bluuebunny/crossref_metadata_2025_split huggingface dataset:
Joint–coordinate adjoint method for optimal control of multibody systems
Authors: Maciąg Paweł , Malczyk Paweł , Frączek Janusz 
Published: November 2022

AbstractThis paper presents a joint–coordinate adjoint method for optimal control of multi-rigid-body systems. Initially formulated as a set of differential-algebraic equations, the adjoint system is brought into a minimal form by projecting the original expressions into the joint’s motion and constraint force subspaces. Consequently, cumbersome partial derivatives corresponding to joint-space equations of motion are avoided, and the approach is algorithmically more straightforward. The analogies between the formulation of Hamilton’s equations of motion in a mixed redundant-joint set of coordinates and the necessary conditions arising from the minimization of the cost functional are demonstrated in the text. The observed parallels directly lead to the definition of a joint set of adjoint variables. Through numerical studies, the performance of the proposed approach is investigated for optimal control of a double pendulum on a cart. The results demonstrate a successful application of the joint-coordinate adjoint method. The outcome can be easily generalized to optimal control of more complex systems.
Three-dimensional location methods for the vision system of strawberry-harvesting robots: development and comparison
Authors: Ge Yuanyue , Xiong Ya , From Pål Johan 
Published: November 2022

AbstractFor most fruit-harvesting robots, an essential task of the machine vision system is to provide the manipulator with an accurate three-dimensional location of the target. However, the accuracy of this location can be affected by various factors. This study aimed to develop seven location methods, to investigate their effectiveness, as well as the influences of different camera modes and camera types, and, ultimately, to ascertain which was the optimal method. These methods utilized the pixels of the detected targets in each image, the corresponding depth values, as well as the locations of the 2D bounding boxes extracted from the detection results. These location methods differed in the way that they obtained the position of the 3D bounding box, and in their use of point clustering or colour thresholding. The images were collected via two types of 3D camera, patterned structured light and time-of-flight. Comparative analysis showed that methods using the 2D bounding box and the selected depth value to calculate the 3D bounding box were faster (0.2–8.4 ms compared to 151.9–325.2 ms) and performed better than the 3D clustering methods. In addition, four modes of the structured light camera were tested and compared. The results showed that the high-accuracy mode had fewer noise points but a lower location rate (89.2–89.9%), while the high-density mode created more noise points but a higher location rate (98.9%). Evaluations also indicated that the data from the time-of-flight camera better represented the 3D shape (26.3% more accurate along the camera’s depth direction). Therefore, time-of-flight camera was considered better for the applications that required more accurate 3D shape. This paper, thus, provided references in the selection of location methods, cameras and corresponding modes for related work.
High-performance and balanced parallel graph coloring on multicore platforms
Authors: Giannoula Christina , Peppas Athanasios , Goumas Georgios , Koziris Nectarios 
Published: November 2022

AbstractGraph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application. In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications. We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98$$\times$$
×
and 1.78$$\times$$
×
on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high-performance improvements in real-world end-applications across various input data given.
CH4 and CH2O Oxidation in Lean Gas Engine Exhaust Using Fe2O3 Catalysts
Authors: Mehne M. , Kureti S. 
Published: November 2022

