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Background: Metatarsalgia refers to localized or generalized forefoot pain in the region of the metatarsal heads. Often this pain is plantar, beneath the metatarsal heads, and arises from either mechanical or iatrogenic causes. The treatment of metatarsalgia remains controversial. A thorough understanding of the biomechanics of the forefoot and the underlying pathology of the particular type of metatarsalgia affecting the patient is a prerequisite to selecting the proper treatment. In recent years, massage therapy has been increasingly accepted by patients due to its lower costs, fewer unwanted side effects, and safety for clinical use. In this systematic review, we aim to evaluate the effectiveness and safety of massage therapy for patients with metatarsal pain. Methods: We will search the following electronic databases for randomized controlled trials to evaluate the effectiveness and safety of massage therapy in treating metatarsal pain: Wanfang and PubMed Database, CNKI, CENTRAL, CINAHL, and EMBASE. Each database will be searched from inception to October 2020. The entire process will include study selection, data extraction, risk of bias assessment, and meta-analyses. Results: This proposed study will evaluate the effectiveness and safety of massage therapy for patients with metatarsal pain. The outcomes will include changes in metatarsal pain relief and adverse effect. Conclusions: This proposed systematic review will evaluate the existing evidence on the effectiveness and safety of massage therapy for patients with metatarsalgia. Dissemination and ethics: The results of this review will be disseminated through peer-reviewed publication. Because all of the data used in this systematic review and meta-analysis has been published, this review does not require ethical approval. Furthermore, all data will be analyzed anonymously during the review process. OSF Registration number: DOI 10.17605/OSF.IO/C6KFJ

Background Thoracic facet joint disorder is a common thoracic disorder in clinic, inducing pain and discomfort at the dislocated thoracic vertebrae, radiating to pain of the neck and back. The incidence of thoracic facet joint disorder is higher than the facet disorder of the cervical and lumbar vertebrae. Therefore, an ideal strategy to relieve thoracic facet joint disorder is urgently needed. In recent years, massage therapy has been increasingly accepted by thoracic facet joint disorder patients due to its lower costs, fewer unwanted side effects and safety for clinical use. In this systematic review, we aim to evaluate the effectiveness and safety of massage therapy for patients with thoracic facet joint disorder. Methods We will search the following electronic databases for randomized controlled trials to evaluate the effectiveness of massage therapy in treating thoracic facet joint disorder: Wanfang and PubMed Database, CNKI, CENTRAL, CINAHL and EMBASE. Each database will be searched from inception to October 2020. The entire process will include study selection, data extraction, risk of bias assessment and meta-analyses. Results This proposed study will evaluate the effectiveness of massage therapy for patients with thoracic facet joint disorder. Conclusions This proposed systematic review will evaluate the existing evidence on the effectiveness and safety of massage therapy for patients with thoracic facet joint disorder. Dissemination and ethics The results of this review will be disseminated through peer-reviewed publication. Because all of the data used in this systematic review and meta-analysis has been published, this review does not require ethical approval. Furthermore, all data will be analyzed anonymously during the review process. Osf registration number DOI 10.17605/OSF.IO/XMEJD.

Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.