bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improved Wake Velocity Distribution Behind a Rising Bubble for Isothermal and Thermally Stratified Liquid Layers
Authors: Agarwal Shashwat S. , Kumar Kunal , Chandra Laltu , Ghosh Pradyumna 
Published: April 2022

Abstract
This paper aims at (a) improving the vertical velocity distribution in the wake of a rising isolated bubble for isothermal water layers and (b) evaluating the proposed distribution for thermally stratified therminol layers before and after the initiation of vortex shedding. To address these objectives, numerical investigations are performed, for the rise of an isolated bubble in isothermal and thermally stratified liquid layers, with a combination of the monotonic upwind scheme for conservation laws and pressure implicit with splitting of operators numerical scheme. The analysis revealed that the vertical velocity in the wake of a rising isolated bubble, for isothermal and thermally stratified liquid layers, differs remarkably from the Gaussian distribution. Based on the detailed investigations, region-wise wake velocity distribution comprising a linear superposition of Gaussian approximation with Burr distribution is proposed. Furthermore, this distribution is utilized to predict the rise velocity for a chain of bubbles having different frequencies of departure. Thus, the findings will be useful for the design of heat exchangers or cooling devices, which rely on the heat transfer augmentation with rising air bubbles from a heated surface for isothermal (buoyancy suppressed) and thermally stratified (buoyancy assisted) liquid layers.
Temperature Response Characteristics of the Production Profile for Multilayered Gas Wells Based on Distributed Temperature Sensing Monitoring
Authors: Wei Mingqiang , Long Tengyi , Duan Xiyu , Dejam Morteza , Sun Yuping 
Published: April 2022

Abstract
The distributed temperature sensing (DTS) is used to overcome the defects of traditional production profile testing technology and realize the real-time and accurate temperature monitoring of complex underground reservoirs. However, the temperature response characteristics of the production profile for multilayered gas wells have not been studied clearly, which leads to technical problems of the production profile interpretation of such gas wells monitored by DTS. Therefore, considering the influence of the fluid heat convection, viscous dissipation, and heat conduction which are the involved mechanisms in this process, a model coupling pressure and temperature fields of a multilayered gas reservoir are established in the current study. Subsequently, the formation temperature variation for transient testing of a multilayered gas reservoir is solved by programming, and the effect of model parameters (e.g., gas production, permeability, and rock heat capacity ratio) on the temperature response characteristics of the production profile is analyzed. Finally, the accuracy and reliability of temperature response prediction are verified by fitting the actual DTS temperature test data of an offshore-multilayered gas well. The results of this study provide ideas regarding quantitative interpretation and analysis of DTS monitoring data for the production profile of multilayered gas wells.
Mass Conserving Analysis of Steadily Loaded, Oscillating Partial Arc Journal Bearings Using a Generalized Warner Bearing Formulation
Authors: Boedo S. 
Published: April 2022

Abstract
This paper presents the first numerical study of self-acting grooveless, partial arc bearings under steady load and sinusoidal journal oscillation accounting for mass-conserving cavitation, with the objective of determining a range of bearing loads, oscillation speeds, and dimensional specifications under which a fully hydrodynamic lubrication regime can be achieved. A generalized Warner bearing (GWB) formulation is introduced to address the long computation times associated with this bearing application and to account for local flattening of the pressure region expected in very thin films. The computational speed of the GWB formulation is approximately 80 times faster than the complete formulation with comparable accuracy for the examples provided in the paper. Mixed or boundary lubrication is predicted for oscillation amplitudes less than π/4 radians regardless of load number, which concurs with experimental trends. Fully hydrodynamic lubrication is predicted over a large range of load numbers for minimum oscillation amplitudes of approximately π/2 radians.
Charged radiation collapse in Einstein–Gauss–Bonnet gravity
Authors: Brassel Byron P. , Maharaj Sunil D. , Goswami Rituparno 
Published: April 2022

