bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of Poststorage Cardiac Function From Cardioplegic Effluent in an Ex Vivo Porcine Heart Model
Authors: Kappler Benjamin , van Tuijl Sjoerd , van Laar Teus J. , Pabittei Dara R. , Buijsrogge Marc P. , Stijnen Marco , de Mol Bas A. J. M. 
Published: October 2019

Abstract
The use of slaughterhouse-based hearts has advantages over hearts obtained from laboratory animals for preclinical testing. However, slaughterhouse hearts have greater variability in cardiac function; this has resulted in a dispute over their actual reproducibility. This study explores the feasibility of examining the cardioplegic effluent during hypothermic cardiac arrest for the presence of biomarkers to predict poststorage heart function of slaughterhouse hearts. This may enable proactive measures to optimize preservation strategies and improve the initial cardiac performance of slaughterhouse heart experiments. Slaughterhouse pig hearts (n = 9; 420 ± 30 g) were arrested and flushed with an additional liter cardioplegia after 1 h. Effluent samples were examined for ammonia, lactate, troponin, and inorganic phosphate. After 2 h, hearts were hemoreperfused in the ex vivo heart platform PhysioHeart™ to restore physiological cardiac functions and to identify correlations between biomarkers and cardiac output. There was a negative correlation between cardiac output of revived hearts and levels of ammonia (r = −0.865; p = 0.002) and lactate (r = −0.763; p = 0.01). No correlation was found between cardiac output and levels of phosphate (r = −0.553; p = 0.12) and troponin (r = −0.367; p = 0.331). The analysis approach to assess cardioplegic biomarkers was feasible and enabled the estimation of the effectiveness of organ protection and cardiac function before reperfusion. Ammonia is a predictor for cardiac dysfunction. Effluent analysis prior to heart revival can uncover poststorage cardiac dysfunction in isolated hearts and may prevent failed experiments while improving reproducibility and standardization.
Investigating Force-Feedback in Mid-Air Sketching of Multi-Planar Three-Dimensional Curve-Soups
Authors: Mohanty Ronak R. , Castillo Ricardo M. , Ragan Eric D. , Krishnamurthy Vinayak R. 
Published: October 2019

Abstract
In this paper, we report on our investigation of haptics-enabled mid-air interactions for sketching three-dimensional (3D) curve-soups—collections of three-dimensional multi-planar curves. We study pen-based mid-air interactions for free-form curve input from the perspective of manual labor, controllability, and kinesthetic feedback. We specifically study the role of kinesthetic feedback for two aspects of mid-air sketching, namely, drawing curves on planar surfaces and spatial rotation of 3D curve-soups. For this, we implemented a simple haptics-enabled workflow for users to draw and compose collections of planar curves on a force-enabled virtual canvas. The qualitative and quantitative analyses of our study-tasks show that there is a rich interaction design space of kinesthetic feedback methods for mid-air sketching beyond physically currently prevalent models.
Evaluating the Accuracy of RANS Wind Flow Modeling Over Forested Terrain. Part 2: Impact on Capacity Factor for Moderately Complex Topography
Authors: Morales Garza Viridiana G. , Sumner Jonathon , Nathan Jörn , Masson Christian 
Published: October 2019

Abstract
This study evaluates the uncertainty in speed-up factors predicted using the Reynolds-Averaged Navier–Stokes equations to model flow over moderately complex forested terrain and considers its effect on the uncertainty in wind energy calculations. All simulations are solved using the open-source software openfoam v.2.4.0 with a modified k–ε turbulence closure. The forest drag effect is calculated with two models: a displacement height model and a canopy model that estimates the pressure loss due to the forest through analogy with porous media. Two years of concurrent wind data from three meteorological masts at a potential wind farm site in Canada are used for validation purposes. In all, these experimental data are compared with the predictions of four wind flow models: (A) a terrain only model, (B) a displacement height model, (C) a uniform forest canopy model, and (D) a non-uniform forest canopy model. Overall, the canopy models provide better agreement with the mean statistical results than the displacement height model. In this case, the 2.76% uncertainty in the speed-up factor associated with the wind flow predictions of the non-uniform forest distribution model leads to an uncertainty in the energy calculation of just 5.94%.
Rojiroti microfinance and child nutrition: a cluster randomised trial
Authors: Ojha Shalini , Szatkowski Lisa , Sinha Ranjeet , Yaron Gil , Fogarty Andrew , Allen Stephen John , Choudhary Sunil , Smyth Alan Robert 
Published: October 2019

