bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bowel stimulation before loop ileostomy closure to reduce postoperative ileus: a multicenter, single-blinded, randomized controlled trial
Authors: Garfinkle Richard , Demian Marie , Sabboobeh Sarah , Moon Jeongyoon , Hulme-Moir Michael , Liberman A. Sender , Feinberg Stan , Hayden Dana M. , Chadi Sami A. , Demyttenaere Sebastian , Samuel Louise , Hotakorzian Nevart , Quintin Laurence , Morin Nancy , Faria Julio , Ghitulescu Gabriela , Vasilevsky Carol-Ann , Boutros Marylise ,   Bowel Stimulation Research Collaborative, Jarvis John , Herd Andrew , Moot Andrew , Rajaratnam Siraj , Nisbet Sherry , Charlebois Patrick , Lee Lawrence , Stein Barry , Stotland Peter , Hameed Usmaan , Bhama Anuradha R , Quereshy Fayez , Tataryn Donna 
Published: August 2022

Abstract
Introduction
The objective of this study was to evaluate the impact of preoperative bowel stimulation on the development of postoperative ileus (POI) after loop ileostomy closure.

Methods
This was a multicenter, randomized controlled trial (NCT025596350) including adult (≥ 18 years old) patients who underwent elective loop ileostomy closure at 7 participating hospitals. Participants were randomly assigned (1:1) using a centralized computer-generated sequence with block randomization to either preoperative bowel stimulation or no stimulation (control group). Bowel stimulation consisted of 10 outpatient sessions within the 3 weeks prior to ileostomy closure and was performed by trained Enterostomal Therapy nurses. The primary outcome was POI, defined as an intolerance to oral food in the absence of clinical or radiological signs of obstruction, on or after postoperative day 3, that either (a) required nasogastric tube insertion; or (b) was associated with two of the following: nausea/vomiting, abdominal distension, or the absence of flatus.

Results
Between January 2017 and November 2020, 101 patients were randomized, and 5 patients never underwent ileostomy closure; thus, 96 patients (47 stimulated vs. 49 control) were analyzed according to a modified intention-to-treat protocol. Baseline characteristics were well balanced in both groups. The incidence of POI was lower among patients randomized to stimulation (6.4% vs. 24.5%, p = 0.034; unadjusted RR: 0.26, 95% CI 0.078–0.87). Stimulated patients also had earlier median time to first flatus (2.0 days (1.0–2.0) vs. 2.0 days (2.0–3.0), p = 0.025), were more likely to pass flatus on postoperative day 1 (46.8% vs. 22.4%, p = 0.022), and had a shorter median postoperative hospital stay (3.0 days (2.0–3.5) vs. 4.0 days (2.0–6.0), p = 0.003).

Conclusions
Preoperative bowel stimulation via the efferent limb of the ileostomy reduced POI after elective loop ileostomy closure.
Estimation of unrecorded groundwater abstractions in Jordan through regional groundwater modelling
Authors: Gropius M. , Dahabiyeh M. , Al Hyari M. , Brückner F. , Lindenmaier F. , Vassolo S. 
Published: August 2022

AbstractJordan suffers from water scarcity and groundwater covers the majority of Jordan’s water supply. Therefore, there is an urgent need to manage this resource conscientiously. A regional numerical groundwater flow model, developed as part of a decision support system for the country of Jordan, allows for quantification of the overexploitation of groundwater resources and enables determination of the extent of unrecorded agricultural groundwater abstraction. Groundwater in Jordan is abstracted from three main aquifers partly separated by aquitards. With updated geological, structural, and hydrogeological data available in the country, a regional numerical groundwater flow model for the whole of Jordan and the southernmost part of Syria was developed using MODFLOW. It was first calibrated for a steady-state condition using data from the 1960s, when groundwater abstraction was negligible. After transient calibration using groundwater level measurements from all aquifers, model results reproduce the large groundwater-level declines experienced in the last decades, which have led to the drying out of numerous springs. They show a reversal of groundwater flow directions in some regions, due to over-abstraction, and demonstrate that documented abstractions are not sufficient to cause the observed groundwater-level decline. Only after considering irrigation water demand derived from remote sensing data, the model is able to simulate these declines. Illegal abstractions can be quantified and predictive scenarios show the potential impact of different management strategies on future groundwater resources.
Russian coal in a changing climate: risks and opportunities for industry and government
Authors: Martus Ellie , Fortescue Stephen 
Published: August 2022

