A short-term electricity price forecasting scheme for power market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July 7th 2010

Video-assisted thoracoscopic surgery is more favorable than thoracotomy for administration of adjuvant chemotherapy after lobectomy for non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>Video-assisted thoracoscopic surgery (VATS) lobectomy is a newly developed type of surgery for lung cancer and has been demonstrated obvious minimally-invasive advantages compared with traditional thoracotomy. Theoretically, that less trauma leads to quicker recovery and may facilitate administration of adjuvant chemotherapy. We tested this hypothesis in this study.</p> <p>Methods</p> <p>One hundred and ten NSCLC patients underwent lobectomy and adjuvant chemotherapy from June 2004 to June 2010 was analyzed. The baseline characteristic criteria, variables related to surgery and accomplishing status of chemotherapy were analyzed.</p> <p>Results</p> <p>All 110 patients underwent lobectomy through VATS (n = 54) or thracotomy (n = 56) and adjuvant chemotherapy. There was no significant difference in patients' age, preoperative pulmonary function, co-morbidity, pathologic staging between the two groups, whereas, blood loss, operation time and postoperative complications, chest tube duration and length of stay were less in VATS group. There were no significant differences in time to initiation chemotherapy. Cases in VATS group received more cycles of chemotherapy (3.6 vs. 3.0, p = 0.002). A higher proportion of patients received full dose on schedule in VATS group (57.4% vs. 33.9%, p = 0.013) and a higher proportion of patients completed ≥75% planed dose, (88.9% vs. 71.4%, p = 0.022); slightly higher proportion of patients in thoracotomy group had grade 3 or more toxicity (20.4% vs. 35.7%, p = 0.074).</p> <p>Conclusions</p> <p>Patients underwent lobectomy by VATS have better compliance and fewer delayed or reduced dose on adjuvant chemotherapy than those by thoracotomy.</p

A Case of Premature Ventricular Complexes from the Proximal Left Bundle Branch Successfully Ablated from the Right Coronary Cusp

Background: Premature ventricular complexes (PVCs) from the proximal left bundle branch (LBB) can be ablated in the left ventricular outflow tract but can easily damage normal conduction bundles. Here, we report a case of successful ablation of PVCs from the proximal LBB within the right coronary cusp (RCC). Case presentation: Our patient was a 70-year-old woman with PVCs from the proximal LBB that were successfully ablated via the RCC through radiofrequency catheter ablation with a 3D mapping system; she had a complication of incomplete right bundle branch block (RBBB) and remained asymptomatic during follow-up. Conclusion: The RCC provides an alternative approach for ablating PVCs originating from the proximal LBB, owing to the close relationship between the RCC and proximal LBB

Hierarchical Masked 3D Diffusion Model for Video Outpainting

Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/.Comment: ACM MM 2023 accepte

Identification and Functional Analysis of ThADH1 and ThADH4 Genes Involved in Tolerance to Waterlogging Stress in Taxodium hybrid ‘Zhongshanshan 406’

The Taxodium hybrid ‘Zhongshanshan 406’ (T. hybrid ‘Zhongshanshan 406’) [Taxodium mucronatum Tenore × Taxodium distichum (L.). Rich] has an outstanding advantage in flooding tolerance and thus has been widely used in wetland afforestation in China. Alcohol dehydrogenase genes (ADHs) played key roles in ethanol metabolism to maintain energy supply for plants in low-oxygen conditions. Two ADH genes were isolated and characterized—ThADH1 and ThADH4 (GenBank ID: AWL83216 and AWL83217—basing on the transcriptome data of T. hybrid ‘Zhongshanshan 406’ grown under waterlogging stress. Then the functions of these two genes were investigated through transient expression and overexpression. The results showed that the ThADH1 and ThADH4 proteins both fall under ADH III subfamily. ThADH1 was localized in the cytoplasm and nucleus, whereas ThADH4 was only localized in the cytoplasm. The expression of the two genes was stimulated by waterlogging and the expression level in roots was significantly higher than those in stems and leaves. The respective overexpression of ThADH1 and ThADH4 in Populus caused the opposite phenotype, while waterlogging tolerance of the two transgenic Populus significantly improved. Collectively, these results indicated that genes ThADH1 and ThADH4 were involved in the tolerance and adaptation to anaerobic conditions in T. hybrid ‘Zhongshanshan 406’

Dual-view Curricular Optimal Transport for Cross-lingual Cross-modal Retrieval

Current research on cross-modal retrieval is mostly English-oriented, as the availability of a large number of English-oriented human-labeled vision-language corpora. In order to break the limit of non-English labeled data, cross-lingual cross-modal retrieval (CCR) has attracted increasing attention. Most CCR methods construct pseudo-parallel vision-language corpora via Machine Translation (MT) to achieve cross-lingual transfer. However, the translated sentences from MT are generally imperfect in describing the corresponding visual contents. Improperly assuming the pseudo-parallel data are correctly correlated will make the networks overfit to the noisy correspondence. Therefore, we propose Dual-view Curricular Optimal Transport (DCOT) to learn with noisy correspondence in CCR. In particular, we quantify the confidence of the sample pair correlation with optimal transport theory from both the cross-lingual and cross-modal views, and design dual-view curriculum learning to dynamically model the transportation costs according to the learning stage of the two views. Extensive experiments are conducted on two multilingual image-text datasets and one video-text dataset, and the results demonstrate the effectiveness and robustness of the proposed method. Besides, our proposed method also shows a good expansibility to cross-lingual image-text baselines and a decent generalization on out-of-domain data

Extremely fast decision tree mining for evolving data streams

Nowadays real-time industrial applications are generating a huge amount of data continuously every day. To process these large data streams, we need fast and efficient methodologies and systems. A useful feature desired for data scientists and analysts is to have easy to visualize and understand machine learning models. Decision trees are preferred in many real-time applications for this reason, and also, because combined in an ensemble, they are one of the most powerful methods in machine learning. In this paper, we present a new system called STREAMDM-C++, that implements decision trees for data streams in C++, and that has been used extensively at Huawei. Streaming decision trees adapt to changes on streams, a huge advantage since standard decision trees are built using a snapshot of data, and can not evolve over time. STREAMDM-C++ is easy to extend, and contains more powerful ensemble methods, and a more efficient and easy to use adaptive decision trees. We compare our new implementation with VFML, the current state of the art implementation in C, and show how our new system outperforms VFML in speed using less resources