bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pregnant nurses’ experiences of working shifts: a qualitative systematic review protocol
Authors: Ooshige Narumi , Matsunaka Eriko , Ueki Shingo 
Published: August 2022

Objective:
The proposed systematic review will synthesize the experiences of nurses working in any shifts during pregnancy.

Introduction:
Nurses are among the most burdened health care workers and are at risk of diseases of the circulatory system due to prolonged standing. For pregnant nurses, this could reduce blood circulation and blood flow to the uterus and placenta. Contributing factors include long working hours and frequent heavy lifting, which may induce uterine contractions and premature birth; irregular shifts, which exacerbate fatigue; and high levels of psychosocial stress. There is no qualitative evidence as to what nurses are experiencing while working shifts when pregnant. This review will evaluate and integrate the current available findings related to the experience of shift-working nurses who are pregnant.

Inclusion criteria:
This review will include qualitative studies focusing on the experience of shift work among pregnant nurses. No language or date limits will be applied. All types of pregnancy among nurses will be included.

Methods:
The databases to be searched will include CINAHL (EBSCOhost), MEDLINE (EBSCOhost), PsycINFO (EBSCOhost), and Igaku Chuo Zasshi (the Japan Medical Abstracts Society). Sources of gray literature will include MedNar and DANS, as well as the websites of the American Nurses Association and the Japanese Nursing Association. Study selection, critical appraisal, data extraction, and data synthesis will be performed independently by 2 reviewers. The synthesized findings will be graded according to the ConQual approach for establishing confidence in findings.

Systematic review registration:
PROSPERO CRD42022309674
Small impact cratering processes produce distinctive charcoal assemblages
Authors: Losiak A. , Belcher C.M. , Plado J. , Jõeleht A. , Herd C.D.K. , Kofman R.S. , Szokaluk M. , Szczuciński W. , Muszyński A. , Wild E.M. , Baker S.J. 
Published: August 2022

Abstract
The frequency of crater-producing asteroid impacts on Earth is not known. Of the predicted Holocene asteroid impact craters of &lt;200 m diameter, only ~30% have been located. Until now there has been no way to distinguish them from “normal” terrestrial structures unless pieces of iron meteorites were found nearby. We show that the reflective properties of charcoal found in the proximal ejecta of small impact craters are distinct from those produced by wildfires. Impact-produced charcoals and wildfire charcoals must derive from different heating regimes. We suggest that charcoal with specific reflective properties may help to recognize the meteoritic origin of small craters.
Onset of long-lived silicic and alkaline magmatism in eastern North America preceded Central Atlantic Magmatic Province emplacement
Authors: Kinney Sean T. , MacLennan Scott A. , Szymanowski Dawid , Keller C. Brenhin , VanTongeren Jill A. , Setera Jacob B. , Jaret Steven J. , Town C. Forrest , Strauss Justin V. , Bradley Dwight C. , Olsen Paul E. , Schoene Blair 
Published: August 2022

Abstract
The White Mountain magma series is the largest Mesozoic felsic igneous province on the eastern North American margin. Previous geochronology suggests that magmatism occurred over 50 m.y., with ages for the oldest units apparently coeval with the ca. 201 Ma Central Atlantic Magmatic Province, the flood basalt province associated with the end-Triassic mass extinction and the opening of the Atlantic Ocean. We use zircon U-Pb geochronology to show that emplacement of White Mountain magma series plutons was already underway at 207.5 Ma. The largest volcanic-plutonic complex, the White Mountain batholith, was emplaced episodically from ca. 198.5 Ma to ca. 180 Ma and is ~25 m.y. older than published ages suggest, and all samples we dated from the Moat Volcanics are between ca. 185 Ma and 180 Ma. The Moat Volcanics and the White Mountain batholith are broadly comagmatic, which constrains the age of a key Jurassic paleomagnetic pole. Our data indicate that a regional mantle thermal anomaly in eastern North America developed at least 5 m.y. prior to the main stage of Central Atlantic Magmatic Province flood basalt volcanism and suggest a geodynamic link between the White Mountain magma series and the Central Atlantic Magmatic Province.
Time constraints on hydrocarbon migration and caprock formation recorded by calcite spar in a Carboniferous–Permian carbonate-evaporite succession, Finnmark Platform, Barents Sea
Authors: Hodgskiss Malcolm S.W. , Thiagarajan Nivedita , Wang Yue , Rameil Niels , Brunstad Harald , Hammer Erik , Pedersen Jon Halvard , Kirsimae Kalle , Mørk Atle , Lepland Aivo 
Published: August 2022

