Bi-LSTM architecture diagram. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

STL Linear Combination Forecast Graph. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

LOSS curves for BWO-BiLSTM model training. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Analysis of STL-PCA prediction results. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Accumulated contribution rate of PCA. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Figure of ablation experiment. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Flowchart of the STL-PCA-BWO-BiLSTM model. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Parameter optimization results of BiLSTM. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Descriptive statistical analysis of data. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

The MAE value of the model under raw data. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Three error values under raw data. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Decomposition of time scries plot. by Xiangjuan Liu (618000)

“…First, seven benchmark models including Prophet, ARIMA, and LSTM were applied to raw price series, where results demonstrated that deep learning models significantly outperformed traditional methods. Subsequently, STL decomposition decoupled the series into trend, seasonal, and residual components for component-specific modeling, achieving a 22.6% reduction in average MAE compared to raw data modeling. …”

Achieving Improved Ion Swarm Shaping Based on Ion Leakage Control in Ion Mobility Spectrometry by Jiyao Wang (2121157)

“…In Ion Mobility Spectrometry (IMS), ion gates are essential for controlling ion flow, significantly impacting detection sensitivity and resolution. …”

AO/EtBr Staining of Cancer cell lines: <i>To check whether vitamin D3 promoted cell death in cancer cells, a dual staining protocol was implemented as detailed in the methods secti... by Vidya G. Bettada (22208808)

Dynamics and functional role of TOR signaling during cell cycle re-entry in Vasa2+/Piwi1+ cells after refeeding. by Eudald Pascual-Carreras (12115380)

Gating strategy of experiment using 30 min EdU pulses experiments in mOr2-Piwi1 juvenile polyps. by Eudald Pascual-Carreras (12115380)

Unraveling the Mechanism of 1‑Deoxynojirimycin (DNJ) Accumulation: The Role of SWEET3 in Mulberry Chloroplasts by Zhen Yang (107412)

“…Transient overexpression or RNA interference of <i>SWEET3</i> in mulberry leaves significantly increased or decreased DNJ levels, respectively, while stable overexpression in hairy roots enhanced DNJ accumulation. …”

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

“…By compiling a global data set of 1,782 paired observations from 185 publications, we show that land use conversion from natural to managed ecosystems significantly reduced NNM by 7.5% (−11.5, −2.8%) and increased NN by 150% (86, 194%), indicating decreasing N availability while increasing potential N loss through denitrification and nitrate leaching. …”

BPCA ratios and proxies evaluated in this study. The variable <i>n</i> in the equation for the Average CAS refers to the number of carboxylic acid substitutions (3, 4, 5 or 6). The... by Ivy Notterpek (21368133)

Raw data of Fig 2-Fig 13 in this study. by Wen Wen Zheng (17721404)

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