bluuebunny/crossref_metadata_2025_split huggingface dataset:
Restoring steppe landscapes: patterns, drivers and implications in Russia’s steppes
Authors: Pazur Robert , Prishchepov Alexander V. , Myachina Ksenya , Verburg Peter H. , Levykin Sergey , Ponkina Elena V. , Kazachkov Grigory , Yakovlev Ilya , Akhmetov Renat , Rogova Natalia , Bürgi Matthias 
Published: December 2020

AbstractContextAgricultural land abandonment across the steppe belt of Eurasia has provided an opportunity for the restoration of steppe landscapes in recent decades. However, global food demands are about to revert this trajectory and put restored steppe landscapes at risk.ObjectivesWe analysed steppe development in southern Russia in the last 40 years, assessed its spatial patterns and drivers of change for several periods.MethodsUsing Landsat imagery, we mapped the permanent steppe and steppe restoration from 1990 to 2018. Based on regression tree models, we evaluate and explain its dynamics. Results were compared with district-level trends in land-use intensities of cropland.ResultsWe found 70% of the steppe in 2018 represented permanent steppe and 30% of former cropland dominantly abandoned in the postsocialism (1990–2000). The permanent steppe and steppe restored in the postsocialism (1990–2000) were located far from settlements, on rough terrain and in districts of the Virgin Land Campaign (1954–1963). In recent decades, the patterns of steppe restoration (2000–2018) were mostly determined by unfavourable agroclimatic conditions and distance from grain storage facilities. The restoration pattern reflects regional differences in land-use intensities, e.g., isolated steppe patches mostly appeared in areas of intensive agricultural land-use.ConclusionsSteppe restoration has appeared in areas marginal for agricultural production, with poor natural conditions and little human footprint. Consequently, the permanent steppe became less fragmented and a more continuous steppe landscape resulted. The remaining isolated steppe patches require attention in restoration programs as they are mostly located in areas of intensive agricultural land-use.
Global optimization based on active preference learning with radial basis functions
Authors: Bemporad Alberto , Piga Dario 
Published: December 2020

AbstractThis paper proposes a method for solving optimization problems in which the decision-maker cannot evaluate the objective function, but rather can only express apreferencesuch as “this is better than that” between two candidate decision vectors. The algorithm described in this paper aims at reaching the global optimizer by iteratively proposing the decision maker a new comparison to make, based on actively learning a surrogate of the latent (unknown and perhaps unquantifiable) objective function from past sampled decision vectors and pairwise preferences. A radial-basis function surrogate is fit via linear or quadratic programming, satisfying if possible the preferences expressed by the decision maker on existing samples. The surrogate is used to propose a new sample of the decision vector for comparison with the current best candidate based on two possible criteria: minimize a combination of the surrogate and an inverse weighting distance function to balance between exploitation of the surrogate and exploration of the decision space, or maximize a function related to the probability that the new candidate will be preferred. Compared to active preference learning based on Bayesian optimization, we show that our approach is competitive in that, within the same number of comparisons, it usually approaches the global optimum more closely and is computationally lighter. Applications of the proposed algorithm to solve a set of benchmark global optimization problems, for multi-objective optimization, and for optimal tuning of a cost-sensitive neural network classifier for object recognition from images are described in the paper. MATLAB and a Python implementations of the algorithms described in the paper are available athttp://cse.lab.imtlucca.it/~bemporad/glis.
Identifying spatial technology clusters from patenting concentrations using heat map kernel density estimation
Authors: Stek Pieter E. 
Published: December 2020

AbstractIn this paper a methodology for identifying and delineating spatial technology clusters based on patenting concentration is developed. The methodology involves the automated geocoding of patent inventor addresses, the application of a home bias correction factor and a sensitivity analysis to determine the optimal parameters of the kernel density estimation interpolation distance and the minimum concentration threshold to identify clusters. The methodology’s performance is compared to a number of other cluster identification methods and it is validated across 18 individual sectors, including mature broad-based high-technology sectors and emerging niche sustainable energy technology sectors. The results suggest that the performance of the methodology exceed that of alternative cluster identification methods, although there is some variation in performance between different sectors. This demonstrates that the methodology provides researchers, practitioners and policy makers with a useful tool to gain insight into the spatial distribution of sectoral innovation activity at a global scale and sub-national regional level and to monitor changes over time, thereby supplementing more readily available global statistical data which is available at the national level.
Ontology, neural networks, and the social sciences
Authors: Strohmaier David 
Published: December 2020

