bluuebunny/crossref_metadata_2025_split huggingface dataset:
UPLC-ESI-TQD-MS/MS Identification and Antioxidant, Anti-Inflammatory, Anti-Diabetic, Anti-Obesity and Anticancer Properties of Polyphenolic Compounds of Hawthorn Seeds
Authors: Żurek Natalia , Świeca Michał , Kapusta Ireneusz 
Published: May 2024

AbstractHawthorn seeds are a by-product of fruit processing and due to the scale of processing of this raw material, they can be an important source of bioactive compounds. This work is the first report on the phenolic composition of hawthorn seeds and their antioxidant, anti-inflammatory, antidiabetic, antiobesity and anticancer activities. In the isolated phenolic fraction of six seed species, 23 phenolic compounds were identified using the UPLC-ESI-TQD-MS/MS method, the key ones of which included the B-type procyanidin dimer. The seeds of the tested species showed high antioxidant activity (mainly by scavenging O2•− and OH• radicals), anti-inflammatory (mainly through LOX inhibition), anti-diabetic, anti-obesity and anti-cancer, with the highest activity against colon cancer cells (Dld-1 line), showing no activity against healthy colon epithelial cells (CCD841CoN). This activity was significantly dependent on the analyzed hawthorn species and, according to PCA analysis, on the content of flavan-3-ols. These discoveries provided the theoretical basis for the possibility of industrial use of hawthorn seeds.
Graphical Abstract
Optimizing sparse general matrix–matrix multiplication for DCUs
Authors: Guo Hengliang , Wang Haolei , Chen Wanting , Zhang Congxiang , Han Yubo , Zhu Shengguang , Zhang Dujuan , Guo Yang , Shang Jiandong , Wan Tao , Li Qingyang , Wu Gang 
Published: May 2024

AbstractSparse general matrix–matrix multiplication (SpGEMM) is a crucial and complex computational task in many practical applications. Improving the performance of SpGEMM on SIMT processors like modern GPUs is challenging due to the unpredictable sparsity of sparse matrices. Although existing GPU solutions have made progress in improving performance through advanced algorithm design, they ignore some optimizations related to specific processor architectures. This can result in a partially inefficient implementation of their algorithms. This paper focuses on optimizing four inefficient parts of the NSparse algorithm on DCU (a GPU-like accelerator). The optimizations include: 1) setting parameters to improve the load balance of the second matrix by extracting maximum row information at runtime; 2) reducing overhead of binning operations by making full use of registers and shared memory effectively; 3) improving numerical SpGEMM performance by adjusting its calculation mode; and 4) enhancing global load balance through finer-grained grouping and kernel configurations. Experiment results demonstrate that when compared to five state-of-the-art SpGEMM algorithms (bhSparse, KokkosKernels, NSparse, rocSparse, and spECK), our optimized method achieves an average of 7.99x (up to 18.2x), 8.01x (up to 20.83x), 2.37x (up to 6.16x), 1.82x (up to 4.20x), and 1.63x (up to 5.01x) speedups on 29 sparse matrices with different sparse structures, respectively.
Behavioral economics and the nature of neoclassical paradigm
Authors: Esposito Lorenzo , Mastromatteo Giuseppe 
Published: May 2024

AbstractPsychological observations are by now well integrated into economics, especially in the theory of finance, as can also be seen in the Nobel Prize awarded to Thaler. On the contrary, Simon’s attempt to reforge economic theory on the paradigm of bounded rationality failed. Starting from the birth of the neoclassical paradigm, we’ll describe the attempt to give it psychological foundations with a direct measurement of utility, then the axiomatic turn of the paradigm and its first anomalies. We’ll then sum up the debate on rationality, taking place in the group of economists led by Simon, which brought to the rational expectations hypothesis. Finally, we’ll discuss the development of behavioral economics and its progressive acceptance in economic theory. This historical reconstruction allows us to understand the actual hard core of the neoclassical paradigm and the growing need of the paradigm for practical flexibility that determines how to choose arguments, methods and evidence that can be useful to its development, including psychological ones.
On the Damping Performance and Mechanical Response of Additive-Manufactured and Thermo-Mechanical Processed AlSiMg Alloy
Authors: Isil Canay , Radi Amin , Yapici Guney Guven 
Published: May 2024

