bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Water Sound Synthesis using Coupled Bubbles
Authors: Xue Kangrui , Aronson Ryan M. , Wang Jui-Hsien , Langlois Timothy R. , James Doug L.
Published: July 2023

We introduce a practical framework for synthesizing bubble-based water sounds that captures the rich inter-bubble coupling effects responsible for low-frequency acoustic emissions from bubble clouds. We propose coupled-bubble oscillator models with regularized singularities, and techniques to reduce the computational cost of time stepping with dense, time-varying mass matrices. Airborne acoustic emissions are estimated using finite-difference time-domain (FDTD) methods. We propose a simple, analytical surface-acceleration model, and a sample-and-hold GPU wavesolver that is simple and faster than prior CPU wavesolvers.

Sound synthesis results are demonstrated using bubbly flows from incompressible, two-phase simulations, as well as procedurally generated examples using single-phase FLIP fluid animations. Our results demonstrate sound simulations with hundreds of thousands of bubbles, and perceptually significant frequency transformations with fuller low-frequency content.
An Extensible, Data-Oriented Architecture for High-Performance, Many-World Simulation
Authors: Shacklett Brennan , Rosenzweig Luc Guy , Xie Zhiqiang , Sarkar Bidipta , Szot Andrew , Wijmans Erik , Koltun Vladlen , Batra Dhruv , Fatahalian Kayvon
Published: July 2023

Training AI agents to perform complex tasks in simulated worlds requires millions to billions of steps of experience. To achieve high performance, today's fastest simulators for training AI agents adopt the idea of batch simulation: using a single simulation engine to simultaneously step many environments in parallel. We introduce a framework for productively authoring novel training environments (including custom logic for environment generation, environment time stepping, and generating agent observations and rewards) that execute as high-performance, GPU-accelerated batched simulators. Our key observation is that the entity-component-system (ECS) design pattern, popular for expressing CPU-side game logic today, is also well-suited for providing the structure needed for high-performance batched simulators. We contribute the first fully-GPU accelerated ECS implementation that natively supports batch environment simulation. We demonstrate how ECS abstractions impose structure on a training environment's logic and state that allows the system to efficiently manage state, amortize work, and identify GPU-friendly coherent parallel computations within and across different environments. We implement several learning environments in this framework, and demonstrate GPU speedups of two to three orders of magnitude over open source CPU baselines and 5-33× over strong baselines running on a 32-thread CPU. An implementation of the OpenAI hide and seek 3D environment written in our framework, which performs rigid body physics and ray tracing in each simulator step, achieves over 1.9 million environment steps per second on a single GPU.
In-Timestep Remeshing for Contacting Elastodynamics
Authors: Ferguson Zachary , Schneider Teseo , Kaufman Danny , Panozzo Daniele
Published: July 2023

We propose In-Timestep Remeshing, a fully coupled, adaptive meshing algorithm for contacting elastodynamics where remeshing steps are tightly integrated, implicitly, within the timestep solve. Our algorithm refines and coarsens the domain automatically by measuring physical energy changes within each ongoing timestep solve. This provides consistent, degree-of-freedom-efficient, productive remeshing that, by construction, is physics-aware and so avoids the errors, over-refinements, artifacts, per-example hand-tuning, and instabilities commonly encountered when remeshing with timestepping methods. Our in-timestep computation then ensures that each simulation step's output is both a converged stable solution on the updated mesh and a temporally consistent trajectory with respect to the model and solution of the last timestep. At the same time, the output is guaranteed safe (intersection- and inversion-free) across all operations. We demonstrate applications across a wide range of extreme stress tests with challenging contacts, sharp geometries, extreme compressions, large timesteps, and wide material stiffness ranges - all scenarios well-appreciated to challenge existing remeshing methods.
Virtual Mirrors: Non-Line-of-Sight Imaging Beyond the Third Bounce
Authors: Royo Diego , Sultan Talha , Muñoz Adolfo , Masumnia-Bisheh Khadijeh , Brandt Eric , Gutierrez Diego , Velten Andreas , Marco Julio
Published: July 2023

