bluuebunny/crossref_metadata_2025 huggingface dataset:
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers
Authors: Wu Ronghuan , Su Wanchao , Ma Kede , Liao Jing
Published: December 2023

Scalable Vector Graphics (SVG) is a popular vector image format that offers good support for interactivity and animation. Despite its appealing characteristics, creating custom SVG content can be challenging for users due to the steep learning curve required to understand SVG grammars or get familiar with professional editing software. Recent advancements in text-to-image generation have inspired researchers to explore vector graphics synthesis using either image-based methods (i.e., text → raster image → vector graphics) combining text-to-image generation models with image vectorization, or language-based methods (i.e., text → vector graphics script) through pretrained large language models. Nevertheless, these methods suffer from limitations in terms of generation quality, diversity, and flexibility. In this paper, we introduce IconShop, a text-guided vector icon synthesis method using autoregressive transformers. The key to success of our approach is to sequentialize and tokenize SVG paths (and textual descriptions as guidance) into a uniquely decodable token sequence. With that, we are able to exploit the sequence learning power of autoregressive transformers, while enabling both unconditional and text-conditioned icon synthesis. Through standard training to predict the next token on a large-scale vector icon dataset accompanied by textural descriptions, the proposed IconShop consistently exhibits better icon synthesis capability than existing image-based and language-based methods both quantitatively (using the FID and CLIP scores) and qualitatively (through formal subjective user studies). Meanwhile, we observe a dramatic improvement in generation diversity, which is validated by the objective Uniqueness and Novelty measures. More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.
An Adaptive Fast-Multipole-Accelerated Hybrid Boundary Integral Equation Method for Accurate Diffusion Curves
Authors: Bang Seungbae , Serkh Kirill , Stein Oded , Jacobson Alec
Published: December 2023

In theory, diffusion curves promise complex color gradations for infinite-resolution vector graphics. In practice, existing realizations suffer from poor scaling, discretization artifacts, or insufficient support for rich boundary conditions. Previous applications of the boundary element method to diffusion curves have relied on polygonal approximations, which either forfeit the high-order smoothness of Bézier curves, or, when the polygonal approximation is extremely detailed, result in large and costly systems of equations that must be solved. In this paper, we utilize the boundary integral equation method to accurately and efficiently solve the underlying partial differential equation. Given a desired resolution and viewport, we then interpolate this solution and use the boundary element method to render it. We couple this hybrid approach with the fast multipole method on a non-uniform quadtree for efficient computation. Furthermore, we introduce an adaptive strategy to enable truly scalable infinite-resolution diffusion curves.
ClothCombo: Modeling Inter-Cloth Interaction for Draping Multi-Layered Clothes
Authors: Lee Dohae , Kang Hyun , Lee In-Kwon
Published: December 2023

We present ClothCombo, a pipeline to drape arbitrary combinations of clothes on 3D human models with varying body shapes and poses. While existing learning-based approaches for draping clothes have shown promising results, multi-layered clothing remains challenging as it is non-trivial to model inter-cloth interaction. To this end, our method utilizes a GNN-based network to efficiently model the interaction between clothes in different layers, thus enabling multi-layered clothing. Specifically, we first create feature embedding for each cloth using a topology-agnostic network. Then, the draping network deforms all clothes to fit the target body shape and pose without considering inter-cloth interaction. Lastly, the untangling network predicts the per-vertex displacements in a way that resolves interpenetration between clothes. In experiments, the proposed model demonstrates strong performance in complex multi-layered scenarios. Being agnostic to cloth topology, our method can be readily used for layered virtual try-on of real clothes in diverse poses and combinations of clothes.
Discontinuity-Aware 2D Neural Fields
Authors: Belhe Yash , Gharbi Michaël , Fisher Matthew , Georgiev Iliyan , Ramamoorthi Ravi , Li Tzu-Mao
Published: December 2023

Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadratic, or cubic Bézier curves, and construct a feature field that is discontinuous across these locations and smooth everywhere else. Finally, we use a shallow multi-layer perceptron to decode the features into the signal value. To construct the feature field, we develop a new data structure based on a curved triangular mesh, with features stored on the vertices and on a subset of the edges that are marked as discontinuous. We show that our method can be used to compress a 100, 000
2
-pixel rendered image into a 25MB file; can be used as a new diffusion-curve solver by combining with Monte-Carlo-based methods or directly supervised by the diffusion-curve energy; or can be used for compressing 2D physics simulation data.
Collapsing Embedded Cell Complexes for Safer Hexahedral Meshing
Authors: Brückler Hendrik , Campen Marcel
Published: December 2023

