peaks located in the end region could be due to assembly problems or to repetitive elements.

Additional file 2: Figure S1. Linear mixed model analysis for calf size at first (A) and later calvings (B) for the targeted QTL region on bovine chromosome 23. Association results for calf size for the targeted region. Figure S2. Linear mixed model analysis for calving ease at first (A) and later calvings (B) for the targeted QTL region on bovine chromosome 23. Association results for calf size for the targeted region.

In the past decade, Africa has developed from being an extremely impoverished continent with discouraging prospects to a more promising destination and home to some of the fastest growing Frontier Market economies. Approximately 75% of Africans rely on agriculture for their livelihoods, making it the most important economic sector. Despite possessing the world´s largest reservoir of unused arable land (about 60%), Africa has the lowest agricultural productivity, amounting to approximately 10% of global agricultural output. This suggests that scaling up agricultural production in SSA could be the new growth frontier. Evidence has shown that if countries in SSA where using the same level of technology utilized by industrial countries, income levels in SSA would be significantly higher. The paper aims to address this issue, and study how Danish agriculture firms can use technology transfer to create growth opportunities in Frontier Markets of Sub-Saharan Africa.

Complex marine operations are moving further from shore, into deeper waters, and harsher environments. The operating hours of a vessel are weather dependent, and good knowledge of the prevailing weather conditions may ensure cost-efficient and safe operations. This paper considers the estimation of the peak wave frequency of the on-site sea state based on the vessel’s motion in waves. A sea state can be described by significant wave height, peak wave frequency, wave direction, and often wind speed and direction are added as well. The signal-based algorithm presented in this paper is based on Fourier transforms of the vessel response in heave, roll and pitch. The measurements are used directly to obtain an estimate of the peak frequency of the waves. Experimental results from model-scale offshore ship runs at the Marine Cybernetics Laboratory (MCLab) at NTNU demonstrate the performance of the proposed sea state estimation algorithm.

Low frequency radars, also known as sounders, can be used for subsurface measurements of Earth’s massive ice sheets. Radar data are essential to improving ice sheet models for better prediction of the response of these ice sheets to global climate change. While airborne sounders are needed for detailed measurements of fast-flowing outlet glaciers, a space-based sounder is potentially capable of broad coverage with high spatial and uniform sampling over the interior of the ice sheets. For both types of systems, however, surface clutter that obscures the depth signal of interest is a major technical challenge.This dissertation deals with tomographic techniques based on multiphase-center radars that represent state-of-the-art technology within the field of ice sounding. The use of advanced tomographic processing for clutter suppression is investigated, which up to this point has been largely unexplored in the literature. The investigation also includes a theoretical study of beam forming and direction-of-arrival (DOA) estimation techniques. In addition to the primary treatment of clutter suppression,additional novel applications of tomography are also explored.Based on an experimental multi-phase-center dataset acquired with the POLarimetric Airborne Radar Ice Sounder (POLARIS), single-pass tomographic surface clutter suppression capabilities are demonstrated for the system. Using repeat-pass POLARIS data, a method based on data-driven DOA estimation is used to show an along-track variation of the effective scattering center of the surface return, which is caused by a varying penetration depth.As an alternative to the traditional echogram, a new DOA representation that offers a better visualization of the desired signals and clutter is suggested. Based on this alternative presentation, a novel technique for discrimination of the desired bed return from strong surface clutter is presented. The technique is applied to data from the channel of the challenging Jakobshavn Glacier acquired with the Multi-channel Coherent Radar Depth Sounder/Imager (MCoRDS/I), where it is shown how the technique can be used to close some of the critical gaps in bed detection along the channel.Finally, a geometric model is used to show how the across-track slope of the bed is related to the DOA pattern of the bed return. Based on this, a technique for estimation of the backscattering characteristics is presented. Furthermore, waveform analysis is investigated for estimation of the bed roughness.

The purpose of the master-thesis was to examine how Blue Ocean Robotics can structure its strategic alliances to gain a competitive advantage in the market development of robotics in international markets.