Non-line-of-sight (NLOS) imaging methods are capable of reconstructing complex scenes that are not visible to an observer using indirect illumination. However, they assume only third-bounce illumination, so they are currently limited to single-corner configurations, and present limited visibility when imaging surfaces at certain orientations. To reason about and tackle these limitations, we make the key observation that planar diffuse surfaces behave specularly at wavelengths used in the computational wave-based NLOS imaging domain. We call such surfaces
virtual mirrors.
We leverage this observation to expand the capabilities of NLOS imaging using illumination beyond the third bounce, addressing two problems: imaging single-corner objects at limited visibility angles, and imaging objects hidden behind two corners. To image objects at limited visibility angles, we first analyze the reflections of the known illuminated point on surfaces of the scene as an estimator of the position and orientation of objects with limited visibility. We then image those limited visibility objects by computationally building secondary apertures at other surfaces that observe the target object from a direct visibility perspective. Beyond single-corner NLOS imaging, we exploit the specular behavior of virtual mirrors to image objects hidden behind a second corner by imaging the space behind such virtual mirrors, where the mirror image of objects hidden around two corners is formed. No specular surfaces were involved in the making of this paper.
Skin-Screen: A Computational Fabrication Framework for Color Tattoos
Authors: Piovarci Michal , Chapiro Alexandre , Bickel Bernd
Published: July 2023

Tattoos are a highly popular medium, with both artistic and medical applications. Although the mechanical process of tattoo application has evolved historically, the results are reliant on the artisanal skill of the artist. This can be especially challenging for some skin tones, or in cases where artists lack experience. We provide the first systematic overview of tattooing as a computational fabrication technique. We built an automated tattooing rig and a recipe for the creation of silicone sheets mimicking realistic skin tones, which allowed us to create an accurate model predicting tattoo appearance. This enables several exciting applications including tattoo previewing, color retargeting, novel ink spectra optimization, color-accurate prosthetics, and more.
Revisiting controlled mixture sampling for rendering applications
Authors: Hua Qingqin , Grittmann Pascal , Slusallek Philipp
Published: July 2023

Monte Carlo rendering makes heavy use of mixture sampling and multiple importance sampling (MIS). Previous work has shown that control variates can be used to make such mixtures more efficient and more robust. However, the existing approaches failed to yield practical applications, chiefly because their underlying theory is based on the unrealistic assumption that a single mixture is optimized for a single integral. This is in stark contrast with rendering reality, where millions of integrals are computed---one per pixel---and each is infinitely recursive. We adapt and extend the theory introduced by previous work to tackle the challenges of real-world rendering applications. We achieve robust mixture sampling and (approximately) optimal MIS weighting for common applications such as light selection, BSDF sampling, and path guiding.
Min-Deviation-Flow in Bi-directed Graphs for T-Mesh Quantization
Authors: Heistermann Martin , Warnett Jethro , Bommes David
Published: July 2023

Subdividing non-conforming T-mesh layouts into conforming quadrangular meshes is a core component of state-of-the-art (re-)meshing methods. Typically, the required constrained assignment of integer lengths to T-Mesh edges is left to generic branch-and-cut solvers, greedy heuristics, or a combination of the two. This either does not scale well with input complexity or delivers suboptimal result quality. We introduce the
Minimum-Deviation-Flow Problem
in bi-directed networks (Bi-MDF) and demonstrate its use in modeling and efficiently solving a variety of T-Mesh quantization problems. We develop a fast approximate solver as well as an iterative refinement algorithm based on matching in graphs that solves Bi-MDF exactly. Compared to the state-of-the-art QuadWild [Pietroni et al. 2021] implementation on the authors'
300
dataset, our exact solver finishes after only 0.49% (total 17.06s) of their runtime (3491s) and achieves 11% lower energy while an approximation is computed after 0.09% (3.19s) of their runtime at the cost of 24% increased energy. A novel half-arc-based T-Mesh quantization formulation extends the feasible solution space to include previously unattainable quad meshes. The Bi-MDF problem is more general than our application in layout quantization, potentially enabling similar speedups for other optimization problems that fit into the scheme, such as quad mesh refinement.
Étendue Expansion in Holographic Near Eye Displays through Sparse Eye-box Generation Using Lens Array Eyepiece
Authors: Chae Minseok , Bang Kiseung , Yoo Dongheon , Jeong Yoonchan
Published: July 2023