AbstractA systematic series of commercial α-Fe2O3 catalysts was investigated with respect to the after-treatment of the lean exhaust emissions of gas engines. The samples were physico-chemically characterized by X-ray diffraction, Laser Raman spectroscopy, N2 physisorption, temperature-programmed reduction with CO and the temperature-programmed desorption of CO2, whereas the catalytic efficiency was evaluated using a model exhaust gas. Structure–activity correlations showed that for the oxidation of CH4 the number of active Fe sites and the availability of surface and subsurface oxygen are crucial properties of the catalysts. By contrast, the conversion of CH2O is driven by the CO2 adsorption capacity and the amount of OH surface species, in line with the mechanistic understanding gained by step function experiments and diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS). The in-situ DRIFTS studies suggested that the CH2O oxidation follows a Cannizzaro-type mechanism including the reaction of two CH2O molecules with a surface OH site to form CH3OH and formate species. The subsequent conversion of the formate moieties with H2O results in the reconstruction of the OH groups and the release of formic acid. The latter is assumed to decompose into CO2 and H2 which finally oxidizes to H2O. The best iron oxide catalyst was upscaled to the level of a real catalytic converter, which was tested in the lean exhaust gas of a 600 kW biomethane engine. As a result, the catalyst demonstrated high activity with regard to CH2O and CO removal above 300 and 500 °C, respectively, while only negligible conversion of CH4 occurred.
The Timing of Pregnancies After Bariatric Surgery has No Impact on Children’s Health—a Nationwide Population-based Registry Analysis
Authors: Beiglböck Hannes , Mörth Eric , Reichardt Berthold , Stamm Tanja , Itariu Bianca , Harreiter Jürgen , Eichelter Jakob , Prager Gerhard , Kautzky-Willer Alexandra , Wolf Peter , Krebs Michael 
Published: November 2022

Abstract
Purpose
Bariatric surgery has a favorable effect on fertility in women. However, due to a lack of data regarding children’s outcomes, the ideal time for conception following bariatric surgery is unknown. Current guidelines advise avoiding pregnancy during the initial weight loss phase (12–24 months after surgery) as there may be potential risks to offspring. Thus, we aimed to analyze health outcomes in children born to mothers who had undergone bariatric surgery. The surgery-to-delivery interval was studied.

Materials and Methods
A nationwide registry belonging to the Austrian health insurance funds and containing health-related data claims was searched. Data for all women who had bariatric surgery in Austria between 01/2010 and 12/2018 were analyzed. A total of 1057 women gave birth to 1369 children. The offspring’s data were analyzed for medical health claims based on International Classification of Diseases (ICD) codes and number of days hospitalized. Three different surgery-to-delivery intervals were assessed: 12, 18, and 24 months.

Results
Overall, 421 deliveries (31%) were observed in the first 2 years after surgery. Of these, 70 births (5%) occurred within 12 months after surgery. The median time from surgery to delivery was 34 months. Overall, there were no differences noted in frequency of hospitalization and diagnoses leading to hospitalization in the first year of life, regardless of the surgery-to-delivery interval.

Conclusion
Pregnancies in the first 24 months after bariatric surgery were common. Importantly, the surgery-to-delivery interval had no significant impact on the health outcome of the children.

Graphical Abstract
Generating Explanations for Conceptual Validation of Graph Neural Networks: An Investigation of Symbolic Predicates Learned on Relevance-Ranked Sub-Graphs
Authors: Finzel Bettina , Saranti Anna , Angerschmid Alessa , Tafler David , Pfeifer Bastian , Holzinger Andreas 
Published: November 2022

AbstractGraph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain’s and user’s perspective have not been thoroughly studied. We argue that combining symbolic learning methods, such as Inductive Logic Programming (ILP), with statistical machine learning methods, especially GNNs, is an essential forward-looking step to perform powerful and validatable relational concept learning. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. We further provide a novel validation framework that can be used to generate comprehensible explanations with ILP on top of the relevance output of GNN explainers and human-expected relevance for concepts learned by GNNs. Our experiments conducted on our benchmark data set demonstrate that it is possible to extract symbolic concepts from the most relevant explanations that are representative of what a GNN has learned. Our findings open up a variety of avenues for future research on validatable explanations for GNNs.
Composting date palm residues promotes circular agriculture in oases
Authors: El Janati Mustapha , Robin Paul , Akkal-Corfini Nouraya , Bouaziz Ahmed , Sabri Ahmed , Chikhaoui Mohammed , Thomas Zahra , Oukarroum Abdallah 
Published: November 2022

