bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Light Probes for Real-Time Global Illumination
Authors: Guo Jie , Zong Zijing , Song Yadong , Fu Xihao , Tao Chengzhi , Guo Yanwen , Yan Ling-Qi
Published: November 2022

Reproducing physically-based global illumination (GI) effects has been a long-standing demand for many real-time graphical applications. In pursuit of this goal, many recent engines resort to some form of light probes baked in a precomputation stage. Unfortunately, the GI effects stemming from the precomputed probes are rather limited due to the constraints in the probe storage, representation or query. In this paper, we propose a new method for probe-based GI rendering which can generate a wide range of GI effects, including glossy reflection with multiple bounces, in complex scenes. The key contributions behind our work include a gradient-based search algorithm and a neural image reconstruction method. The search algorithm is designed to reproject the probes' contents to any query viewpoint, without introducing parallax errors, and converges fast to the optimal solution. The neural image reconstruction method, based on a dedicated neural network and several G-buffers, tries to recover high-quality images from low-quality inputs due to limited resolution or (potential) low sampling rate of the probes. This neural method makes the generation of light probes efficient. Moreover, a temporal reprojection strategy and a temporal loss are employed to improve temporal stability for animation sequences. The whole pipeline runs in realtime (>30 frames per second) even for high-resolution (1920×1080) outputs, thanks to the fast convergence rate of the gradient-based search algorithm and a light-weight design of the neural network. Extensive experiments on multiple complex scenes have been conducted to show the superiority of our method over the state-of-the-arts.
SurfaceVoronoi
Authors: Xin Shiqing , Wang Pengfei , Xu Rui , Yan Dongming , Chen Shuangmin , Wang Wenping , Zhang Caiming , Tu Changhe
Published: November 2022

In this paper, we propose to compute Voronoi diagrams over mesh surfaces driven by an arbitrary geodesic distance solver, assuming that the input is a triangle mesh as well as a collection of sites P = {
Pi
}

m

i
=1

on the surface. We propose two key techniques to solve this problem. First, as the partition is determined by minimizing the
m
distance fields, each of which rooted at a source site, we suggest keeping one or more distance triples, for each triangle, that may help determine the Voronoi bisectors when one uses a mark-and-sweep geodesic algorithm to predict the multi-source distance field. Second, rather than keep the distance itself at a mesh vertex, we use the squared distance to characterize the linear change of distance field restricted in a triangle, which is proved to induce an exact VD when the base surface reduces to a planar triangle mesh. Specially, our algorithm also supports the Euclidean distance, which can handle thin-sheet models (e.g. leaf) and runs faster than the traditional restricted Voronoi diagram (RVD) algorithm. It is very extensible to deal with various variants of surface-based Voronoi diagrams including (1) surface-based power diagram, (2) constrained Voronoi diagram with curve-type breaklines, and (3) curve-type generators. We conduct extensive experimental results to validate the ability to approximate the exact VD in different distance-driven scenarios.
SCULPTOR
Authors: Qiu Zesong , Li Yuwei , He Dongming , Zhang Qixuan , Zhang Longwen , Zhang Yinghao , Wang Jingya , Xu Lan , Wang Xudong , Zhang Yuyao , Yu Jingyi
Published: November 2022

Recent years have seen growing interest in 3D human face modeling due to its wide applications in digital human, character generation and animation. Existing approaches overwhelmingly emphasized on modeling the exterior shapes, textures and skin properties of faces, ignoring the inherent correlation between inner skeletal structures and appearance. In this paper, we present SCULPTOR, 3D face creations with
Skeleton Consistency Using a Learned Parametric facial generaTOR
, aiming to facilitate the easy creation of both anatomically correct and visually convincing face models via a hybrid parametric-physical representation. At the core of SCULPTOR is LUCY, the first large-scale shape-skeleton face dataset in collaboration with plastic surgeons. Named after the fossils of one of the oldest known human ancestors, our LUCY dataset contains high-quality Computed Tomography (CT) scans of the complete human head before and after orthognathic surgeries, which are critical for evaluating surgery results. LUCY consists of 144 scans of 72 subjects (31 male and 41 female), where each subject has two CT scans taken pre- and post-orthognathic operations. Based on our LUCY dataset, we learned a novel skeleton consistent parametric facial generator, SCULPTOR, which can create unique and nuanced facial features that help define a character and at the same time maintain physiological soundness. Our SCULPTOR jointly models the skull, face geometry and face appearance under a unified data-driven framework by separating the depiction of a 3D face into shape blend shape, pose blend shape and facial expression blend shape. SCULPTOR preserves both anatomic correctness and visual realism in facial generation tasks compared with existing methods. Finally, we showcase the robustness and effectiveness of SCULPTOR in various fancy applications unseen before, like archaeological skeletal facial completion, bone-aware character fusion, skull inference from images, face generation with lipo-Level change and facial animations, etc.
BoolSurf
Authors: Riso Marzia , Nazzaro Giacomo , Puppo Enrico , Jacobson Alec , Zhou Qingnan , Pellacini Fabio
Published: November 2022

