This paper aimed to study the application of local anesthetics combined with transversus abdominis plane (TAP) block in gynecological laparoscopy (GLS) surgery during perioperative period under the guidance of ultrasound image enhanced by the wavelet transform image enhancement (WTIE) algorithm. 56 patients who underwent GLS surgery in hospital were selected and classified as the infiltrating group and block group. The puncture needle was guided by ultrasound images under WTIE algorithm, and 0.375% ropivacaine was adopted to block TAP. The results showed that the dosage of propofol in the infiltrating group (313.23 ± 19.67 mg) was remarkably inferior to the infiltrating group (377.67 ± 21.56 mg)
P
<
0.05
. The hospitalization time of patients in the infiltrating group (2.14 ± 0.18 days) was obviously shorter than that of the infiltrating group (3.23 ± 0.27 days)
P
<
0.05
. 3 h, 6 h, and 12 h after the operation, the visual analogue scores (3.82 ± 1.58 points, 2.97 ± 1.53 points, and 1.38 ± 0.57 points) of the patients in the infiltration group were considerably higher than the infiltrating group (2.31 ± 1.46 points, 1.06 ± 1.28 points, and 0.95 ± 0.43 points)
P
<
0.05
. 3 h, 6 h, and 12 h after the operation, the number of patients in the infiltrating group who used tramadol for salvage analgesia (2 cases, 1 case, and 1 case) was notably less than that in the infiltration group (9 cases, 7 cases, and 3 cases)
P
<
0.05
. In short, local anesthetics combined with TAP block can reduce postoperative VAS score and postoperative nausea and vomiting (PONV) score, which also reduced the incidence of postoperative analgesia.
In this paper, we propose a new influence spread model, namely, Complementary&Competitive Independent Cascade (C 2 IC) model. C 2 IC model generalizes three well known influence model, i.e., influence boosting (IB) model, campaign oblivious (CO)IC model and the IC-N (IC model with negative opinions) model. This is the first model that considers both complementary and competitive influence spread comprehensively under multi-agent environment. Correspondingly, we propose the Complementary&Competitive influence maximization (C 2 IM) problem. Given an ally seed set and a rival seed set, the C 2 IM problem aims to select a set of assistant nodes that can boost the ally spread and prevent the rival spread concurrently. We show the problem is NP-hard and can generalize the influence boosting problem and the influence blocking problem. With classifying the different cascade priorities into 4 cases by the monotonicity and submodularity (M&S) holding conditions, we design 4 algorithms respectively, with theoretical approximation bounds provided. We conduct extensive experiments on real social networks and the experimental results demonstrate the effectiveness of the proposed algorithms. We hope this work can inspire abundant future exploration for constructing more generalized influence models that help streamline the works of this area.
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