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工作家庭雙介面之要求、資源與職家衝突關連之性別差異
No full text本研究目的為探討工作要求、家庭要求、工作資源、與家庭資源等前因變項與職家衝突關連的性別差異。樣本為台灣264名全職工作者，資料分析主要以分群階層迴歸檢驗兩性於職家衝突（包括工作－家庭衝突、家庭－工作衝突）前因之差異。首先，T檢定顯示職家衝突感受均無性別差異。再者，工作負荷與家庭責任是男性與女性職家衝突的顯著預測因子。第三，組織家庭支持文化與主管理念性支持對於男性的雙向職家衝突有顯著預測力，但這兩項工作資源中，僅有「主管理念性支持」對女性工作－家庭衝突有顯著的預測力。第四，「來自配偶的家事協助」是男性工作－家庭衝突的顯著預測因子；然而，對女性來說，「來自父母的家事協助」才可有效降低工作－家庭衝突。是故，組織需了解兩性在職家衝突歷程中的差異，方能協助員工找出最佳因應方式，在職家兩者間取得最終的平衡。補正完畢TW
From Handwriting to Calligraphy: A GAN-Based Intuitive System with Cross-Attention and Skeleton Modeling
No full text補正完畢國際高雄市，台灣TW
Development and Validation of the Online Learning Experience Scale (OLES)
No full text補正完畢國際Hokkaido, JapanJP
TPR (Turnitin-Paraphrase training-peer Review) for academic writing
No full text補正完畢國際Taipei, TaiwanTW
A novel approach for Supply Chain Shipment Pricing Prediction using Temporal Convolutional Network- Residual Neural Network
No full textThe supply chain comprises an interconnected system of warehouses, suppliers, shipping companies, distribution hubs, carriers, and logistics firms collaborating to facilitate the progression and commercialization of a product until its final handover to the ultimate consumer. Moreover, efficiently managing overseas supply chains necessitates precise forecasting of shipping times, as it is a serious aspect of operations and advanced information systems. Nonetheless, the feasibility of generating real-time Global Positioning System data and employing optimization methods for short-term and long-term shipping prediction remains an important challenge. Thus, this study develops a novel approach for the supply chain shipment pricing prediction using a hybrid deep learning approach. At first, pre-processing is executed by data normalization and data transformation. Subsequently, feature fusion is performed by Atkinson index and Double Exponential Dung beetle Optimizer (DEDBO) algorithm, that is a combination of Double Exponential Smoothing (DES) and Dung beetle Optimizer (DBO). Ultimately, supply chain shipment prediction is executed by employing the Temporal Convolutional Network- Residual Neural Network (TCN-RNN), which is a combination of TCN and RNN models. The experimentation evaluation shows that DEDBO-based TCN-RNN attains minimal MSE, RMSE, MAE and MAPE with values of 0.0001, 0.0104, 0.0054 and 0.329.補正完畢SG