We present a set of operators to perform modifications, in particular collapses and splits, in volumetric cell complexes which are discretely embedded in a background mesh. Topological integrity and geometric embedding validity are carefully maintained. We apply these operators strategically to volumetric block decompositions, so-called T-meshes or base complexes, in the context of hexahedral mesh generation. This allows circumventing the expensive and unreliable global volumetric remapping step in the versatile meshing pipeline based on 3D integer-grid maps. In essence, we reduce this step to simpler local cube mapping problems, for which reliable solutions are available. As a consequence, the robustness of the mesh generation process is increased, especially when targeting coarse or block-structured hexahedral meshes. We furthermore extend this pipeline to support feature alignment constraints, and systematically respect these throughout, enabling the generation of meshes that align to points, curves, and surfaces of special interest, whether on the boundary or in the interior of the domain.
Shrink & Morph: 3D-Printed Self-Shaping Shells Actuated by a Shape Memory Effect
Authors: Jourdan David , Hugron Pierre-Alexandre , Schreck Camille , Martínez Jonàs , Lefebvre Sylvain
Published: December 2023

While 3D printing enables the customization and home fabrication of a wide range of shapes, fabricating freeform thin-shells remains challenging. As layers misalign with the curvature, they incur structural deficiencies, while the curved shells require large support structures, typically using more material than the part itself.

We present a computational framework for optimizing the internal structure of 3D printed plates such that they morph into a desired freeform shell when heated. This exploits the shrinkage effect of thermoplastics such as PLA, which store internal stresses along the deposition directions. These stresses get released when the material is heated again above its glass transition temperature, causing an anisotropic deformation that induces curvature.

Our inverse design method takes as input a freeform surface and finds an optimized set of deposition trajectories in each layer such that their anisotropic shrinkage deforms the plate into the prescribed surface geometry. We optimize for a continuous vector field that varies across the plate and within its thickness. The algorithm then extracts a set of deposition trajectories from the vector field in order to fabricate the flat plates on standard FFF printers. We validate our algorithm on freeform, doubly-curved surfaces.
ReShader: View-Dependent Highlights for Single Image View-Synthesis
Authors: Paliwal Avinash , Nguyen Brandon G. , Tsarov Andrii , Kalantari Nima Khademi
Published: December 2023

In recent years, novel view synthesis from a single image has seen significant progress thanks to the rapid advancements in 3D scene representation and image inpainting techniques. While the current approaches are able to synthesize geometrically consistent novel views, they often do not handle the view-dependent effects properly. Specifically, the highlights in their synthesized images usually appear to be glued to the surfaces, making the novel views unrealistic. To address this major problem, we make a key observation that the process of synthesizing novel views requires changing the shading of the pixels based on the novel camera, and moving them to appropriate locations. Therefore, we propose to split the view synthesis process into two independent tasks of pixel reshading and relocation. During the reshading process, we take the single image as the input and adjust its shading based on the novel camera. This reshaded image is then used as the input to an existing view synthesis method to relocate the pixels and produce the final novel view image. We propose to use a neural network to perform reshading and generate a large set of synthetic input-reshaded pairs to train our network. We demonstrate that our approach produces plausible novel view images with realistic moving highlights on a variety of real world scenes.
FLARE: Fast Learning of Animatable and Relightable Mesh Avatars
Authors: Bharadwaj Shrisha , Zheng Yufeng , Hilliges Otmar , Black Michael J. , Abrevaya Victoria Fernandez
Published: December 2023

Our goal is to efficiently learn personalized animatable 3D head avatars from videos that are geometrically accurate, realistic, relightable, and compatible with current rendering systems. While 3D meshes enable efficient processing and are highly portable, they lack realism in terms of shape and appearance. Neural representations, on the other hand, are realistic but lack compatibility and are slow to train and render. Our key insight is that it is possible to efficiently learn high-fidelity 3D mesh representations via differentiable rendering by exploiting highly-optimized methods from traditional computer graphics and approximating some of the components with neural networks. To that end, we introduce FLARE, a technique that enables the creation of animatable and relightable mesh avatars from a single monocular video. First, we learn a canonical geometry using a mesh representation, enabling efficient differentiable rasterization and straightforward animation via learned blendshapes and linear blend skinning weights. Second, we follow physically-based rendering and factor observed colors into intrinsic albedo, roughness, and a neural representation of the illumination, allowing the learned avatars to be relit in novel scenes. Since our input videos are captured on a single device with a narrow field of view, modeling the surrounding environment light is non-trivial. Based on the split-sum approximation for modeling specular reflections, we address this by approximating the prefiltered environment map with a multi-layer perceptron (MLP) modulated by the surface roughness, eliminating the need to explicitly model the light. We demonstrate that our mesh-based avatar formulation, combined with learned deformation, material, and lighting MLPs, produces avatars with high-quality geometry and appearance, while also being efficient to train and render compared to existing approaches.
Optimal Design of Robotic Character Kinematics
Authors: Maloisel Guirec , Schumacher Christian , Knoop Espen , Grandia Ruben , Bächer Moritz
Published: December 2023

The kinematic motion of a robotic character is defined by its mechanical joints and actuators that restrict the relative motion of its rigid components. Designing robots that perform a given target motion as closely as possible with a fixed number of actuated degrees of freedom is challenging, especially for robots that form kinematic loops. In this paper, we propose a technique that simultaneously solves for optimal design and control parameters for a robotic character whose design is parameterized with configurable joints. At the technical core of our technique is an efficient solution strategy that uses dynamic programming to solve for optimal state, control, and design parameters, together with a strategy to remove redundant constraints that commonly exist in general robot assemblies with kinematic loops. We demonstrate the efficacy of our approach by either editing the design of an existing robotic character, or by optimizing the design of a new character to perform a desired motion.
Efficient Cone Singularity Construction for Conformal Parameterizations
Authors: Li Mo , Fang Qing , Zhang Zheng , Liu Ligang , Fu Xiao-Ming
Published: December 2023

