Enhanced crow search algorithm with multi-stage search integration for global optimization problems.

Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows' behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.

Hessian Semisupervised Scatter Regularized Classification Model With Geometric and Discriminative Information for Nonlinear Process.

The integration of semisupervised modeling and discriminative information has been sporadically discussed in the research literature of traditional classification modeling, while the former one would make full use of the collected data and the latter one would further improve the classification performance. In this article, the Hessian semisupervised scatter regularized classification model is proposed as a coherent framework for the nonlinear process classification upon both labeled and unlabeled data. It is innovatively designed with a loss function to evaluate the classification accuracy and three regularization terms, respectively, corresponding to the geometry information, discriminative information, and model complexity. Both cases of the coherent framework, respectively, casted to the reproducing kernel Hilbert space and linear space, enjoy a theoretically guaranteed analytical solution. Experiments on process classification tasks on a benchmark dataset and a real industrial polyethylene process illustrate the merits of the proposed method in a sense that the class information of novel collected data is accurately predicted.

Bisphosphonates Can Maintain Periprosthetic Bone Mass Density after Total Hip Replacement, with Controversy in Region of Interest 5.

BACKGROUND: Total hip replacement (THR) is the standard surgical treatment of hip diseases. Periprosthetic bone mass density (BMD) loss may be a cause for revision surgery. Bisphosphonates (BPs) are now the principal class medications for osteoporosis. OBJECTIVE: To demonstrate the effect of BPs on treating periprosthetic osteoporosis after THR via a meta-analysis of randomized controlled trials (RCTs). METHODS: A comprehensive search of PubMed, EMBASE, the Web of Science and the Cochrane Central Register of Controlled Trials was performed for RCTs on the effect of BPs on treating periprosthetic osteoporosis after THR and clinical outcomes relative to controls. The primary outcome measures were the change in BMD in each region of interest (ROI), the change in serum bone turnover marker levels, the change in functional parameters and the risk of adverse effects (AEs). The final search was performed in March, 2020. RESULTS: Nine RCTs were included. A total of 359 patients met the inclusion criteria. BPs can clearly maintain periprosthetic BMD in ROIs at 1, 2, 3, 4, 6 and 7 at 6, 12 and 24 months. In addition, BPs can clearly decrease serum procollagen type 1 N-terminal propeptide (P1NP) levels at 12 months. There was no significant difference in the risk of AEs between the BP and control groups; however, BPs can cause more patients to decline participation. CONCLUSION: BPs can effectively maintain overall periprosthetic BMD, but BMD in ROI 5 remains controversial. In addition, the safety of BPs is relatively high, but the compliance may be relatively low.

Pioglitazone Therapy Decreases Bone Mass Density and Increases Fat Mass: A Meta-Analysis.

BACKGROUND: Pioglitazone is mainly used for the management of type 2 diabetes and other insulinassociated diseases. However, the molecular mechanism of pioglitazone can lead to an imbalance in bone metabolism, thus decreasing bone mass density (BMD) and increasing the risk for fractures. OBJECTIVE: To demonstrate the effect of pioglitazone therapy on bone metabolism and fat mass. METHODS: A comprehensive search of the PubMed, EMBASE, Web of Science and Cochrane Central databases for randomized controlled trials (RCTs) on the effect of pioglitazone therapy on BMD and fat mass was performed. The primary outcome measures were the measured values of BMD, percentage changes in BMD, measured values of bone turnover markers and bone metabolic hormones, changes in BMI, body and leg fat mass, and fracture rates. The final search was performed in May 2019. RESULTS: Six RCTs were included. A total of 749 patients met the inclusion criteria. Pioglitazone therapy was shown to significantly reduce the BMD of the whole body, lumbar spine, and total hip and serum PTH levels and increase BMI, total body fat mass and leg fat mass. In addition, 30 mg/d and 30 mg/d initially for one month followed by 45 mg/d pioglitazone could reduce the BMD of the lumbar spine. Pioglitazone therapy exerted no significant influence on the BMD of the femoral neck, serum BSAP or 25-OHD levels, or fracture rates. CONCLUSION: Compared with placebo, pioglitazone therapy reduced BMD and serum PTH levels and increased fat mass and BMI with no difference in serum BSAP or 25-OHD levels or fracture rates; 30 mg/d pioglitazone was sufficient to reduce the BMD of the lumbar spine.