In this paper, we present a novel method the étendue expansion of near-eye holographic displays through the generation of a sparse eye-box. Conventional holographic near-eye displays have suffered from narrow field of view or narrow eye-box due to the limited étendue supported by a spatial light modulator. We focus on the fact that these displays typically form a dense eye-box, which could be an excessive investment of the limited étendue. By rearranging the eye-box in a sparse manner, the practical étendue can be extended. With a properly designed sparse eye-box shape, it can provide the 3D holographic images and ensure continuous light entrance to the pupil. To create a sparse eye-box, we utilize a lens array as an eyepiece lens. We optimize the spatial light modulator's phase profile for the proposed system and analyze the impact of the use of the lens array eyepiece on the holographic image quality. In particular, we focus on the lens array specification of the lenslet pitch and the focal length, deriving feasible specifications based on our analysis. We experimentally demonstrate a near eye see-through display using the proposed system and verify the étendue expansion.
Generative Design of Sheet Metal Structures
Authors: Barda Amir , Tevet Guy , Schulz Adriana , Bermano Amit Haim
Published: July 2023

Sheet Metal (SM) fabrication is perhaps one of the most common metalworking technique.

Despite its prevalence, SM design is manual and costly, with rigorous practices that restrict the search space, yielding suboptimal results.

In contrast, we present a framework for the first automatic design of SM parts. Focusing on load bearing applications, our novel system generates a high-performing manufacturable SM that adheres to the numerous constraints that SM design entails:

The resulting part minimizes manufacturing costs while adhering to structural, spatial, and manufacturing constraints. In other words, the part should be strong enough, not disturb the environment, and adhere to the manufacturing process. These desiderata sum up to an elaborate, sparse, and expensive search space.

Our generative approach is a carefully designed exploration process, comprising two steps. In
Segment Discovery
connections from the input load to attachable regions are accumulated, and during
Segment Composition
the most performing valid combination is searched for.

For Discovery, we define a slim grammar, and sample it for parts using a Markov-Chain Monte Carlo (MCMC) approach, ran in intercommunicating instances (i.e, chains) for diversity. This, followed by a short continuous optimization, enables building a diverse and high-quality library of substructures. During Composition, a valid and minimal cost combination of the curated substructures is selected. To improve compliance significantly without additional manufacturing costs, we reinforce candidate parts onto themselves --- a unique SM capability called
self-riveting.
we provide our code and data in https://github.com/amir90/AutoSheetMetal.

We show our generative approach produces viable parts for numerous scenarios. We compare our system against a human expert and observe improvements in both part quality and design time. We further analyze our pipeline's steps with respect to resulting quality, and have fabricated some results for validation.

We hope our system will stretch the field of SM design, replacing costly expert hours with minutes of standard CPU, making this cheap and reliable manufacturing method accessible to anyone.
Temporal Set Inversion for Animated Implicits
Authors: Jazar Kavosh , Kry Paul G.
Published: July 2023

We exploit the temporal coherence of closed-form animated implicit surfaces by locally re-evaluating an octree-like discretization of the implicit field only as and where is necessary to rigorously maintain a global error invariant over time, thereby saving resources in static or slowly-evolving areas far from the motion where per-frame updates are not necessary. We treat implicit surface rendering as a special case of the continuous constraint satisfaction problem of set inversion, which seeks preimages of arbitrary sets under vector-valued functions. From this perspective, we formalize a temporally-coherent set inversion algorithm that localizes changes in the field by range-bounding its time derivatives using interval arithmetic. We implement our algorithm on the GPU using persistent thread scheduling and apply it to the scalar case of implicit surface and swept volume rendering where we achieve significant speedups in complex scenes with localized deformations like those found in games and modelling applications where interactivity is required and bounded-error approximation is acceptable.
DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots
Authors: Grandia Ruben , Farshidian Farbod , Knoop Espen , Schumacher Christian , Hutter Marco , Bächer Moritz
Published: July 2023

Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a
Differentiable Optimal Control
(DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots. Interfacing with either motion capture or animation data, we formulate retargeting objectives whose parameters make them agnostic to differences in proportions and numbers of degrees of freedom between input and robot. Optimizing these parameters over the manifold spanned by optimal state and control trajectories, we minimize the retargeting error. We demonstrate the utility and efficacy of our modeling by applying DOC to a
Model-Predictive Control
(MPC) formulation, showing retargeting results for a family of robots of varying proportions and mass distribution. With a hardware deployment, we further show that the retargeted motions are physically feasible, while MPC ensures that the robots retain their capability to react to unexpected disturbances.
Locally Meshable Frame Fields
Authors: Liu Heng , Bommes David
Published: July 2023