Accuracy of genomic prediction depends on the information in the reference population. Achieving an adequate sized reference population is a challenge for genomic prediction in small cattle populations. One way to increase the size of reference population is to combine reference data from different populations. The objective of this study was to assess the gain of genomic prediction accuracy when including US Jersey bulls in the Danish Jersey reference population. The data included 1,262 Danish progeny-tested bulls and 1,157 US progeny-tested bulls. Genomic breeding values (GEBV) were predicted using a GBLUP model from the Danish reference population and the joint Danish-US reference population. The traits in the analysis were milk yield, fat yield, protein yield, fertility, mastitis, longevity, body conformation, feet & legs, and longevity. Eight of the nine traits benefitted from the inclusion of US Jersey bulls in the reference population. The gains ranged from 1.6% points for fertility to 12.5% points for udder conformation. The exception was longevity for which the joint reference population resulted in a loss of 5.5% points in reliability of GEBV. Averaged over all nine traits, reliability of GEBV using the joint reference population was 4.0% points higher than the reliability of GEBV using the Danish reference population alone. The results confirm that exchanging reference data to increase the size of reference population is an efficient approach to increase the accuracy of genomic prediction, especially for the populations with small number of reference bulls

This study investigated the phenomenon of bias in the trend of genomic predictions and attempted to find the reason and solution for this bias. The data used in this study include Danish Jersey data and simulation data. In Jersey data, the bias was reduced when cows were included in the reference population. In simulated data, there was no bias when the test animals were unselected cows. When the G matrix was derived from genotypes of causal genes, the bias was reduced. The results suggest that the main reasons for causing the bias of the prediction trends are the selection of bulls and bull dams as well as the inaccurate relationship matrix. The possible strategies to eliminate the bias could be to use cow reference and improve genomic relationship matrix

A thorough understanding of the developmental signals that direct pluripotent stem cells (PSCs) toward a cardiac fate is essential for translational applications in disease modeling and therapy. We screened a panel of 44 cytokines/signaling molecules for their ability to enhance Nkx2.5(+) cardiac progenitor cell (CPC) formation during in vitro embryonic stem cell (ESC) differentiation. Treatment of murine ESCs with insulin or insulin-like growth factors (IGF1/2) during early differentiation increased mesodermal cell proliferation and, consequently, CPC formation. Furthermore, we show that downstream mediators of IGF signaling (e.g., phospho-Akt and mTOR) are required for this effect. These data support a novel role for IGF family ligands to expand the developing mesoderm and promote cardiac differentiation. Insulin or IGF treatment could provide an effective strategy to increase the PSC-based generation of CPCs and cardiomyocytes for applications in regenerative medicine.

In this paper we provide new evidence showing that fair behavior is intuitive to most people. We find a strong association between a short response time and fair behavior in the dictator game. This association is robust to controls that take account of the fact that response time might be affected by the decision-maker's cognitive ability and swiftness. The experiment was conducted with a large and heterogeneous sample recruited from the general population in Denmark. We find a striking similarity in the association between response time and fair behavior across groups in the society, which suggests that the predisposition to act fairly is a general human trait.

The Nordic Holstein yield evaluation model describes all available milk, protein and fat test-day yields from Denmark, Finland and Sweden. In its current form all variance components are estimated from observations recorded under conventional milking systems. Also the model for heterogeneity of variance correction is developed for the same observations. As automated milking systems are becoming more popular the current evaluation model needs to be enhanced to account for the different measurement error variances of observations from automated milking systems. In this simulation study different models and different approaches to account for heterogeneous variance when observations have different measurement error variances were investigated. Based on the results we propose to upgrade the currently applied models and to calibrate the heterogeneous variance adjustment method to yield same genetic variance for both milking systems

We use the strategy method to classify subjects into cooperator types in a large-scale online Public Goods Game and find that free riders spend more time on making their decisions than conditional cooperators and other cooperator types. This result is robust to reversing the framing of the game and is not driven by free riders lacking cognitive ability, confusion, or natural swiftness in responding. Our results suggest that conditional cooperation serves as a norm and that free riders need time to resolve a moral dilemma.