Abstract
A late Carboniferous to early Permian carbonate and evaporite succession from the Finnmark Platform (southern Barents Sea) contains nodules of anhydrite partially to fully replaced by calcite spar and native sulfur genetically linked to hydrocarbon migration and/or oxidation, analogous to processes observed in salt diapir caprocks in the Gulf of Mexico. In situ U-Pb dating of this calcite spar therefore has the potential to directly date hydrocarbon migration and provide further insight into the geochemical and temperature conditions during this event when coupled with traditional stable and clumped isotope ratios (δ13C, δ18O, and Δ47). Results indicate calcite formed ca. 50–15 Ma, postdating host-rock deposition by 250–285 m.y. Strongly negative δ13C values in the calcite spar (mean = −15‰) are consistent with a major contribution of carbon from hydrocarbons, and Δ47 paleothermometry indicates a mean precipitation temperature of 46 ± 11 °C. These geochemical results are consistent with the local burial history and suggest protracted hydrocarbon migration and/or oxidation and caprock formation spanning ~35 m.y.
The northern Hikurangi margin three-dimensional plate interface in New Zealand remains rough 100 km from the trench
Authors: Leah Harold , Fagereng Åke , Bastow Ian , Bell Rebecca , Lane Victoria , Henrys Stuart , Jacobs Katie , Fry Bill 
Published: August 2022

AbstractAt the northern Hikurangi margin (North Island, New Zealand), shallow slow slip events (SSEs) frequently accommodate subduction-interface plate motion from landward of the trench to &lt;20 km depth. SSEs may be spatially related to geometrical interface heterogeneity, though kilometer-scale plate-interface roughness imaged by active-source seismic methods is only constrained offshore at &lt;12 km depth. Onshore constraints are comparatively lacking, but we mapped the Hikurangi margin plate interface using receiver functions from data collected by a dense 22 × 10 km array of 49 broadband seismometers. The plate interface manifests as a positive-amplitude conversion (velocity increase with depth) dipping west from 10 to 17 km depth. This interface corroborates relocated earthquake hypocenters, seismic velocity models, and downdip extrapolation of depth-converted two-dimensional active-source lines. Our mapped plate interface has kilometer-amplitude roughness we interpret as oceanic volcanics or seamounts, and is 1–4 km shallower than the regional-scale plate-interface model used in geodetic inversions. Slip during SSEs may thus have different magnitudes and/or distributions than previously thought. We show interface roughness also leads to shear-strength variability, where slip may nucleate in locally weak areas and propagate across areas of low shear-strength gradient. Heterogeneous shear strength throughout the depth range of the northern Hikurangi margin may govern the nature of plate deformation, including the localization of both slow slip and hazardous earthquakes.
Orogenic belt resulting from ocean-continent collision
Authors: Zhao Jun-Hong , Yang Ting , Wang Wei 
Published: August 2022

Abstract
Orogenic belts have been thought to form through plate convergence, involving subduction of oceanic lithosphere at continental margins (accretionary orogens), which may ultimately lead to ocean closure and continent-continent collision (collisional orogens). Intraplate orogens away from plate margins have been known, but the mechanisms controlling their evolution are poorly understood. The South China craton, including the Yangtze and Cathaysia blocks, underwent a Paleozoic orogenesis that formed a &gt;500-km-wide orogenic belt with widespread granitoids that are unconformably overlain by Devonian cover sequences. The pre-Devonian basement rocks were subjected to strong deformation and greenschist-to amphibolite-facies metamorphism at 460–400 Ma. Paleozoic magmatism was characterized by voluminous crustally derived Silurian granitoids associated with incorporation of ancient crustal materials at 450–440 Ma and addition of juvenile mantle-derived melts at 420–410 Ma. Based on the absence of arc-like magmatism and the existence of ophiolites in the West Cathaysia terrane, geochemical evidence that oceanic crust existed beneath the East Cathaysia terrane, and geophysical evidence of contrasting lithospheres on both sides and two discrete slabs beneath their fault boundary, we propose that this Paleozoic orogenic belt was formed by collision between the two terranes that was driven by far-field forces during the assembly of Gondwana, and the East Cathaysia terrane represents oceanic lithosphere that was overthrusted by the continental crustal materials of the West Cathaysia terrane. Numerical modeling shows that this type of collision can explain the dynamics of the Paleozoic orogenesis in the South China craton and may be a mechanism for some orogens in which subduction-related igneous and metamorphic rocks are lacking.
Compact quantum metric spaces from free graph algebras
Authors: Aguilar Konrad , Hartglass Michael , Penneys David 
Published: August 2022