ObjectiveTo determine whether Rojiroti microfinance, for poor Indian women, improves child nutrition.DesignCluster randomised trial.SettingTolas (village communities) in Bihar State.ParticipantsWomen and children under 5 years.InterventionsWith Rojiroti microfinance, women form self-help groups and save their money to provide loans to group members. After 6 months, they receive larger external loans. Tolas were randomised to receive Rojiroti immediately or after 18 months.Outcome measuresThe primary analysis compared the mean weight for height Z score (WHZ) of children under 5 years in the intervention versus control tolas who attended for weight and height measurement 18 months after randomisation. Secondary outcomes were weight for age Z score (WAZ), height for age Z score, mid-upper arm circumference (MUAC), wasting, underweight and stunting.ResultsWe randomised 28 tolas to each arm and collected data from 2469 children (1560 mothers) at baseline and 2064 children (1326 mothers) at follow-up. WHZ was calculated for 1718 children at baseline and 1377 (674 intervention and 703 control) at follow-up. At 18 months, mean WHZ was significantly higher for intervention (−1.02) versus controls (−1.37; regression coefficient adjusted for clustering β=0.38, 95% CI 0.16 to 0.61, p=0.001). Significantly fewer children were wasted in the intervention group (122, 18%) versus control (200, 29%; OR=0.46, 95% CI 0.28 to 0.74, p=0.002). Mean WAZ was better in the intervention group (−2.13 vs −2.37; β=0.27, 95% CI 0.11 to 0.43, p=0.001) as was MUAC (13.6 cm vs 13.4 cm; β=0.22, 95% CI 0.03 to 0.40, p=0.02). In an analysis adjusting for baseline nutritional measures (259 intervention children and 300 control), only WAZ and % underweight showed significant differences in favour of the intervention.ConclusionIn marginalised communities in rural India, child nutrition was better in those who received Rojiroti microfinance, compared with controls.Trial registration numberNCT01845545.
Firearm ownership in California: A latent class analysis
Authors: Schleimer Julia P , Kravitz-Wirtz Nicole , Pallin Rocco , Charbonneau Amanda K , Buggs Shani A , Wintemute Garen J 
Published: October 2019

ObjectiveTo examine whether firearm ownership and ownership-related motivations and practices can be classified into reasonably distinct types.MethodsCross-sectional data on firearm owners (n=429) were obtained from the 2018 California Safety and Well-Being Survey, a state-representative web-based survey. We conducted a latent class analysis using six self-reported indicators of firearm ownership: (1) number of firearms owned, (2) types of firearms owned, (3) primary reason for firearm ownership, (4) firearm storage, (5) loaded handgun carrying and (6) high-capacity magazine ownership.ResultsWe identified five markedly different classes of firearm ownership. There were two classes of single-firearm owners and three classes of multiple-firearm owners. Only members of one class (9% of owners) were likely to have carried a loaded handgun and to own high-capacity magazines or assault-type weapons. Members of this class were also likely to own 5+ firearms, own for protection against people, and store a firearm in the least secure manner (loaded and unlocked).ConclusionThere were distinct classes of firearm ownership in California, and all higher-risk behaviours studied were exhibited disproportionately by members of a single class. This latent class structure, which may help identify higher-risk groups of firearm owners, could inform future research on risk assessment and on focused interventions to reduce firearm injury and death.
Collapsing stellar filament and exotic matter in Palatini f(R) gravity
Authors: Manzoor Rubab , Jawad Abdul , Adeel Muhammad , Saeed Muhammad , Rani Shamaila 
Published: October 2019

