Trajectory Prediction of Port Container Trucks Based on DeepPBM-Attention

Existing tracking algorithms mostly rely on model-driven approaches, which can be prone to inaccuracies due to unpredictable human behaviours. This article aims to address the issue of transient errors in tracking port container trucks (PCTrucks) when encountering obstructions. A data-driven algorithm for predicting vehicle trajectories is proposed in this study. The approach involves preprocessing an extensive dataset of GPS information, training a DeepLSTM-Attention model, and integrating the proposed model with the population-based training (PBT) algorithm to optimise network hyperparameters. The objective is to enhance the accuracy of predicting trajectories for vehicles moving horizontally. The trajectory data used are collected from real-world port operations. This research is conducted across nine trajectory segments and benchmarked against traditional approaches like Kalman filtering, machine learning techniques such as support vector regression (SVR) and standard long short-term memory (LSTM) networks. The results demonstrate that the proposed prediction method, that is, DeepPBM-Attention, outperforms other techniques in several evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), F1 score and trajectory reconstruction error (TRE). Compared to LSTM networks, the performance of DeepPBM-Attention is improved by approximately 40%. The proposed data-driven trajectory prediction algorithm exhibits high accuracy and practicality, which can effectively be applied to the positioning prediction of horizontally moving vehicles in port environments

Global divergent responses of primary productivity to water, energy, and CO2

The directionality of the response of gross primary productivity (GPP) to climate has been shown to vary across the globe. This effect has been hypothesized to be the result of the interaction between multiple bioclimatic factors, including environmental energy (i.e., temperature and radiation) and water availability. This is due to the tight coupling between water and carbon cycling in plants and the fact that temperature often drives plant water demand. Using GPP data extracted from 188 sites of FLUXNET2015 and observation-driven terrestrial biosphere models, we disentangled the confounding effects of temperature, precipitation and carbon dioxide on GPP, and examined their long-term effects on productivity across the globe. Based on the FLUXNET2015 data, we observed a decline in the positive effect of temperature on GPP, while the positive effects of precipitation and CO2 were becoming stronger during 2000-2014. Using data derived from terrestrial biosphere models between 1980 and 2010 we found similar effects globally. The modeled data allowed us to investigate these effects more thoroughly over space and time. In arid regions, the modeled response to precipitation increased since 1950, approximately 30 years earlier than in humid regions. We further observed the negative effects of summer temperature on GPP in arid regions, suggesting greater aridity stress on productivity under global warming. Our results imply that aridity stress, triggered by rising temperatures, has reduced the positive influence of temperature on GPP, while increased precipitation and elevated CO2 may alleviate negative aridity impacts.Peer reviewe

Global intron retention mediated gene regulation during CD4+ T cell activation.

T cell activation is a well-established model for studying cellular responses to exogenous stimulation. Using strand-specific RNA-seq, we observed that intron retention is prevalent in polyadenylated transcripts in resting CD4(+) T cells and is significantly reduced upon T cell activation. Several lines of evidence suggest that intron-retained transcripts are less stable than fully spliced transcripts. Strikingly, the decrease in intron retention (IR) levels correlate with the increase in steady-state mRNA levels. Further, the majority of the genes upregulated in activated T cells are accompanied by a significant reduction in IR. Of these 1583 genes, 185 genes are predominantly regulated at the IR level, and highly enriched in the proteasome pathway, which is essential for proper T cell proliferation and cytokine release. These observations were corroborated in both human and mouse CD4(+) T cells. Our study revealed a novel post-transcriptional regulatory mechanism that may potentially contribute to coordinated and/or quick cellular responses to extracellular stimuli such as an acute infection

Self-passivated freestanding superconducting oxide film for flexible electronics

The integration of high-temperature superconducting YBa2Cu3O6+x (YBCO) into flexible electronic devices has the potential to revolutionize the technology industry. The effective preparation of high-quality flexible YBCO films therefore plays a key role in this development. We present a novel approach for transferring water-sensitive YBCO films onto flexible substrates without any buffer layer. Freestanding YBCO film on a polydimethylsiloxane substrate is extracted by etching the Sr3Al2O6 sacrificial layer from the LaAlO3 substrate. In addition to the obtained freestanding YBCO thin film having a Tc of 89.1 K, the freestanding YBCO thin films under inward and outward bending conditions have Tc of 89.6 K and 88.9 K, respectively. A comprehensive characterization involving multiple experimental techniques including high-resolution transmission electron microscopy, scanning electron microscopy, Raman and X-ray Absorption Spectroscopy is conducted to investigate the morphology, structural and electronic properties of the YBCO film before and after the extraction process where it shows the preservation of the structural and superconductive properties of the freestanding YBCO virtually in its pristine state. Further investigation reveals the formation of a YBCO passivated layer serves as a protective layer which effectively preserves the inner section of the freestanding YBCO during the etching process. This work plays a key role in actualizing the fabrication of flexible oxide thin films and opens up new possibilities for a diverse range of device applications involving thin-films and low-dimensional materials.Comment: 22 pages,4 figures,references adde

Preoperative diagnosis of solitary pulmonary nodules with a novel hematological index model based on circulating tumor cells