We propose an efficient method to construct sparse cone singularities under distortion-bounded constraints for conformal parameterizations. Central to our algorithm is using the technique of shape derivatives to move cones for distortion reduction without changing the number of cones. In particular, the supernodal sparse Cholesky update significantly accelerates this movement process. To satisfy the distortion-bounded constraint, we alternately move cones and add cones. The capability and feasibility of our approach are demonstrated over a data set containing 3885 models. Compared with the state-of-the-art method, we achieve an average acceleration of 15 times and slightly fewer cones for the same amount of distortion.
A Hardware Design Framework for Computer Vision Models Based on Reconfigurable Devices
Authors: Fan Zimeng , Hu Wei , Liu Fang , Xu Dian , Guo Hong , He Yanxiang , Peng Min
Published: December 2023

In computer vision, the joint development of the algorithm and computing dimensions cannot be separated. Models and algorithms are constantly evolving, while hardware designs must adapt to new or updated algorithms. Reconfigurable devices are recognized as important platforms for computer vision applications because of their reconfigurability. There are two typical design approaches: customized and overlay design. However, existing work is unable to achieve both efficient performance and scalability to adapt to a wide range of models. To address both considerations, we propose a design framework based on reconfigurable devices to provide unified support for computer vision models. It provides software-programmable modules while leaving unit design space for problem-specific algorithms. Based on the proposed framework, we design a model mapping method and a hardware architecture with two processor arrays to enable dynamic and static reconfiguration, thereby relieving redesign pressure. In addition, resource consumption and efficiency can be balanced by adjusting the hyperparameter. In experiments on CNN, vision Transformer, and vision MLP models, our work’s throughput is improved by 18.8x–33.6x and 1.4x–2.0x compared to CPU and GPU. Compared to others on the same platform, accelerators based on our framework can better balance resource consumption and efficiency.
Promptable Game Models: Text-guided Game Simulation via Masked Diffusion Models
Authors: Menapace Willi , Siarohin Aliaksandr , Lathuilière Stéphane , Achlioptas Panos , Golyanik Vladislav , Tulyakov Sergey , Ricci Elisa
Published: December 2023

Neural video game simulators emerged as powerful tools to generate and edit videos. Their idea is to represent games as the evolution of an environment’s state driven by the actions of its agents. While such a paradigm enables users toplaya game action-by-action, its rigidity precludes more semantic forms of control. To overcome this limitation, we augment game models withpromptsspecified as a set ofnatural languageactions anddesired states. The result—a Promptable Game Model (PGM)—makes it possible for a user toplaythe game by prompting it with high- and low-level action sequences. Most captivatingly, our PGM unlocks thedirector’s mode, where the game is played by specifying goals for the agents in the form of a prompt. This requires learning “game AI,” encapsulated by our animation model, to navigate the scene using high-level constraints, play against an adversary, and devise a strategy to win a point. To render the resulting state, we use a compositional NeRF representation encapsulated in our synthesis model. To foster future research, we present newly collected, annotated and calibrated Tennis and Minecraft datasets. Our method significantly outperforms existing neural video game simulators in terms of rendering quality and unlocks applications beyond the capabilities of the current state-of-the-art. Our framework, data, and models are available at snap-research.github.io/promptable-game-models.
Compositional Verification of First-Order Masking Countermeasures against Power Side-Channel Attacks
Authors: Gao Pengfei , Song Fu , Chen Taolue
Published: December 2023

Power side-channel attacks allow an adversary to efficiently and effectively steal secret information (e.g., keys) by exploiting the correlation between secret data and runtime power consumption, hence posing a serious threat to software security, particularly cryptographic implementations. Masking is a commonly used countermeasure against such attacks, which breaks the statistical dependence between secret data and side-channel leaks via randomization. In a nutshell, a variable is represented by a vector of shares armed with random variables, called
masking encoding
, on which cryptographic computations are performed. While compositional verification for the security of masked cryptographic implementations has received much attention because of its high efficiency, existing compositional approaches either use implicitly fixed pre-conditions that may not be fulfilled by state-of-the-art efficient implementations, or require user-provided hard-coded pre-conditions that are time consuming and highly non-trivial, even for an expert. In this article, we tackle the compositional verification problem of first-order masking countermeasures, where first-order means that the adversary is allowed to access only one intermediate computation result. Following the literature, we consider countermeasures given as gadgets, which are special procedures whose inputs are masking encodings of variables. We introduce a new security notion parameterized by an explicit pre-condition for each gadget, as well as composition rules for reasoning about masking countermeasures against power side-channel attacks. We propose accompanying efficient algorithms to automatically infer proper pre-conditions, based on which our new compositional approach can efficiently and automatically prove security for masked implementations. We implement our approaches as a tool
MaskCV
and conduct experiments on publicly available masked cryptographic implementations including 10 different full AES implementations. The experimental results confirm the effectiveness and efficiency of our approach.
Consistently Tuned Battery Lifetime Predictive Model of Capacity Loss, Resistance Increase, and Irreversible Thickness Growth
Authors: Pannala Sravan , Movahedi Hamidreza , Garrick Taylor R. , Stefanopoulou Anna G. , Siegel Jason B.
Published: December 2023

