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AIM: To explore the variance in efficacy between botulinum toxin A(BTA)injection and extraocular muscle surgery in managing large-angle(≥+60 PD)acute acquired concomitant esotropia(AACE).METHODS: A retrospective analysis was conducted on clinical data of 60 patients with AACE treated at our hospital from June 2020 to December 2022. Patients were divided into three groups based on different treatments: 2.5 IU BTA injection group(14 cases), 5.0 IU BTA injection group(29 cases), and surgical group(17 cases). Follow-up was conducted for 6 mo after treatment to observe the degree of strabismus after the correction of refractive error, visual function, treatment effectiveness, and occurrence of complications after BTA injection.RESULTS: At 6 mo post-treatment, the degree of strabismus in the surgical group and the 5.0 IU BTA injection group was lower than that in the 2.5 IU BTA injection group(P0.017). The effective rate of the 5.0 IU BTA injection group was higher than that of the 2.5 IU BTA injection group(86% vs 43%, P0.05). The incidence of complications after treatment was not significantly different between the 2.5 IU BTA injection group and the 5.0 IU BTA injection group(43% vs 52%, P>0.05).CONCLUSION: For AACE patients with esotropia degree ≥+60 PD, bilateral medial rectus injection of 5.0 IU BTA can yield outcomes comparable to traditional extraocular muscle surgery, with the advantages of minimal trauma and simple and convenient operation.

Reliability is one of important index in the analysis and evaluation of mechanical structure. Aiming at the problems of various failure modes and low efficiency of reliability evaluation for complex mechanical structures, the reliability analysis method based on polynomial chaos expansions is applied to the reliability analysis of mechanical structures by combining Monte Carlo random sampling and polynomial chaos expansions. In addition, considering the correlation of parameters and various failure modes in the limit state function, the reliability evaluation method of mechanical structure considering failure correlation is given. Finally, a worm gear two-stage reducer is taken as an example to verify the proposed method according to analyzes the various limit state function of different failure form. The computational results show that the reliability evaluation method based on polynomial chaos expansions is efficient and accurate in mechanical structure reliability analysis. And when failure correlation is considered, it is also simple because of avoiding the complex transformation calculation problem.

With the advancement of power systems, the integration of a substantial portion of renewable energy often leads to frequent voltage surges and increased fluctuations in distribution networks (DNs), significantly affecting the safety of DNs. Active distribution networks (ADNs) can address voltage issues arising from a high proportion of renewable energy by regulating distributed controllable resources. However, the conventional mathematical optimization-based approach to voltage reactive power control has certain limitations. It heavily depends on precise DN parameters, and its online implementation requires iterative solutions, resulting in prolonged computation time. In this study, we propose a Volt-VAR control (VVC) framework in ADNs based on multi-agent reinforcement learning (MARL). To simplify the control of photovoltaic (PV) inverters, the ADNs are initially divided into several distributed autonomous sub-networks based on the electrical distance of reactive voltage sensitivity. Subsequently, the Multi-Agent Soft Actor-Critic (MASAC) algorithm is employed to address the partitioned cooperative voltage control problem. During online deployment, the agents execute distributed cooperative control based on local observations. Comparative tests involving various methods are conducted on IEEE 33-bus and IEEE 141-bus medium-voltage DNs. The results demonstrate the effectiveness and versatility of this method in managing voltage fluctuations and mitigating reactive power loss. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting could provide a scientific basis for the rapid response and stable operation of a modern power system. To take advantage of the meteorological big data to improve short-term forecasting accuracy, while considering the nonlinear and spatiotemporal correlation characteristics of the power load data, this paper proposes a short-term power load forecasting method based on meteorological data dimensionality reduction and a hybrid deep neural network. First, the elastic network is used to reduce the dimensions of high-dimensional meteorological big data, eliminate irrelevant meteorological factors, and improve the quality of input data. Then, taking the dimension-reduced meteorological data and historical load data as input, a load forecasting model based on a novel deep neural network is established. This model uses a convolution neural network (CNN) and a bi-directional long short-term memory (BiLSTM) neural network to extract the spatial and temporal correlation features of power load related data, and combines the attention mechanism to enhance the learning weight of the load series in important periods, and adopts residual connection (RC) to optimize the network training speed and alleviate the overfitting problem. Finally, taking the open data set of the New York Independent System Operating Agency (NYISO) as an example, single-step and multi-step advance prediction experiments are carried out to verify the advantages of the proposed method. [ABSTRACT FROM AUTHOR]