AbstractDry leaves of date palms (DPs) are by-products of date cultivation that are often mismanaged in oasis agroecosystems. Ideally, they should be upcycled in a circular agriculture model before producing and exporting high-value products. Compost inputs enhance soil organic matter and conserve soil. This study investigated composting of four mixtures based on farming system requirements and the availability of organic resources. The DPs were used alone or combined with sheep manure and rock phosphate. We experimented with farm-scale windrow composting to improve understanding and support decision-making. The results showed that temperature increased rapidly to 70 °C at the beginning of composting in the mixtures with manure. Organic matter content decreased by 40% in all windrows during composting, which indicated organic matter mineralisation and loss of volatile solids. Composting DP with or without sheep manure for 136 days with two turnings conserved nitrogen and increased the nitrogen content of compost. Mixing DP with sheep manure and rock phosphate increased the final total phosphorus content of compost to 0.52% (dry matter basis). Our results highlight that composting DP is a feasible alternative to improve circularity in oasis agroecosystems, by increasing organic matter content and fertiliser value, thus paving the way for sustainable production of high-value products from DP.
Image Processing Robustness Assessment of Small-Body Shapes
Authors: Buonagura Carmine , Pugliatti Mattia , Topputo Francesco 
Published: November 2022

AbstractAsteroids and comets are triggering interest due to the richness of precious materials, their scientific value as well as for their potential hazardousness. Owing to their significant diversity, minor bodies do not exhibit uniform shapes: they can range from spherical to irregularly shaped objects with rocky, uneven, and cratered surface. Nowadays, space probes rely more and more on optical navigation techniques, due to the increasing demand for autonomy. When dealing with minor bodies, the diversified range of shapes can significantly affect the performance of these techniques. In order to enable deep space probes to confidently deal with uncertainties, the need for robust image processing methods arises. Commonly, few image processing methods are designed and tested with limited shapes to meet mission requirements. In this work, we depart from this paradigm by developing a new framework, which includes extensive testing of the image processing algorithms with various shapes. The shapes are not randomly analyzed, yet they are arranged in a hierarchical structure called hyper-cube. The cube allows for a better understanding of the methods performance and to infer the way they shift from one shape to the other. The novelty of this approach lies both in the cube representation, which allows a better understanding of the link between the image processing algorithms and shape of the object, but also in the extensive number of shapes that have been tested, which has never been done before. In this analysis, four methods are considered, namely: center of brightness, intensity weighted centroiding, correlation with Lambertian spheres, and least-squares-based ellipse fitting. Results from this test allow us correlating the methods performances to the bodies shape, to suggest the best performing method for each shape family, and to assess their robustness.
Urinary epidermal growth factor/monocyte chemotactic peptide 1 ratio as non-invasive predictor of Mayo clinic imaging classes in autosomal dominant polycystic kidney disease
Authors: Rocchetti Maria Teresa , Pesce Francesco , Matino Silvia , Piscopo Giovanni , di Bari Ighli , Trepiccione Francesco , Capolongo Giovanna , Perniola Maria Antonietta , Song Xuewen , Khowaja Saima , Haghighi Amirreza , Peters Dorien , Paolicelli Simona , Pontrelli Paola , Netti Giuseppe Stefano , Ranieri Elena , Capasso Giovambattista , Moschetta Marco , Pei York , Gesualdo Loreto ,   (Studio PRE. MED. (MEDicina di PREcisione) Prog.n.F/050065/01-02/X32)
Published: November 2022

Abstract
Background
Age- and height-adjusted total kidney volume is currently considered the best prognosticator in patients with autosomal dominant polycystic kidney disease. We tested the ratio of urinary epidermal growth factor and monocyte chemotactic peptide 1 for the prediction of the Mayo Clinic Imaging Classes.

Methods
Urinary epidermal growth factor and monocyte chemotactic peptide 1 levels were measured in two independent cohorts (discovery, n = 74 and validation set, n = 177) and healthy controls (n = 59) by immunological assay. Magnetic resonance imaging parameters were used for total kidney volume calculation and the Mayo Clinic Imaging Classification defined slow (1A–1B) and fast progressors (1C–1E). Microarray and quantitative gene expression analysis were used to test epidermal growth factor and monocyte chemotactic peptide 1 gene expression.

