Development And Evaluation Of A Simplified Modeling Approach For Hydraulic Systems

This paper presents how a hydraulic system can be properly modeled for hydraulic balancing, knowledge of flow distribution, coupled simulation, and evaluation of control, etc. It focuses on water-based heating and cooling systems, which generally have high energy efficiency in design but could perform poorly in reality due to the undersensing condition and strong thermal-hydraulic coupling. The study introduces the motivation, presents the simplified modeling methodology, and illustrates the model and simulating structure. A preliminary evaluation of the method is conducted with two simple simulations. The proposed “node-branch-state” modeling approach could be easily modified, expanded and integrated into a detailed thermal model. The paper concludes with some discussions on future work

Poverty alleviation and policy dynamics in Hong Kong : a study of the community care fund

published_or_final_versionPolitics and Public AdministrationMasterMaster of Public Administratio

A Measurement of the Assembly of Milky Way Analogues at Redshifts 0.5<z<20.5 < z < 2 with Resolved Stellar Mass and Star-Formation Rate Profiles

The resolved mass assembly of Milky-Way-mass galaxies has been previously studied in simulations, the local universe, and at higher redshifts using infrared (IR) light profiles. To better characterize the mass assembly of Milky Way Analogues (MWAs), as well as their changes in star-formation rate and color gradients, we construct resolved stellar mass and star-formation rate maps of MWA progenitors selected with abundance matching techniques up to z $\sim$ 2 using deep, multi-wavelength imaging data from the Hubble Frontier Fields. Our results using stellar mass profiles agree well with previous studies that utilize IR light profiles, showing that the inner 2 kpc of the galaxies and the regions beyond 2 kpc exhibit similar rates of stellar mass growth. This indicates the progenitors of MWAs from $z\sim 2$ to the present do not preferentially grow their bulges or their disks. The evolution of the star-formation rate (SFR) profiles indicate greater decrease in SFR density in the inner regions versus the outer regions. S\'ersic parameters indicate modest growth in the central regions at lower redshifts, perhaps indicating slight bulge growth. However, the S\'ersic index does not rise above $n \sim 2$ until $z < 0.5$, meaning these galaxies are still disk dominated systems. We find that the half-mass radii of the MWA progenitors increase between $1.5 < z < 2$, but remain constant at later epochs ($z < 1.5$). This implies mild bulge growth since $z\sim 2$ in MWA progenitors, in line with previous MWA mass assembly studies.Comment: 19 Pages, 12 figures, accepted by Ap

Cost-effectiveness analysis of left atrial appendage occlusion compared with pharmacological strategies for stroke prevention in atrial fibrillation

Background Transcatheter left atrial appendage occlusion (LAAO) is a promising therapy for stroke prophylaxis in non-valvular atrial fibrillation (NVAF) but its cost-effectiveness remains understudied. This study evaluated the cost- effectiveness of LAAO for stroke prophylaxis in NVAF. Methods A Markov decision analytic model was used to compare the cost-effectiveness of LAAO with 7 pharmacological strategies: aspirin alone, clopidogrel plus aspirin, warfarin, dabigatran 110 mg, dabigatran 150 mg, apixaban, and rivaroxaban. Outcome measures included quality-adjusted life years (QALYs), lifetime costs and incremental cost-effectiveness ratios (ICERs). Base-case data were derived from ACTIVE, RE-LY, ARISTOTLE, ROCKET-AF, PROTECT-AF and PREVAIL trials. One- way sensitivity analysis varied by CHADS2 score, HAS-BLED score, time horizons, and LAAO costs; and probabilistic sensitivity analysis using 10,000 Monte Carlo simulations was conducted to assess parameter uncertainty. Results LAAO was considered cost-effective compared with aspirin, clopidogrel plus aspirin, and warfarin, with ICER of US$5,115,$2,447, and $6,298 per QALY gained, respectively. LAAO was dominant (i.e. less costly but more effective) compared to other strategies. Sensitivity analysis demonstrated favorable ICERs of LAAO against other strategies in varied CHADS2 score, HAS-BLED score, time horizons (5 to 15 years) and LAAO costs. LAAO was cost-effective in 86.24$50,000/QALY. Conclusions Transcatheter LAAO is cost-effective for prevention of stroke in NVAF compared with 7 pharmacological strategies. Condensed abstract The transcatheter left atrial appendage occlusion (LAAO) is considered cost-effective against the standard 7 oral pharmacological strategies including acetylsalicylic acid (ASA) alone, clopidogrel plus ASA, warfarin, dabigatran 110 mg, dabigatran 150 mg, apixaban, and rivaroxaban for stroke prophylaxis in non-valvular atrial fibrillation management

Frequent mutation of receptor protein tyrosine phosphatases provides a mechanism for STAT3 hyperactivation in head and neck cancer

