37 research outputs found
Effect of viscoelastic fluid on the lift force in lubricated contacts
We consider a cylinder immersed in viscous fluid moving near a flat substrate
covered by an incompressible viscoelastic fluid layer, and study the effect of
the fluid viscoelasticity on the lift force exerted on the cylinder. The lift
force is zero when the viscoelastic layer is not deformed, but becomes non-zero
when it is deformed. We calculate the lift force by considering both the
tangential stress and the normal stress applied at the surface of the
viscoelastic layer. Our analysis indicates that as the layer changes from the
elastic limit to the viscous limit, the lift force decreases with the decrease
of the Deborah number (De). For small De, the effect of the layer elasticity is
taken over by the surface tension and the lift force can become negative. We
also show that the tangential stress and the interface slip velocity (the
surface velocity relative to the substrate), which have been ignored in the
previous analysis, give important contributions to the lift force. Especially
for thin elastic layer, they give dominant contributions to the lift force
Channelling Multimodality Through a Unimodalizing Transport: Warp-U Sampler and Stochastic Bridge Sampling
Monte Carlo integration is fundamental in scientific and statistical
computation, but requires reliable samples from the target distribution, which
poses a substantial challenge in the case of multi-modal distributions.
Existing methods often involve time-consuming tuning, and typically lack
tailored estimators for efficient use of the samples. This paper adapts the
Warp-U transformation [Wang et al., 2022] to form multi-modal sampling strategy
called Warp-U sampling. It constructs a stochastic map to transport a
multi-modal density into a uni-modal one, and subsequently inverts the
transport but with new stochasticity injected. For efficient use of the samples
for normalising constant estimation, we propose (i) an unbiased estimation
scheme based coupled chains, where the Warp-U sampling is used to reduce the
coupling time; and (ii) a stochastic Warp-U bridge sampling estimator, which
improves its deterministic counterpart given in Wang et al. [2022]. Our overall
approach requires less tuning and is easier to apply than common alternatives.
Theoretically, we establish the ergodicity of our sampling algorithm and that
our stochastic Warp-U bridge sampling estimator has greater (asymptotic)
precision per CPU second compared to the Warp-U bridge estimator of Wang et al.
[2022] under practical conditions. The advantages and current limitations of
our approach are demonstrated through simulation studies and an application to
exoplanet detection
Overcoming Wntâβ-catenin dependent anticancer therapy resistance in leukaemia stem cells
Leukaemia stem cells (LSCs) underlie cancer therapy resistance but targeting these cells remains difficult. The Wntâβ-catenin and PI3KâAkt pathways cooperate to promote tumorigenesis and resistance to therapy. In a mouse model in which both pathways are activated in stem and progenitor cells, LSCs expanded under chemotherapy-induced stress. Since Akt can activate β-catenin, inhibiting this interaction might target therapy-resistant LSCs. High-throughput screening identified doxorubicin (DXR) as an inhibitor of the Aktâβ-catenin interaction at low doses. Here we repurposed DXR as a targeted inhibitor rather than a broadly cytotoxic chemotherapy. Targeted DXR reduced Akt-activated β-catenin levels in chemoresistant LSCs and reduced LSC tumorigenic activity. Mechanistically, β-catenin binds multiple immune-checkpoint gene loci, and targeted DXR treatment inhibited expression of multiple immune checkpoints specifically in LSCs, including PD-L1, TIM3 and CD24. Overall, LSCs exhibit distinct properties of immune resistance that are reduced by inhibiting Akt-activated β-catenin. These findings suggest a strategy for overcoming cancer therapy resistance and immune escape
Battle of Postdisaster Response and Restoration