AbstractRecent advancements in additive manufacturing (AM) fuel efforts for expanding the design envelopes for components obtained via this technology through continuous improvement in mechanical behavior. Damping properties can also be altered depending on the microstructure evolved during AM. Therefore, achieving enhanced monotonic mechanical response with better damping properties is highly sought-after. In this respect, thermo-mechanical processing via severe plastic deformation (SPD) and artificial aging is imparted on the additive-manufactured samples with the target of grain refinement and densification to further improve mechanical and damping properties. Employing microstructural characterizations and mechanical experiments, a multi-scale exploration is carried out to develop a relation between the evolved microstructure and the resulting behavior. It is concluded that introducing a refined microstructure decorated with well-distributed (Mg,Si)-rich phase and favorable dislocation substructure in AlSi10Mg positively affects the resulting mechanical behavior. Moreover, it is shown that artificial aging can be employed to improve the damping characteristics of severely deformed additive-manufactured AlSi10Mg alloy.
Graphical Abstract
Super-Resolution Imaging of Sub-diffraction-Limited Pattern with Superlens Based on Deep Learning
Authors: Guan Yizhao , Masui Shuzo , Kadoya Shotaro , Michihata Masaki , Takahashi Satoru 
Published: May 2024

AbstractThe development of super-resolution imaging techniques has revolutionized our ability to study the nano-scale world, where objects are often smaller than the diffraction limit of traditional optical microscopes. Super-resolution superlenses have been proposed to solve this problem by manipulating the light wave in the near field. A superlens is a kind of metamaterial-based lens that can enhance the evanescent waves generated by nano-scale objects, utilizing the surface plasmon phenomenon. The superlens allows for the imaging of nano-scale objects that would otherwise be impossible to resolve using traditional lenses. Previous research has shown that nanostructures can be imaged using superlenses, but the exact shape of the superlens must be known in advance, and an analytical calculation is needed to reconstruct the image. Localized plasmon structured illumination microscopy is an approach to achieve super-resolution by imaging the superlens-enhanced evanescent wave with illumination shifts. This study proposes a new approach utilizing a conditional generative adversarial network to obtain super-resolution images of arbitrary nano-scale patterns. To test the efficacy of this approach, finite-difference time-domain simulation was utilized to obtain superlens imaging results. The data from the simulation were then used for deep learning to develop the model. With the help of deep learning, the inverse calculation of complex sub-diffraction-limited patterns can be achieved. The super-resolution feature of the superlens based on deep learning is investigated. The findings of this study have significant implications for the field of nano-scale imaging, where the ability to resolve arbitrary nano-scale patterns will be crucial for advances in nanotechnology and materials science.
Application of artificial intelligence to predict rock strength and drilling efficiency using in-cutter sensing data and vibration modes
Authors: Koulidis Alexis , Ooi Guang , Ahmed Shehab 
Published: May 2024

AbstractDrilling is a complex destructive action that induces vibrations due to the rock-bit interaction, which affects the overall drilling efficiency and wellbore quality. This study aims to enhance drilling efficiency by deploying artificial neural networks (ANNs) to integrate in-cutter force sensing and vibration data. Data is collected from experiments conducted with sharp cutters on rock samples of varying mechanical properties, measuring variables such as weight on bit, torque, rotational speed, in-cutter force, and vibration measurements. A scoring system is used to evaluate the drilling efficiency by coupling the mechanical specific energy and vibration modes. An ANN is trained with these variables to predict the rate of penetration and rock strength, which are also measured in the experiments to be used as ground truth. The reliability of the framework is demonstrated by testing the validity of the ANN model on samples with various mechanical properties. It introduces a reliable and swift method for determining optimal drilling parameters, supported by a sensitivity analysis to fine-tune the ANN and assess the influence of each parameter on performance. This study demonstrates that ANN could be successfully implemented to predict the rate of penetration and rock strength on a laboratory-scaled drilling rig. The results show that the ANN model accurately predicts training and testing datasets for scoring while drilling multiple layers with a correlation coefficient of 0.966. Integration of in-cutter sensing technology, vibration data, and ANN can be of significant interest and be used on field applications to provide a reliable and rapid decision about drilling efficiency.
Experimental design and manufacturing of a smart control system for horizontal separator based on PID controller and integrated production model
Authors: Fadaei Mehdi , Ameri Mohammad Javad , Rafiei Yousef , Asghari Morteza , Ghasemi Mehran 
Published: May 2024