AbstractThe ontology of social objects and facts remains a field of continued controversy. This situation complicates the life of social scientists who seek to make predictive models of social phenomena. For the purposes of modelling a social phenomenon, we would like to avoid having to make any controversial ontological commitments. The overwhelming majority of models in the social sciences, including statistical models, are built upon ontological assumptions that can be questioned. Recently, however, artificial neural networks (ANNs) have made their way into the social sciences, raising the question whether they can avoid controversial ontological assumptions. ANNs are largely distinguished from other statistical and machine learning techniques by being a representation-learning technique. That is, researchers can let the neural networks select which features of the data to use for internal representation instead of imposing their preconceptions. On this basis, I argue that neural networks can avoid ontological assumptions to a greater degree than common statistical models in the social sciences. I then go on, however, to establish that ANNs are not ontologically innocent either. The use of ANNs in the social sciences introduces ontological assumptions typically in at least two ways, via the input and via the architecture.
Analysis of stress pins for the local prestressing of cold forging tools
Authors: Killmann Martin , Merklein Marion 
Published: December 2020

AbstractThe trend towards lightweight design leads to an increasing demand for sophisticated part geometries with high functional integration. In order to use the advantages of cold forging regarding the time- and resource-efficient production of high-quality parts, high local stresses causing fatigue failure in geometrically complex tools have to be controlled. The objective of this manuscript is to analyse the use of stress pins for a local influence on the stress state within forging, especially in non-circular symmetrical cold forging dies. For this purpose, a closed-die forging process for elliptical parts is designed and analysed regarding the die stresses. Distinct areas with local compressive and tensile stresses occur in the process. To counteract the tensile stresses critical for fatigue failure, the effect of stress pins pressed into the die creating a local prestress is analysed. Around the pins, compressive radial and tensile tangential stresses occur. While large pin diameters, interferences and close positioning to the tensile area lead to an increasing prestressing effect, too high values of these parameters cause a detrimental superposition of tensile process and pin stresses. If used correctly, there is high potential to improve the stress state and tool life especially for locally stressed complex tools.
A novel reduced-dimensional dictionary grid screening strategy for SR-STAP
Authors: Cui Ning , Yu Zhongjun , Xing Kun 
Published: December 2020

AbstractThe traditional space-time adaptive processing (STAP) methods require large training samples to estimate the clutter covariance matrix. Sparse recovery STAP (SR-STAP) can get a highly accurate estimation in the case of insufficient samples and it alleviates the above problem of conventional STAP methods, however, the price of SR-STAP is that it involves a huge computational load in optimization, especially when the input data size is very big. This paper proposed a novel SR-STAP dictionary construction method that can significantly improve computational efficiency. We introduce the noise grids as the supplement of the spectrum-aid methods and design a second grids screening method based on the prior spectrum information. After the process of the proposed method, the dimension of the space-time dictionary has a sharp decrease compared to the over-complete dictionary, moreover, the sparsity of variable can be maintained better than the common spectrum-aid methods. The numerical experiments verify that the proposed method not only can significantly reduce the optimization time to far smaller than the SR-STAP but also more robust than spectrum-aid methods even in the small input size.
A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler
Authors: Turgut Oguz Emrah 
Published: December 2020

AbstractThis research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.
Resection of Colorectal Liver Metastasis: Prognostic Impact of Tumor Burden vs KRAS Mutational Status
Authors: Tsilimigras Diamantis I. , Hyer Madison J. , Bagante Fabio , Guglielmi Alfredo , Ruzzenente Andrea , Alexandrescu Sorin , Poultsides George , Sasaki Kazunari , Aucejo Federico , Pawlik Timothy M. 
Published: December 2020

BACKGROUND:
The prognostic impact of colorectal liver metastasis (CRLM) morphologic characteristics relative to KRAS mutational status after hepatic resection remains ill defined.

STUDY DESIGN:
Patients undergoing hepatectomy for CRLM between 2001 and 2018 were identified using an international multi-institutional database. Tumor burden score (TBS) was defined as distance from origin on a Cartesian plane that incorporated maximum tumor size (x-axis) and number of lesions (y-axis). Impact of TBS on overall survival (OS) relative to KRAS status (wild type [wtKRAS] vs mutated [mutKRAS]) was assessed.