AbstractWe generalise the continual gravitational collapse of a spherically symmetric radiation shell of matter in five dimensional Einstein–Gauss–Bonnet gravity to include the electromagnetic field. The presence of charge has a significant effect in the collapse dynamics. We note that there exists a maximal charge contribution for which the metric functions in Einstein–Gauss–Bonnet gravity remain real, which is not the case in general relativity. Beyond this maximal charge the spacetime metric is complex. The final fate of collapse for the uncharged matter field, with positive mass, is an extended, weak and initially naked central conical singularity. With the presence of an electromagnetic field, collapse terminates with the emergence of a branch singularity separating the physical spacetime from the complex region. We show that this marked difference in singularity formation is only prevalent in five dimensions. We extend our analysis to higher dimensions and show that for all dimensions $$N\ge 5$$

N
≥
5

, charged collapse ceases with the above mentioned branch singularity. This is significantly different than the uncharged scenario where a strong curvature singularity forms post collapse for all $$N\ge 6$$

N
≥
6

and a weak conical singularity forms when $$N=5$$

N
=
5

. A comparison with charged radiation collapse in general relativity is also given.
OPTIMIZATION OF EROSION–CORROSION BEHAVIOR OF NICHROME COATED 2205 DUPLEX STAINLESS STEEL USING GREY RELATIONAL ANALYSIS
Authors: KURUVILA ROSHAN , KUMARAN S. THIRUMALAI , KHAN M. ADAM 
Published: April 2022

Erosion–corrosion is a common mechanism in the degradation of systems in the mining, chemical, and petrochemical industries. Surface modification can help to reduce the detrimental effects of erosion–corrosion. This study uses the plasma spray process for coating nichrome on duplex stainless steel (DSS2205) to improve the surface characteristics. Erosion tests were performed to analyze the effect of parameters such as impact velocity (150, 175 and 200[Formula: see text]m/s), impact angle (30∘, 60∘and 90∘), and erodent particle discharge rate (2.5, 3.75 and 5[Formula: see text]g/min). According to the findings, the maximum erosion rate ([Formula: see text][Formula: see text]g/g) was found at a flow velocity of 200[Formula: see text]m/s and 30∘impact angle with a discharge rate of 2.5[Formula: see text]g/min. While the minimal erosion rate was observed at a low velocity of 150[Formula: see text]m/s, 90∘impact angle, and 5[Formula: see text]g/min discharge rate. Electrochemical polarization studies were carried out on the eroded specimens using a corrosive solution containing 3.5% NaCl. Corrosion potentials and current densities were estimated from polarization graphs using the Tafel extrapolation method. The erosion–corrosion properties of the samples were subjected to metallurgical characterization to evaluate the influence of nichrome plasma coatings to assess the potential causes of failure. The grey relational analysis (GRA) was further used for improving the erosion–corrosion process parameters and found the optimum solution as flow velocity of 150[Formula: see text]m/s, 30∘impact angle with a discharge rate of 5[Formula: see text]g/min. The validation test confirmed an increase of 20–30% in grade when using the optimum process parameters.
Minerals for future technologies: how Germany copes with challenges
Authors: Watzel Ralph 
Published: April 2022

Abstract
Energy transition, de-carbonization and the future of manufacturing will not end the import dependency of Germany's national economy, but it will most probably result in a shift to new and different dependencies. The need for new raw materials is rapidly increasing. Domestic resources may contribute to but not entirely meet the evolving demand. The supply of new raw materials requires new approaches in exploration supported by sound science. Here, we report in a summarized way on recent results of scientific research projects on critical minerals. Mineral commodities that have important uses and no viable substitutes, yet face potential disruption in supply, are defined as critical to a nation's economic and national security. The main topics are the assessment of critical mineral potential in primary ore deposits and mining waste, exploration of the deep subsurface and thermal brines, bioleaching and deep sea mineral deposits. Raw material development and extraction are affected by environmental concerns, especially groundwater and soil, and societal aspects, such as fair trade in supply chains.
Antioxidative Stress and Antiapoptosis Effect of Chitosan Nanoparticles to Protect Cardiac Cell Damage on Streptozotocin-Induced Diabetic Rat
Authors: Wardani Giftania , Nugraha Jusak , Mustafa Mohd. Rais , Kurnijasanti Rochmah , Sudjarwo Sri Agus 
Published: April 2022

