This paper presents an effective image retrieval method by combining high-level features from convolutional neural network (CNN) model and low-level features from dot-diffused block truncation coding (DDBTC). The low-level features, e.g., texture and color, are constructed by vector quantization -indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate and average recall rate (ARR), are employed to examine various data sets. As documented in the experimental results, the proposed schemes can achieve superior performance compared with the state-of-the-art methods with either low-or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

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Background: There is a need for postoperative pain control that minimizes/eliminates opioid use during the first 72 hours following surgery, when pain is most severe. HTX-011 is an extended-release, dual-acting local anesthetic that demonstrated superior 72-hour analgesia over standard of care bupivacaine hydrochloride (HCl) and saline placebo in a phase 3 bunionectomy study (EPOCH-1). Having shown HTX-011 monotherapy is superior to bupivacaine HCl in reducing postoperative pain intensity and opioid use, this follow-on study evaluated the safety and efficacy of HTX-011 as the foundation of a multimodal analgesia (MMA) regimen using over-the-counter medications recommended by practice guidelines for pain management. Methods: Following regional anesthesia administered as a lidocaine block, patients underwent unilateral bunionectomy with osteotomy and internal fixation. Prior to closure, HTX-011 (up to 60 mg bupivacaine/1.8 mg meloxicam) was applied without a needle. Patients received scheduled postoperative MMA alternating ibuprofen (600 mg) and acetaminophen (1 g) every 3 hours for 72 hours. Efficacy was assessed based on pain intensity (numeric rating scale [NRS; 0-10]) and consumption of opioid rescue medication (intravenous morphine milligram equivalents [MME]). Adverse event and vital sign monitoring, plus laboratory and wound healing assessments, were used to determine safety. Results: Over the 72-hour assessment period following bunionectomy, mean pain scores were mild in severity (NRS <4) and 22/31 patients (71%) experienced no severe pain (NRS {greater than or equal to}7) with HTX-011 as the foundation of scheduled, non-opioid MMA. Patients consumed an average of 1.61 MME total, with 24/31 (77%) requiring no opioid rescue medication (opioid-free). HTX-011 was well-tolerated and demonstrated no safety concerns with the inclusion of postoperative MMA. Conclusions: HTX-011 as the foundation of an MMA regimen including scheduled ibuprofen and acetaminophen maintained mean postoperative pain scores in the mild range and enabled opioid-free recovery for 77% of bunionectomy patients through the 28-day recovery period.

Singular perturbation reaction-diffusion problem with Dirichlet boundary condition is considered. It is a multi-scale problem. Presence of small parameter leads to boundary layer phenomena in both sides of the region. A non-equidistant finite difference method is presented according to the property of boundary layer. The region is divided into an inner boundary layer region and an outer boundary layer region according to transition point of Shishkin. The steps sizes are equidistant in the outer boundary layer region. The step sizes are gradually increased in the inner boundary layer region such that half of the step sizes are different from each other. Truncation error is estimated. The proposed method is stable and uniformly convergent with the order higher than 2. Numerical results are given, which are in agreement with the theoretical result.

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