[EN] The paper presents the results of the Battle of Postdisaster Response and Restoration (BPDRR) presented in a special session at the first International water distribution systems analysis & computing and control in the water industry (WDSA/CCWI) Joint Conference, held in Kingston, Ontario, Canada, in July 2018. The BPDRR problem focused on how to respond and restore water service after the occurrence of five earthquake scenarios that cause structural damage in a water distribution system. Participants were required to propose a prioritization schedule to fix the damages of each scenario while following restrictions on visibility/nonvisibility of damages. Each team/approach was evaluated against six performance criteria: (1) time without supply for hospital/firefighting, (2) rapidity of recovery, (3) resilience loss, (4) average time of no user service, (5) number of users without service for eight consecutive hours, and (6) water loss. Three main types of approaches were identified from the submissions: (1) general-purpose metaheuristic algorithms, (2) greedy algorithms, and (3) ranking-based prioritizations. All three approaches showed potential to solve the challenge efficiently. The results of the participants showed that for this network, the impact of a large-diameter pipe failure on the network is more significant than several smaller pipes failures. The location of isolation valves and the size of hydraulic segments influenced the resilience of the system during emergencies. On average, the interruptions to water supply (hospitals and firefighting) varied considerably among solutions and emergency scenarios, highlighting the importance of private water storage for emergencies. The effects of damages and repair work were more noticeable during the peak demand periods (morning and noontime) than during the low-flow periods; and tank storage helped to preserve functionality of the network in the first few hours after a simulated event. (C) 2020 American Society of Civil Engineers.Paez, D.; Filion, Y.; Castro-Gama, M.; Quintiliani, C.; Santopietro, S.; Sweetapple, C.; Meng, F.... (2020). Battle of Postdisaster Response and Restoration. Journal of Water Resources Planning and Management. 146(8):1-13. https://doi.org/10.1061/(ASCE)WR.1943-5452.0001239S1131468Balut A. R. Brodziak J. Bylka and P. Zakrzewski. 2018. âBattle of post-disaster response and restauration (BPDRR).â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Bibok A. 2018. âNear-optimal restoration scheduling of damaged drinking water distribution systems using machine learning.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Castro-Gama M. C. Quintiliani and S. Santopietro. 2018. âAfter earthquake post-disaster response using a many-objective approach a greedy and engineering interventions.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Cimellaro, G. P., Tinebra, A., Renschler, C., & Fragiadakis, M. (2016). New Resilience Index for Urban Water Distribution Networks. Journal of Structural Engineering, 142(8). doi:10.1061/(asce)st.1943-541x.0001433Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. doi:10.1109/tit.1967.1053964Creaco, E., Franchini, M., & Alvisi, S. (2010). Optimal Placement of Isolation Valves in Water Distribution Systems Based on Valve Cost and Weighted Average Demand Shortfall. Water Resources Management, 24(15), 4317-4338. doi:10.1007/s11269-010-9661-5Deb, K., Mohan, M., & Mishra, S. (2005). Evaluating the Îľ-Domination Based Multi-Objective Evolutionary Algorithm for a Quick Computation of Pareto-Optimal Solutions. Evolutionary Computation, 13(4), 501-525. doi:10.1162/106365605774666895Deuerlein J. D. Gilbert E. Abraham and O. Piller. 2018. âA greedy scheduling of post-disaster response and restoration using pressure-driven models and graph segment analysis.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Deuerlein, J. W. (2008). Decomposition Model of a General Water Supply Network Graph. Journal of Hydraulic Engineering, 134(6), 822-832. doi:10.1061/(asce)0733-9429(2008)134:6(822)Di Nardo, A., Di Natale, M., Giudicianni, C., Santonastaso, G. F., & Savic, D. (2018). Simplified Approach to Water Distribution System Management via Identification of a Primary Network. Journal of Water Resources Planning and Management, 144(2), 04017089. doi:10.1061/(asce)wr.1943-5452.0000885Eliades D. G. M. Kyriakou S. Vrachimis and M. M. Polycarpou. 2016. âEPANET-MATLAB toolkit: An open-source software for interfacing EPANET with MATLAB.