Starting with a vertex-weighted pointed graph [Formula: see text], we form the free loop algebra [Formula: see text] defined in Hartglass–Penneys’ article on canonical [Formula: see text]-algebras associated to a planar algebra. Under mild conditions, [Formula: see text] is a non-nuclear simple [Formula: see text]-algebra with unique tracial state. There is a canonical polynomial subalgebra [Formula: see text] together with a Dirac number operator [Formula: see text] such that [Formula: see text] is a spectral triple. We prove the Haagerup-type bound of Ozawa–Rieffel to verify [Formula: see text] yields a compact quantum metric space in the sense of Rieffel.We give a weighted analog of Benjamini–Schramm convergence for vertex-weighted pointed graphs. As our [Formula: see text]-algebras are non-nuclear, we adjust the Lip-norm coming from [Formula: see text] to utilize the finite dimensional filtration of [Formula: see text]. We then prove that convergence of vertex-weighted pointed graphs leads to quantum Gromov–Hausdorff convergence of the associated adjusted compact quantum metric spaces.As an application, we apply our construction to the Guionnet–Jones–Shyakhtenko (GJS) [Formula: see text]-algebra associated to a planar algebra. We conclude that the compact quantum metric spaces coming from the GJS [Formula: see text]-algebras of many infinite families of planar algebras converge in quantum Gromov–Hausdorff distance.
Extension of a Simulation Software to Incorporate Quality-Related Factors in Investigations on Software Engineering Economics
Authors: Kuhlen David , Speck Andreas 
Published: August 2022

The economics of software engineering [B. W. Boehm, Software Engineering Economics, Prentice-Hall, Englewood Cliffs, New Jersey, 1981] are strongly influenced by the way how software development processes are performed. Simulations give valuable insights in the performance of these processes. In order to research the economics of software engineering, a process analysis studio (PAS) was developed and presented [D. Kuhlen and A. Speck, Business process analysis by model checking, in Proc. 5th Int. Symp. Data-Driven Process Discovery and Analysis SIMPDA, eds. P. Ceravolo and S. Rinderle-Ma, 2015; D. Kuhlen and A. Speck, The way of designing a simulation software in order to evaluate the economic performance in software development, in ICCMS ’17 Proc. 8th Int. Conf. Computer Modeling and Simulation, Int. Conf. Proc. Series by ACM, 2016; D. Kuhlen, Kostensenkungspotenzialfunktion der Softwareproduktion, dissertation, Christian-Albrechts-Universität zu Kiel, 2019]. This simulation software was built to analyze the capacitive configurations of the software production. It helps to assess the impact of different configurations on the process output. However, this research emphasizes the impact of quality-related factors (QRFs) on the process output. This paper presents a proposal how to extend the simulation software model in order to facilitate qualitative analysis. In order to facilitate the understanding of the model and the evaluation of the contribution, offered by the proposal, Prolog codes are used.
Potential of phthalocyanine on streptozotocin-induced diabetic rats: An initiative of dye in substitution of therapeutics
Authors: Govardhane Sharayu , Shende Pravin 
Published: August 2022