AbstractAs one of the world’s major coal-producing and exporting states, Russia is central to discussions on the future of the industry in the context of global climate change. This is a question that has become particularly salient in light of Russia’s invasion of Ukraine in February 2022. Yet despite this, Russia remains understudied in the literature. As a result, we know little about the extent to which Russia is likely to contribute to the alleviation of coal-driven climate change or to its exacerbation. This analysis seeks to examine this question from the perspective of the coal industry, by exploring whether and how Russia’s coal companies incorporate the vulnerabilities and risks around climate change into their corporate presence and behaviour, and how this aligns with broader government policy on the coal industry. Drawing on a range of primary and secondary sources, including corporate reporting, government documents, and media commentary, this analysis identifies two central narratives emerging from coal companies. The first is focused on the risks around climate change, primarily in relation to reputation, increased regulation, and access to markets that a climate-driven global shift away from coal might entail. The second narrative acknowledges the potential opportunities around technology and ‘green coal’. Within government, both the pro-coal and coal-sceptic lobby recognise the significance of climate change; however, it is used purely instrumentally in policy debates. The coal sceptics employ climate to highlight risks around global reductions in coal demand but are unable to challenge government support for the industry. Ultimately, our findings suggest that both company behaviour and government policy in Russia remain strongly pro-coal, and that it will take a significant drop in global demand for that to change. We conclude that, before the invasion of Ukraine, selling as much coal as possible was a greater government priority than climate change policy, and the invasion is unlikely to shift the balance the other way.
Regular measurements of EMF in a representative Norwegian city—constant exposure over time despite introduction of new technologies
Authors: Markussen Atle C. , Sjoemoen Tone-Mette , Unander Edith Helene , Klaeboe Lars 
Published: August 2022

AbstractThe rapid growth of the wireless communication industry has resulted in the installation of numerous of base stations, everywhere in our surroundings. The population is exposed to Radio Frequency Electromagnetic Fields of varying frequency and strength. This, and introduction of new systems have risen public concerns regarding potential health effects from this RF-EMF exposure. The purpose of this project is to get an overview of any changes in exposure when new technologies are introduced. From June 2013 to October 2019, measurements were made at 16 measurement points in Kristiansand and surrounding areas in the same order, on the same day of the week and at the same time of day. The measurements are performed on the frequency bands 390, 450, 800, 900, 1800, 2100, 2400, and 2600 MHz. When we summed up the exposure for all the frequency bands relative to the limit values in a measuring point, the total values per measuring point showed that the exposure outdoors in most cases is less than 1‰ of the limit value. In 2017, a temporary increase was registered for most measurement points, but during 2018 the levels returned to the levels registered before 2017. During the increase, the levels were still low, around 3‰ of the limit values. The increase may be due to the fact that two mobile operators during this period made a comprehensive reconfiguration of their networks. The measurements presented in this report show that the exposure of the population is low, thousandths of the limit values, and relatively constant over time even though new technologies are introduced.
Models as Fundamental Entities in Set Theory: A Naturalistic and Practice-based Approach
Authors: Antos Carolin 
Published: August 2022

AbstractThis article addresses the question of fundamental entities in set theory. It takes up J. Hamkins’ claim that models of set theory are such fundamental entities and investigates it using the methodology of P. Maddy’s naturalism, Second Philosophy. In accordance with this methodology, I investigate the historical case study of the use of models in the introduction of forcing, compare this case to contemporary practice and give a systematic account of how set-theoretic practice can be said to introduce models as new entities. In conclusion, I argue for a view that takes both sets and models to be fundamental entities in set theory.
A decision support tool for the selection of 15 N analysis methods of ammonium and nitrate
Authors: Jia Mengru , Bol Roland , Kooijman Annemieke , Wessel Wim W. , Tietema Albert 
Published: August 2022

