1,006 research outputs found
sj-docx-1-pie-10.1177_09544089231215967 - Supplemental material for Modelling and simulation of MRR in electro-chemical surface grinding of Al-SiC-Gr using hybrid analytical approach
No full textSupplemental material, sj-docx-1-pie-10.1177_09544089231215967 for Modelling and simulation of MRR in electro-chemical surface grinding of Al-SiC-Gr using hybrid analytical approach by Dhruv Kant Rahi and Avanish Kumar Dubey in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
sj-docx-2-pie-10.1177_09544089231215967 - Supplemental material for Modelling and simulation of MRR in electro-chemical surface grinding of Al-SiC-Gr using hybrid analytical approach
No full textSupplemental material, sj-docx-2-pie-10.1177_09544089231215967 for Modelling and simulation of MRR in electro-chemical surface grinding of Al-SiC-Gr using hybrid analytical approach by Dhruv Kant Rahi and Avanish Kumar Dubey in Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering</p
Geometric Modeling and Physics Simulation Framework for Building a Digital Twin of Extrusion-based Additive Manufacturing
No full textAccurate simulation of the printing process is essential for improving print quality, reducing waste, and optimizing the printing parameters of extrusion-based additive manufacturing. Traditional additive manufacturing simulations are very compute-intensive and are not scalable to simulate even moderately-sized geometries. In this paper, we propose a general framework for creating a digital twin of the dynamic printing process by performing physics simulations with the intermediate print geometries. Our framework takes a general extrusion-based additive manufacturing G-code, generates an analysis-suitable voxelized geometry representation from the print schedule, and performs physics-based (transient thermal and phase change) simulations of the printing process. Our approach leverages parallel adaptive octree meshes for both voxelated geometry representation as well as for fast simulations to address real-time predictions. We demonstrate the effectiveness of our method by simulating the printing of complex geometries at high voxel resolutions with both sparse and dense infills. Our results show that this approach scales to high voxel resolutions and can predict the transient heat distribution as the print progresses. This work lays the computational and algorithmic foundations for building real-time digital twins and performing rapid virtual print sequence exploration to improve print quality and further reduce material waste.This is a pre-print of the article Gamdha, Dhruv, Kumar Saurabh, Baskar Ganapathysubramanian, and Adarsh Krishnamurthy. "Geometric Modeling and Physics Simulation Framework for Building a Digital Twin of Extrusion-based Additive Manufacturing." arXiv preprint arXiv:2305.07120 (2023).
DOI: 10.48550/arXiv.2305.07120.
Attribution 4.0 International (CC BY 4.0).
Posted with permission
PaRTE
No full textWe present a test set of 1,126 sentence pairs to evaluate whether Recognizing Textual Entailment (RTE) models are robust to paraphrasing. We posit that if RTE models understand language, their predictions should be consistent across inputs that share the same meaning. We develop an evaluation RTE set consisting of paraphrased examples that we use to determine if models' predictions change when examples are paraphrased
Spectroscopic surface scattering of confined acoustic phonons in silicon nanostructures
No full textThe specularity of phonons at rough crystal surfaces is a fundamental aspect of phonon transport in nanostructures. It directly impacts engineering problems such as heat conduction in nanostructures and dissipation in nanomechanical resonators. At room temperature, the available guidance from theory is limited to fully diffuse transport under the condition of small surface roughness. Recent experiments in thermal transport suggest that there may exist large gaps in understanding phonon interactions with rough surfaces, especially when the roughness dimensions are comparable to phonon wavelengths. To consider such refinements, this thesis focuses on spectroscopic measurements of specularity in silicon nanostructures with well-characterized surface morphologies. We employ a femtosecond laser pump-probe setup to excite and detect confined acoustic phonons (∼ 18 - 200 GHz) in freely- suspended silicon membranes and nanowires. Surface scattering dominates intrinsic Akhiezer damping at frequencies > 60 GHz, thereby enabling us to probe phonon-boundary interactions over wavelengths ∼ 42 - 140 nm. To quantitatively understand the dependence of boundary scattering on RMS roughness and correlation length, we obtained detailed statistics of the surfaces using HRTEM and AFM imaging. For silicon membranes, we find that both Ziman and perturbation approach for roughness scattering successfully explain the nearly specular reflection of ∼ 0.1 THz phonons from surface with ∼ 1-nm scale roughness. The measured phonon specularities for silicon nanowires, however, are significantly lower in comparison to membranes for the frequency range ν ∼ 18−100 GHz. The reduction in specularity is caused by additional scattering from multiple surfaces introduced in a nanowire. Using a remarkably simple normalization scheme, we show that the scattering from multiple surfaces can be effectively decoupled. The magnitudes of the (normalized) phonon lifetimes are in good quantitative agreement with the predictions of Ziman approach but does not perfectly explain the frequency dependence. The τ ∼ ν^−1.7 dependence observed in our experiments is suggestive of weak phonon localization which cannot be understood within the existing framework of single scattering (or equivalently first Born approximation). This work helps to advance the fundamental understanding of phonon scattering at the surfaces of nanostructures.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2020-12-01The student, Dhruv Gelda, accepted the attached license on 2018-11-15 at 12:58.The student, Dhruv Gelda, submitted this Dissertation for approval on 2018-11-15 at 13:10.This Dissertation was approved for publication on 2018-11-15 at 16:13.DSpace SAF Submission Ingestion Package generated from Vireo submission #13089 on 2019-02-08 at 11:39:00Made available in DSpace on 2019-02-08T18:39:46Z (GMT). No. of bitstreams: 3