BackgroundA single-step blending approach allows genomic prediction using information of genotyped and non-genotyped animals simultaneously. However, the combined relationship matrix in a single-step method may need to be adjusted because marker-based and pedigree-based relationship matrices may not be on the same scale. The same may apply when a GBLUP model includes both genomic breeding values and residual polygenic effects. The objective of this study was to compare single-step blending methods and GBLUP methods with and without adjustment of the genomic relationship matrix for genomic prediction of 16 traits in the Nordic Holstein population.MethodsThe data consisted of de-regressed proofs (DRP) for 5 214 genotyped and 9 374 non-genotyped bulls. The bulls were divided into a training and a validation population by birth date, October 1, 2001. Five approaches for genomic prediction were used: 1) a simple GBLUP method, 2) a GBLUP method with a polygenic effect, 3) an adjusted GBLUP method with a polygenic effect, 4) a single-step blending method, and 5) an adjusted single-step blending method. In the adjusted GBLUP and single-step methods, the genomic relationship matrix was adjusted for the difference of scale between the genomic and the pedigree relationship matrices. A set of weights on the pedigree relationship matrix (ranging from 0.05 to 0.40) was used to build the combined relationship matrix in the single-step blending method and the GBLUP method with a polygenetic effect.ResultsAveraged over the 16 traits, reliabilities of genomic breeding values predicted using the GBLUP method with a polygenic effect (relative weight of 0.20) were 0.3% higher than reliabilities from the simple GBLUP method (without a polygenic effect). The adjusted single-step blending and original single-step blending methods (relative weight of 0.20) had average reliabilities that were 2.1% and 1.8% higher than the simple GBLUP method, respectively. In addition, the GBLUP method with a polygenic effect led to less bias of genomic predictions than the simple GBLUP method, and both single-step blending methods yielded less bias of predictions than all GBLUP methods.ConclusionsThe single-step blending method is an appealing approach for practical genomic prediction in dairy cattle. Genomic prediction from the single-step blending method can be improved by adjusting the scale of the genomic relationship matrix

Automated milking systems (AMS) are becoming more popular in dairy farms. In this paper we present an approach for estimation of residual error covariance matrices for AMS and conventional milking system (CMS) observations. The variances for other random effects are kept as defined in the evaluation model. AMS residual variances were found to be 16 to 37 percent smaller for milk and protein yield and 42 to 47 percent larger for fat yield compared to CMS

Denne artikel beskriver resultaterne af et udredningsarbejde igangsat af Den Danske MaritimeFond. Udredningsarbejdet udmundede i en rapport, der beskriver de ingeniørkompetencer, derefterspørges af den danske maritime industri i dag, hvor Danmark ikke længere har nogenbetragtelig skibsbygningsaktivitet. Yderligere konkluderer rapporten, at efterspørgslen eftermaritime ingeniører er større end det antal, der bliver uddannet fra universiteterne. Rapportenpræsenterer en række anbefalinger til industrien og til universiteterne, der skal muliggøre, atbehovet for maritime ingeniørerne mødes. Anbefalingerne er kort beskrevet sammen med detarbejde, der er igangsat på Danmarks Tekniske Universitet (DTU) og Syddansk Universitet(SDU) for at følge op på rapportens anbefalinger. This article describes the outcome of a survey initiated by the Danish Maritime Fund (DMF).The survey resulted in a report that describes the engineering competencies requested by theDanish maritime industry. This is of interest since the desired competencies have changed inthe past one to two decades, where Denmark no longer has a considerable ship-buildingindustry. Furthermore, the DMF initiated report concludes that the demand for maritimeengineers in the industry is larger than the output. The report sets forth a series ofrecommendations to the industry as well as the universities to enable meeting the demand formaritime engineers in Denmark. The recommendations are outlined together with the workcommenced at the Technical University of Denmark (DTU) and the University of SouthernDenmark (SDU) to follow up on the recommendations in the report.

It has been shown that participants in the dictator game are less willing to give money to the other participant when their choice set also includes the option to take money. We examine whether this effect is due to the choice set providing a signal about entitlements in a setting where entitlements initially may be considered unclear. We find that the share of positive transfers depends on the choice set even when there is no uncertainty about entitlements, and that this choice-set effect is robust across a heterogenous group of participants recruited from the general adult population in Denmark. The findings are consistent with dictator giving partly being motivated by a desire to signal that one is not entirely selfish or by a desire to follow a social norm that is choice-set dependent.

CD communication 24-02