â In Proc. 14th Int. Conf. on Computing and Control for the Water Industry (CCWI) 8. The Hague The Netherlands: International Water Conferences. https://doi.org/10.5281/zenodo.831493.Fragiadakis, M., Christodoulou, S. E., & Vamvatsikos, D. (2013). Reliability Assessment of Urban Water Distribution Networks Under Seismic Loads. Water Resources Management, 27(10), 3739-3764. doi:10.1007/s11269-013-0378-0Gilbert, D., Abraham, E., Montalvo, I., & Piller, O. (2017). Iterative Multistage Method for a Large Water Network Sectorization into DMAs under Multiple Design Objectives. Journal of Water Resources Planning and Management, 143(11), 04017067. doi:10.1061/(asce)wr.1943-5452.0000835Hill, D., Kerkez, B., Rasekh, A., Ostfeld, A., Minsker, B., & Banks, M. K. (2014). Sensing and Cyberinfrastructure for Smarter Water Management: The Promise and Challenge of Ubiquity. Journal of Water Resources Planning and Management, 140(7), 01814002. doi:10.1061/(asce)wr.1943-5452.0000449Hwang, H. H. M., Lin, H., & Shinozuka, M. (1998). Seismic Performance Assessment of Water Delivery Systems. Journal of Infrastructure Systems, 4(3), 118-125. doi:10.1061/(asce)1076-0342(1998)4:3(118)Li Y. J. Gao C. Jian C. Ou and S. Hu. 2018. âA two-stage post-disaster response and restoration method for the water distribution system.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Liu, W., Zhao, Y., & Li, J. (2014). Seismic functional reliability analysis of water distribution networks. Structure and Infrastructure Engineering, 11(3), 363-375. doi:10.1080/15732479.2014.887121Luong, H. T., & Nagarur, N. N. (2005). Optimal Maintenance Policy and Fund Allocation in Water Distribution Networks. Journal of Water Resources Planning and Management, 131(4), 299-306. doi:10.1061/(asce)0733-9496(2005)131:4(299)MacQueen J. B. 1967. âSome methods for classification and analysis of multivariate observations.â In Vol. 1 of Proc. 5th Berkeley Symp. on Mathematical Statistics and Probability 281â297. Berkeley: University of California Press.Mahmoud, H. A., Kapelan, Z., & SaviÄ, D. (2018). Real-Time Operational Response Methodology for Reducing Failure Impacts in Water Distribution Systems. Journal of Water Resources Planning and Management, 144(7), 04018029. doi:10.1061/(asce)wr.1943-5452.0000956Meng, F., Fu, G., Farmani, R., Sweetapple, C., & Butler, D. (2018). Topological attributes of network resilience: A study in water distribution systems. Water Research, 143, 376-386. doi:10.1016/j.watres.2018.06.048Ostfeld, A., Uber, J. G., Salomons, E., Berry, J. W., Hart, W. E., Phillips, C. A., ⌠Walski, T. (2008). The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. Journal of Water Resources Planning and Management, 134(6), 556-568. doi:10.1061/(asce)0733-9496(2008)134:6(556)Paez D. Y. Filion and M. Hulley. 2018a. âBattle of post-disaster response and restoration (BPDRR)âProblem description and rules.â Accessed June 14 2019. https://www.queensu.ca/wdsa-ccwi2018/problem-description-and-files.Paez, D., Suribabu, C. R., & Filion, Y. (2018). Method for Extended Period Simulation of Water Distribution Networks with Pressure Driven Demands. Water Resources Management, 32(8), 2837-2846. doi:10.1007/s11269-018-1961-1Salcedo C. A. Aguilar P. Cuero S. Gonzalez S. MuĂąoz J. PĂŠrez A. Posada J. Robles and K. Vargas. 2018. âDetermination of the hydraulic restoration capacity of b-city involving a multi-criteria decision support model.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Santonastaso G. F. E. Creaco A. Di Nardo and M. Di Natale. 2018. âPost-disaster response and restauration of B-town network based on primary network.â In Vol. 1 of Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. Kingston Canada: Open Journal Systems.Sophocleous S. E. Nikoloudi H. A. Mahmoud K. Woodward and M. Romano. 2018. âSimulation-based framework for the restoration of earthquake-damaged water distribution networks using a genetic algorithm.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Sweetapple C. F. Meng R. Farmani G. Fu and D. Butler. 2018. âA heuristic approach to water network post-disaster response and restoration.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems.Tabucchi, T., Davidson, R., & Brink, S. (2010). Simulation of post-earthquake water supply system restoration. Civil Engineering and Environmental Systems, 27(4), 263-279. doi:10.1080/10286600902862615Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., ⌠Ohar, Z. (2018). Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks. Journal of Water Resources Planning and Management, 144(8), 04018048. doi:10.1061/(asce)wr.1943-5452.0000969Walski, T. M. (1993). Water distribution valve topology for reliability analysis. Reliability Engineering & System Safety, 42(1), 21-27. doi:10.1016/0951-8320(93)90051-yWang, Y., Au, S.-K., & Fu, Q. (2010). Seismic Risk Assessment and Mitigation of Water Supply Systems. Earthquake Spectra, 26(1), 257-274. doi:10.1193/1.3276900Yoo, D. G., Kang, D., & Kim, J. H. (2016). Optimal design of water supply networks for enhancing seismic reliability. Reliability Engineering & System Safety, 146, 79-88. doi:10.1016/j.ress.2015.10.001Zhang Q. F. Zheng K. Diao B. Ulanicki and Y. Huang. 2018. âSolving the battle of post-disaster response and restauration (BPDRR) problem with the aid of multi-phase optimization framework.â In Proc. 1st Int. WDSA/CCWI 2018 Joint Conf. 14. Kingston Canada: Open Journal Systems
