To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning.

1. Motor imagery represents a widely recognized paradigm in brain-computer interface (BCI) research (George et al. 2021; Lee et al. 2024; Chen et al. 2025). Currently, most of the research on EEG-b...

This article introduces a unique, multiparametric behavioral phenotyping protocol for MPTP-induced Parkinson's disease (PD) in larval zebrafish. Chemical induction of PD-like pathology in zebrafish provides a high-throughput, genetically tractable system, yet most studies have focused narrowly on basic locomotor assays or simple light-dark transitions. Such limited approaches fail to capture the broader spectrum of PD-related dysfunctions, ranging from bradykinesia and sensorimotor deficits to anxiety-like behaviors and sleep disturbances. To address this gap, this study developed an integrated battery of 6 complementary assays: locomotor test to assess general locomotor and exploratory drive; thigmotaxis assessment to detect anxiety-related edge-preference behaviors; startle response test to record bradykinesia and impaired motor reactivity; light-dark challenge assays to evaluate risk-taking and stress responsiveness; photomotor response paradigms (including strobe light stimulation) to quantify sensorimotor and reflexive movement; and sleep-wake regulation monitoring to measure sleep-wake behavior. Larval zebrafish are exposed to sublethal concentrations of MPTP from 1 day post-fertilization (dpf) to 5 dpf, with behavioral assessments initiated from 4 to 6 dpf. High-resolution automated tracking generates several behavioral endpoints per assay. This approach uncovers previously undetected PD-like alterations, such as disorderly sleep architecture, impaired habituation learning, sensorimotor and reflexive movement, reflecting early nonmotor symptoms. By combining these assays, researchers obtain a comprehensive behavioral symptom that more closely mirrors the complexity of human PD. This protocol expands the paradigm for studying PD pathogenesis, screening candidate therapeutics, and exploring environmental modulators. It exemplifies the collection's goal of presenting innovative animal models that enhance translational relevance through robust, multifaceted phenotyping.