Laparoscopic intra-peritoneal ventral hernia repair associated with traditional parietal closure (hybrid technique).

editorial reviewe

Uncertainty reduction in residual stress measurements by an optimised inverse solution using nonconsecutive polynomials

Many destructive methods for measuring residual stresses such as the slittingmethod require an inverse analysis to solve the problem. The accuracy of theresult as well as an uncertainty component (the model uncertainty) dependson the basis functions used in the inverse solution. The use of a series expan-sion as the basis functions for the inverse solution was analysed in a previouswork for the particular case where functions orders grew consecutively. Thepresent work presents a new estimation of the model uncertainty and a newimproved methodology to select the final basis functions for the case wherethe basis is composed of polynomials. Including nonconsecutive polynomialorders in the basis generates a larger space of possible solutions to be evaluatedand allows the possibility to include higher-order polynomials. The paperincludes a comparison with two other inverse analyses methodologies appliedto synthetically generated data. With the new methodology, the final error isreduced and the uncertainty estimation improved

Sensor-Carrying Platforms

Information and communication technology, autonomy, and miniaturization in terms of, for example, microelectromechanical systems are enabling technologies with significant impact on the development of sensors, sensor-carrying platforms, control systems, data gathering, storage, and analysis methods. Sensor-carrying platforms are grouped in stationary devices such as landers and moorings to dynamic platforms such as marine robotics, ships, aerial systems, and remote-sensing satellites from space. Lately, the development of low-cost small satellites with customized payload sensors and accessible mission control centers has opened for a democratization of the space for remote sensing as well. The mapping and monitoring strategy may be carried out by each type of sensor-carrying platform suitable for the mission. However, we see a quantum leap by operating heterogeneous sensor-carrying platforms for the most efficient mapping and monitoring in spatial and temporal scales. We are facing a paradigm shift in terms of resolution and coverage capabilities. There have been several research efforts to improve the technology and methodology for mapping and monitoring of the oceans. Today, we see that the mapping coverage may be 100–1000 times higher than the state-of-the-art technology 6 years ago. The entailed increase in data harvesting does also create new challenges in handling of big data sets. It is an increasing need to update the oceanographic and ecosystem numerical model capabilities, taking full benefit of the ongoing shift in technology. The Arctic can truly be characterized as a remote and harsh environment for scientific operations and even more demanding during the Polar Night due to the darkness. During winter operations, extreme coldness may also be a challenge dependent on the weather conditions. Enabling technology and proper operational procedures may be the only way to reveal and understand the processes taking place there. The spatial scale is enormous, and as several research campaigns have already taught us, the variability is huge not only during the seasons but also over the years. This clearly also tells us the importance of prolonged presence. In this chapter, we will briefly present the various sensor-carrying platforms and payload sensors. We will also describe the philosophy behind integrated operations using heterogenous platforms and why and how to bridge science and technology being successful in the development of autonomous systems for efficient and safe operations. Examples and experience from Arctic missions will also be presented.acceptedVersionThis is a post-peer-review, pre-copyedit version of an article. Locked until 9/4-2022 due to copyright restrictions. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-33208-

Outcomes and Risk Score for Distal Pancreatectomy with Celiac Axis Resection (DP-CAR): An International Multicenter Analysis

Background: Distal pancreatectomy with celiac axis resection (DP-CAR) is a treatment option for selected patients with pancreatic cancer involving the celiac axis. A recent multicenter European study reported a 90-day mortality rate of 16%, highlighting the importance of patient selection. The authors constructed a risk score to predict 90-day mortality and assessed oncologic outcomes. Methods: This multicenter retrospective cohort study investigated patients undergoing DP-CAR at 20 European centers from 12 countries (model design 2000–2016) and three very-high-volume international centers in the United States and Japan (model validation 2004–2017). The area under receiver operator curve (AUC) and calibration plots were used for validation of the 90-day mortality risk model. Secondary outcomes included resection margin status, adjuvant therapy, and survival. Results: For 191 DP-CAR patients, the 90-day mortality rate was 5.5% (95 confidence interval [CI], 2.2–11%) at 5 high-volume (≥ 1 DP-CAR/year) and 18% (95 CI, 9–30%) at 18 low-volume DP-CAR centers (P = 0.015). A risk score with age, sex, body mass index (BMI), American Society of Anesthesiologists (ASA) score, multivisceral resection, open versus minimally invasive surgery, and low- versus high-volume center performed well in both the design and validation cohorts (AUC, 0.79 vs 0.74; P = 0.642). For 174 patients with pancreatic ductal adenocarcinoma, the R0 resection rate was 60%, neoadjuvant and adjuvant therapies were applied for respectively 69% and 67% of the patients, and the median overall survival period was 19 months (95 CI, 15–25 months). Conclusions: When performed for selected patients at high-volume centers, DP-CAR is associated with acceptable 90-day mortality and overall survival. The authors propose a 90-day mortality risk score to improve patient selection and outcomes, with DP-CAR volume as the dominant predictor