Predicting changes in cell resistance and thickness as the battery ages can be as important as capacity fade in informing vehicle resale value, pack replacement schedules, and viability for repurposing before eventual recycling. Three well-known degradation mechanisms, namely SEI growth, lithium plating, and electrode particle fracture due to reversible expansion and contraction, are revisited and tuned using capacity fade data from cells experiencing plating and having significant loss of anode active material as the dominant degradation mechanism. Therefore, lifetime prediction achieved in this work cannot rely on SEI degradation that can be tuned mostly with calendar aging data. More importantly, our model uses capacity-predictive irreversible SEI growth, net growth in plating, and accumulation of particle fracture as the cells cycle to predict the irreversible increases in cell thickness through a single set of tuned parameters. Last but not least, the capacity-predictive degradation can also predict the increased resistance through another single set of parameters. These two sets of scaling parameters achieve for the first time a consistently tuned model of degradation mechanisms for lifetime prediction of changes in battery capacity, resistance, and thickness as the battery ages. The model prediction has been validated in conditions not used for parameter tuning.
Electrochemical Performance of Mixed Redox-Active Organic Molecules in Redox Flow Batteries
Authors: Amini Kiana , Jing Yan , Gao Jinxu , Sosa Jordan D. , Gordon Roy G. , Aziz Michael J.
Published: December 2023

Designing electrolytes based on mixture of different organic redox active molecules brings the opportunity of enhancing the volumetric energy density of flow batteries and removes the requirement of high solubility for individual organic species in the mixture. In the present work, we conduct computational and experimental analysis to investigate the electrochemical performance of mixed redox-active organic molecules. A zero-dimensional transient model is employed to investigate the changes in the half-cell potential and the concentrations and partial currents of individual redox reactions in a mixture of organic molecules over time. The model demonstrates the effects of individual properties of species such as kinetic rate constants, mass transfer coefficients, concentration ratios and standard redox potentials and reports the effect of energy-losing homogenous chemical redox reaction on the voltage efficiency and concentration ratios of the mixed species. Pairs of anthraquinone negolyte species were selected for an experimental case study. A mixture of 2,6-N-TSAQ and 2,6-DHAQ showed 40% increase in the volumetric energy density compared to the performance of 2,6-DHAQ alone. Based on the results of the experimental and computational analysis, we propose guidelines for the design of suitable mixed redox-active organic species.
Understanding the Effect of Applied Current and Potential on Pitting in 316L Stainless Steels
Authors: Vanegas Mayra , Wang Ke , Iannuzzi Mariano
Published: December 2023

The pit morphologies of Type 316 L stainless steel were evaluated with “sandwiched” foil electrodes to identify pit growth characteristics under galvanostatic and potentiostatic polarization through ex-situ and in-situ tests. The applied current and potential values influenced the pit morphologies and lacy cover formation. A direct correlation was found between the type of lacy cover and the observed pit morphologies. The faster pit depth growth occurred with only one opening on the lacy cover due to “pit dissolution induced convection,” while the shorter mass transfer path led to faster lateral pit growth with a perforated lacy cover or open pit.
Voltammetric Analysis of Thiram with Bimetallic Nanosensor and Investigation of Adsorption Mechanism by DFT-D3 Method
Authors: Celik Murat , Kanbes-Dindar Cigdem , Karayel Arzu , Bozal-Palabiyik Burcin , Uslu Bengi
Published: December 2023

In this study, to detect thiram electrochemically, a simple nanosensor based on a glassy carbon electrode (GCE) modified with green-synthesized Ag and Au nanoparticles was designed. AuNPs@AgNPs/GCE nanosensor gave considerably greater signal for 5 ppm thiram in pH 3.0 phosphate buffer solution using adsorptive stripping differential pulse voltammetry compared to bare GCE. Under ideal conditions, the nanosensor produced a linear concentration calibration curve extending from 0.2 to 1.4 ppm for thiram, with limits of detection and quantification of 0.033 ppm and 0.100 ppm, respectively. The developed electrochemical bimetallic nanosensor demonstrated high sensitivity and stability, showing that it is a novel and promising platform for thiram determination. Furthermore, the nanosensor was used to assess thiram in human serum and recovery was obtained as 103.6%. DFT-D3 results showed that covalent modification of GCE with AuNPs/AgNPs occurs with the electron transfer between the electrode surface and thiram by bonding sulfur atoms of thiram to AgNPs and AuNPs. Both experimental and theoretical analyses showed that the modification with Ag and Au, GCE appeared to be a key property to improve the electronic activity at the surface and increase the charge transfer that facilitates the adsorption of the selected guest.
Evaluation of Anode Water Electrolyzed with Anion Exchange Membrane for Cleaning EUV Semiconductor
Authors: Kim Hyungwon , Lim Sungmin , Ryoo Kunkul , Choi Insik , Choi Byungsun , Kim Jaeyoung , Jung Younwon
Published: December 2023

