A decomposition method for finding optimal container stowage plans

In transportation of goods in large container ships, shipping industries need to minimize the time spent at ports to load/unload containers. An optimal stowage of containers on board minimizes unnecessary unloading/reloading movements, while satisfying many operational constraints. We address the basic container stowage planning problem (CSPP). Different heuristics and formulations have been proposed for the CSPP, but finding an optimal stowage plan remains an open problem even for small-sized instances. We introduce a novel formulation that decomposes CSPPs into two sets of decision variables: the first defining how single container stacks evolve over time and the second modeling port-dependent constraints. Its linear relaxation is solved through stabilized column generation and with different heuristic and exact pricing algorithms. The lower bound achieved is then used to find an optimal stowage plan by solving a mixed-integer programming model. The proposed solution method outperforms the methods from the literature and can solve to optimality instances with up to 10 ports and 5,000 containers in a few minutes of computing time

Improving Rigid 3-D Calibration for Robotic Surgery

Autonomy is the next frontier of research in robotic surgery and its aim is to improve the quality of surgical procedures in the next future. One fundamental requirement for autonomy is advanced perception capability through vision sensors. In this article, we propose a novel calibration technique for a surgical scenario with a da Vinci Research Kit (dVRK) robot. Camera and robotic arms calibration are necessary to precise position and emulate expert surgeon. The novel calibration technique is tailored for RGB-D cameras. Different tests performed on relevant use cases prove that we significantly improve precision and accuracy with respect to state of the art solutions for similar devices on a surgical-size setups. Moreover, our calibration method can be easily extended to standard surgical endoscope used in real surgical scenario

Estimation of emission rate from experimental data

The estimation of the source pollutant strength is a relevant issue for atmospheric environment. This characterizes an inverse problem in the atmospheric pollution dispersion studies. In the inverse analysis, a time-dependent pollutant source is considered, where the location of such source term is assumed known. The inverse problem is formulated as a non-linear optimization approach, whose objective function is given by the least-square difference between the measured and simulated by the mathematical model, pollutant concentration, associated with a regularization operator. The forward problem is addressed by a Lagrangian model, and a quasi-Newton method is employed for minimizing the objective function. The second-order Tikhonov regularization is applied and the regularization parameter is computed by using the L-curve scheme. The inverse-problem methodology is verified with data from the tracer Copenhagen experiment

An Ecophisiological Proposal to Manage Natural Grasslands: A Long Term Trial

Natural grasslands on Southern Brazil comprise the so called “Rio de La Plata Grasslands” in South America. They are an important fodder source for ruminant pastoral systems and contribute to regional ecosystem services. Strength of these grasslands is its floristic diversity that poses a dilemma to farmers: how to choose management protocols that could be applied for hundreds of species. We propose to use a functional ecophysiological approach based on groups of grasses, the most abundant on aerial biomass of this natural grasslands. We clustered the most frequent grasses in two groups based on its leaf traits (leaf dry matter content and specific leaf area). These traits are functional clues to growth rhythms and nutritive value that could separate grasses in “resource capture” and “resource conservation” groups, both important for forage production and ecosystem services. Evaluating the most frequent grasses in each group we found they have an average of 375 degree-days, for “resource capture” and 750 degree-day for “resource conservation” groups, as its leaf elongation duration. So we evaluated a rotational grazing system based on this morphogenic trait for beef heifers rearing on natural grasslands from 2010 to 2019. We chose these experimental animals, as a model by its nutrient requirements and relevance for regional rearing and breeding systems. Our results indicate an average daily gain that is adequate to reach mating age and weight targets (0,3 kg/heifer/day to mate at 24 months) and allowed a higher stocking rate and gain per area when compared to regional standards (1,100 kg of live weight/ha and 370 kg/ha versus 600 and 70 kg/ha). All this animal performance was obtained without changing floristic diversity and also enhancing ecosystem services as CO2 sequestration. We concluded that this approach could allow farmers to conciliate the dilemma of production and conservation in pastoral ecosystems

Citicoline in ophthalmological neurodegenerative disease: A comprehensive review

Cytidine 5'-diphosphocholine has been widely studied in systemic neurodegenerative diseases, like Alzheimer's disease, Parkinson's disease, and brain ischemia. The rationale for the use of citicoline in ophthalmological neurodegenerative diseases, including glaucoma, anterior ischemic optic neuropathy, and diabetic retinopathy, is founded on its multifactorial mechanism of action and the involvement in several metabolic pathways, including phospholipid homeostasis, mitochondrial dynamics, as well as cholinergic and dopaminergic transmission, all being involved in the complexity of the visual transmission. This narrative review is aimed at reporting both pre-clinical data regarding the involvement of citicoline in such metabolic pathways (including new insights about its role in the intracellular proteostasis through an interaction with the proteasome) and its effects on clinical psychophysical, electrophysiological, and morphological outcomes following its use in ophthalmological neurodegenerative diseases (including the results of the most recent prospective randomized clinical trials)

Artificial intelligence weights the importance of factors predicting complete cytoreduction at secondary cytoreductive surgery for recurrent ovarian cancer

Objective: Accumulating evidence support that complete cytoreduction (CC) at the time of secondary cytoreductive surgery (SCS) improves survival in patients affected by recurrent ovarian cancer (ROC). Here, we aimed to determine whether artificial intelligence (AI) might be useful in weighting the importance of clinical variables predicting CC and survival. Methods: This is a retrospective study evaluating 194 patients having SCS for ROC. Using artificial neuronal network (ANN) analysis was estimated the importance of different variables, used in predicting CC and survival. ANN simulates a biological neuronal system. Like neurons, ANN acquires knowledge through a learning-phase process and allows weighting the importance of covariates, thus establishing how much a variable influences a multifactor phenomenon. Results: Overall, 82.9% of patients had CC at the time of SCS. Using ANN, we observed that the 3 main factors driving the ability of achieve CC included: disease-free interval (DFI) (importance: 0.231), retroperitoneal recurrence (importance: 0.178), residual disease at primary surgical treatment (importance: 0.138), and International Federation of Gynecology and Obstetrics (FIGO) stage at presentation (importance: 0.088). Looking at connections between different covariates and overall survival (OS), we observed that DFI is the most important variable influencing OS (importance: 0.306). Other important variables included: CC (importance: 0.217), and FIGO stage at presentation (importance: 0.100). Conclusion: According to our results, DFI should be considered as the most important factor predicting both CC and OS. Further studies are needed to estimate the clinical utility of AI in providing help in decision making process