AbstractThe stable nitrogen isotope (15 N) analysis of ammonium (NH4+) and nitrate (NO3–) is widely used in ecological research, providing insights into N cycling and its underlying regulating mechanisms in both aquatic and terrestrial ecosystems. To date, a large number of methods have been developed for the preparation and measurement of 15 N abundance of NH4+ and NO3– in liquid environmental samples at either natural abundance or enriched levels. However, these methods are all subject to certain specific advantages and limitations, and ecologists might be looking for an efficient way to select the most suitable methods in face of shifting sampling and analytical conditions. Based on our extensive review of these 15 N analysis methods we developed a decision support tool (DST) to provide quick and proper guidance for environmental researchers in finding the optimal method for preparing their liquid samples for 15 N analysis in NH4+ or NO3–. The DST is a decision tree based on several key criteria that users need to take into account when choosing the preferred sample preparation method for their samples. The criteria concern: the sample matrix, the 15 N abundance and the concentration of the target N species, the contamination by other N-containing chemicals, the isotopic fractionation, the availability of equipment, concerns about toxicity of reagents, and the preparation time. This work links field-scale experiments and laboratory 15 N analysis. Potential applications of our decision trees include 15 N studies ranging from natural abundance to tracer level in a wide range of terrestrial, freshwater and marine ecosystems.
Bridging Conceptual Divides Between Colonial and Modern Worlds: Insular Narratives and the Archaeologies of Modern Spanish Colonialism
Authors: Senatore Maria Ximena 
Published: August 2022

Abstract
Narratives embedded in studies of modern Spanish colonialism have conspired against a deep understanding of colonialism as a global and current issue and have influenced or limited the directions for research. By focusing on particular narratives that separate and disconnect the realities of the colonies from those of the Iberian Peninsula, this article discusses the conceptual divide between the study of colonial and early modern realms, and the tenuous connections between the archaeology of Spanish colonialism developed in America and in the Iberian Peninsula. This paper attempts to counter those insular narratives by offering a view on how even remote settlements in Ibero-America show connections that tell stories of sixteenth-century Spain and pose questions that often cannot be answered due to the lack of shared perspectives between the study of modern Spanish colonialism in America and the Iberian Peninsula. To illustrate this view, a case study focused on Ciudad del Nombre de Jesús settled during the failed Spanish plan for the fortification of the Strait of Magellan at the end of the sixteenth century is provided. The interpretation of the results of archaeological and historical lines of research allows the establishment of material connections among individuals, stories and places of the Iberian Peninsula and America. The implication of this case contributes to considering the role that archaeology can play in questioning the enduring effects of modern Spanish colonialism.
The Bio Economic Seaweed Model (BESeM) for modelling tropical seaweed cultivation – experimentation and modelling
Authors: van Oort P. A. J. , Rukminasari N. , Latama G. , Verhagen A. , van der Werf A.K. 
Published: August 2022

AbstractThe Bio Economic Seaweed Model (BESeM) is a model designed for modelling tropical seaweed cultivation. BESeM can simulate the common tropical seaweed cultivation system with multiple harvests per year, clonal reproduction and labour intensive harvesting and replanting activities. Biomass growth is modelled as a sigmoid, with growth being initially exponentially and eventually flattening off towards a maximum weight per plant or per square meter (wf,max). To estimate the latter, longer duration experiments than normal are needed – in the order of 100 days rather than 45 days. Drying (on platforms on the beach) is simulated as well as increase in harvested chemical concentration over time since planting, for harvested chemicals such as agar extracted from Gracilaria or carrageenan extracted from Kappaphycus or Euchema. BESeM has a limited number of parameters which makes it easily amenable to new sites and species. An experiment is presented for a site in Indonesia in which Gracilaria was monitored for 120 days in 6 nearby sites and from which BESeM model parameters were estimated. A simulation example is presented which illustrates how BESeM can be used to find the optimum combination of replanting weight and harvest cycle length (in days) for maximising gross and net farm income.
Navigation Algorithm-Agnostic Integrity Monitoring based on Solution Separation with Constrained Computation Time and Sensor Noise Overbounding
Authors: Bosdelekidis Vasileios , Bryne Torleiv H. , Sokolova Nadezda , Johansen Tor A. 
Published: August 2022