The main robustness issue of state-of-the-art frame field based hexahedral mesh generation algorithms originates from non-meshable topological configurations, which do not admit the construction of an integer-grid map but frequently occur in smooth frame fields. In this article, we investigate the topology of frame fields and derive conditions on their meshability, which are the basis for a novel algorithm to automatically turn a given non-meshable frame field into a similar but locally meshable one. Despite local meshability is only a necessary but not sufficient condition for the stronger requirement of meshability, our algorithm increases the 2% success rate of generating valid integer-grid maps with state-of-the-art methods to 58%, when compared on the challenging HexMe dataset [Beaufort et al. 2022]. The source code of our implementation and the data of our experiments are available at https://lib.algohex.eu.
Real-Time Radiance Fields for Single-Image Portrait View Synthesis
Authors: Trevithick Alex , Chan Matthew , Stengel Michael , Chan Eric , Liu Chao , Yu Zhiding , Khamis Sameh , Chandraker Manmohan , Ramamoorthi Ravi , Nagano Koki
Published: July 2023

We present a one-shot method to infer and render a photorealistic 3D representation from a single unposed image (e.g., face portrait) in real-time. Given a single RGB input, our image encoder directly predicts a canonical triplane representation of a neural radiance field for 3D-aware novel view synthesis via volume rendering. Our method is fast (24 fps) on consumer hardware, and produces higher quality results than strong GAN-inversion baselines that require test-time optimization. To train our triplane encoder pipeline, we use only synthetic data, showing how to distill the knowledge from a pretrained 3D GAN into a feedforward encoder. Technical contributions include a Vision Transformer-based triplane encoder, a camera data augmentation strategy, and a well-designed loss function for synthetic data training. We benchmark against the state-of-the-art methods, demonstrating significant improvements in robustness and image quality in challenging real-world settings. We showcase our results on portraits of faces (FFHQ) and cats (AFHQ), but our algorithm can also be applied in the future to other categories with a 3D-aware image generator.
Gut Microbial Intersections with Human Ecology and Evolution
Authors: Amato Katherine R. , Carmody Rachel N.
Published: July 2023

Although microbiome science is relatively young, our knowledge of human-microbiome interactions is growing rapidly and has already begun to transform our understanding of human ecology and evolution. Here we summarize our current understanding of three-way interactions between the gut microbiota, human ecology, and human evolution. We review the factors driving microbiome variation within and between individuals and populations, as well as comparative data from nonhuman primates that allow a more direct examination of microbial relationships with host ecology and evolution. Collectively, these data sets can help illuminate generalizable principles governing host-microbiome-environment interactions, the processes contributing to bidirectional influences between the human gut microbiota and the human ecological niche, and past changes in the human microbiome that may have harbored consequences for human adaptation. Developing richer insight into host-microbiome-environment interactions will ultimately broaden our view of human biology and its response to changing environments.
Environment, Epigenetics, and the Pace of Human Aging
Authors: Goldman Elisabeth A. , Sterner Kirstin N.
Published: July 2023

The trajectory of human aging varies widely from one individual to the next due to complex interactions between the genome and the environment that influence the aging process. Such differences in age-specific mortality and disease risk among same-aged individuals reflect variation in the pace of biological aging. Certain mechanisms involved in the progression of biological aging originate in the epigenome, where chemical modifications to the genome are able to alter gene expression without modifying the underlying DNA sequence. The epigenome serves as an interface for environmental signals, which are able to “get under the skin” to influence health and aging. A number of the molecular mechanisms involved in the aging process have been identified, although few aging phenotypes have been definitively traced to their underlying molecular causes thus far. In this review, we discuss variation in human biological aging and the epigenome's role in promoting heterogeneity in human longevity and healthspan.
Social Movements, Power, and Mediated Visibility
Authors: Bajoghli Narges
Published: July 2023

This article focuses on how the anthropological study of media—through an examination of its production, circulation, and consumption—elucidates issues of social organization, political economy, and alternative visions for political futures. By bringing together the studies of visual media, social movements, and hegemonic power by anthropologists and ethnographers of media since the turn of the twenty-first century, this review article provides a critical understanding of research about our current media environment, where scholarship within anthropology is heading in these domains, and what looking at these three fields together can mean for a more robust understanding of our political, social, and cultural futures.
The Effects of Lithium Ions and pH on the Function of Polyacrylic Acid Binder for Silicon Anodes
Authors: Sun Fei , Wheeler Dean R.
Published: July 2023