AbstractDuring oil production, the reservoir pressure declines, causing changes in the hydrocarbon components. To ensure better separation of produced phases, separator dimensions should also be adjusted. It is not possible to change the dimensions of the separator during production. Therefore, to improve the separation of the phases, the level of the separator needs to be adjusted. An intelligent system is required to ensure that the liquid level is maintained at the desired level for optimal phase separation during changes in reservoir pressure. In this study, a novel correlation is presented to measure the desired liquid level using new separator pressures. For this purpose, an intelligent system was built in the laboratory and tested in different operational conditions. The intelligent system effectively maintained the desired liquid level of the separator through a new correlation technique. The system accomplished this by acquiring new separator pressure readings collected by installed sensors. This approach helped mitigate the negative effects of the slug flow regime and minimized issues such as foam formation and over-flushing of the separator. It could achieve a 99.1% separation efficiency between gas and liquid phases. This was possible during liquid and gas flow rates ranging from 0 to 2.35 and 8–17 m3/h, respectively. The system could operate under bubble, stratified, plug, and slug flow regimes. Then the intelligent model obtained from lab experiments was integrated into the production model for the southern Iranian oil field. The smart model increased oil production by 13% and prevented the separator from over-flushing in 840 days.
Optimizing cluster spacing in multistage hydraulically fractured shale gas wells: balancing fracture interference and stress shadow impact
Authors: Ibrahim Ahmed Farid 
Published: May 2024

AbstractHorizontal drilling and multistage hydraulic fracturing have seen widespread application in shale formations during the past decade, leading to significant economic productivity gains through the creation of extensive fracture surfaces. The determination of the ideal cluster spacing in shale gas wells is contingent upon the unique geological and formation characteristics. Generally, reducing the spacing between clusters has the potential to augment gas recovery, albeit at the expense of higher drilling and completion costs, as well as the influence of stress shadows on fracture propagation. This study introduces an integrated methodology designed to explore the impact of cluster interference on well performance. Commencing with a fracture propagation model accommodating stress shadow effects for an equivalent slurry volume injection, analytical rate transient analysis (RTA) was amalgamated with reservoir numerical simulation to compute the effective fracture surface area (Aca.) for hydrocarbon production. The correlation between the effective fracture surface area determined by RTA and the actual stimulated fracture area (Aca.) derived from numerical simulations was established in relation to cluster spacing. The findings of this research reveal that wells featuring a greater number of stages and tighter cluster spacing tend to exhibit elevated cluster interference, resulting in a lower effective-to-actual fracture surface area ratio and heightened stress shadow effects impeding fracture propagation. A cluster spacing of 33 feet with six clusters per stage emerges as the optimal choice at formation permeability of 0.00005 md that decreased to 18 ft at formation permeability of 0.00001 md. ACe either stabilizes or decreases above the optimal value, suggesting that more clusters would not have a major impact on increasing the effective stimulated area. Allowing 20% interference, regardless of the permeability of the formation, maximized cumulative production while preventing thief zones and excessive cluster interference. The insights gained from this study will serve as a valuable resource for completion and reservoir engineers, enabling them to fine-tune cluster spacing to maximize well revenue in the dynamic landscape of shale gas extraction.
Ideas in transition? Policymakers’ ideas of the social dimension of the green transition
Authors: Gerstenberg Anne 
Published: May 2024

