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Predicting the remaining useful life (RUL) is an effective way to indicate the health of lithium-ion batteries, which can help to improve the reliability and safety of battery-powered systems. To predict the RUL, the line of research focuses on using the empirical degradation model followed by the particle filter (PF) algorithm, which is used for online updating the model's parameters. However, this works well for specific batteries under specific discharge conditions. When the degradation trends cannot be presented by the chosen empirical model or the standard PF encounters impoverishment and degeneracy problem, the RUL prediction would be inaccurate. To improve the RUL prediction accuracy, we propose a novel approach by enhancing the existing method from two aspects. First, we introduce a neural network (NN) to model battery degradation trends under various operation conditions. As NN's generalization and nonlinear representing ability, it outperforms the typical empirical degradation model. Second, the NN model's parameters are recursively updated by the bat-based particle filter. The bat algorithm is used to move the particles to the high likelihood regions, which optimizes the particle distribution and thus reduces the degeneracy and impoverishment of PF. In this paper, quantitative evaluation is presented using two datasets with different batteries under different aging conditions. The results indicate that the proposed the approach can achieve higher RUL prediction accuracy than conventional empirical model and standard PF. INDEX TERMS Lithium-ion batteries, neural network, capacity degradation, remaining useful life prediction, bat algorithm, particle filter.

Railway alignment optimization is considered one of the most complicated and time‐consuming problems in railway planning and design. It requires searching among the infinite potential alternatives in huge three‐dimensional (3D) search spaces for a near‐optimal alignment, while considering complex constraints and a nonlinear objective function. In mountainous regions, the complex terrain and constructions require additional and more complex constraints than in topographically simpler regions. In this paper, the authors solve this problem with an algorithm based on a 3D distance transform (3D‐DT). Compared with previous two‐dimensional distance transform (2D‐DT) methods developed in this field, the feasible search spaces of 3D‐DT are greatly increased. Consequently, this new method can find more alternatives with higher qualities. In this approach, an erythrocyte‐shaped 3D neighboring mask is developed to narrow local search spaces and speed up the search process. Besides, a stepwise‐backstepping strategy is designed to dynamically determine feasible 3D search spaces and efficiently search the study area. During the 3D‐DT search process, multiple constraints, including geometric, construction, and location constraints, are effectively handled. After the 3D‐DT search, a genetic algorithm is employed to optimize the 3D‐DT paths into final alignments. Finally, this novel approach is applied to an actual case in a complex mountainous region. The comprehensive cost of the best solution generated by 3D‐DT is 16% below a manual solution produced by very experienced human designers. Furthermore, the total number of feasible alternatives found by 3D‐DT is 4.3 times greater than by 2D‐DT. The comprehensive cost of the best 3D‐DT solution is 10% below the best one generated by 2D‐DT.

The use of geoengineering techniques for phosphorus management offers the promise of greater and quicker chemical and ecological recovery. It can be attractive when used with other restoration measures but should not be considered a panacea. The range of materials being proposed for use as well as the in-lake processes targeted for manipulation continues to grow. With increasing political imperatives to meet regulatory goals for water quality, we recommend a coordinated approach to the scientific understanding, costs, and integration of geoengineering with other approaches to lake management.

School engagement (SE) refers to the intensity and quality of emotions experienced by students when commencing and carrying out learning activities, and includes behavioral, emotional, and cognitive engagement. A high SE level promotes academic achievement, reduces students' behavioral problems, and prevents school dropout. This study, whose participants were 819 students from Tibetan areas, explored the impact of teacher autonomy support (TAS) on students' SE and the mechanisms involved in this relationship. The results showed that TAS had a positive impact on SE, while students' self-efficacy had a mediating effect between TAS and SE. On the one hand, TAS affected self-efficacy through academic interest and ultimately influenced SE; moreover, TAS negatively affected academic anxiety, indirectly inhibiting the negative effect of academic anxiety on SE through self-efficacy. The theoretical and practical implications of the study findings are discussed.

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