Results
Baseline ratio of urinary epidermal growth factor and monocyte chemotactic peptide 1 correlated with total kidney volume adjusted for height (r = − 0.6, p < 0.001), estimated glomerular filtration rate (r = 0.69 p < 0.001), discriminated between Mayo Clinic Imaging Classes (p < 0.001), and predicted the variation of estimated glomerular filtration rate at 10 years (r = − 0.51, p < 0.001). Conditional Inference Trees identified cut-off levels of the ratio of urinary epidermal growth factor and monocyte chemotactic peptide 1 for slow and fast progressors at > 132 (100% slow) and < 25.76 (89% and 86% fast, according to age), with 94% sensitivity and 66% specificity (p = 6.51E−16). Further, the ratio of urinary epidermal growth factor and monocyte chemotactic peptide 1 at baseline showed a positive correlation (p = 0.006, r = 0.36) with renal outcome (delta-estimated glomerular filtration rate per year, over a mean follow-up of 4.2 ± 1.2 years). Changes in the urinary epidermal growth factor and monocyte chemotactic peptide 1 were mirrored by gene expression levels in both human kidney cysts (epidermal growth factor: − 5.6-fold, fdr = 0.001; monocyte chemotactic peptide 1: 3.1-fold, fdr = 0.03) and Pkd1 knock-out mouse kidney (Egf: − 14.8-fold, fdr = 2.37E-20, Mcp1: 2.8-fold, fdr = 6.82E−15).

Conclusion
The ratio of urinary epidermal growth factor and monocyte chemotactic peptide 1 is a non-invasive pathophysiological biomarker that can be used for clinical risk stratification in autosomal dominant polycystic kidney disease.
Negative calcium balance despite normal plasma ionized calcium concentrations during citrate anticoagulated continuous venovenous hemofiltration (CVVH) in ICU patients
Authors: de Jonge Evert , van der Vooren Marije , Gillis Judith M. E. P. , del Prado Michael R. , Wigbers Jeanette , Bakhshi-Raiez Ferishta , Elzo Kraemer Carlos V. 
Published: November 2022

Abstract
Background
Supplementation of calcium during continuous venovenous hemofiltration (CVVH) with citrate anticoagulation is usually titrated using a target blood ionized calcium concentration. Plasma calcium concentrations may be normal despite substantial calcium loss, by mobilization of calcium from the skeleton. Aim of our study is to develop an equation to calculate CVVH calcium and to retrospectively calculate CVVH calcium balance in a cohort of ICU-patients.

Methods
This is a single-center retrospective observational cohort study. In a subcohort of patients, all calcium excretion measurements in patients treated with citrate CVVH were randomly divided into a development set (n = 324 in 42 patients) and a validation set (n = 441 in 42 different patients). Using mixed linear models, we developed an equation to calculate calcium excretion from routinely available parameters. We retrospectively calculated calcium balance in 788 patients treated with citrate CVVH between 2014 and 2021.

Results
Calcium excretion (mmol/24 h) was − 1.2877 + 0.646*[Ca]blood,total * ultrafiltrate (l/24 h) + 0.107*blood flow (ml/h). The mean error of the estimation was − 1.0 ± 6.7 mmol/24 h, the mean absolute error was 4.8 ± 4.8 mmol/24 h. Calculated calcium excretion was 105.8 ± 19.3 mmol/24 h. Mean daily CVVH calcium balance was − 12.0 ± 20.0 mmol/24 h. Mean cumulative calcium balance ranged from − 3687 to 448 mmol.

Conclusion
During citrate CVVH, calcium balance was negative in most patients, despite supplementation of calcium based on plasma ionized calcium levels. This may contribute to demineralization of the skeleton. We propose that calcium supplementation should be based on both plasma ionized calcium and a simple calculation of calcium excretion by CVVH.