The antioxidant can inhibit oxidative stress and apoptosis, which has a role in an important mechanism on diabetic-induced cardiac cell damage. The research goal was to prove the antioxidative stress and antiapoptosis effect of chitosan nanoparticles as a cardioprotector in streptozotocin-induced diabetic rats. Scanning electron microscope (SEM) and dynamic light scattering (DLS) characterize the chitosan nanoparticles. This research is a laboratory experiment which consists of the control group (rats were given distilled water), the streptozotocin group (rats were injected streptozotocin at dose of 55 mg/kg BW i.p), and the chitosan nanoparticle group (rats were given streptozotocin at dose 55 mg/kg BW i.p, and then given chitosan nanoparticles at dose 75 mg/kg BW, 150 mg/kg BW, and 300 mg/kg BW peroral). Creatine kinase-myoglobin (CK-MB) and lactate dehydrogenase (LDH) were measured from the blood sample. Malondialdehyde (MDA), superoxide dismutase (SOD), and glutathione peroxidase (GPx) from cardiac tissue were examined by ELISA; nuclear factor erythroid 2-related factor 2 (Nrf2) was evaluated by western blotting; B-cell lymphoma 2 (Bcl-2) and Caspase-3 expression were investigated by immunohistochemical staining and also were evaluated histological preparation by hematoxylin & eosin (H&E) staining. The chitosan nanoparticles have a rough surface and an irregular shape. Its size is

247.3
±
38.1

μ
m

. Streptozotocin injection significantly increased the levels of CK-MB, LDH, MDA, and expression of caspase-3. In contrast, the levels of SOD, GPx, Nrf2, and expression of Bcl-2 decreased as compared with the control group (

p
<
0.05

). This is accompanied by the loss of normal cardiac cell structure and necrosis. The administration of chitosan nanoparticles significantly reduced levels of CK-MB, LDH, MDA, and expression of Caspase-3. However, the levels of SOD, GPx, Nrf2, and expression of Bcl-2 increased as compared with the streptozotocin group (

p
<
0.05

). And also, chitosan nanoparticles inhibited cell necrosis in diabetic rats. This study suggests that the administration of chitosan nanoparticles can protect cardiac cell damage in diabetic rats through antioxidative stress by decreasing ROS and increasing Nrf2 expression, level of SOD, and GPx and through antiapoptosis by increasing expression of Bcl-2 and decreasing expression of Caspase-3.
Linking Genotype to Phenotype: Bench to Bedside
Authors: George Suzanne , Bertagnolli Monica M. 
Published: April 2022

Summary
Over the past three decades, researchers in the NCI-funded cancer cooperative groups have routinely incorporated a collection of biospecimens, quality-of-life assessments, diet and physical activity data, and other health outcome variables from clinical trial participants to provide an expanding resource for correlative science in cancer clinical research.
See related articles by Nixon et al., p. 2771 and 2779
Peripheral Blood Monocyte Abundance Predicts Outcomes in Patients with Breast Cancer
Authors: Axelrod Margaret L. , Wang Yu , Xu Yaomin , Sun Xiaopeng , Bejan Cosmin A. , Gonzalez-Ericsson Paula I. , Nunnery Sara , Bergman Riley E. , Donaldson Joshua , Guerrero-Zotano Angel L. , Massa Chiara , Seliger Barbara , Sanders Melinda , Mayer Ingrid A. , Balko Justin M. 
Published: April 2022