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Reducing extrinsic damping of surface acoustic waves at gigahertz frequencies
No full textHigh-frequency surface acoustic waves (SAWs) in the GHz range can be generated using absorption from an ultrafast laser in a patterned metallic grating on a substrate. Reducing the attenuation at these frequencies can yield better sensors as well as enable them to better probe phonon and electron-phonon interactions near surfaces. It is not clear from existing experiments which mechanisms dominate damping at high frequencies. We calculate damping times of SAWs due to various mechanism in the 1-100 GHz range to find that mechanical loading of the grating on the substrate dominates dissipation by radiating energy from the surface into the bulk. To overcome this and enable future measurements to probe intrinsic damping, we propose incorporating distributed acoustic Bragg reflectors (DABRs) in the experimental structure. Layers of alternating materials with contrasting acoustic impedances embedded a wavelength away from the surface serve to reflect energy back to the surface. Using numerical simulations, we show that a single Bragg reflector is sufficient to increase the energy density at the surface by more than five times. We quantify the resulting damping time to find that it is longer than the intrinsic damping time. The proposed structure can enable future measurements of intrinsic damping in SAWs at 100 GHz.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2019-05-01The student, Dhruv Gelda, accepted the attached license on 2017-01-31 at 12:44.The student, Dhruv Gelda, submitted this Thesis for approval on 2017-01-31 at 13:04.This Thesis was approved for publication on 2017-01-31 at 15:56.DSpace SAF Submission Ingestion Package generated from Vireo submission #10551 on 2018-08-14 at 16:00:20Made available in DSpace on 2018-08-14T21:37:12Z (GMT). No. of bitstreams: 3
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Optimizing Aggregation and Join Queries in Geo-Distributed Data Analytics
No full textUniversity of Minnesota Ph.D. dissertation. 2022. Major: Computer Science. Advisor: Abhishek Chandra. 1 computer file (PDF); 171 pages.Large-scale data analytics services require collection and analysis of data from end-user applications and devices distributed around the globe. These services are increasingly deployed on a geographically distributed infrastructure comprising a multi-tier topology of edge servers and cloud data centers (DCs). Such geo-distributed analytics (GDA) involves data transfer over the wide area network (WAN) links connecting the various processing sites (edges and DCs). These WAN links are highly constrained and heterogeneous in nature, making the data transfer over the WAN slow and costly. Additionally, the edge nodes can also be constrained in terms of compute capacity. While the prior work on GDA has tried to address these challenges to some degree, this thesis identifies and solves a number of unidentified challenges associated with two fundamental operations in any GDA system: data aggregation and relational joins. Real-time aggregation and processing of geo-distributed data streams continuously over time often has two competing requirements: first, the results be available at the center within a certain acceptable delay bound and second, the WAN traffic needs to be minimized due to constrained and expensive WAN bandwidth. This delay-traffic tradeoff forms a fundamental component of streaming analytics. This thesis proposes a Time-To-Live (TTL-) based aggregation model which provides a theoretical basis for understanding the aforementioned delay-traffic tradeoff. The TTL-based aggregation model is then utilized to solve a variety of optimization problems such as jointly minimizing the delay and traffic costs, minimizing delay subject to a traffic bound and minimizing traffic subject to a delay bound in the context of hub-and-spoke like edge-cloud infrastructure where multiple edges are connected to a central cloud data center. Next, this thesis also proposes aggregation networks to efficiently perform continuous aggregation over a general multi-tiered distributed edge-cloud infrastructure. In doing so, it identifies a number of less studied tradeoffs such as tradeoff between traffic and traffic cost. The identified tradeoffs are then utilized to propose AggNet, a cost-aware system for minimizing traffic cost across aggregation networks while satisfying the resource constraints in the network as well as the delay sensitivity of the streaming aggregation queries. Computing joins in a geo-distributed setting remains a challenging problem, as joins often form the most heavyweight component in an analytics query, both in terms of compute and data shuffle over the WAN. This thesis first looks at queries comprising both join and aggregation operators. It proposes AggFirstJoin, an approach to minimize the cost of geo-distributed joins using a theoretically sound query transformation technique. The optimization approach takes a combined view of the join and aggregation operations which are often part of the same query, and pushes (a transformed) aggregation before join so as to produce the same results as the original query. The query transformation technique is further augmented with a WAN-aware task placement and a Bloom filtering approach to further reduce query execution time and WAN usage respectively. Next, this thesis studies queries with join operators on their own. Computing exact results for such queries is much more challenging since there are no aggregation operators which could have reduced the data shuffle over WAN. Hence, this thesis proposes a geo-distributed join sampling approach which can efficiently generate random samples from geo-distributed tables in order to finally produce a random sample of the joined result. All of the proposed techniques in this thesis are implemented on top of popular data analytics engines such as Apache Spark and Apache Flink. Evaluations are carried out using both real and synthetic traces on a real geo-distributed testbed on AWS as well as an emulated test-bed. The proposed techniques show remarkable improvements over the existing state-of-the-art.Kumar, Dhruv. (2022). Optimizing Aggregation and Join Queries in Geo-Distributed Data Analytics. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/227914
Ethics: What is the decent thing?