Type 1 diabetes mellitus, is one of the severe chronic metabolic diseases with severe worldwide complications due to increased levels of glucose affecting many organ functionalities. Antioxidant properties reduce the free radicals and inhibit the oxidation that promotes cellular development in many conditions such as diabetes, Alzheimer’s disease and cardiovascular disorders. Phthalocyanine is a large blue colored aromatic compound with high potential of anti-oxidant ability owing to the presence of porphyrazine ring units. The present study has aimed at examining the potential effect of phthalocyanine on the streptozotocin-induced diabetic model over 28 days. For the study, the animals were assigned into 4 groups: control, diabetic, phthalocyanine (10 [Formula: see text]g/kg/day) treated diabetic group and phthalocyanine (20 [Formula: see text]g/kg/day) treated diabetic group. After the induction of diabetes using streptozotocin (53 mg/kg body weight), phthalocyanine (10 [Formula: see text]g/kg/day) and phthalocyanine (20 [Formula: see text]g/kg/day) were given daily for 28 days and the blood glucose level was checked on 7th, 14th, 21st and 28th day. A significant reduction in blood glucose within the period on the 28th day with increase in body weight was observed. Phthalocyanine also showed a decrease in the total protein content and blood urea nitrogen with improved oxidative stress parameters. The histopathological analyses displayed improvement in pancreatic structure with basal rounded nuclei and well-defined boundaries normal acini with a phthalocyanine dose (10 [Formula: see text]g/kg). It was concluded that phthalocyanine at the dose of 10 [Formula: see text]g/kg presented anti-diabetic and anti-hyperlipidemic actions with protective effect to the pancreatic [Formula: see text]-cell within 28 days.
Program adverbs and Tlön embeddings
Authors: Li Yao , Weirich Stephanie 
Published: August 2022

Free monads (and their variants) have become a popular general-purpose tool for representing the semantics of effectful programs in proof assistants. These data structures support the compositional definition of semantics parameterized by uninterpreted events, while admitting a rich equational theory of equivalence. But monads are not the only way to structure effectful computation, why should we limit ourselves?
In this paper, inspired by applicative functors, selective functors, and other structures, we define a collection of data structures and theories, which we call program adverbs, that capture a variety of computational patterns. Program adverbs are themselves composable, allowing them to be used to specify the semantics of languages with multiple computation patterns. We use program adverbs as the basis for a new class of semantic embeddings called Tlön embeddings. Compared with embeddings based on free monads, Tlön embeddings allow more flexibility in computational modeling of effects, while retaining more information about the program's syntactic structure.
Approximating (
k,ℓ
)-Median Clustering for Polygonal Curves
Authors: Buchin Maike , Driemel Anne , Rohde Dennis 
Published: August 2022

In 2015, Driemel, Krivošija, and Sohler introduced the
k,ℓ
-median clustering problem for polygonal curves under the Fréchet distance. Given a set of input curves, the problem asks to find
k
median curves of at most ℓ vertices each that minimize the sum of Fréchet distances over all input curves to their closest median curve. A major shortcoming of their algorithm is that the input curves are restricted to lie on the real line. In this article, we present a randomized bicriteria-approximation algorithm that works for polygonal curves in ℝ

d

and achieves approximation factor (1+ɛ) with respect to the clustering costs. The algorithm has worst-case running time linear in the number of curves, polynomial in the maximum number of vertices per curve (i.e., their complexity), and exponential in
d
, ℓ, 1/ɛ and 1/δ (i.e., the failure probability). We achieve this result through a shortcutting lemma, which guarantees the existence of a polygonal curve with similar cost as an optimal median curve of complexity ℓ, but of complexity at most 2ℓ -2, and whose vertices can be computed efficiently. We combine this lemma with the superset sampling technique by Kumar et al. to derive our clustering result. In doing so, we describe and analyze a generalization of the algorithm by Ackermann et al., which may be of independent interest.
DOLL
Authors: Jiang Harry , Zhang Xiaoxi , Joe-Wong Carlee 
Published: August 2022