Graphical abstract
Defect Passivation on Lead-Free CsSnI3 Perovskite Nanowires Enables High-Performance Photodetectors with Ultra-High Stability
Authors: Gao Zheng , Zhou Hai , Dong Kailian , Wang Chen , Wei Jiayun , Li Zhe , Li Jiashuai , Liu Yongjie , Zhao Jiang , Fang Guojia 
Published: November 2022

AbstractIn recent years, Pb-free CsSnI3 perovskite materials with excellent photoelectric properties as well as low toxicity are attracting much attention in photoelectric devices. However, deep level defects in CsSnI3, such as high density of tin vacancies, structural deformation of SnI6− octahedra and oxidation of Sn2+ states, are the major challenge to achieve high-performance CsSnI3-based photoelectric devices with good stability. In this work, defect passivation method is adopted to solve the above issues, and the ultra-stable and high-performance CsSnI3 nanowires (NWs) photodetectors (PDs) are fabricated via incorporating 1-butyl-2,3-dimethylimidazolium chloride salt (BMIMCl) into perovskites. Through materials analysis and theoretical calculations, BMIM+ ions can effectively passivate the Sn-related defects and reduce the dark current of CsSnI3 NW PDs. To further reduce the dark current of the devices, the polymethyl methacrylate is introduced, and finally, the dual passivated CsSnI3 NWPDs show ultra-high performance with an ultra-low dark current of 2 × 10–11 A, a responsivity of up to 0.237 A W−1, a high detectivity of 1.18 × 1012 Jones and a linear dynamic range of 180 dB. Furthermore, the unpackaged devices exhibit ultra-high stability in device performance after 60 days of storage in air (25 °C, 50% humidity), with the device performance remaining above 90%.
Identification and analysis of long-term changes in river deltas and riparian zones using time-series multispectral data
Authors: Kontopoulos Christos , Kitsiou Dimitra , Chlorokostas Stavros , Charalampopoulou Betty , Grammalidis Nikos , Tzepkenlis Anastasios 
Published: November 2022

AbstractToday’s remote sensing data and technologies offer the capability to effectively monitor diverse and challenging environments around the world, such as coastal river and riparian zones. Coastal riparian zones and river deltas usually demonstrate extreme coastline changes in terms of the extent of water coverage of inland territories due to flood events, low and high tides, the climate, specific environmental characteristics, etc. In this paper, we exploit freely available multispectral time series data for previous decades, utilizing Landsat missions in order to develop an open-source-based image processing pipeline for the extraction of the actual yearly average coastline status of riparian river delta areas. The latter present significant temporal coastline changes between years, semesters, and months. Average mean maps are generated and then compared to several temporal levels in order to distinguish long-term significant changes and ecosystem threats. Additionally, a custom long short-term memory (LSTM) neural network is deployed to forecast the evolution of the coastline by exploiting the average value for each pixel across all available images as a training sample and producing a forecast output for the next period. The network achieves accuracy scores of 89.77% over 'non-water' depicting pixels and 84.26% over 'water' depicting pixels, for regions that present frequent changes between land and water coverage over time. The predicted map presents high statistical agreement with the respective average map generated in two different validation approaches, with kappa coefficients of 85.9% and 91.4%, respectively.
Öffentliches Sprechen über Suizide zwischen Eindeutigkeit und Uneindeutigkeit
Authors: Stein Stephan 
Published: November 2022

ZusammenfassungDas Thema »Sterben und Tod« gilt als Bereich gesellschaftlicher Tabuisierung par excellence. Mehr noch scheint das für den Umgang mit Suiziden zu gelten. Naheliegend und erwartbar ist beim Sprechen über Suizide eine Ausdruckstypik, die durch Formen des Verbergens und Verhüllens geprägt ist. Der Beitrag geht am Beispiel verschiedener Textsorten der Frage nach, wie aus der jeweiligen Perspektive – Berichterstattung in den Medien, Todes- und Traueranzeigen von Hinterbliebenen, Gedenk‑/Kondolenztexte von Anteilnahme Bekundenden, Abschiedsbriefe von Suizidenten – mit dem Thema »Suizid« umgegangen wird, um das Spannungsverhältnis zwischen Offenlegen und Verbergen zu verdeutlichen.