Abstract
We explore the dynamics of collapsing stellar filament in the presence of exotic material like dark matter. We use Palatini f(R) theory to include exotic substance in the collapsing process. We derive a collapse equation by applying Darmois junction conditions on collapsing surface boundary $$\Sigma $$Σ. It is found that the radial pressure related to baryonic matter remains non-zero at $$\Sigma $$Σ. We then discuss the stability criteria of the collapsing process in the framework of three parameteric model, $$f(R)=R+\lambda R_{c}[ 1-(1+\frac{R^{2}}{R^{2}_{c}})^{-n}]$$f(R)=R+λRc[1-(1+R2Rc2)-n]. It is concluded that the stability of collapsing filament depends upon a directly proportional relation of gravitational effects of exotic terms with the radial pressure of seen matter. Stability criteria of family of polytropic filamentary structures are also discussed. For all stable polytropic filaments, it is found that the density of seen material is exponentially related to the exotic forces. Finally, we explore theoretical relation between gravitational waves and dark terms. It is theoretically predicted that the presence of exotic material can affect the propagation of gravitational waves.
The pelagic archive of short-term sea-level change in the Cretaceous: a review of proxies linked to orbital forcing
Authors: Wagreich Michael , Koukal Veronika 
Published: October 2019

Abstract
Deep-time sea-level oscillations in the Milankovitch-band of orbital cyclicities govern deposition in the pelagic realm mainly by varying siliciclastic input. Pelagic sediments from the Cretaceous greenhouse climate phase provide a valuable archive for sea-level change. Although sea-level variations are of negligible amplitude compared with depositional water-depths, direct physical proxy data are based on higher and coarser siliciclastic input during sea-level lowstand and regressions, and include coarser grain size and grain-size parameters as well as the heavy mineral and clay content. Chemical proxies that relate to siliciclastics are manganese, titanium and zirconium, often normalized v. aluminium. Further proxies provide the ratios of strontium v. calcium, controlled by shelf carbonate erosion, and partly redox-sensitive elements like uranium and thorium. From a mineralogical point of view, the total amount of siliciclastics and their diversity relating to heavy minerals provides sea-level information in hemipelagites, as well as the phyllosilicate content v. biogenic pelagic background deposition of carbonate and siliceous microfossils in pelagites. In addition, measurements of gamma ray emission, linked to U, Th, K content and magnetic susceptibility may relate to sea-level cycles and various other more climate-dependent proxies like oxygen isotopes of fossil calcite and compositional maturity of hemipelagic sediments.
TF-Net
Authors: Yu Jiecao , Lukefahr Andrew , Das Reetuparna , Mahlke Scott 
Published: October 2019

Deep Neural Networks (DNNs) have become an essential component of various applications. While today’s DNNs are mainly restricted to cloud services, network connectivity, energy, and data privacy problems make it important to support efficient DNN computation on low-cost, low-power processors like microcontrollers. However, due to the constrained computation resources, it is challenging to execute large DNN models on microcontrollers. Using sub-byte low-precision input activations and weights is a typical method to reduce DNN computation. But on byte-addressable microcontrollers, the sub-byte computation is not well supported. The sub-byte inputs and weights need to be unpacked from bitstreams before computation, which incurs significant computation and energy overhead.
In this paper, we propose the TF-Net pipeline to efficiently deploy sub-byte DNNs on microcontrollers. While TF-Net allows for a range of weight and input precision, we find Ternary weights and Four-bit inputs provide the optimal balance between model accuracy, computation performance, and energy efficiency. TF-Net first includes a training framework for sub-byte low-precision DNN models. Two algorithms are then introduced to accelerate the trained models. The first, direct buffer convolution, amortizes unpacking overhead by caching unpacked inputs. The second, packed sub-byte multiply-accumulate, utilizes a single multiplication instruction to perform multiple sub-byte multiply-accumulate computations. To further accelerate DNN computation, we propose two instructions, Multiply-Shift-Accumulate and Unpack, to extend the existing microcontroller instruction set. On the tested networks, TF-Net can help improve the computation performance and energy efficiency by 1.83× and 2.28× on average, respectively.
IGOR, Get Me the Optimum! Prioritizing Important Design Decisions During the DSE of Embedded Systems
Authors: Smirnov Fedor , Pourmohseni Behnaz , Glaß Michael , Teich Jürgen 
Published: October 2019