Electrically nonconducting UPW was electrolyzed without electrolyte through anion exchange membrane for evaluating applicability to EUV semiconductor cleaning. Produced anode water held positive ORP up to 900 mV, which is very oxidative. ORP, pH, and conductivity measurements showed delicately complementary each other for understanding anode water. Correlation of concurrent ORP decrease and conductivity increase in ultra-pure anode water domain was observed first time. The oxidative OH° was formed as the major species in anode water, causing positive ORP during ORP measurement. H+ and OH− ions, and OH° radical coexisted in anode water at amphoteric nonequilibrium, while pH was less than 6. It was concluded that OH°, as a strong oxidant, transformed itself to OH− by ORP measurement. OH° radical would oxidize selectively and then remove nano-contaminants. Anode water is considered to fulfill the requirement of EUV semiconductor cleaning where no oxygen species should be required because of likely oxide layer formation during cleaning, and it will even remove the native oxide developed unintentionally before cleaning.
Lack of a transcriptional response of primary bronchial epithelial cells from patients with asthma and controls to IL-33
Authors: Saikumar Jayalatha Akshaya Keerthi , Jonker Marnix R. , Carpaij Orestes A. , van den Berge Maarten , Affleck Karen X. , Koppelman Gerard H. , Nawijn Martijn C.
Published: December 2023

The IL-33/IL-1RL1 pathway stands as a formidable genetic predisposition for asthma, with ongoing clinical developments of various drugs designed to mitigate its influence in patients with asthma. The absence of a transcriptomic reaction to IL-33 within the bronchial epithelium holds significance in the pursuit of identifying biomarkers that can aid in pinpointing those individuals who would derive the greatest benefit from therapies targeting the IL-33 pathway.
Machining Characteristics Investigations of DSS‐2205 Using RSM–ANN and Gray Relational Analysis
Authors: Gutema Endalkachew Mosisa , Gopal Mahesh
Published: December 2023

DSS has low machinability characteristics due to its high strength, machining is complicated, and careful attention is required when selecting machining parameters. The main criteria discussed in this paper concern the turning optimization parameters and machining time reduction of DSS 2205 as the work material. The input parameters are cutting velocity, feeds, cutting depth, and tooltip nose radius of the cutting tool. The design of experiments methodology is employed to design the experiments using Design‐Expert V12 software. The second‐order mathematical model was developed, and analysis of variance was performed to analyze the performance characteristics to recognize the critical variables influencing the output parameter. An artificial neural network (ANN) backpropagation algorithm using MATLAB software was used to develop the mathematical model and optimize the output. The model was developed, and the results were optimized using MATLAB software’s ANN back propagation method to find the best possible solutions. The generated models were significant based on the analysis of variance and the R‐squared value, and these results indicate that the cutting velocity is the most critical factor. For a low machining time, the cutting velocity should be between 100 and 140 m/min, and the tooltip nose radius should be 2.8 mm. The optimal parameter settings are validated by performing a lower is better confirmation test using gray relational analysis (GRA). The GRA exposed the lower machining time at a cutting velocity of 140 m/min, rate of feed of 0.5 mm/rev, cutting depth of 0.5 mm, and tooltip nose radius of 2.4 mm. The predicted values were close to the experimental values, and the result indicates the optimal level of the highest GRA grade of the machining variable.
A Review of Birth-Death and Other Markovian Discrete-Time Queues
Authors: El-Taha Muhammad
Published: December 2023

In this review article, we consider discrete-time birth-death processes and their applications to discrete-time queues. To make the analysis simpler to follow, we focus on transform-free methods and consider instances of non-birth-death Markovian discrete-time systems. We present a number of results within one discrete-time framework that parallels the treatment of continuous time models. This approach has two advantages; first, it unifies the treatment of several discrete-time models in one framework, and second, it parallels to the extent possible the treatment of continuous time models. This allows us to draw parallels and contrasts between the discrete and continuous time queues. Specifically, we focus on birth-death applications to the single server discrete-time model with Bernoulli arrivals and geometric service times and provide the reader with a simple rigorous detailed analysis that covers all five scheduling rules considered in the literature, with attention to stationary distributions at slot edges, slot centers, and prearrival epochs. We also cover the waiting time distributions. Moreover, we cover three Markovian models that fit the global balance equations. Our approach provides interesting insights into the behavior of discrete-time queues. The article is intended for those who are familiar with queueing theory basics and would like a simple, yet rigorous introductory treatment to discrete-time queues.
Experimental Study of Multiple Physical Parameters Monitoring during Uniaxial Loading Process of Sandstone
Authors: Zhao Yongchuan , Yang Tianhong , Shi Wenhao
Published: December 2023