No full textWhat is the decent thing? As VW feels the fallout from breaking its promises, architects Irena Bauman, author of How to be a Happy Architect, and Dhruv Sookhoo, ethics investigator, tackle some everyday ethical dilemmas
Hybrid adaptive chassis control for vehicle lateral stability in the presence of uncertainty
No full textTo guarantee the safety of passengers in a wide range of driving situations, vehicle lateral stability should be achieved in the presence of nonlinear dynamics (consequence of critical maneuvers) and uncertainty (consequence of uncertain parameters). This paper designs a hybrid adaptive strategy to attain vehicle stability in these situations. The design is based on a piecewise affine (PWA) description of the vehicle model where partitions describe both the linear and the nonlinear regimes, and where parametric uncertainties are handled by estimators for the control gains that can adapt to different conditions acting on the system. Comparisons with strategies that merely exploits the linear region of the vehicle dynamics are provided for different driving conditions, and performance improvements of the proposed methodology are assessed.Accepted Author ManuscriptTeam Bart De Schutte
Abstract 2970: Mitigating tumor-stroma metabolic symbiosis for cancer therapy
No full textAbstract
Head and neck squamous cell carcinoma (HNSCC) affects 40,000 patients annually and is associated with &lt;50% 5-year survival. There is an urgent need to better understand the biology of the disease in order to develop more effective therapeutic approaches. HNSCC tumors are dysplastic with up to 80% fibroblasts. We recently reported that tumor-associated fibroblasts (TAFs) make HNSCC more aggressive. Furthermore, we reported that TAFs produce hepatocyte growth factor (HGF), which binds to the c-Met receptor expressed on HNSCC to drive aggressiveness. Although we did not detect HGF secretion from HNSCC cell lines, we reported that paracrine activation of c-Met by TAF-secreted HGF facilitates HNSCC progression. Reciprocal signaling between the tumor and stroma has been reported in several cancers to facilitate tumor growth, invasion and resistance to therapy. Recent studies have shown that c-Met activation promotes glycolysis. Although highly glycolytic, the mechanisms regulating HNSCC glycolysis remain unknown. We show that TAF-secreted HGF through c-Met activation on HNSCC (a) induces aerobic glycolysis accompanied by lactate production, and b) regulates expression of basic fibroblast growth factor (bFGF). Studies have shown that HNSCC tumors have high lactate levels resulting from increased glycolysis, and this correlates with reduced survival. Our data demonstrate that TAF-secreted HGF increases key glycolytic enzymes including hexokinase II. Furthermore, HGF increases glycolysis and lactate production from HNSCC. We demonstrate that HGF also increases levels of the bidirectional lactate transporter, monocarboxylate transporter 1 (MCT1). We demonstrate that MCT1 levels are increased in both HNSCC and TAFs under co-culture conditions. In addition, HGF stimulation increases levels of MCT1 in HNSCC indicating a possible mechanism whereby HNSCC remove the excess lactate generated during glycolysis. The mechanisms whereby HNSCC tumors survive highly acidic conditions remain unknown. Since MCT1 levels are increased in TAFs as well, we sought to determine if TAFs utilize the lactate as a carbon source to generate energy. Indeed, we found that bFGF secreted by HNSCC, binds to its cognate FGF receptor (FGFR) on TAFs to facilitate latate utilization through mitochondrial oxidative phosphorylation (OXPHOS). Thus there exists a metabolic symbiosis between HNSCC and TAFs that contribute to tumor growth. Through these studies, we delineate the mechanisms of glycolysis regulation in HNSCC and demonstrate that inhibition of the cross-talk between HNSCC and TAFs can be used as a novel therapeutic approach.
Citation Format: Dhruv Kumar, Jacob New, Vikalp Vishwakarma, Hemant Chavan, Partha Kasturi, Sufi M. Thomas. Mitigating tumor-stroma metabolic symbiosis for cancer therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2970. doi:10.1158/1538-7445.AM2017-2970</jats:p
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