Design Space Exploration (DSE) techniques for complex embedded systems must cope with a huge variety of applications and target architectures as well as a wide spectrum of objectives and constraints. In particular, existing design automation approaches are either problem-independent, in that they do not exploit any knowledge about the optimization problem at hand, or are tailored to specific a priori assumptions about the problem and/or a specific set of design objectives. While the latter are only applicable within a very limited scope of design problems, the former may struggle to deliver high-quality solutions for problems with large design spaces and/or complex design objectives. As a remedy, we propose
Importance-Guided Order Rearrangement (IGOR)
as a novel approach for DSE of embedded systems. Instead of relying on an a priori problem knowledge, IGOR uses a machine-learning-inspired technique to dynamically analyze the importance of design decisions, i.e., the impact that these decisions—within the specific problem that is being optimized—have on the quality of explored problem solutions w.r.t. the given design objectives. Throughout the DSE, IGOR uses this information to guide the optimization towards the most promising regions of the design space. Experimental results for a variety of applications from different domains of embedded computing and for different optimization scenarios give evidence that the proposed approach is both scalable and adaptable, as it can be used for the optimization of systems described by several thousands constraints, where it outperforms both problem-specific and problem-independent optimization approaches and achieves ε-dominance improvements of up to 95%.
Aggregation Strategies in Reachable Set Computation of Hybrid Systems
Authors: Duggirala Parasara Sridhar , Bak Stanley 
Published: October 2019

Computing the set of reachable states is a widely used technique for proving that a hybrid system satisfies its safety specification. Flow-pipe construction methods interleave phases of computing continuous successors and phases of computing discrete successors. Directly doing this leads to a combinatorial explosion problem, though, as with each discrete successor there may be an interval of time where the transition can occur, so that the number of paths becomes exponential in the number of discrete transitions. For this reason, most reachable set computation tools implement some form of set aggregation for discrete transitions, such as, performing a template-based overapproximation or convex hull aggregation. These aggregation methods, however, in theory can lead to unbounded error, and in practice are often the root cause of why a safety specification cannot be proven.
This paper proposes techniques for improving the accuracy of the aggregation operations performed for reachable set computation. First, we present two aggregation strategies over generalized stars, namely convex hull aggregation and template based aggregation. Second, we perform adaptive deaggregation using a data structure called Aggregated Directed Acyclic Graph (AGGDAG). Our deaggregation strategy is driven by counterexamples and hence has soundness and relative completeness guarantees. We demonstrate the computational benefits of our approach through two case studies involving satellite rendezvous and gearbox meshing.
Code-Inherent Traffic Shaping for Hard Real-Time Systems
Authors: Oehlert Dominic , Saidi Selma , Falk Heiko 
Published: October 2019

Modern hard real-time systems evolved from isolated single-core architectures to complex multi-core architectures which are often connected in a distributed manner. With the increasing influence of interconnections in hard real-time systems, the access behavior to shared resources of single tasks or cores becomes a crucial factor for the system’s overall worst-case timing properties. Traffic shaping is a powerful technique to decrease contention in a network and deliver guarantees on network streams. In this paper we present a novel approach to automatically integrate a traffic shaping behavior into the code of a program for different traffic shaping profiles while being as least invasive as possible. As this approach is solely depending on modifying programs on a code-level, it does not rely on any additional hardware or operating system-based functions.
We show how different traffic shaping profiles can be implemented into programs using a greedy heuristic and an evolutionary algorithm, as well as their influences on the modified programs. It is demonstrated that the presented approaches can be used to decrease worst-case execution times in multi-core systems and lower buffer requirements in distributed systems.
A Phase II Trial of Hu14.18K322A in Combination with Induction Chemotherapy in Children with Newly Diagnosed High-Risk Neuroblastoma
Authors: Furman Wayne L. , Federico Sara M. , McCarville Mary Beth , Shulkin Barry L. , Davidoff Andrew M. , Krasin Matthew J. , Sahr Natasha , Sykes April , Wu Jianrong , Brennan Rachel C. , Bishop Michael William , Helmig Sara , Stewart Elizabeth , Navid Fariba , Triplett Brandon , Santana Victor M. , Bahrami Armita , Anthony Gwendolyn , Yu Alice L. , Hank Jacquelyn , Gillies Stephen D. , Sondel Paul M. , Leung Wing H. , Pappo Alberto S. 
Published: October 2019