AbstractClimate change and its associated mitigation policies have an impact on people’s livelihoods. Therefore, how best to react to such impacts is currently a prominent topic in academic and policymaking debates. The article contributes to this new and rapidly developing academic discourse by identifying different understandings of the social dimension within current paradigms of the green transition and analysing policymakers’ understandings of this dimension in Germany and the European Union. The integration of the social dimension ultimately depends on underlying understandings of redistribution and the state-society relationship. The market-liberal Green Economy approach is dominant in the discourse; its narrow social dimension is mostly seeking to increase the acceptance of climate policy through earmarking revenues of carbon trading. Green Keynesian arguments, which examine structural policies and allow for more integrated approaches of social protection are fighting to be heard. Ideas from the Degrowth sphere gain only fragmented access to the policy debates, as they are often framed as unrealistic and politically unfeasible. An interest-based understanding of the welfare state would be suited to mediate interests within the transformation process and socially legitimise climate policy, but current Green Economy discourses primarily aim for social policy to fulfil an appeasing role.
Manufacture and use of cutting inserts made of different glass ceramics
Authors: Breidenstein Bernd , Grzeschik Florian , Wolters Philipp , Petersen Hilke 
Published: May 2024

AbstractThe most commonly used materials for cutting tools worldwide are carbide-based. Its production requires the increasingly scarce metals tungsten and cobalt, the latter is often obtained under ethically questionable circumstances. In addition, a lot of manufacturing effort along the process chain is required to produce the final tool. The intensive manufacturing efforts are what carbide-based tools have in common with other important cutting materials such as cubic boron nitride, ceramics, or synthetic diamond. For this reason, efforts are underway to expand the range of cutting materials beyond current options such as e.g. cemented carbides, high-speed steel, or cubic boron nitride. This work shows that, in principle, glass ceramics can also be included in the investigations. The glass ceramic materials examined here can be ground into indexable inserts and have been successfully used in the machining of polyether ether ketone and aluminum EN AW 2007. These first results are intended to pave the way for further research in this area.
Helicobacter pylori infection in Africa: comprehensive insight into its pathogenesis, management, and future perspectives
Authors: Emmanuel Blessing N. , Peter Deborah A. , Peter Mercy O. , Adedayo Ibrahim S. , Olaifa Kayode 
Published: May 2024

AbstractHelicobacter pylori is a widespread bacterium that has effectively colonized half of the global population, with Africa having over 70% of the total burden of H. pylori infections (HPI). Considering its acknowledged classification of as bacterial carcinogens and their significant contribution to the development of gastrointestinal disorders such as gastritis, peptic ulcers, and gastric neoplasia, together with their growing resistance to antibiotics. Gaining insight into the etiology of this organism is crucial in order to investigate and develop appropriate treatment strategies. Furthermore, the rise of bacteria that are resistant to antibiotics presents an extra danger in managing this detrimental bacterium. Our review focuses on investigating the presence of H. pylori in Africa and analyzing the various factors that contribute to its extensive prevalence. We simplified the complex mechanisms that H. pylori utilizes to flourish in the human body, with a specific emphasis on its virulence factors and antibiotic resistance. These variables pose significant challenges to conventional treatment strategies. In addition, we analyze both conventional and developing diagnostic methods, as well as the current treatment approaches implemented in various African nations. In addition, we tackle the distinct healthcare obstacles of the region and put-up practical remedies. The main goal of this review is to improve the formulation of more efficient methods for the management and treatment of HPI in Africa.
Geographical flows: a fresh perspective on quantifying urban function
Authors: Pei Tao , Yan Xiaorui , Shu Hua , Wang Xi , Chen Xiao , Guo Sihui , Jiang Jingyu , Song Ci , Chen Jie 
Published: May 2024

