A multi-stage approach for empty container repositioning under coordination among linear carriers

This paper studies the empty container repositioning (ECR) problem considering the exchange of slots and empty containers among liner shipping companies. It is common for an individual shipping company to seek an optimal solution for ECR and cargo routing to maximize its own benefits. To achieve cooperation among shipping companies, a multi-stage solution strategy is proposed. With the inverse optimization technique, the guide leasing prices of slots and empty containers among shipping companies are derived considering the schedule of vessels and cargo routing. Based on the guide leasing price, a cooperative model is formulated to minimize the total cost, which includes the transportation cost for laden containers, the inventory holding cost, the container leasing cost, and the repositioning cost. All the involved shipping companies are expected to follow the best solution of ECR and cargo routing to achieve a cooperative and stable optimum. A real-world shipping network operated by three liner shipping companies is used as a case study with promising numerical results

Input-output-based genuine value added and genuine productivity in China\u27s industrial sectors (1995-2010)

The rapid growth of China\u27s economy has brought about huge losses of natural capital in the form of natural resource depletion and damages from carbon emissions. This paper recalculates value added, capital formation, capital stock, and related multifactor productivity in China\u27s industrial sectors by further developing the genuine savings method of the World Bank. The sector-level natural capital loss was calculated using China\u27s official input–output table and their extensions for tracing final consumers. The capital output elasticity in the productivity estimation was adjusted based on these tables. The results show that although the loss of natural capital in China\u27s industrial sectors in terms of value added has slowed, the impacts on their productivity during the past decades is still quite clear

A 10 km daily-level ultraviolet radiation predicting dataset based on machine learning models in China from 2005 to 2020

Ultraviolet (UV) radiation is closely related to health, but limited measurements hindered further investigation of its health effects in China. Machine learning algorithm has been widely used in predicting environmental factors with high accuracy, but limited studies have done for UV radiation. This study aimed to develop UV radiation prediction model based on random forest method, and predict UV radiation at daily level and 10 km resolution in mainland China in 2005–2020. A random forest model was employed to predict UV radiation by integrating ground UV radiation measurements from monitoring stations and multiple predictors, such as UV radiation data from satellite. Missing data of satellite-based UV radiation was filled by three-day moving average method. The model's performance was evaluated through multiple cross-validation (CV) methods. The overall R2 (root mean square error, RMSE) between measured and predicted UV radiation from model development and model 10-fold CV was 0.97 (15.64 W m-2) and 0.83 (37.44 W m-2) at daily level, respectively. The model with OMI EDD performed higher predicting accuracy than the one without it. Based on predictions of UV radiation at daily level and 10 km spatial resolution and nearly 100 % spatiotemporal coverage, we found UV radiation increased by 4.20 % while PM2.5 levels decreased by 48.51 % and O3 levels rose by 22.70 % in 2013–2020, suggesting a potential correlation among these environmental factors. Uneven spatial distribution of UV radiation was found to be associated with factors such as latitude, elevation, meteorological factors and seasons. The eastern areas of China posed higher risk with both high population density and UV radiation intensity. Based on machine learning algorithm, this study generated a gridded dataset characterized by relatively high precision and extensive spatiotemporal coverage of UV radiation, which demonstrates the spatiotemporal variability of UV radiation levels in China and can facilitate health-related research in the future. This dataset is currently freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024)

Exploiting prompt learning with pre-trained language models for Alzheimer's Disease detection

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care and to delay further progression. Speech based automatic AD screening systems provide a non-intrusive and more scalable alternative to other clinical screening techniques. Textual embedding features produced by pre-trained language models (PLMs) such as BERT are widely used in such systems. However, PLM domain fine-tuning is commonly based on the masked word or sentence prediction costs that are inconsistent with the back-end AD detection task. To this end, this paper investigates the use of prompt-based fine-tuning of PLMs that consistently uses AD classification errors as the training objective function. Disfluency features based on hesitation or pause filler token frequencies are further incorporated into prompt phrases during PLM fine-tuning. The decision voting based combination among systems using different PLMs (BERT and RoBERTa) or systems with different fine-tuning paradigms (conventional masked-language modelling fine-tuning and prompt-based fine-tuning) is further applied. Mean, standard deviation and the maximum among accuracy scores over 15 experiment runs are adopted as performance measurements for the AD detection system. Mean detection accuracy of 84.20% (with std 2.09%, best 87.5%) and 82.64% (with std 4.0%, best 89.58%) were obtained using manual and ASR speech transcripts respectively on the ADReSS20 test set consisting of 48 elderly speakers.Comment: Accepted ICASSP 2023 (will update with IEEE vision later