AbstractIntegrity monitoring (IM) in autonomous navigation has been extensively researched, but currently available solutions are mainly applicable to specific algorithms and sensors, or limited by linearity or 'Gaussianity' assumptions. This study investigates a Solution Separation (SS) based framework for universal IM, scalable to multi-sensor fusion as each hypothesis assumes a whole sensor measurement set as faulty. Architecturally we consider that: 1) multi sensor systems must account for various sensor noise models which lead to inconsistent estimates of uncertainties, 2) a module must be able to detect sensor failure or sensor noise mismodeling and suggest better bounds for the error, without being constantly conservative, 3) some algorithms are computationally heavy to monitor in the SS setting or the provided covariances cannot be interpreted in IM. A hybrid SS architecture can be practical, where some solutions are evaluated with a navigation algorithm with known characteristics, although the all-sensor-in solution is evaluated with the monitored algorithm. Experiments are run on filter and smoothing-based navigation algorithms. In addition, we experiment with hybrid SS monitoring and time-correlated noise to evaluate the appropriability of our framework in the context of the above-mentioned requirements. This is a novel framework in the IM domain, directly integrable in existing navigation solutions and, in our opinion, it will facilitate the quantification of the effect of different sensors in navigation safety.
EFIX: Exact fixed point methods for distributed optimization
Authors: Jakovetić Dušan , Krejić Nataša , Krklec Jerinkić Nataša 
Published: August 2022

AbstractWe consider strongly convex distributed consensus optimization over connected networks. EFIX, the proposed method, is derived using quadratic penalty approach. In more detail, we use the standard reformulation—transforming the original problem into a constrained problem in a higher dimensional space—to define a sequence of suitable quadratic penalty subproblems with increasing penalty parameters. For quadratic objectives, the corresponding sequence consists of quadratic penalty subproblems. For generic strongly convex case, the objective function is approximated with a quadratic model and hence the sequence of the resulting penalty subproblems is again quadratic. EFIX is then derived by solving each of the quadratic penalty subproblems via a fixed point (R)-linear solver, e.g., Jacobi Over-Relaxation method. The exact convergence is proved as well as the worst case complexity of order $${{\mathcal {O}}}(\epsilon ^{-1})$$

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for the quadratic case. In the case of strongly convex generic functions, the standard result for penalty methods is obtained. Numerical results indicate that the method is highly competitive with state-of-the-art exact first order methods, requires smaller computational and communication effort, and is robust to the choice of algorithm parameters.
Predicting Spray Dried Dispersion Particle Size Via Machine Learning Regression Methods
Authors: Schmitt John M. , Baumann John M. , Morgen Michael M. 
Published: August 2022

AbstractSpray dried dispersion particle size is a critical quality attribute that impacts bioavailability and manufacturability of the spray drying process and final dosage form. Substantial experimentation has been required to relate formulation and process parameters to particle size with the results limited to a single active pharmaceutical ingredient (API). This is the first study that demonstrates prediction of particle size independent of API for a wide range of formulation and process parameters at pilot and commercial scale. Additionally we developed a strategy with formulation and target particle size as inputs to define a set of “first to try” process parameters. An ensemble machine learning model was created to predict dried particle size across pilot and production scale spray dryers, with prediction errors between −7.7% and 18.6% (25th/75th percentiles) for a hold-out evaluation set. Shapley additive explanations identified how changes in formulation and process parameters drove variations in model predictions of dried particle size and were found to be consistent with mechanistic understanding of the particle formation process. Additionally, an optimization strategy used the predictive model to determine initial estimates for process parameter values that best achieve a target particle size for a provided formulation. The optimization strategy was employed to estimate process parameters in the hold-out evaluation set and to illustrate selection of process parameters during scale-up. The results of this study illustrate how trained regression models can reduce the experimental effort required to create an in-silico design space for new molecules during early-stage process development and subsequent scale-up.
Forecasting National and Regional Youth Unemployment in Spain Using Google Trends
Authors: Simionescu Mihaela , Cifuentes-Faura Javier 
Published: August 2022