Biomarkers of response are needed in breast cancer to stratify patients to appropriate therapies and avoid unnecessary toxicity. We used peripheral blood gene expression and cell-type abundance to identify biomarkers of response and recurrence in neoadjuvant chemotherapy–treated patients with breast cancer. We identified a signature of IFN and complement response that was higher in the blood of patients with pathologic complete response. This signature was preferentially expressed by monocytes in single-cell RNA sequencing. Monocytes are routinely measured clinically, enabling examination of clinically measured monocytes in multiple independent cohorts. We found that peripheral monocytes were higher in patients with good outcomes in four cohorts of patients with breast cancer. Blood gene expression and cell type abundance biomarkers may be useful for prognostication in breast cancer.Significance:Biomarkers are needed in breast cancer to identify patients at risk for recurrence. Blood is an attractive site for biomarker identification due to the relative ease of longitudinal sampling. Our study suggests that blood-based gene expression and cell-type abundance biomarkers may have clinical utility in breast cancer.
Searching for Computer Vision North Stars
Authors: Fei-Fei Li , Krishna Ranjay 
Published: April 2022

Abstract
Computer vision is one of the most fundamental areas of artificial intelligence research. It has contributed to the tremendous progress in the recent deep learning revolution in AI. In this essay, we provide a perspective of the recent evolution of object recognition in computer vision, a flagship research topic that led to the breakthrough data set of ImageNet and its ensuing algorithm developments. We argue that much of this progress is rooted in the pursuit of research “north stars,” wherein researchers focus on critical problems of a scientific discipline that can galvanize major efforts and groundbreaking progress. Following the success of ImageNet and object recognition, we observe a number of exciting areas of research and a growing list of north star problems to tackle. This essay recounts the brief history of ImageNet, its related work, and the follow-up progress. The goal is to inspire more north star work to advance the field, and AI at large.
Human Language Understanding & Reasoning
Authors: Manning Christopher D. 
Published: April 2022

Abstract
The last decade has yielded dramatic and quite surprising breakthroughs in natural language processing through the use of simple artificial neural network computations, replicated on a very large scale and trained over exceedingly large amounts of data. The resulting pretrained language models, such as BERT and GPT-3, have provided a powerful universal language understanding and generation base, which can easily be adapted to many understanding, writing, and reasoning tasks. These models show the first inklings of a more general form of artificial intelligence, which may lead to powerful foundation models in domains of sensory experience beyond just language.
The Curious Case of Commonsense Intelligence
Authors: Choi Yejin 
Published: April 2022

Abstract
Commonsense intelligence is a long-standing puzzle in AI. Despite considerable advances in deep learning, AI continues to be narrow and brittle due to its lack of common sense. Why is common sense so trivial for humans but so hard for machines? In this essay, I map the twists and turns in recent research adventures toward commonsense AI. As we will see, the latest advances on common sense are riddled with new, potentially counterintuitive perspectives and questions. In particular, I discuss the significance of language for modeling intuitive reasoning, the fundamental limitations of logic formalisms despite their intellectual appeal, the case for on-the-fly generative reasoning through language, the continuum between knowledge and reasoning, and the blend between symbolic and neural knowledge representations.
Toward a Theory of Justice for Artificial Intelligence
Authors: Gabriel Iason 
Published: April 2022

Abstract
This essay explores the relationship between artificial intelligence and principles of distributive justice. Drawing upon the political philosophy of John Rawls, it holds that the basic structure of society should be understood as a composite of sociotechnical systems, and that the operation of these systems is increasingly shaped and influenced by AI. Consequently, egalitarian norms of justice apply to the technology when it is deployed in these contexts. These norms entail that the relevant AI systems must meet a certain standard of public justification, support citizens' rights, and promote substantively fair outcomes, something that requires particular attention to the impact they have on the worst-off members of society.
Artificial Intelligence, Humanistic Ethics
Authors: Tasioulas John 
Published: April 2022

Abstract
Ethics is concerned with what it is to live a flourishing life and what it is we morally owe to others. The optimizing mindset prevalent among computer scientists and economists, among other powerful actors, has led to an approach focused on maximizing the fulfilment of human preferences, an approach that has acquired considerable influence in the ethics of AI. But this preference-based utilitarianism is open to serious objections. This essay sketches an alternative, “humanistic” ethics for AI that is sensitive to aspects of human engagement with the ethical often missed by the dominant approach. Three elements of this humanistic approach are outlined: its commitment to a plurality of values, its stress on the importance of the procedures we adopt, not just the outcomes they yield, and the centrality it accords to individual and collective participation in our understanding of human well-being and morality. The essay concludes with thoughts on how the prospect of artificial general intelligence bears on this humanistic outlook.
Distrust of Artificial Intelligence: Sources & Responses from Computer
Science & Law
Authors: Dwork Cynthia , Minow Martha 
Published: April 2022