The complete plastid genome of Bauhinia variegata L. var. variegata (Leguminosae)
Bauhinia variegata L. var. variegata is a popular ornamental tree in the tropical and subtropical region. We herein report and characterize the complete plastid genome of B. variegata var. variegata in an effort to provide genomic resources for genetic utilization. The complete plastome is 155,415âbp in length and contains the typical quadripartite structure, including two inverted repeat (IR) regions of 25,549âbp, a large single-copy (LSC) region of 86,110âbp and a small single-copy (SSC) region of 18,207âbp. 130 genes are annotated, including 85 protein-coding genes, 37 transfer RNA genes, and four unique ribosomal RNA genes. The phylogenetic analysis based on the plastomes from B. variegata var. variegata and 12 previously reported species of Cercidoideae suggested a sister-relationship between B. variegata var. variegata and B. Ă blakeana with a strong bootstrap value
Single-phase inďŹow performance relationship in stress-sensitive reservoirs
  For stress-sensitive reservoirs, understanding the characteristics of the inďŹow performance relationship is vital for evaluating the performance of a well and designing an optimal stimulation. In this study, a reservoir simulator was used to establish the inďŹow performance relationship of a well for a wide variety of reservoirs and wellbore conditions. First, a base case was simulated using typical reservoir, wellbore, and ďŹuid parameters. Subsequently, variations from the base case were investigated. The results of the simulation indicate that the dimensionless inďŹow performance relationship in the stress-sensitive reservoir is similar to the Vogel-type inďŹow performance relationship, which is used for evaluating the productivity of a vertical well in a solution-gas-drive reservoir. Unlike the two-phase ďŹow in a solution-gas-drive reservoir, the nonlinear characteristic of the inďŹow performance relationship in stress-sensitive reservoirs is caused by stress-dependent permeability. Furthermore, the stress sensitivity level is the only parameter that affects the nonlinearity coefďŹcient of the dimensionless inďŹow performance relationship equation. The nonlinearity coefďŹcient was plotted against the stress sensitivity index, and the nonlinearity coefďŹcient was found to be linearly proportional to the stress sensitivity index. This study provides a realistic and less expensive methodology to evaluate the reservoir productivity of stress-sensitive reservoirs when the reservoir stress sensitivity level is known and to predict the reservoir stress sensitivity level when the inďŹow performance relationship of the stress-sensitive reservoirs is known.Cited as: Wang, F., Gong, R., Huang, Z., Meng, Q., Zhang, Q., Zhan, S. Single-phase inďŹow performance relationship in stress-sensitive reservoirs. Advances in Geo-Energy Research, 2021, 5(2): 202-211, doi: 10.46690/ager.2021.02.0
Quality of Life in Patients with Acromegaly before and after Transsphenoidal Surgical Resection
Objective. We aimed to determine the perioperative changes in the quality of life (QoL) in patients with acromegaly and to reveal the relationship between biochemical indicators and quality of life change after tumor resection. Methods. Patients with acromegaly were enrolled from a tertiary pituitary center. SF-36 scale and AcroQoL scale were used to determine the QoL before and after surgery. We analyzed changes in QoL using a generalized linear model for repeated measurements. We compared the changes in QoL among three groups (remission, active, and discordant group) based on postoperative growth hormone (GH) and insulin-like growth factor-1. Results. 