Binder plays a critical role in the performance of silicon anodes for lithium-ion batteries, specifically by connecting particles of active material and promoting adhesion to the current collector. Recent studies have differed on the relative cycle life of silicon anodes made from water-based polyacrylic acid (PAA) vs LiOH-PAA binders. Differences between the two may be due to the pH value or the extra Li+ in the binder, both of which change when LiOH is added to PAA. Here we investigate the impact of these two variables on the performance of silicon anodes. Regarding the effect of Li+, cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) results confirm our hypothesis that the extra Li+ facilitates ion transport. Regarding pH, we find that high pH in binders is detrimental to the electrode mechanical integrity, as observed in peeling tests and cross-sectional imaging. However, viscosity tests reveal that increased pH benefits the coating and mixing process. Our cycling results show that LiOH-PAA binder maintains greater cell capacity than does PAA, and further that LiOH-PAA at pH 4.5 leads to a cell with the highest capacity. Therefore, an intermediate pH is an optimal compromise between benefits observed for the low and high pH experiments.
1 mm2, 3.6 kV, 4.8 A NiO/Ga2O3 Heterojunction Rectifiers
Authors: Li Jian-Sian , Chiang Chao-Ching , Xia Xinyi , Wan Hsiao-Hsuan , Ren Fan , Pearton S.J.
Published: July 2023

Large area (1 mm2) vertical NiO/β n-Ga2O/n+ Ga2O3 heterojunction rectifiers are demonstrated with simultaneous high breakdown voltage and large conducting currents. The devices showed breakdown voltages (VB) of 3.6 kV for a drift layer doping of 8 × 1015 cm−3, with 4.8 A forward current. This performance is higher than the unipolar 1D limit for GaN, showing the promise of β-Ga2O3 for future generations of high-power rectification devices. The breakdown voltage was a strong function of drift region carrier concentration, with VB dropping to 1.76 kV for epi layer doping of 2 × 1016 cm−3. The power figure-of-merit, VB
2/RON, was 8.64 GW·cm−2, where RON is the on-state resistance (1.5 mΩ cm2). The on-off ratio switching from 12 to 0 V was 2.8 × 1013, while it was 2 × 1012 switching from 100 V. The turn-on voltage was 1.8 V. The reverse recovery time was 42 ns, with a reverse recovery current of 34 mA.
Impact of short-term, pharmacological CARM1 inhibition on skeletal muscle mass, function, and atrophy in mice
Authors: Webb Erin K. , Ng Sean Y. , Mikhail Andrew I. , Stouth Derek W. , vanLieshout Tiffany L. , Syroid Anika L. , Ljubicic Vladimir
Published: July 2023

Short-term pharmacological inhibition of coactivator-associated arginine methyltransferase 1 (CARM1) was effective at significantly reducing CARM1 methyltransferase function in skeletal muscle. CARM1 inhibition did not impact muscle mass, but exercise capacity was impaired, particularly in male mice, whereas morphological and molecular signatures of denervation-induced muscle atrophy were largely maintained in animals administered the inhibitor. Altogether, the role of CARM1 in neuromuscular biology remains complex and requires further investigation of its therapeutic potential in muscle-wasting conditions.
New frontiers in the measurement of energy metabolism
Authors: Niclou Alexandra , St-Martin Philippe , Redman Leanne M. , Bergouignan Audrey
Published: July 2023

This perspective highlights three key areas of current and future energy metabolism research: intergenerational health, climate change, and interplanetary exploration. We describe the recent advances in determining estimated energy requirements for a large subset of the general population using the gold standard method for free-living total daily energy expenditure estimates, the doubly labeled water method. The global rise in overweight and obesity demands particular attention to energy requirements in pregnancy and early life, as accumulating evidence contributes to our understanding of intergenerational health transmission and the potential for epigenetic programming in utero. We also acknowledge some gaps in necessary guidelines and understandings of energy requirements for underrepresented populations (i.e., individuals from low and middle-income countries) or those who undergo major physiological changes in new environment (e.g., astronauts). The rising prevalence of excess weight gain, together with climate change, cumulate into a global syndemic exposing vulnerable populations to both malnutrition and the effects of unpredictable and severe weather events, emphasizing the need for varied energetic data accounting for rapid physiological and socioeconomic changes. Finally, we relate how specific estimated energy requirements are needed to account for the energetic challenges specific to extended space travel and ensure the success of interplanetary exploration.
Laplacian reference is optimal for steady-state visual-evoked potentials
Authors: Zhang Yuan , Valsecchi Matteo , Gegenfurtner Karl R. , Chen Jing
Published: July 2023

