Multiple-Phase Modeling of Degradation Signal for Condition Monitoring and Remaining Useful Life Prediction

Remaining useful life prediction plays an important role in ensuring the safety, availability, and efficiency of various engineering systems. In this paper, we propose a flexible Bayesian multiple-phase modeling approach to characterize degradation signals for prognosis. The priors are specified with a novel stochastic process and the multiple-phase model is formulated to a novel state-space model to facilitate online monitoring and prediction. A particle filtering algorithm with stratified sampling and partial Gibbs resample-move strategy is developed for online model updating and residual life prediction. The advantages of the proposed method are demonstrated through extensive numerical studies and real case studies

Do Target Shareholders Lose To Private Acquirers?

Abstract Focusing on private equity firms, I find that targets receive a higher premium when acquirers are private firms. When the target management is in the private acquirer’s team, the targets receive a lower premium. When I look at cumulative abnormal returns (CARs), targets of private acquirers have lower CARs than those of public acquirers, and private acquisitions with management participation have higher CARs than without management participation. Also, private acquisitions are more likely to be successful than public acquisitions, and within private acquisitions, those with target’s management participation have even better odds

Therapeutic role of MiR-140-5p for the treatment of non-small cell lung cancer

Background/Aim: Lung cancer is the second most common cancer in both men and women, after prostate and breast cancer, respectively. There are two main types of primary lung cancer, non-small cell lung cancer (NSCLC), which accounts for approximately 85-90% of all lung cancer cases, and small cell lung cancer (SCLC), which accounts for the other 10-15% of lung cancers. MiRNAs are small molecules that post-transcriptionally regulate many genes and contribute to many disease aetiologies, including tumours. In lung cancer, the down-regulation of miR-140-5p leads to disease progression. Materials and Methods: In this study a miR-140-5p-only treatment and miR-140-5p combined with other chemotherapeutics have been studied in vitro. Results: When transfected into NSCLC, the overexpression of miR-140-5p reduced the migration and invasion properties of malignant cells and, also improved their adhesion onto the artificial extracellular matrix. When miRNA-140-5p replacement treatment was combined with other drugs commonly used in clinical practice, such as gefinitib, DMH1 and cisplatin, it enhanced their efficacy by reducing the migration and invasion ability of cancer cells, thus suggesting that it acts synergistically with known compounds for the treatment of NSCLC. Additionally, some endothelial mesenchymal transition (EMT) markers appeared to be regulated by miR-140-5p. Conclusion: Novel direct targets of miR-140-5p have not been investigated in this study, but our results indicate the involvement of miR-140-5p in lung cancer invasion. The preliminary data from this study imply that when miR-140-5p levels are restored; maybe synergistically support current therapies for NSCLC though further validation, especially in vivo is required

Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust

Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at https://github.com/YuxinWenRick/tree-ring-watermark.Comment: 16 pages, 8 figures, code is available at https://github.com/YuxinWenRick/tree-ring-watermark, fixed the repo lin

Significance and therapeutic implications of endothelial progenitorcells in angiogenic-mediated tumour metastasis

Cancer conveys profound social and economic consequences throughout the world. Metastasis is respon-sible for approximately 90% of cancer-associated mortality and, when it occurs, cancer becomes almostincurable. During metastatic dissemination, cancer cells pass through a series of complex steps includingthe establishment of tumour-associated angiogenesis. The human endothelial progenitor cells (hEPCs)are a cell population derived from the bone marrow which are required for endothelial tubulogenesisand neovascularization. They also express abundant inflammatory cytokines and paracrine angiogenicfactors. Clinically hEPCs are highly correlated with relapse, disease progression, metastasis and treatmentresponse in malignancies such as breast cancer, ovarian cancer and non-small-cell lung carcinoma. It hasbecome evident that the hEPCs are involved in the angiogenesis-required progression and metastasis oftumours. However, it is not clear in what way the signalling pathways, controlling the normal cellularfunction of human BM-derived EPCs, are hijacked by aggressive tumour cells to facilitate tumour metas-tasis. In addition, the actual roles of hEPCs in tumour angiogenesis-mediated metastasis are not wellcharacterised. In this paper we reviewed the clinical relevance of the hEPCs with cancer diagnosis, pro-gression and prognosis. We further summarised the effects of tumour microenvironment on the hEPCsand underlying mechanisms. We also hypothesized the roles of altered hEPCs in tumour angiogenesisand metastasis. We hope this review may enhance our understanding of the interaction between hEPCsand tumour cells thus aiding the development of cellular-targeted anti-tumour therapies

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors and Machine Learning Algorithms

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe strike) utilizing wearable body sensors. Thirty-six children (Age 9.4±2.8 years) diagnosed with ITW participated in this study. Six ML algorithms, consisting of Support Vector Machines (SVM), decision tree (DT), random forest (RF), K-nearest neighbors (KNN), Multi-layer Perceptron (MLP), and Gaussian process (GP), could successfully classify initial contact walking patterns among ITW. We found that a simple KNN algorithm resulted in the highest accuracy of 92.92% and an F1-score of 93.20% to differentiate toe walking gait versus best heel strike when using all four body sensors. We also found that toe walking resulted in higher variability in the sacral vertical accelerations among children diagnosed with ITW. Accurate quantification of toe walking steps in clinical applications is critical for assessing rehabilitation progress and designing new interventions for children diagnosed with ITW