AbstractPurpose:We sought to evaluate whether combining a humanized antidisialoganglioside mAb (hu14.18K322A) with induction chemotherapy improves early responses and outcomes in children with newly diagnosed high-risk neuroblastoma.Patients and Methods:We conducted a prospective nonrandomized, single-arm, two-stage, phase II clinical trial. Six courses of induction chemotherapy were coadministered with hu14.18K322A and followed with granulocyte–macrophage colony-stimulating factor (GM-CSF) and low-dose IL2. Consolidation was performed with a busulfan/melphalan preparative regimen. An additional course of hu14.18K322A was administered with parent-derived natural killer cells, when available, during consolidation. Hu14.18K322A, GM-CSF, IL2, and isotretinoin were then administered. Secondary outcomes included reduced tumor volume and semiquantitative 123I-metaiodobenzylguanidine scoring [i.e., Curie scores (CS)] at the end of induction.Results:Forty-two patients received hu14.18K322A and induction chemotherapy. This regimen was well tolerated, with continuous-infusion narcotics adjusted to patient tolerance. Partial responses (PR) or better after the first two chemoimmunotherapy courses occurred in 32 patients [76.2%; 95% confidence interval (CI), 60.6–88.0]. This was accompanied by primary tumor volume reductions (median, –76%; range, –100% to 5%). Of 35 patients with stage IV disease who completed induction, 31 had end-of-induction CSs of 2 or less. No patients experienced progression during induction. Two-year event-free survival (EFS) was 85.7% (95% CI, 70.9–93.3).Conclusions:Adding hu14.18K322A to induction chemotherapy produced early PR or better in most patients, reduced tumor volumes, improved CSs at the end of induction, and yielded an encouraging 2-year EFS. These results, if validated in a larger study, may change the standard of care for children with high-risk neuroblastoma.
Dynamical Mechanisms Supporting Extreme Rainfall Accumulations in the Houston “Tax Day” 2016 Flood
Authors: Nielsen Erik R. , Schumacher Russ S. 
Published: October 2019

AbstractThis research examines the environmental and storm-scale characteristics of the extreme rainfall and flooding in the Houston, Texas, area on 18 April 2016, known as the “Tax Day” flood. Radar and local mesonet rain gauge observations were used to identify the locations and structures of extreme rain-rate-producing cells, with special attention given to rotating updrafts. To supplement this observation-based analysis, a WRF-ARW simulation of the Tax Day storm in 2016 was examined for the influence of any attendant rotation on both the dynamics and microphysics of the cells producing the most intense short-term (i.e., subhourly to hourly) rainfall accumulations. Results show that the most intense rainfall accumulations in the model analysis, as in the observational analysis, are associated with rotating convective elements. A lowering of the updraft base, enhancement of the low-level vertical velocities, and increased low-level rainwater production is seen in rotating updrafts, compared to those without rotation. These differences are also maintained despite increased hydrometeor loading. The results agree with the findings of previous idealized model simulations that show dynamical accelerations associated with meso-γ-scale rotation can enhance convective rainfall rates.
Collective Action and Tragedy of Tank Water
Authors: Sarkar Sukumar , Ray Biswajit 
Published: October 2019

Tank irrigation is important in agriculture-dependent developing economies such as India. Since tank water is a common pool resource, managing tanks cost-effectively through collective action (CA) is a challenge. This notion raises the following principal questions on the tank commons: What is the relationship between transaction costs (that is, cost of cooperation) and CA? And what drive the use of tank water for irrigation? For satisfactory answers, we carried out field studies on 127 farmers of seven tank villages in the districts of Bardhaman and West Midnapore in West Bengal, India between September 2015 and May 2017. Interesting findings have emerged from our study. We notice that CAs are inversely correlated with transaction costs in the study sites. Regarding the factors influencing tank water use (WU) we find based on Logit models that CA matters in WU significantly and positively. This impact of CA on WU is also robust irrespective of the model specifications. However, low income households participate more by offering labour than money but unfortunately they seem to have no primary motives for tank reconstruction. Moreover, in communities where tanks are large, local people’s participation more in terms of labour and such participation in reconstruction of the tanks is their primary motive, and also where the institutional arrangements are both formal and informal, people tend to use tank water more. The practical implication is that the absence of Water User Association and hence proper collaborative management coupled with weak nestedness between the village people and the State is one of the major causes of inadequate tank WU, leading to the decline of the tank commons. JEL: Q01, Q25