The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis.
Dry Matter Yield and Nutritional Composition of Natural Pasture in East Gojjam Zone, Amhara Region
Authors: Desta Alemu Gashe , Ayele Shashie , Tiruneh Workinesh , Alemu Berhanu , Addis Mesganaw
Published: July 2023

Next to crop production, rearing livestock in Ethiopia is essential for the community’s food and nutrition security. However, a major obstacle to global livestock production, particularly in Ethiopia, is a shortage of high-quality and sufficient livestock feed. Designing development plans and intervention options for both improving natural pasture and rearing livestock would require an evaluation of the natural pasture yield and nutritional composition. As a result, the purpose of this study was to evaluate the nutrient content and dry matter yield of natural pasture in the study areas. The study area was divided into highland, midland, and lowland agroecologies, and from each agroecology, protected and communal grazing areas were selected randomly. The results showed that the average dry matter yield, crude protein (CP), and neutral detergent fiber (NDF) of natural pasture varied across agroecologies and between grazing sites. The average dry matter yields of natural pasture in highland, midland, and lowland agroecologies were about 1.60, 3.02, and 1.96 t/ha, respectively. The average dry matter yield in protected and communal grazing areas of natural pasture was about 2.70 and 1.69 t/ha, respectively. The crude protein content of the natural pasture was 7.12, 7.63, and 6.90% in the highlands, midlands, and lowlands, respectively. The crude protein content of the natural pasture in protected and communal grazing areas was 6.69 and 7.73%, respectively. In general, the dry matter yield and crude protein contents of natural pasture were low, which would have an effect on livestock productivity, and the overall NDF content of natural pasture was 65.9%, which is classified as low-quality feed and thus would have an impact on feed intake, digestibility, and livestock productivity.
Adaptive Threshold Residual Energy‐Based Efficient Sensor Network Protocol (ATREEN)
Authors: Mote Tushar S. , Jagtap S. K.
Published: July 2023

Wireless sensor network (WSN) hierarchical (clustering) routing involves the gathering of sensor nodes to form clusters in which sensor node communicates to cluster head (CH). The CH is responsible for data aggregation and forwarding collected data toward base station (BS). Energy expenditure in usual protocols becomes unequal because of clusters having diverse lengths. In this paper, we propose another new enhanced clustering protocol called adaptive threshold residual energy‐based efficient sensor network protocol (ATREEN) for adaptive selection of CH in WSNs. In ATREEN, CHs are chosen by comparing threshold of node value with respect to distance between cluster member and CH, residual energy of sensor nodes, and position of sensor node with respect to BS. Simulation results show that ATREEN protocol extends network lifetime and stability with decline in energy dissipation compared to clustering protocols such as low‐energy adaptive clustering hierarchy (LEACH), power‐efficient gathering in sensor information systems (PEGASIS), and distributed energy‐efficient clustering (DEEC) scheme for heterogeneous WSNs.
An Explainable Probabilistic Model for Health Monitoring of Concrete Dam via Optimized Sparse Bayesian Learning and Sensitivity Analysis
Authors: Lin Chaoning , Chen Siyu , Hariri-Ardebili Mohammad Amin , Li Tongchun
Published: July 2023

Machine learning has become increasingly popular for modeling dam behavior due to its ability to capture complex relationships between input parameters and dam behavior responses. However, the use of sophisticated machine learning methods for monitoring dam behaviors and making decisions is often hindered by model uncertainty and a lack of interpretability. This paper introduces a novel model for dam health monitoring, focused on monitoring radial displacement and seepage, using optimized sparse Bayesian learning and sensitivity analysis. The model hyperparameters are optimized using an intelligent optimization method integrating the multi-population Rao algorithm and blocked cross-validation, while sensitivity analysis is employed to calculate the relative importance of input variables for a better understanding of the dam’s state. The effectiveness of the proposed model is verified by using long-term monitoring data of a prototype concrete arch dam. The results confirm that the proposed model provides satisfactory performance on both the point predictions and the interval predictions for dam structural behaviors while obtaining effective explainability.
Applications of Artificial Intelligence in Nursing Care: A Systematic Review
Authors: Martinez-Ortigosa Adrian , Martinez-Granados Alejandro , Gil-Hernández Esther , Rodriguez-Arrastia Miguel , Ropero-Padilla Carmen , Roman Pablo
Published: July 2023