ObjectivePreoperative noninvasive diagnosis of the benign or malignant solitary pulmonary nodule (SPN) is still important and difficult for clinical decisions and treatment. This study aimed to assist in the preoperative diagnosis of benign or malignant SPN using blood biomarkers.MethodsA total of 286 patients were recruited for this study. The serum FR+CTC, TK1, TP, TPS, ALB, Pre-ALB, ProGRP, CYFRA21-1, NSE, CA50, CA199, and CA242 were detected and analyzed.ResultsIn the univariate analysis, age, FR+CTC, TK1, CA50, CA19.9, CA242, ProGRP, NSE, CYFRA21-1, and TPS showed the statistical significance of a correlation with malignant SPNs (P <0.05). The highest performing biomarker is FR+CTC (odd ratio [OR], 4.47; 95% CI: 2.57–7.89; P <0.001). The multivariate analysis identified that age (OR, 2.69; 95% CI: 1.34–5.59, P = 0.006), FR+CTC (OR, 6.26; 95% CI: 3.09–13.37, P <0.001), TK1 (OR, 4.82; 95% CI: 2.4–10.27, P <0.001), and NSE (OR, 2.06; 95% CI: 1.07–4.06, P = 0.033) are independent predictors. A prediction model based on age, FR+CTC, TK1, CA50, CA242, ProGRP, NSE, and TPS was developed and presented as a nomogram, with a sensitivity of 71.1% and a specificity of 81.3%, and the AUC was 0.826 (95% CI: 0.768–0.884).ConclusionsThe novel prediction model based on FR+CTC showed much stronger performance than any single biomarker, and it can assist in predicting benign or malignant SPNs

Artificial intelligence for diagnosis and Gleason grading of prostate cancer: The PANDA challenge

Through a community-driven competition, the PANDA challenge provides a curated diverse dataset and a catalog of models for prostate cancer pathology, and represents a blueprint for evaluating AI algorithms in digital pathology. Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge-the largest histopathology competition to date, joined by 1,290 developers-to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted kappa, 95% confidence interval (CI), 0.840-0.884) and 0.868 (95% CI, 0.835-0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.KWF Kankerbestrijding ; Netherlands Organization for Scientific Research (NWO) ; Swedish Research Council European Commission ; Swedish Cancer Society ; Swedish eScience Research Center ; Ake Wiberg Foundation ; Prostatacancerforbundet ; Academy of Finland ; Cancer Foundation Finland ; Google Incorporated ; MICCAI board challenge working group ; Verily Life Sciences ; EIT Health ; Karolinska Institutet ; MICCAI 2020 satellite event team ; ERAPerMe

Genome-Wide uH2A Localization Analysis Highlights Bmi1-Dependent Deposition of the Mark at Repressed Genes

Polycomb group (PcG) proteins control organism development by regulating the expression of developmental genes. Transcriptional regulation by PcG proteins is achieved, at least partly, through the PRC2-mediated methylation on lysine 27 of histone H3 (H3K27) and PRC1-mediated ubiquitylation on lysine 119 of histone H2A (uH2A). As an integral component of PRC1, Bmi1 has been demonstrated to be critical for H2A ubiquitylation. Although recent studies have revealed the genome-wide binding patterns of some of the PRC1 and PRC2 components, as well as the H3K27me3 mark, there have been no reports describing genome-wide localization of uH2A. Using the recently developed ChIP-Seq technology, here, we report genome-wide localization of the Bmi1-dependent uH2A mark in MEF cells. Gene promoter averaging analysis indicates a peak of uH2A just inside the transcription start site (TSS) of well-annotated genes. This peak is enriched at promoters containing the H3K27me3 mark and represents the least expressed genes in WT MEF cells. In addition, peak finding reveals regions of local uH2A enrichment throughout the mouse genome, including almost 700 gene promoters. Genes with promoter peaks of uH2A exhibit lower-level expression when compared to genes that do not contain promoter peaks of uH2A. Moreover, we demonstrate that genes with uH2A peaks have increased expression upon Bmi1 knockout. Importantly, local enrichment of uH2A is not limited to regions containing the H3K27me3 mark. We describe the enrichment of H2A ubiquitylation at high-density CpG promoters and provide evidence to suggest that DNA methylation may be linked to uH2A at these regions. Thus, our work not only reveals Bmi1-dependent H2A ubiquitylation, but also suggests that uH2A targeting in differentiated cells may employ a different mechanism from that in ES cells

A Data-Driven Method for Predicting the Cutterhead Torque of EPB Shield Machine

The prediction of cutterhead torque of earth pressure balance (EPB) shield machine is mainly studied. First, the idea of shield tunneling stage division is proposed. The process of shield tunneling from start to stop (or pause) is divided into start-up and stationary driving stages. Using the change point detection method based on linear regression, the separation points between start-up stage and stationary driving stage are identified from the original construction data, and the datasets of the two stages are extracted, respectively. Then, for the start-up stage, the linear regression method is suggested for the cutterhead torque prediction, since there is a strong linear correlation between the key parameters such as the cutterhead torque and the thrust force. Meanwhile, for the stationary driving stage, considering the fact that the key parameters vary smoothly and show obvious inertia, the long short-term memory (LSTM) network method can be used to establish the relationship model between cutterhead torque and other key parameters, such as the thrust force. Through the test experiments of construction data in Zhengzhou, Luoyang, and Dalian shield projects, the results show that the proposed segmented modeling method possesses good adaptability and the cutterhead torque prediction model has high prediction accuracy

Optimized dispatch of wind farms with power control capability for power system restoration

Abstract As the power control technology of wind farms develops, the output power of wind farms can be constant, which makes it possible for wind farms to participate in power system restoration. However, due to the uncertainty of wind energy, the actual output power can’t reach a constant dispatch power in all time intervals, resulting in uncertain power sags which may induce the frequency of the system being restored to go outside the security limits. Therefore, it is necessary to optimize the dispatch of wind farms participating in power system restoration. Considering that the probability distribution function (PDF) of transient power sags is hard to obtain, a robust optimization model is proposed in this paper, which can maximize the output power of wind farms participating in power system restoration. Simulation results demonstrate that the security constraints of the restored system can be kept within security limits when wind farm dispatch is optimized by the proposed method