Abstract
Social distrust of AI stems in part from incomplete and faulty data sources, inappropriate redeployment of data, and frequently exposed errors that reflect and amplify existing social cleavages and failures, such as racial and gender biases. Other sources of distrust include the lack of “ground truth” against which to measure the results of learned algorithms, divergence of interests between those affected and those designing the tools, invasion of individual privacy, and the inapplicability of measures such as transparency and participation that build trust in other institutions. Needed steps to increase trust in AI systems include involvement of broader and diverse stakeholders in decisions around selection of uses, data, and predictors; investment in methods of recourse for errors and bias commensurate with the risks of errors and bias; and regulation prompting competition for trust.
Democracy & Distrust in an Era of Artificial Intelligence
Authors: Katyal Sonia K. 
Published: April 2022

Abstract
Our legal system has historically operated under the general view that courts should defer to the legislature. There is one significant exception to this view: cases in which it appears that the political process has failed to recognize the rights or interests of minorities. This basic approach provides much of the foundational justifications for the role of judicial review in protecting minorities from discrimination by the legislature. Today, the rise of AI decision-making poses a similar challenge to democracy's basic framework. As I argue in this essay, the rise of three trends-privatization, prediction, and automation in AI - have combined to pose similar risks to minorities. In this essay, I outline what a theory of judicial review would look like in an era of artificial intelligence, analyzing both the limitations and the possibilities of judicial review of AI. Here, I draw on cases in which AI decision-making has been challenged in courts, to show how concepts of due process and equal protection can be recuperated in a modern AI era, and even integrated into AI, to provide for better oversight and accountability.
Rethinking AI for Good Governance
Authors: Margetts Helen 
Published: April 2022

Abstract
This essay examines what AI can dofor government, specifically through three generic tools at the heart of governance: detection, prediction, and data-driven decision-making. Public sector functions, such as resource allocation and the protection of rights, are more normatively loaded than those of firms, and AI poses greater ethical challenges than earlier generations of digital technology, threatening transparency, fairness, and accountability. The essay discusses how AI might be developed specifically for government, with a public digital ethos to protect these values. Three moves that could maximize the transformative possibilities for a distinctively public sector AI are the development of government capacity to foster innovation through AI; the building of integrated and generalized models for policy-making; and the detection and tackling of structural inequalities. Combined, these developments could offer a model of data-intensive government that is more efficient, ethical, fair, prescient, and resilient than ever before in administrative history.
Luminance Contrast Drives Interactions between Perception and Working Memory
Authors: Kosilo Maciej , Martinovic Jasna , Haenschel Corinna 
Published: April 2022

AbstractVisual working memory (WM) enables the use of past sensory experience in guiding behavior. Yet, laboratory tasks commonly evaluate WM in a way that separates it from its sensory bottleneck. To understand how perception interacts with visual memory, we used a delayed shape recognition task to probe how WM may differ for stimuli that bias processing toward different visual pathways. Luminance compared with chromatic signals are more efficient in driving the processing of shapes and may thus also lead to better WM encoding, maintenance, and memory recognition. To evaluate this prediction, we conducted two experiments. In the first psychophysical experiment, we measured contrast thresholds for different WM loads. Luminance contrast was encoded into WM more efficiently than chromatic contrast, even when both sets of stimuli were equated for discriminability. In the second experiment, which also equated stimuli for discriminability, early sensory responses in the EEG that are specific to luminance pathways were modulated by WM load and thus likely reflect the neural substrate of the increased efficiency. Our results cannot be accounted for by simple saliency differences between luminance and color. Rather, they provide evidence for a direct connection between low-level perceptual mechanisms and WM by showing a crucial role of luminance for forming WM representations of shape.