Aiming at the damage evolution and energy release characteristics of sandstone in failure process, uniaxial and cyclic loading experiments were conducted with the average temperature, infrared temperature field, acoustic emission (AE), and displacement field being monitored simultaneously. The results show that in the low stress stage, the initial pores and fissures are compressed and the thermoelastic effect is insignificant. At the same time, the AE events are active, but most of the events are released with low energy. Then, the surface of the specimen shows stratified displacement phenomenon in the vertical direction. In the following stage, the thermoelastic effect is significant, and the average temperature of specimens shows a linear correlation with stress, whereas the AE is relatively inactive, and vertical surface deformation shows further homogenization and saliency. Because of the top and bottom constraints, the horizontal displacement field shows axis symmetrical distribution of double half-ellipse like a drum. During the yielding phase and the rupture moment, the average temperature rises obviously because of the heat from the friction of the rupture surface, and the temperature field also appears the phenomenon of differentiation; in the meantime, a large amount of AE events occur and the proportion of high energy events increases; further, the deformation field is significantly gathered near the ruptured position. Finally, the time sequence of the multiple physical parameters (AE parameters, average temperature, temperature fields, horizontal and vertical displacement fields) is summarized, which can be a reference for the stress state and failure analysis.
The Application of Advanced Bone Imaging Technologies in Sports Medicine
Authors: Tadros Samuel S. , Epsley Scott , Mehta Sameer , Jones Brandon C. , Rajapakse Hiran I. , Madi Rashad , Alecxih Austin , Kargilis Daniel , Rajapakse Chamith S.
Published: December 2023

Until recently, the evaluation of bone health and fracture risk through imaging has been limited to dual-energy X-ray absorptiometry (DXA) and plain radiographs, with a limited application in the athletic population. Several novel imaging technologies are now available for the clinical assessment of bone health, including bone injury risk and healing progression, with a potential for use in sports medicine. Among these imaging modalities is high-resolution peripheral quantitative computed tomography (HR-pQCT) which is a promising technology that has been developed to examine the bone microarchitecture in both cortical and trabecular bone at peripheral anatomical sites. Technologies that do not expose patients to ionizing radiation are optimal, particularly for athletes who may require frequent imaging. One such alternative is diagnostic ultrasound, which is preferable due to its low cost and lack of radiation exposure. Furthermore, ultrasound, which has not been a common imaging modality for monitoring fracture healing, has been shown to potentially demonstrate earlier signs of union compared to conventional radiographs, including callus mineralization and density at the healing site. Through the use of conventional magnetic resonance imaging (MRI), finite element analysis (FEA) can be used to simulate the structural and mechanical properties of bone. On the other hand, the ultrashort echo time (UTE) MRI can evaluate cortical bone quality by detecting water bound to the organic bone matrix and free water, providing important information about bone porosity. Several novel bone imaging techniques originally developed for osteoporosis assessment have great potential to be utilized to improve the standard of care in bone fracture risk assessment and healing in sports medicine with much greater precision and less adverse radiation exposure.
Genetic Factors and Long-term Treatment-Related Neurocognitive Deficits, Anxiety, and Depression in Childhood Leukemia Survivors: An Exome-Wide Association Study
Authors: Petrykey Kateryna , Lippé Sarah , Sultan Serge , Robaey Philippe , Drouin Simon , Affret-Bertout Laurence , Beaulieu Patrick , St-Onge Pascal , Baedke Jessica L. , Yasui Yutaka , Hudson Melissa M. , Laverdière Caroline , Sinnett Daniel , Krajinovic Maja
Published: December 2023

Background:

An increased risk of neurocognitive deficits, anxiety, and depression has been reported in childhood cancer survivors.

Methods:

We analyzed associations of neurocognitive deficits, as well as anxiety and depression, with common and rare genetic variants derived from whole-exome sequencing data of acute lymphoblastic leukemia (ALL) survivors from the PETALE cohort. In addition, significant associations were assessed using stratified and multivariable analyses. Next, top-ranking common associations were analyzed in an independent SJLIFE replication cohort of ALL survivors.

Results:

Significant associations were identified in the entire discovery cohort (N = 229) between the AK8 gene and changes in neurocognitive function, whereas PTPRZ1, MUC16, TNRC6C-AS1 were associated with anxiety. Following stratification according to sex, the ZNF382 gene was linked to a neurocognitive deficit in males, whereas APOL2 and C6orf165 were associated with anxiety and EXO5 with depression. Following stratification according to prognostic risk groups, the modulatory effect of rare variants on depression was additionally found in the CYP2W1 and PCMTD1 genes. In the replication SJLIFE cohort (N = 688), the male-specific association in the ZNF382 gene was not significant; however, a P value<0.05 was observed when the entire SJLIFE cohort was analyzed. ZNF382 was significant in males in the combined cohorts as shown by meta-analyses as well as the depression-associated gene EXO5.

Conclusions:

Further research is needed to confirm whether the current findings, along with other known risk factors, may be valuable in identifying patients at increased risk of these long-term complications.

