HYDRODYNAMIC INTERACTION BETWEEN SHIPS AND RESTRICTED WATERWAYS

In open and unrestricted waters the water displaced by a forward sailing vessel can travel without major obstruction underneath and along the ship. In restricted and shallow sailing conditions, the displaced water is squeezed between the hull and the bottom and/or the bank. This results in higher flow velocities and as a consequence a pressure drop around the same hull. In the vicinity of a bank this pressure drop generates a combination of forces and moments on the vessel, known as bank effects. The major achievement of the presented research is the development of a realistic and robust formulation for these bank effects. This knowledge is acquired with an extensive literature study on one hand and with dedicated model tests carried out in different towing tanks on the other. The majority of the utilised model tests were carried out in the shallow water towing tank at Flanders Hydraulics Research in Antwerp, Belgium. The data set on bank effects consists of more than 8 000 unique model test setups (which is by far the most elaborate research ever carried out on this subject). These model tests provide the input for the analysis of bank effects and the creation of the mathematical model.

Significant DBSCAN+: Statistically Robust Density-based Clustering

Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.

Improving Seismic Inversion Robustness via Deformed Jackson Gaussian

The seismic data inversion from observations contaminated by spurious measures (outliers) remains a significant challenge for the industrial and scientific communities. This difficulty is due to slow processing work to mitigate the influence of the outliers. In this work, we introduce a robust formulation to mitigate the influence of spurious measurements in the seismic inversion process. In this regard, we put forth an outlier-resistant seismic inversion methodology for model estimation based on the deformed Jackson Gaussian distribution. To demonstrate the effectiveness of our proposal, we investigated a classic geophysical data-inverse problem in three different scenarios: (i) in the first one, we analyzed the sensitivity of the seismic inversion to incorrect seismic sources; (ii) in the second one, we considered a dataset polluted by Gaussian errors with different noise intensities; and (iii) in the last one we considered a dataset contaminated by many outliers. The results reveal that the deformed Jackson Gaussian outperforms the classical approach, which is based on the standard Gaussian distribution.

Red-Emitting SBBF (Single-Benzene-Based Fluorophore)-Silica Hybrid Material: One-Pot Synthesis, Characterization, and Biomedical Applications

We report, for the first time, a new red-emitting hybrid material based on a single-benzene-based fluorophore (SBBF) and silica. This robust formulation shows several features, including bright emissions at a red wavelength (>600 nm), high scalability (>gram-scale), facile synthesis (one-pot reaction; SBBF formation, hydrolytic condensation, propagation), high stability (under different humidity, pH, light), bio-imaging applicability with low cellular toxicity, and an antibacterial effect within Gram-negative/Gram-positive strains. Based on our findings, we believe that these hybrid materials can pave the way for the further development of dye-hybrid materials and applications in various fields.

$$\Gamma $$-Robust electricity market equilibrium models with transmission and generation investments

We consider uncertain robust electricity market equilibrium problems including transmission and generation investments. Electricity market equilibrium modeling has a long tradition but is, in most of the cases, applied in a deterministic setting in which all data of the model are known. Whereas there exist some literature on stochastic equilibrium problems, the field of robust equilibrium models is still in its infancy. We contribute to this new field of research by considering $$\Gamma $$ Γ -robust electricity market equilibrium models on lossless DC networks with transmission and generation investments. We state the nominal market equilibrium problem as a mixed complementarity problem as well as its variational inequality and welfare optimization counterparts. For the latter, we then derive a $$\Gamma $$ Γ -robust formulation and show that it is indeed the counterpart of a market equilibrium problem with robustified player problems. Finally, we present two case studies to gain insights into the general effects of robustification on electricity market models. In particular, our case studies reveal that the transmission system operator tends to act more risk-neutral in the robust setting, whereas generating firms clearly behave more risk-averse.

Branch-Cut-and-Price for the Robust Capacitated Vehicle Routing Problem with Knapsack Uncertainty

Capacitated vehicle routing problems are widely studied combinatorial optimization problems, and branch-and-cut-and-price algorithms can solve instances harder than ever before. These models, however, neglect that demands volumes are often not known with precision when planning the vehicle routes, thus incentivizing decision makers to significantly overestimate the volumes for avoiding coping with infeasible routes. A robust formulation that models demand uncertainty through a knapsack polytope is considered. A new branch-and-cut-and-price algorithm for the problem is provided, which combines efficient algorithms for the problem with no uncertainty with profound results in robust combinatorial optimization and includes novel heuristics and new valid inequalities. The numerical results illustrate that the resulting approach is two orders of magnitude faster that the best algorithm from the literature, solving twice as many instances to optimality.

Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach

We study statistical inference and distributionally robust solution methods for stochastic optimization problems, focusing on confidence intervals for optimal values and solutions that achieve exact coverage asymptotically. We develop a generalized empirical likelihood framework—based on distributional uncertainty sets constructed from nonparametric f-divergence balls—for Hadamard differentiable functionals, and in particular, stochastic optimization problems. As consequences of this theory, we provide a principled method for choosing the size of distributional uncertainty regions to provide one- and two-sided confidence intervals that achieve exact coverage. We also give an asymptotic expansion for our distributionally robust formulation, showing how robustification regularizes problems by their variance. Finally, we show that optimizers of the distributionally robust formulations we study enjoy (essentially) the same consistency properties as those in classical sample average approximations. Our general approach applies to quickly mixing stationary sequences, including geometrically ergodic Harris recurrent Markov chains.