151 patients (75 males and 76 females) diagnosed with acromegaly were included. The average age was 43.9âÂąâ12.3 years. The median total SF-36 scale was 65.3% (IQR: 63.2%â69.2%). Overall AcroQoL score at baseline was 59.1% (IQR: 51.8%â71.8%). Nadir GH levels (coefficient â0.08, p=0.047), T3 levels (coefficient 2.8, p=0.001), and testosterone levels (coefficient â0.20, p=0.033) in males were independent predictive factors of the total SF-36 score. During the follow-up, the median overall SF-36 score increased to 66.1% at 3 months and 75.3% at 6 months (p<0.001) after surgery. The median overall AcroQoL score increased to 74.5% at 3 months and 77.3% at 6 months (p<0.001) after surgery. At 6-month follow-up, median scores were still less than 70% in appearance, vitality, and mental health dimensions. The QoL after surgery were similar among the three groups, although higher GH and more preoperative somatostatin analogs usage were observed in the active group. Conclusion. In conclusion, acromegalic patients were associated with low QoL, which could be reversed partially by surgery. The improvement was independent of the endocrine remission. Appearance, vitality, and mental health were three major aspects that warrant further attention from physicians and caregivers after surgery
Magnetic Treatment Improves the Seedling Growth, Nitrogen Metabolism, and Mineral Nutrient Contents in Populus × euramericana ‘Neva’ under Cadmium Stress
This pot experiment was carried out to investigate the mechanism underlying nutrient metabolism and seedling growth responses to magnetic treatment following exposure to cadmium (Cd) stress. A magnetic device of 300 Gs was applied during Cd(NO3)2 solution treatment at 0 and 100 mM·L−1. One-year-old seedlings of Populus × euramericana ‘Neva’ were treated with different Cd(NO3)2 solutions in the presence or absence of magnetic treatment for 30 days. Seedling growth and physiological–biochemical indexes were measured under Cd stress. The contents of ammonium (NH4+–N), nitrate (NO3––N), and total nitrogen (TN) in leaves, as well as NH4+–N and TN in roots, were increased by magnetic treatment combined with Cd stress, although the NO3––N content was decreased. The activities of nitrate reductase (NR), nitrite reductase (NiR), glutathione reductase (GR), and glutamate synthase (GOGAT) in leaves and the activities of NR, glutamine synthetase (GS), and GOGAT in roots were stimulated by magnetic treatment; conversely, the NiR activity in roots was inhibited by magnetic effects. Magnetic treatment improved the synthesis of cysteine (Cys) and glutamine (Gln) in leaves and reduced the contents of glutamic acid (Glu) and glycine (Gly), while the contents of Cys, Glu, Gln, and Gly were increased in roots. The contents of Ca, Mg, Fe, Mn, Zn, and Cu in leaves were increased by magnetic treatment under Cd stress, whereas the content of K was reduced. In roots, the contents of K, Ca, and Fe were increased by magnetic treatment under Cd stress, but the contents of Na, Mg, Mn, Zn, and Cu were decreased. Magnetization could regulate the uptake of mineral nutrients by roots and translocation from the roots to the aboveground parts by affecting root morphology. Magnetic treatment could also improve nitrogen assimilation and the synthesis of free amino acids by stimulating the activities of key enzymes
An Ontology-Driven Approach for Integrating Intelligence to Manage Human and Ecological Health Risks in the Geospatial Sensor Web
Due to the rapid installation of a massive number of fixed and mobile sensors, monitoring machines are intentionally or unintentionally involved in the production of a large amount of geospatial data. Environmental sensors and related software applications are rapidly altering human lifestyles and even impacting ecological and human health. However, there are rarely specific geospatial sensor web (GSW) applications for certain ecological public health questions. In this paper, we propose an ontology-driven approach for integrating intelligence to manage human and ecological health risks in the GSW. We design a Human and Ecological health Risks Ontology (HERO) based on a semantic sensor network ontology template. We also illustrate a web-based prototype, the Human and Ecological Health Risk Management System (HaEHMS), which helps health experts and decision makers to estimate human and ecological health risks. We demonstrate this intelligent system through a case study of automatic prediction of air quality and related health risk