RESULTS:
Among 1,361 patients, the median number of metastatic lesions was 2 (interquartile range [IQR] 1–3), and median size of the largest metastatic lesion was 3.0 cm (IQR 2.0–5.0 cm), resulting in a median TBS of 4.1 (IQR 2.8–6.1); KRAS status was wtKRAS (n = 420, 30.9%), mutKRAS (n = 251, 18.4%), and unknown (n = 690, 50.7%). Overall median and 5-year OS were 49.5 months (95%CI 45.2–53.8) and 43.2%, respectively. In examining the entire cohort, TBS was associated with long-term prognosis (5-year OS, low TBS: 49.4% vs high TBS: 36.7%), as was KRAS mutational status (5-year OS, wtKRAS: 48.2% vs mutKRAS: 31.1%; unknown KRAS: 44.0%)(both p < 0.01). Among patients with wtKRAS tumors, TBS was strongly associated with improved OS (5-year OS, low TBS: 59.1% vs high TBS: 38.4%, p = 0.002); however, TBS failed to discriminate long-term prognosis among patients with mutKRAS tumors (5-year OS, low TBS: 37.4% vs high TBS: 26.7%, p = 0.19). In fact, patients with high TBS/wtKRAS CRLM had comparable outcomes to patients with low TBS/mutKRAS tumors (5-year OS, 38.4% vs 37.4%, respectively; p = 0.59). On multivariable analysis, while TBS was associated with OS among patients with wtKRAS CRLM (hazard ratio 1.43, 95%CI 1.02–2.00; p = 0.03), TBS was not an independent predictor of survival among patients with mutKRAS CRLM (HR 1.36, 95%CI 0.92–1.99; p = 0.12).

CONCLUSIONS:
While TBS was associated with survival among patients with wtKRAS tumors, CRLM morphology was not predictive of long-term outcomes among patients with mutKRAS CRLM.
Uniformly positive entropy of induced transformations
Authors: BERNARDES NILSON C. , DARJI UDAYAN B. , VERMERSCH RÔMULO M. 
Published: December 2020

AbstractLet

$(X,T)$
be a topological dynamical system consisting of a compact metric space X and a continuous surjective map

$T : X \to X$
. By using local entropy theory, we prove that

$(X,T)$
has uniformly positive entropy if and only if so does the induced system

$({\mathcal {M}}(X),\widetilde {T})$
on the space of Borel probability measures endowed with the weak* topology. This result can be seen as a version for the notion of uniformly positive entropy of the corresponding result for topological entropy due to Glasner and Weiss.
A new cobia (Teleostei, Rachycentridae) species from the Miocene St. Marys Formation along Calvert Cliffs, Maryland, USA
Authors: Godfrey Stephen J. , Carnevale Giorgio 
Published: December 2020

AbstractThe highly fossiliferous St. Marys Formation is exposed along Calvert Cliffs, Maryland, and comprises one of the best available records of late Miocene life in the northeastern United States. Rachycentron stremphaencus new species, a cobia from the late Miocene (Tortonian) of the St. Marys Formation is described herein on the basis of a single three-dimensional neurocranium. This fossil represents the earliest known occurrence of neurocranial remains of the genus Rachycentron in the record. Rachycentron stremphaencus differs from Rachycentron canadum (Linnaeus, 1766) in many ways. The most obvious include a different ornamentation of the outer surface of the cranial bones; a notably pronounced lateral ridge resulting in a considerable gradient from the dorsal-medial exposure of the frontal to its lateralmost supraorbital margin; the size, shape, and position of the sphenotic that is located in the posterior half of the neurocranium and its lateralmost edge being adjacent to the anteriormost extent of the wedge-shaped trough in the dorsal surface of the skull formed by the lateral and medial ridges; the two contralateral medial ridges forming a proportionately much wider trough on either side of the supraoccipital; the epioccipitals not reaching the rear edge of the neurocranium; and the lack of a conspicuous posterolateral prong of the intercalar.UUID: http://zoobank.org/c8523bb4-2de2-4318-9a37-027f463a0441
OP366 Characterizing The Population At Risk Of Chronic Obstructive Pulmonary Disease In China Using A Real-World Population Survey
Authors: Koch Marco , Butt Thomas , Guo Wudong , Li Xue , Chen Yirong , Tan Diana , Liu Gordon G. 
Published: December 2020

IntroductionChronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality in China. However, early identification of patients with COPD in the community is challenging. This study used a real-world survey of the Chinese urban adult population to estimate the prevalence of COPD diagnosis or COPD-risk, examine the health outcomes and healthcare resource use of these groups, and investigate the sociodemographic factors associated with these statuses.MethodsRespondents to the 2017 National Health and Wellness Survey in China (n = 19,994) were classified into: COPD (diagnosed), COPD-risk (undiagnosed), and control (undiagnosed, not at-risk) using their self-reported diagnosis and Lung Function Questionnaire (LFQ) score. These groups were compared by healthcare resource use and health outcomes (EuroQol [EQ-5D] and Work Productivity and Activity Impairment questionnaires). Factors associated with being in these groups were investigated using pairwise comparisons (t-tests and chi-square tests) and multivariable logistic regression.ResultsIn total, 3,320 respondents (16.6%) had a suspected risk of COPD but did not report receiving a diagnosis. This was projected to 105.3 million people (16.9% of urban adults). Relative to the controls, COPD-risk and COPD-diagnosed respondents had higher healthcare resource use, lower productivity, and lower health-related quality of life (HRQoL) (p < 0.05). Age, smoking, alcohol consumption, weight, exercise, comorbidities, gender, education, employment, and air pollution were associated with increased odds of COPD-risk relative to the controls (p < 0.05).ConclusionsA substantial group of individuals, undiagnosed, but with a risk of COPD, have impaired HRQoL, lower productivity, and elevated healthcare resource use. A range of sociodemographic factors are predictive of COPD risk, which may support targeted screening. Case-detection tools such as the LFQ may offer a convenient approach for identifying individuals for further definitive testing and appropriate treatment in China.
OP388 17-Year Disease Reduction Predicted By A Transmission Dynamic Model After Pneumococcal Conjugate Vaccine Introduction In The United States
Authors: Snedecor Sonya 
Published: December 2020