Impact:

Our results suggest that specific genes may be related to increased neuropsychological consequences.
A Phase Ib Study of the DNA-PK Inhibitor Peposertib Combined with Neoadjuvant Chemoradiation in Patients with Locally Advanced Rectal Cancer
Authors: Romesser Paul B. , Capdevila Jaume , Garcia-Carbonero Rocio , Philip Tony , Fernandez Martos Carlos , Tuli Richard , Rodriguez-Gutierrez Almudena , Kuipers Mirjam , Becker Andreas , Coenen-Stass Anna , Sarholz Barbara , You Xiaoli , Miller Eric D.
Published: December 2023

Purpose:

Peposertib—an orally administered DNA-dependent protein kinase inhibitor—has shown potent radiosensitization in preclinical models. This dose-escalation study (NCT03770689) aimed to define the maximum tolerated dose (MTD) and recommended phase II dose (RP2D) of peposertib plus capecitabine-based chemoradiotherapy (CRT) and assessed its safety and efficacy in locally advanced rectal cancer.

Patients and Methods:

Patients were treated for 5 to 5.5 weeks with 50- to 250-mg peposertib once daily, capecitabine 825 mg/m2 twice daily, and radiotherapy (RT), 5 days per week. Following clinical restaging (8 weeks after CRT completion), patients with clinical complete response (cCR) could opt for surveillance. Total mesorectal excision was recommended upon incomplete response (IR).

Results:

Nineteen patients were treated with peposertib at doses of 50 mg (n = 1), 100 mg, 150 mg, and 250 mg (n = 6 each). Dose-limiting toxicities occurred in one out of five (100 mg), one out of six (150 mg), and three out of six (250 mg) evaluable patients. Peposertib ≤150 mg once daily was tolerable in combination with CRT. After 8 weeks of treatment with peposertib and CRT, the cCR was 15.8% (n = 3). Among the three patients with cCR, two underwent surgery and had residual tumors. Among the 16 patients with IR, seven underwent surgery and had residual tumors; five of the remaining nine patients opted for consolidative chemotherapy. The combined cCR/pathologic complete response (pCR) rate was 5.3% (n = 1, 100 mg cohort).

Conclusions:

Peposertib did not improve complete response rates at tolerable dose levels. The study was closed without declaring the MTD/RP2D.
Table S1 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary table S1 is the experimental mice table
Figure S10 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Supplementary figure S10 show correspondence between gene expression and CNV in radiation-induced sarcomas
FIGURE 4 from Temporary Knockdown of p53 During Focal Limb Irradiation Increases the Development of Sarcomas
Authors: Daniel Andrea R. , Su Chang , Williams Nerissa T. , Li Zhiguo , Huang Jianguo , Lopez Omar , Luo Lixia , Ma Yan , Campos Lorraine da Silva , Selitsky Sara R. , Modliszewski Jennifer L. , Liu Siyao , Hernansaiz-Ballesteros Rosa , Mowery Yvonne M. , Cardona Diana M. , Lee Chang-Lung , Kirsch David G.
Published: December 2023

Radiation-induced sarcomas exhibit an increase in proliferative gene programs and a decrease in myogenic differentiation programs. A, Heat map of the top 75 differentially expressed genes in radiation-induced sarcomas (n = 16) versus normal muscle (n = 7). Genes (rows) are colored by scaled, normalized expression values. Both rows and columns are clustered. B, Volcano plot of log2 fold change for all genes with base mean greater than 50 in tumor (n = 16) versus normal (n = 7). Labeled genes have a nominal −log10P value greater than or equal to 30. C, Normalized enrichment scores for all FDR significant pathways for GSEA of Hallmark pathways. Points are colored by −log10 (nominal P value). D, Scatter plot comparing the differentially expressed pathways between tumors that arose in irradiated tissues (Rad) versus sporadic (Spor) tumors in mouse (y-axis) and human (x-axis) datasets. The EMT pathway is highlighted with a green dot. E, Boxplot showing gene expression of EMT pathway targets in mouse radiation-induced UPS compared with p53/RB tumors (top) and in human radiation-associated undifferentiated sarcomas compared with sporadic undifferentiated sarcomas.
Clinical and Biochemical Characteristics of Pediatric Diabetic Ketoacidosis Admissions to COVID-19 Free UAE Tertiary Center during Pandemic
Authors: Marei Shoroogh , Ra’a Said Dalia , Almazrouei Raya , Kaplan Walid , Al Hassani Noura
Published: December 2023