Aim. To synthesise the available evidence on the applicability of artificial intelligence in nursing care. Background. Artificial intelligence involves the replication of human cognitive abilities in machines, allowing to perform tasks that conventionally necessitate human cognition. However, its application in health sciences is a recent one, and its use is currently limited to supporting the diagnosis and prognosis of hospitalised patients, among others. Evaluation. A systematic review was conducted in the PubMed-Medline, Scopus, CINAHL, Web of Science, and Nursing & Allied Health databases until September 2022, following the PRISMA guidelines. Key Issues. A total of 21 articles were selected for the review. The different applications of artificial intelligence in nursing identified comprised (i) advances in early disease detection and clinical decision making; (ii) artificial intelligence-based support systems in nursing for patient monitoring and workflow optimisation; and (iii) artificial intelligence insights for nursing training and education. Conclusion. Artificial intelligence-based systems demonstrated increased autonomy of patients and professionals in care processes such as wound management through guided instructions, improved workflows, and efficiency in terms of time, materials, and human resources. Implications for Nursing Management. Artificial intelligence applied to nursing practice can be a very useful resource for professionals, managers, and supervisors. It has the potential to change current working flow systems and may serve as a down-to-earth resource to support nursing professionals in their decision-making process that ensures high quality and patient safety care.
Blind Image Quality Assessment via Multiperspective Consistency
Authors: Guo Ning , Qingge Letu , Huang YuanChen , Roy Kaushik , Li YangGui , Yang Pei
Published: July 2023

Blind image quality assessment (BIQA) has made significant progress, but it remains a challenging problem due to the wide variation in image content and the diverse nature of distortions. To address these challenges and improve the adaptability of BIQA algorithms to different image contents and distortions, we propose a novel model that incorporates multiperspective consistency. Our approach introduces a multiperspective strategy to extract features from various viewpoints, enabling us to capture more beneficial cues from the image content. To map the extracted features to a scalar score, we employ a content‐aware hypernetwork architecture. Additionally, we integrate all perspectives by introducing a consistency supervision strategy, which leverages cues from each perspective and enforces a learning consistency constraint between them. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments on five representative datasets. The results demonstrate that our method outperforms state‐of‐the‐art techniques on both authentic and synthetic distortion image databases. Furthermore, our approach exhibits excellent generalization ability. The source code is publicly available at https://github.com/gn-share/multi-perspective.
High-Order Fiber Bragg Grating Corrosion Sensor Based on the Detection of a Local Surface Expansion
Authors: Budinski Vedran , Njegovec Matej , Pevec Simon , Macuh Boris , Donlagic Denis
Published: July 2023

This paper presents a fiber optic sensor for the detection of corrosion processes that occur on metal surfaces. In the proposed sensor design, a sensing fiber with an inscribed high-order Bragg grating is attached to the observed metal surface. A broadening of high-order Bragg grating spectral characteristics is observed and used to detect small and highly localized longitudinal strain variations that occur along the fiber as a result of corrosive rust flakes’ formations beneath the sensing fiber. The proposed approach provides a straightforward fabrication method, the possibility for unobtrusive mounting, including mounting of the sensor beneath the corrosion-protective layers, and consistent corrosion detection yields.
Supplementary Table 2 from Hospitalizations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: A Longitudinal Matched Cohort Study
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: July 2023

Multivariable adjusted hazard ratios for all-cause and cause-specific hospitalisations, major cancer diagnostic group.
Data from Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility
Authors: Gregga Isabelle , Pharoah Paul D. P. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: July 2023

AbstractBackground:
Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility.
Methods:
We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis.
Results:
Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein–cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein–cancer associations (FDR < 0.05). Ten of 15 protein–cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62).
Conclusions:
We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect.
Impact:
PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.
Data from Hospitalizations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: A Longitudinal Matched Cohort Study
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: July 2023