The underpinnings of STAT3 hyperphosphorylation resulting in enhanced signaling and cancer progression are incompletely understood. Loss-of-function mutations of enzymes that dephosphorylate STAT3, such as receptor protein tyrosine phosphatases, which are encoded by the PTPR gene family, represent a plausible mechanism of STAT3 hyperactivation. We analyzed whole exome sequencing (n = 374) and reverse-phase protein array data (n = 212) from head and neck squamous cell carcinomas (HNSCCs). PTPR mutations are most common and are associated with significantly increased phospho-STAT3 expression in HNSCC tumors. Expression of receptor-like protein tyrosine phosphatase T (PTPRT) mutant proteins induces STAT3 phosphorylation and cell survival, consistent with a “driver” phenotype. Computational modeling reveals functional consequences of PTPRT mutations on phospho-tyrosine–substrate interactions. A high mutation rate (30%) of PTPRs was found in HNSCC and 14 other solid tumors, suggesting that PTPR alterations, in particular PTPRT mutations, may define a subset of patients where STAT3 pathway inhibitors hold particular promise as effective therapeutic agents.Fil: Lui, Vivian Wai Yan. University of Pittsburgh; Estados UnidosFil: Peyser, Noah D.. University of Pittsburgh; Estados UnidosFil: Ng, Patrick Kwok-Shing. University Of Texas Md Anderson Cancer Center;Fil: Hritz, Jozef. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados Unidos. Masaryk University; República ChecaFil: Zeng, Yan. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Lu, Yiling. University Of Texas Md Anderson Cancer Center;Fil: Li, Hua. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Wang, Lin. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Gilbert, Breean R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: General, Ignacio. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Bahar, Ivet. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Ju, Zhenlin. University Of Texas Md Anderson Cancer Center;Fil: Wang, Zhenghe. Case Western Reserve University; Estados UnidosFil: Pendleton, Kelsey P.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Xiao, Xiao. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Du, Yu. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Vries, John K.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados UnidosFil: Hammerman, Peter S.. Harvard Medical School; Estados UnidosFil: Garraway, Levi A.. Harvard Medical School; Estados UnidosFil: Mills, Gordon B.. University Of Texas Md Anderson Cancer Center;Fil: Johnson, Daniel E.. University of Pittsburgh at Johnstown; Estados Unidos. University of Pittsburgh; Estados UnidosFil: Grandis, Jennifer R.. University of Pittsburgh; Estados Unidos. University of Pittsburgh at Johnstown; Estados Unido

Sharpness-Aware Graph Collaborative Filtering

Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Also, training GNNs requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to choose the minima carefully. Here we propose an effective training schema, called {gSAM}, under the principle that the \textit{flatter} minima has a better generalization ability than the \textit{sharper} ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM

A Novel Bayesian Framework Infers Driver Activation States and Reveals Pathway-Oriented Molecular Subtypes in Head and Neck Cancer

Head and neck squamous cell cancer (HNSCC) is an aggressive cancer resulting from heterogeneous causes. To reveal the underlying drivers and signaling mechanisms of different HNSCC tumors, we developed a novel Bayesian framework to identify drivers of individual tumors and infer the states of driver proteins in cellular signaling system in HNSCC tumors. First, we systematically identify causal relationships between somatic genome alterations (SGAs) and differentially expressed genes (DEGs) for each TCGA HNSCC tumor using the tumor-specific causal inference (TCI) model. Then, we generalize the most statistically significant driver SGAs and their regulated DEGs in TCGA HNSCC cohort. Finally, we develop machine learning models that combine genomic and transcriptomic data to infer the protein functional activation states of driver SGAs in tumors, which enable us to represent a tumor in the space of cellular signaling systems. We discovered four mechanism-oriented subtypes of HNSCC, which show distinguished patterns of activation state of HNSCC driver proteins, and importantly, this subtyping is orthogonal to previously reported transcriptomic-based molecular subtyping of HNSCC. Further, our analysis revealed driver proteins that are likely involved in oncogenic processes induced by HPV infection, even though they are not perturbed by genomic alterations in HPV+ tumors

The Howl - Spring 2018

The Howl is a magazine that is planned, researched, written, photographed and designed by Otterbein University’s ESL and international students. The magazine serves to give them a safe space in which to use their voice to share their cultures, experiences and lives. If you are interested in submitting to the Howl, please e-mail your writing or photography to [email protected]. Enjoy Otterbein ESL’s contribution to the Otterbein community’s literary scene.https://digitalcommons.otterbein.edu/the_howl/1004/thumbnail.jp

Toward a Foundation Model for Time Series Data

A foundation model is a machine learning model trained on a large and diverse set of data, typically using self-supervised learning-based pre-training techniques, that can be adapted to various downstream tasks. However, current research on time series pre-training has mostly focused on models pre-trained solely on data from a single domain, resulting in a lack of knowledge about other types of time series. However, current research on time series pre-training has predominantly focused on models trained exclusively on data from a single domain. As a result, these models possess domain-specific knowledge that may not be easily transferable to time series from other domains. In this paper, we aim to develop an effective time series foundation model by leveraging unlabeled samples from multiple domains. To achieve this, we repurposed the publicly available UCR Archive and evaluated four existing self-supervised learning-based pre-training methods, along with a novel method, on the datasets. We tested these methods using four popular neural network architectures for time series to understand how the pre-training methods interact with different network designs. Our experimental results show that pre-training improves downstream classification tasks by enhancing the convergence of the fine-tuning process. Furthermore, we found that the proposed pre-training method, when combined with the Transformer model, outperforms the alternatives