Background: COVID-19 pandemic led to delayed diagnosis and increase in number and severity of type 1 diabetes mellitus (T1DM) and diabetic ketoacidosis (DKA) cases in pediatric population worldwide. The indirect impact of the pandemic on pediatric DKA admissions to COVID-19-free hospitals worth to be evaluated. Objectives: Our aim was to evaluate the characteristics and severity of DKA admissions before and during the pandemic to COVID-19-free hospital. Methods: This descriptive retrospective study included 130 episodes of DKA for patients aged below 16 years admitted to Tawam Hospital, a COVID-19-free hospital, between March 2017 and Feb 2021. Data from March 2020 to Feb 2021 (pandemic) were compared to the previous 3 years, March 2017 to Feb 2020 (pre-pandemic). Data were retrieved from the electronic records and analyzed using STATA13. Results: We evaluated 130 DKA admissions (63 pandemic and 67 pre-pandemic). The majority of patients in the pandemic group were in, the age group of (6–11.9 years) (54% vs. 23.9%, p = 0.001), and higher proportion of them was diagnosed with new-onset diabetes (42.9% vs. 25.4%, p = 0.035). Overall, there was no significant difference in symptoms duration, DKA severity, or time to DKA resolution, but there was a difference in the median (IQR) HbA1C, 11% (9.4–12.95) vs. 10.15% (9.27–11.80) (p = 0.0297) in the pandemic and pre-pandemic groups, respectively. Conclusion: In our COVID-19-free hospital, the pandemic and service reallocation has led to an increased rate of DKA admissions with increased number of newly diagnosed T1DM. Clinical presentation and severity were not adversely affected.
Clinical Impact of Early Tumour Shrinkage in Metastatic or Unresectable Oesophageal Cancer Treated with Pembrolizumab plus Chemotherapy
Authors: Sugase Takahito , Kanemura Takashi , Takeoka Tomohira , Matsuura Norihiro , Masuike Yasunori , Shinno Naoki , Hara Hisashi , Omori Takeshi , Kitakaze Masatoshi , Kubo Masahiko , Mukai Yosuke , Sueda Toshinori , Hasegawa Shinichiro , Akita Hirofumi , Nishimura Junichi , Wada Hiroshi , Yasui Masayoshi , Miyata Hiroshi
Published: December 2023

Introduction: Metastatic or unresectable locally advanced oesophageal cancer remains a disease with high mortality. More recently, pembrolizumab plus chemotherapy has been indicated as the first-line treatment for those patients, but the predictive factors for treatment efficacy remain controversial. This study investigated the clinical utility of early tumour shrinkage (ETS) and depth of response (DpR) in metastatic or unresectable oesophageal cancer treated with pembrolizumab plus CF therapy. Methods: ETS and DpR, defined as the percent decreases at the second evaluation and the percentage of the maximal tumour shrinkage during treatment, were measured in 53 eligible patients. The ETS and DpR cut-off values were 20% and 30%, respectively, based on survival outcomes. Results: Twenty-seven patients (51%) were treatment naïve, while 26 (49%) had received any treatment before initiating pembrolizumab plus CF therapy. The median progression-free survival (PFS) and overall survival for ETS ≥20% and <20% were 12.7 and 5.5 months and 14.4 and 8.2 months and 12.7 and 4.9 months and 14.4 and 8.0 months for DpR ≥30% and <30%, respectively. ETS <20% showed early tumour growth, whereas ETS ≥20% had a good response rate with sufficient longer response duration. In addition, an ETS cut-off of 20% predicted the best overall response and was not associated with prior treatment. In multivariable analysis, ETS ≥20% and DpR ≥30% were independent factors of longer PFS. Conclusion: Our findings suggest that an ETS is a promising on-treatment marker for early prediction of further sensitivity to pembrolizumab plus CF therapy.
Evaluation of Bone Mineral Density in Lung Transplant Recipients by Chest Computed Tomography
Authors: Mori Ryobu , Handa Tomohiro , Ohsumi Akihiro , Ikezoe Kohei , Tanizawa Kiminobu , Uozumi Ryuji , Tanabe Naoya , Oguma Tsuyoshi , Sakamoto Ryo , Hamaji Masatsugu , Nakajima Daisuke , Yutaka Yojiro , Tanaka Satona , Yamada Yoshito , Oshima Yohei , Sato Susumu , Fukui Motonari , Date Hiroshi , Hirai Toyohiro
Published: December 2023

Introduction: Lung transplantation (LT) recipients are at risk of bone mineral density (BMD) loss. Pre- and post-LT BMD loss has been reported in some cross-sectional studies; however, there are limited studies regarding the serial BMD change in LT recipients. The aim of this study was to investigate the serial BMD changes and the clinical characteristics associated with BMD decline. Methods: This was a single-center, retrospective observational study. BMD was serially measured in thoracic vertebral bodies (Th4, 7, 10) using computed tomography (CT) before and 3 and 12 months after LT. The frequency of osteoporosis and factors associated with pre-LT osteoporosis and post-LT BMD loss were evaluated. The frequency of post-LT compression fracture and its associated factors were also analyzed. Results: This study included 128 adult LT recipients. LT recipients had decreased BMD (151.8 ± 42.2 mg/mL) before LT compared with age-, sex-, and smoking index-matched controls (176.2 ± 35.7 mg/mL). The diagnosis of COPD was associated with pre-LT osteoporosis. LT recipients experience further BMD decline after transplantation, and the percentage of recipients classified as exhibiting osteoporosis increased from 20% at baseline to 43% at 12 months. Recipients who had been taking no or small doses of glucocorticoids before LT had rapid BMD loss after LT. Early bisphosphonate use (within 3 months) after LT attenuated BMD loss and decreased new-onset compression fracture. Conclusion: LT recipients are at high risk for BMD loss and compression fracture after LT. Early bisphosphonate use may decrease BMD loss and compression fracture.