We port Boolean set operations between 2D shapes to surfaces of any genus, with any number of open boundaries. We combine shapes bounded by sets of freely intersecting loops, consisting of geodesic lines and cubic Bézier splines lying on a surface. We compute the arrangement of shapes directly on the surface and assign integer labels to the cells of such arrangement. Differently from the Euclidean case, some arrangements on a manifold may be inconsistent. We detect inconsistent arrangements and help the user to resolve them. Also, we extend to the manifold setting recent work on Boundary-Sampled Halfspaces, thus supporting operations more general than standard Booleans, which are well defined on inconsistent arrangements, too. Our implementation discretizes the input shapes into polylines at an arbitrary resolution, independent of the level of resolution of the underlying mesh. We resolve the arrangement inside each triangle of the mesh independently and combine the results to reconstruct both the boundaries and the interior of each cell in the arrangement. We reconstruct the control points of curves bounding cells, in order to free the result from discretization and provide an output in vector format. We support interactive usage, editing shapes consisting up to 100k line segments on meshes of up to 1M triangles.
Color-Perception-Guided Display Power Reduction for Virtual Reality
Authors: Duinkharjav Budmonde , Chen Kenneth , Tyagi Abhishek , He Jiayi , Zhu Yuhao , Sun Qi
Published: November 2022

Battery life is an increasingly urgent challenge for today's untethered VR and AR devices. However, the power efficiency of head-mounted displays is naturally at odds with growing computational requirements driven by better resolution, refresh rate, and dynamic ranges, all of which reduce the sustained usage time of untethered AR/VR devices. For instance, the Oculus Quest 2, under a fully-charged battery, can sustain only 2 to 3 hours of operation time. Prior display power reduction techniques mostly target smartphone displays. Directly applying smartphone display power reduction techniques, however, degrades the visual perception in AR/VR with noticeable artifacts. For instance, the "power-saving mode" on smartphones
uniformly
lowers the pixel luminance across the display and, as a result, presents an overall darkened visual perception to users if directly applied to VR content.

Our key insight is that VR display power reduction must be cognizant of the gaze-contingent nature of high field-of-view VR displays. To that end, we present a gaze-contingent system that, without degrading luminance, minimizes the display power consumption while preserving high visual fidelity when users actively view immersive video sequences. This is enabled by constructing 1) a gaze-contingent color discrimination model through psychophysical studies, and 2) a display power model (with respect to pixel color) through real-device measurements. Critically, due to the careful design decisions made in constructing the two models, our algorithm is cast as a constrained optimization problem with a
closed-form
solution, which can be implemented as a real-time, image-space shader. We evaluate our system using a series of psychophysical studies and large-scale analyses on natural images. Experiment results show that our system reduces the display power by as much as 24% (14% on average) with little to no perceptual fidelity degradation.
ElastoMonolith
Authors: Takahashi Tetsuya , Batty Christopher
Published: November 2022

Simultaneous coupling of diverse physical systems poses significant computational challenges in terms of speed, quality, and stability. Rather than treating all components with a single discretization methodology (e.g., smoothed particles, material point method, Eulerian grid, etc.) that is ill-suited to some components, our solver,
ElastoMonolith
, addresses three-way interactions among standard particle-in-cell-based viscous and inviscid fluids, Lagrangian mesh-based deformable bodies, and rigid bodies. While prior methods often treat some terms explicitly or in a decoupled fashion for efficiency, often at the cost of robustness or stability, we demonstrate the effectiveness of a strong coupling approach that expresses all of the relevant physics within one consistent and unified optimization problem, including fluid pressure and viscosity, elasticity of the deformables, frictional solid-solid contact, and solid-fluid interface conditions. We further develop a numerical solver to tackle this difficult optimization problem, incorporating projected Newton, an active set method, and a transformation of the inner linear system matrix to ensure symmetric positive definiteness. Our experimental evaluations show that our framework can achieve high quality coupling results that avoid artifacts such as volume loss, instability, sticky contacts, and spurious interpenetrations.
Geo-Metric
Authors: Wolski Krzysztof , Trutoiu Laura , Dong Zhao , Shen Zhengyang , Mackenzie Kevin , Chapiro Alexandre
Published: November 2022

In this work we take a novel perception-centered approach to quantify distortions on 3D geometry of faces, to which humans are particularly sensitive. We generated a dataset, composed of 100 high-quality and demographically-balanced face scans. We then subjected these meshes to distortions that cover relevant use cases in computer graphics, and conducted a large-scale perceptual study to subjectively evaluate them. Our dataset consists of over 84,000 quality comparisons, making it the largest ever psychophysical dataset for geometric distortions. Finally, we demonstrated how our data can be used for applications like metrics, compression, and level-of-detail rendering.
Differentiable Rendering Using RGBXY Derivatives and Optimal Transport
Authors: Xing Jiankai , Luan Fujun , Yan Ling-Qi , Hu Xuejun , Qian Houde , Xu Kun
Published: November 2022