AbstractIn Spain, the youth unemployment rate is one of the highest in the European Union. With the pandemic caused by Covid-19, young people face high unemployment rates and are more vulnerable to a decrease in labour demand. This paper analyses and predicts youth unemployment using Google Trends indices in Spain for the period between the first quarter of 2004 and the second quarter of 2021, being the first work to carry out this study for Spain and the first to use the regional approach for the country. Vector autoregressive Bayesian models and vector error correction models have been used for national data, and Bayesian panel data models and fixed effects model for regional data. The results confirm that forecasts based on Google Trends data are more accurate in predicting the youth unemployment rate.
Motivation, counterfactual predictions and constraints: normativity of predictive mechanisms
Authors: Piekarski Michał 
Published: August 2022

SummaryThe aim of this paper is to present the ontic approach to the normativity of cognitive functions and mechanisms, which is directly related to the understanding of biological normativity in terms of normative mechanisms. This approach assumes the hypothesis that cognitive processes contain a certain normative component independent of external attributions and researchers’ beliefs. This component consists of specific cognitive mechanisms, which I call normative. I argue that a mechanism is normative when it constitutes given actions or behaviors of a system. More precisely, it means that, on the one hand, it is their constitutive cause, and on the other hand, it determines a certain field of possibilities from which the system, guided by its own goals, preferences, environmental constraints, etc., chooses the appropriate action or behavior according to a given situation. The background for the analyses presented here is the predictive processing framework, in which it can be shown that at least some of the predictive mechanisms are in fact normative mechanisms. I refer here to the existence of a motivational relation which determines the normative dependence of the agent’s actions due to specific predictions and environmental constraints.
The case for eliminativism about words
Authors: Tasker Nick 
Published: August 2022

AbstractWords are ubiquitous and familiar, and the concept of a word features both in common-sense ways of understanding the world, and in more theoretical discourse. Nonetheless, it has been repeatedly argued that there is no such thing as words. In this paper, I will set out a range of arguments for eliminativism about words, and indicate the most promising responses. I begin by considering an eliminativist argument based on the alleged mind-dependency of words, before turning to two challenges arising from linguistic theory in the Chomskian tradition. The first of these is issued by Rey in a number of places, including in his recent book (Rey, 2020). The second is Collins’s ( 2010, 2021a) argument based on the alleged explanatory redundancy of words. I will also consider an eliminativist challenge based on the difficulty of providing existence and persistence conditions for words. One general lesson which emerges is that these eliminativist arguments, if they work at all, could be turned against a whole swathe of non-linguistic objects; in other words, the case for eliminativism about words is no stronger than the case for eliminativism about ordinary objects in general.
Stochastic programming with primal–dual dynamics: a mean-field game approach
Authors: Röling Casper T. , Ramirez Stefanny , Bauso Dario , Tembine Hamidou 
Published: August 2022

AbstractThis study addresses primal–dual dynamics for a stochastic programming problem for capacity network design. It is proven that consensus can be achieved on the here and now variables which represent the capacity of the network. The main contribution is a heuristic approach which involves the formulation of the problem as a mean-field game. Every agent in the mean-field game has control over its own primal–dual dynamics and seeks consensus with neighboring agents according to a communication topology. We obtain theoretical results concerning the existence of a mean-field equilibrium. Moreover, we prove that the consensus dynamics converge such that the agents agree on the capacity of the network. Lastly, we emphasize the ways in which penalties on control and state influence the dynamics of agents in the mean-field game.