Background High-flow nasal cannula (HFNC) is a new type of oxygen therapy, but its application in surgery remains unclear, we tried to describe the application of HFNC in microlaryngoscopic surgery for the Chinese population. Methods Nineteen adults, American society of anesthesiology class (ASA) 1–2 patients with body mass index < 30 kg.m−2 underwent microlaryngoscopic surgery using HFNC for airway management. Outcomes included apnoea time, intraoperative oxygenation, carbon dioxide value, lactate value, and the relationship between the duration of apnoea time and carbon dioxide levels. Results A total of 19 patients underwent vocal cord tumor resection under a microlaryngoscope with HFNC as the sole method of ventilation. The mean age was 39.7 years old, and the mean BMI was 23.9 kg.m−2. The mean apnea time was 21.5 min. The SpO2 of 18 patients remained above 90%, and only 1 patient dropped to 88%. The average basal lactate and highest lactate value was 0.58 mmol. L−1 and 0.68 mmol.L−1. The difference between basal and highest lactate values was statistically significant (P < 0.05). The average highest PaCO2 value was 79.4 mmHg. The PaCO2 increased by 1.68 ± 0.12 mmHg every minute linearly. Conclusions In the case series we have observed that HFNC would be safe and effective oxygenation and ventilation technique for selected Chinese patients undergoing non-laser microlaryngoscopic surgery within 30 min. The tubeless technology reduces the complications of tracheal intubation and jet ventilation and clears the surgical field of vision. Trial registration Chinese Clinical Trial Registry (ChiCTR100049144).
The key problem for picking robots is to locate the picking points of fruit. A method based on the moment of inertia and symmetry of apples is proposed in this paper to locate the picking points of apples. Image pre-processing procedures, which are crucial to improving the accuracy of the location, were carried out to remove noise and smooth the edges of apples. The moment of inertia method has the disadvantage of high computational complexity, which should be solved, so convex hull was used to improve this problem. To verify the validity of this algorithm, a test was conducted using four types of apple images containing 107 apple targets. These images were single and unblocked apple images, single and blocked apple images, images containing adjacent apples, and apples in panoramas. The root mean square error values of these four types of apple images were 6.3, 15.0, 21.6 and 18.4, respectively, and the average location errors were 4.9°, 10.2°, 16.3° and 13.8°, respectively. Furthermore, the improved algorithm was effective in terms of average runtime, with 3.7 ms and 9.2 ms for single and unblocked and single and blocked apple images, respectively. For the other two types of apple images, the runtime was determined by the number of apples and blocked apples contained in the images. The results showed that the improved algorithm could extract symmetry axes and locate the picking points of apples more efficiently. In conclusion, the improved algorithm is feasible for extracting symmetry axes and locating the picking points of apples. Additional key words: picking robot; fruit picking point; symmetry axes extraction; moment of inertia; convex hull
Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers' preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users' views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods. INDEX TERMS Many-objective, recommender systems, similarity model, stakeholders.
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