AbstractUrban function refers to various roles and activities that take place in an urban region or a public facility. Previous research only focused on the types of urban function and its quantitative characterization did not attract enough attention. In this paper, we highlight the issue of quantifying the urban function and divide it into two aspects: one is the intensity of the urban function, which can be seen as the capacity to accommodate people engaging in activities related to the function, and the other is its influence on the surrounding area. Due to the cause-and-effect dynamics between urban functions and geographical flows, which refer to the human or objects moving between locations, geographical flows have been used to estimate the intensity and influence of urban function. Based on the review of recent updates, this paper provides a research framework for utilizing flow data to quantify urban function in terms of intensity and influence. Regarding the intensity, the indices are mainly based on the volume of inflow/outflow and their ratio. Regarding the influence, there are two types of indices for evaluating it: one for planar facilities and the other for linear facilities. To describe the intensity and influence of the urban function simultaneously, the volume along with the length of flows are considered into the indices, which can quantify the importance or irreplaceability of a place or facility. The analysis shows that the geographical flows provide a new perspective for quantifying urban function in terms of intensity and influence.
Audio degradation of climax mode transmission in air traffic control
Authors: Vignoli Muniz H. , Parente Ribeiro E. 
Published: May 2024

AbstractAirspace control plays an important role in the safety and fluidity of air traffic. A fundamental service for this purpose is audio communication through frequencies in the VHF bands. This paper describes the evaluation of the audio degradation of voice transmissions from control centre to the aircraft. The effects of more than one station broadcasting on the same frequency with carrier offset (climax mode) are analysed using perceptual evaluation of speech quality (PESQ) perceptual model. Comparative studies are performed to verify the degree of degradation of different audio transport systems and climax situation.
Relationship between body weight and hip width in dairy buffaloes (Bubalus bubalis)
Authors: Cruz-Tamayo Alvar Alonzo , Ramírez-Bautista Marco Antonio , Mota-Rojas Daniel , Escobar-España José Carlos , García-Herrera Ricardo , Gurgel Antonio Leandro Chaves , Dias-Silva Tairon Pannunzio , de Araújo Marcos Jácome , Santana Juliana Caroline Santos , Aguiar Isadora Osorio Maciel , Ítavo Luís Carlos Vinhas , Chay-Canul Alfonso Juventino 
Published: May 2024

AbstractThe objective of the present study was to evaluate the relationship between body weight (BW) and hip width (HW) in dairy buffaloes (Bubalus bubalis). HW was measured in 215 Murrah buffaloes with a BW of 341 ± 161.6 kg, aged between three months and five years, and raised in southeastern Mexico. Linear and non-linear regressions were used to construct the prediction models. The goodness of fit of the models was evaluated using the Akaike information criterion (AIC), Bayesian information criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). Additionally, the developed models were evaluated through internal and external cross-validation (k-folds) using independent data. The ability of the fitted models to predict the observed values was assessed based on the root mean square error of prediction (RMSEP), R2, and mean absolute error (MAE). The relationship between BW and HW showed a high correlation coefficient (r = 0.96, P < 0.001). The chosen fitted model to predict BW was: −176.33 (± 40.83***) + 8.74 (± 1.79***) × HW + 0.04 (± 0.01*) × HW2, because it presented the lowest MSE, RMSE, and AIC values, which were 1228.64, 35.05 and 1532.41, respectively. Therefore, with reasonable accuracy, the quadratic model using hip width may be suitable for predicting body weight in buffaloes.
Countability constraints in order-theoretic approaches to computability
Authors: Hack Pedro , Braun Daniel A. , Gottwald Sebastian 
Published: May 2024

AbstractComputability on uncountable sets has no standard formalization, unlike that on countable sets, which is given by Turing machines. Some of the approaches to define computability in these sets rely on order-theoretic structures to translate such notions from Turing machines to uncountable spaces. Since these machines are used as a baseline for computability in these approaches, countability restrictions on the ordered structures are fundamental. Here, we show several relations between the usual countability restrictions in order-theoretic theories of computability and some more common order-theoretic countability constraints, like order density properties and functional characterizations of the order structure in terms of multi-utilities. As a result, we show how computability can be introduced in some order structures via countability order density and multi-utility constraints.