Most large-scale ML implementations scale to large amounts of data by utilizing multiple servers or virtual machines (VMs) that iteratively compute model updates on local data that are periodically synchronized. Due to the complexity of managing the resulting computing infrastructure, many companies run their ML jobs on external cloud providers' servers. However, cloud resources can be expensive, particularly for large ML jobs with long runtimes.
A particularly popular method to limit the costs of training ML jobs is to utilize preemptible cloud instances. These may be interrupted at the cloud provider's discretion, but they are significantly (up to 90%) cheaper than conventional on-demand instances. Most studies of these ML methods, however, assume the availability of large datasets at training time. In practice, training data may arrive at irregular intervals and models may be trained online as new data samples arrive, e.g., when monitoring data from IoT sensors. While some software frameworks like Apache Kafka can feed online data arrivals to ML algorithms, they provide little insight into the resulting costs of ML training. We extend prior work on provisioning preemptible instances to analyze available pools of data in order to run online ML on incoming datastreams, which presents new challenges due to the need to carefully handle data arrivals. We design, analyze, and optimize DOLL, which to the best of our knowledge is the first system that provides provable performance guarantees for Distributed OnLine Learning over preemptible instances.
Research Challenges and Our Contributions: When pools of data are readily available, the bottleneck to distributed ML training often lies in the time required for each VM to compute its model updates. In our scenario, however, the arrival rate of incoming data may also bottleneck data processing. An intuitive strategy would then be for each VM to process each data point as it arrives. However, since arrivals at different VMs may not be coordinated, synchronizing the model parameters at each VM between data arrivals may introduce additional delays, while asynchronous SGD methods can lead to slow convergence [1]. DOLL uses a batching and grouping process to limit the synchronization delay, which naturally realizes traditional mini-batch SGD so as to provide provable model convergence guarantees.
Handling online data arrivals becomes particularly challenging when we use preemptible instances to compute model updates. Existing methods utilizing preemptible instances for ML jobs largely focus on mitigating training interruptions [2] and their effects on model convergence [3]. When used on datastreams, we face an additional challenge of interruptions pausing the data arrival process, which impedes the rate at which we can compute model updates and thus model convergence. Thus, one should ensure that preemptions do not happen "too often," e.g., by computing some updates on on-demand instances. Our work is the first to optimize the number of preemptible VMs used and demonstrate that we can meet ML convergence guarantees.
Sojourn Time Minimization of Successful Jobs
Authors: Yao Yuan , Paolieri Marco , Golubchik Leana 
Published: August 2022

Due to a growing interest in deep learning applications [5], compute-intensive and long-running (hours to days) training jobs have become a significant component of datacenter workloads. A large fraction of these jobs is often exploratory, with the goal of determining the best model structure (e.g., the number of layers and channels in a convolutional neural network), hyperparameters (e.g., the learning rate), and data augmentation strategies for the target application. Notably, training jobs are often terminated early if their learning metrics (e.g., training and validation accuracy) are not converging, with only a few completing successfully.
For this motivating application, we consider the problem of scheduling a set of jobs that can be terminated at predetermined checkpoints with known probabilities estimated from historical data. We prove that, in order to minimize the time to complete the first K successful jobs on a single server, optimal scheduling does not require preemption (even when preemption overhead is negligible) and provide an optimal policy; advantages of this policy are quantified through simulation.
Related Work. While job scheduling has been investigated extensively in many scenarios (see [6] and [2] for a survey of recent result), most policies require that the cost of waiting times of each job be known at scheduling time; in contrast, in our setting the scheduler does not know which job will be the K-th successful job, and sojourn times of subsequent jobs do not contribute to the target metric.
For example, [4, 3] minimize makespan (i.e., the time to complete all jobs) for known execution times and waiting time costs; similarly, Gittins index [1] and SR rank [7] minimize expected sojourn time of all jobs, i.e., both successfully completed jobs and jobs terminated early. Unfortunately, scheduling policies not distinguishing between these two types of jobs may favor jobs where the next stage is short and leads to early termination with high probability, which is an undesirable outcome in our applications of interest.
Optimized numerical inverse Laplace transformation
Authors: Horvath Illes , Meszaros Andras , Telek Miklos 
Published: August 2022

Among the numerical inverse Laplace transformation (NILT) methods, those that belong to the Abate-Whitt framework (AWF) are considered to be the most efficient ones currently. It is a characteristic feature of the AWF NILT procedures that they are independent of the transform function and the time point of interest.
In this work we propose an NILT procedure that goes beyond this limitation and optimize the accuracy of the NILT utilizing also the transform function and the time point of interest.