Risk Factors for Early and Late Complications after Laparoscopic Sleeve Gastrectomy in One-Year Observation

Background: Although laparoscopic sleeve gastrectomy (LSG) is considered a safe bariatric procedure in the treatment of obesity, it still involves a risk of developing postoperative complications. Knowledge of risk factors for possible complications would allow appropriate preoperative planning, optimization of postoperative care, as well as early diagnosis and treatment. The aim of this study was to evaluate risk factors for complications after laparoscopic sleeve gastrectomy. Methods: A retrospective study of 610 patients who underwent LSG at a tertiary institution were included in the study through retrospective analysis of the medical data. Complications were categorized as early (<30 days) and late (≥30 days) and evaluated according to the Clavien–Dindo Classification. Results: Early complications were observed in 35 patients (5.74%) and late complications occurred in 10 patients (1.64%). Independent risk factors of early complications after laparoscopic sleeve gastrectomy included hypercholesterolemia (OR 3.73; p-value = 0.023) and smoking (OR = 274.66, p-value < 0.001). Other factors that may influence the postoperative course are length of hospital stay and operation time. Smoking, peptic ulcer diseases and co-existence of hiatal hernia were found to be an independent predictors of late complications. Conclusions: Smoking is associated with the higher risk of both, early and late complications, while hypercholesterolemia with only <30 days complications after laparoscopic sleeve gastrectomy.

A Novel Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling

Bird damage to fruit crops causes significant monetary losses to farmers annually. The application of traditional bird repelling methods such as bird cannons and tree netting become inefficient in the long run, requiring high maintenance and reducing mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be beneficial to solve this problem. However, due to their low battery capacity that equals low flight duration, it is necessary to evolve path planning optimization. A novel path planning optimization algorithm of UAVs based on Particle Swarm Optimization (PSO) is presented in this paper. This path planning optimization algorithm aims to manage the drone’s distance and flight time, applying optimization and randomness techniques to overcome the disadvantages of the traditional systems. The proposed algorithm’s performance was tested in three study cases: two of them in simulation to test the variation of each parameter and one in the field to test the influence on battery management and height influence. All cases were tested in the three possible situations: same incidence rate, different rates, and different rates with no bird damage to fruit crops. The field tests were also essential to understand the algorithm’s behavior of the path planning algorithm in the UAV, showing that there is less efficiency with fewer points of interest, but this does not correlate with the flight time. In addition, there is no association between the maximum horizontal speed and the flight time, which means that the function to calculate the total distance for path planning needs to be adjusted. Thus, the proposed algorithm presents promising results with an outstanding reduced average error in the total distance for the path planning obtained and low execution time, being suited for this and other applications.

A Hybrid Grey Wolf Optimizer for Process Planning Optimization with Precedence Constraints

Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup plans. These problems require metaheuristic-based approaches in order to be effectively and efficiently solved. Therefore, to optimize the complex process planning problem, a novel hybrid grey wolf optimizer (HGWO) is proposed. The traditional grey wolf optimizer (GWO) is improved by employing genetic strategies such as selection, crossover and mutation which enhance global search abilities and convergence of the traditional GWO. Precedence relationships among machining operations are taken into account and precedence constraints are modeled using operation precedence graphs and adjacency matrices. Constraint handling heuristic procedure is adopted to move infeasible solutions to a feasible domain. Minimization of the total weighted machining cost of a process plan is adopted as the objective and three experimental studies that consider three different prismatic parts are conducted. Comparative analysis of the obtained cost values, as well as the convergence analysis, are performed and the HGWO approach demonstrated effectiveness and flexibility in finding optimal and near-optimal process plans. On the other side, comparative analysis of computational times and execution times of certain MATLAB functions showed that the HGWO have good time efficiency but limited since it requires more time compared to considered hybrid and traditional algorithms. Potential directions to improving efficiency and performances of the proposed approach are given in conclusions.

Flight Planning Optimization of Multiple UAVs for Internet of Things

This article presents an approach to autonomous flight planning of Unmanned Aerial Vehicles (UAVs)-Drones as data collectors to the Internet of Things (IoT). We have proposed a model for only one aircraft, as well as for multiple ones. A clustering technique that extends the scope of the number of IoT devices (e.g., sensors) visited by UAVs is also addressed. The flight plan generated from the model focuses on preventing breakdowns due to a lack of battery charge to maximize the number of nodes visited. In addition to the drone autonomous flight planning, a data storage limitation aspect is also considered. We have presented the energy consumption of drones based on the aerodynamic characteristics of the type of aircraft. Simulations show the algorithm’s behavior in generating routes, and the model is evaluated using a reliability metric.