AbstractBackground:
The long-term effects of childhood cancer are unclear in the Australian context. We examined hospitalization trends for physical diseases and estimated the associated inpatient care costs in all 5-year childhood cancer survivors (CCS) diagnosed in Western Australia (WA) from 1982 to 2014.
Methods:
Hospitalization records for 2,938 CCS and 24,792 comparisons were extracted from 1987 to 2019 (median follow-up = 12 years, min = 1, max = 32). The adjusted hazard ratio (aHR) of hospitalization with 95% confidence intervals (CI) was estimated using the Andersen–Gill model for recurrent events. The cumulative burden of hospitalizations over time was assessed using the mean cumulative count method. The adjusted mean cost of hospitalization was estimated using the generalized linear models.
Results:
We identified a higher risk of hospitalization for all-cause (aHR, 2.0; 95% CI, 1.8–2.2) physical disease in CCS than comparisons, with the highest risk for subsequent malignant neoplasms (aHR, 15.0; 95% CI, 11.3–19.8) and blood diseases (aHR, 6.9; 95% CI, 2.6–18.2). Characteristics associated with higher hospitalization rates included female gender, diagnosis with bone tumors, cancer diagnosis age between 5 and 9 years, multiple childhood cancer diagnoses, multiple comorbidities, higher deprivation, increased remoteness, and Indigenous status. The difference in the mean total hospitalization costs for any disease was significantly higher in survivors than comparisons (publicly funded $11,483 United States Dollar, P < 0.05).
Conclusions:
The CCS population faces a significantly higher risk of physical morbidity and higher cost of hospital-based care than the comparisons.
Impact:
Our study highlights the need for long-term follow-up healthcare services to prevent disease progression and mitigate the burden of physical morbidity on CCS and hospital services.
Table S6 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Dosing and schedule of abemaciclib and LY3214996
Table S5 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Statistics from DESeq2
Table S3 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Pharmacokinetic properties of LY3214996 and abemaciclib
Figure S9 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Kaplan Meier Survival Curve and Tolerability
Figure S7 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

DNSC Colony Forming Assay Matrix, ipNF05.5 Western Blots, and ipNF95.6 Western Blots
Figure S13 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Etv4, Dusp6, Phlda1, and Dusp4 mRNA expression
Figure S12 from Combined CDK4/6 and ERK1/2 Inhibition Enhances Antitumor Activity in NF1-Associated Plexiform Neurofibroma
Authors: Flint Alyssa C. , Mitchell Dana K. , Angus Steven P. , Smith Abbi E. , Bessler Waylan , Jiang Li , Mang Henry , Li Xiaohong , Lu Qingbo , Rodriguez Brooke , Sandusky George E. , Masters Andi R. , Zhang Chi , Dang Pengtao , Koenig Jenna , Johnson Gary L. , Shen Weihua , Liu Jiangang , Aggarwal Amit , Donoho Gregory P. , Willard Melinda D. , Bhagwat Shripad V. , Clapp D. Wade , Rhodes Steven D.
Published: July 2023

Ki67 Mouse IHC Analysis
Supplementary Table S2 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Table S2. Results of single-sample gene set enrichment analysis (ssGSEA) of transcriptomic profiles from HCC-1428 and HCC-1428/LTED cells.
Supplementary Figure S8 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Figure S8- Individual tumor volumes from mice treated ± E2.
Supplementary Figure S7 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Figure S7- Single-channel fluorescence images of tumors from patients treated with E2.
Supplementary Figure S4 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Figure S4- ER overexpression converts E2 from a growth promoter to a growth suppressor.
Supplementary Figure S14 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Figure S14- Individual growth curves of tumors from mice treated ± E2 and/or olaparib.
Supplementary Figure S13 from Estrogen Therapy Induces Receptor-Dependent DNA Damage Enhanced by PARP Inhibition in ER+ Breast Cancer
Authors: Traphagen Nicole A. , Schwartz Gary N. , Tau Steven , Roberts Alyssa M. , Jiang Amanda , Hosford Sarah R. , Marotti Jonathan D. , Goen Abigail E. , Romo Bianca A. , Johnson Anneka L. , Duffy Emily-Claire K. , Demidenko Eugene , Heverly Paul , Mosesson Yaron , Soucy Shannon M. , Kolling Fred , Miller Todd W.
Published: July 2023

Supplementary Figure S13- E2 and olaparib combine to increase R-loop formation.