IntroductionAfter the introduction of the seven-valent pneumococcal conjugate vaccine (PCV7) in the United States (US) in the year 2000, the incidence of invasive pneumococcal disease (IPD) caused by the seven vaccine serotypes declined by 80 percent in vaccinated children and 30 percent in unvaccinated adults. A transmission dynamic equation model developed in 2009 captured the direct and indirect effects of vaccination in the early years after vaccination. Subsequently, the vaccine program switched to the 13-valent PCV and adult PCV13 vaccination. This work explores the accuracy of the mathematical model to predict long-term IPD due to changes in US immunization practices.MethodsThe model simulates the acquisition of asymptomatic carriage of pneumococci and the development of IPD among individuals aged <2, 2–4, 5–17, 18–49, 50–64, and ≥65 years. Pneumococcal serotypes were stratified into three categories: PCV7-type (4, 6B, 9V, 14, 18C, 19F, and 23F), PCV6-type (1, 3, 5, 6A, 7F, and 19A), and non-PCV-type (all others). Model parameters were calibrated using US IPD surveillance data from 1998–2006. Model results were compared to observed epidemiology.ResultsThe model was previously shown to predict observed IPD well through 2007. After adjusting model parameters for PCV13 efficacy and adult vaccine coverage, modeled IPD closely replicated observed IPD. Observed baseline pre-vaccine incidence for children <2 years of age was 192 cases/100,000 and 13.5 cases in 2016, versus 18.5 cases estimated by the model. Similarly, observed versus modeled cases in the ≥65-year-old age group were 24 and 23.6 cases.ConclusionsThis epidemiologic model accurately simulates the observed US IPD surveillance data 17 years after initial introduction of PCV, highlighting the direct and indirect benefits of vaccination. Well-constructed mathematical models can accurately replicate real-world scenarios. Key input parameters of these models can then be modified to predict the impact of alternate scenarios, providing insights to inform public health policy-making.
OP410 Real-World Benefit Of Endovascular Repair Of Abdominal Aortic Aneurysms - Comparison Of GORE® Global Registry for Endovascular Aortic Treatment And National Institute for Clinical Excellence 2018 Guidance
Authors: O'Neill Frank , Baidoo Bismark , Waltham Matthew 
Published: December 2020

IntroductionEndovascular aneurysm repair (EVAR) is routinely used for treatment of abdominal aortic aneurysm (AAA). In 2018, draft guidance from the National Institute for Clinical Excellence (NICE) suggested EVAR was not cost-effective compared to open surgical repair. The analysis was driven by clinical inputs from randomized control trials which may not reflect current clinical practice. Data from registries may inform more robust economic modelling. The Global Registry for Endovascular Aortic Treatment (GREAT) was initiated to collect contemporary real-world data on the performance of GORE® aortic endografts and includes long-term data on survival, re-interventions and resource use. This study compares the real-world values for mortality and resource use following elective EVAR as collected by GREAT with the 2018 NICE AAA draft guidance.MethodsA total of 1,348 patients (88.7% men; mean age 73.1 years) undergoing elective AAA repair with the GORE® EXCLUDER device. Mortality, re-intervention and resource use was compared with the economic inputs for 2018 NICE draft guidance cost-utility analysis.ResultsAll patients survived EVAR compared to the 0.4 percent mortality indicated in the NICE analysis. All-cause mortality was lower through 1, 3 and 5 years with values of 6.9, 14.8 and 16.2 percent respectively compared to the NICE base case. The average length of stay was 3.7 days in GREAT compared to 8.34 days in the NICE analysis. Short- and long-term re-interventions were also lower with real-world data (3.6% versus 7.3% and 5.5% versus 8.3%).ConclusionsGREAT provides conflicting data on survival and resource use associated with EVAR compared to inputs of the 2018 NICE draft guidance These differences are likely to significantly alter incremental cost-effectiveness ratios. Robust cost-effectiveness modelling in health technology assessments should consider contemporary data, as it is likely more reflective of current clinical practice and more informative for clinical and economic decision making for AAA.