Traditional differentiable rendering approaches are usually hard to converge in inverse rendering optimizations, especially when initial and target object locations are not so close. Inspired by Lagrangian fluid simulation, we present a novel differentiable rendering method to address this problem. We associate each screen-space pixel with the visible 3D geometric point covered by the center of the pixel and compute derivatives on geometric points rather than on pixels. We refer to the associated geometric points as point proxies of pixels. For each point proxy, we compute its 5D RGBXY derivatives which measures how its 3D RGB color and 2D projected screen-space position change with respect to scene parameters. Furthermore, in order to capture global and long-range object motions, we utilize optimal transport based pixel matching to design a more sophisticated loss function. We have conducted experiments to evaluate the effectiveness of our proposed method on various inverse rendering applications and have demonstrated superior convergence behavior compared to state-of-the-art baselines.
Make Your Own Sprites
Authors: Wu Zongwei , Chai Liangyu , Zhao Nanxuan , Deng Bailin , Liu Yongtuo , Wen Qiang , Wang Junle , He Shengfeng
Published: November 2022

Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the pixelization process into specific cell-aware and aliasing-aware stages, mitigating the ambiguities in joint learning of cell size, aliasing effect, and color assignment. To train our model, we construct a dedicated pixel art dataset and augment it with different cell sizes and different degrees of anti-aliasing effects. Extensive experiments demonstrate its superior performance over state-of-the-arts in terms of cell sharpness and perceptual expressiveness. We also show promising results of video game pixelization for the first time. Code and dataset are available at https://github.com/WuZongWei6/Pixelization.
Neural Cloth Simulation
Authors: Bertiche Hugo , Madadi Meysam , Escalera Sergio
Published: November 2022

We present a general framework for the garment animation problem through unsupervised deep learning inspired in physically based simulation. Existing trends in the literature already explore this possibility. Nonetheless, these approaches do not handle cloth dynamics. Here, we propose the first methodology able to learn realistic cloth dynamics unsupervisedly, and henceforth, a general formulation for neural cloth simulation. The key to achieve this is to adapt an existing optimization scheme for motion from simulation based methodologies to deep learning. Then, analyzing the nature of the problem, we devise an architecture able to automatically disentangle static and dynamic cloth subspaces by design. We will show how this improves model performance. Additionally, this opens the possibility of a novel motion augmentation technique that greatly improves generalization. Finally, we show it also allows to control the level of motion in the predictions. This is a useful, never seen before, tool for artists. We provide of detailed analysis of the problem to establish the bases of neural cloth simulation and guide future research into the specifics of this domain.
Affordable Spectral Measurements of Translucent Materials
Authors: Iser Tomáš , Rittig Tobias , Nogué Emilie , Nindel Thomas Klaus , Wilkie Alexander
Published: November 2022

We present a spectral measurement approach for the bulk optical properties of translucent materials using only low-cost components. We focus on the translucent inks used in full-color 3D printing, and develop a technique with a high spectral resolution, which is important for accurate color reproduction. We enable this by developing a new acquisition technique for the three unknown material parameters, namely, the absorption and scattering coefficients, and its phase function anisotropy factor, that only requires three point measurements with a spectrometer. In essence, our technique is based on us finding a three-dimensional
appearance map
, computed using Monte Carlo rendering, that allows the conversion between the three observables and the material parameters. Our measurement setup works without laboratory equipment or expensive optical components. We validate our results on a 3D printed color checker with various ink combinations. Our work paves a path for more accurate appearance modeling and fabrication even for low-budget environments or affordable embedding into other devices.
LaplacianFusion
Authors: Kim Hyomin , Nam Hyeonseo , Kim Jungeon , Park Jaesik , Lee Seungyong
Published: November 2022

We propose
LaplacianFusion
, a novel approach that reconstructs detailed and controllable 3D clothed-human body shapes from an input depth or 3D point cloud sequence. The key idea of our approach is to use Laplacian coordinates, well-known differential coordinates that have been used for mesh editing, for representing the local structures contained in the input scans, instead of implicit 3D functions or vertex displacements used previously. Our approach reconstructs a controllable base mesh using SMPL, and learns a surface function that predicts Laplacian coordinates representing surface details on the base mesh. For a given pose, we first build and subdivide a base mesh, which is a deformed SMPL template, and then estimate Laplacian coordinates for the mesh vertices using the surface function. The final reconstruction for the pose is obtained by integrating the estimated Laplacian coordinates as a whole. Experimental results show that our approach based on Laplacian coordinates successfully reconstructs more visually pleasing shape details than previous methods. The approach also enables various surface detail manipulations, such as detail transfer and enhancement.
Production-Ready Face Re-Aging for Visual Effects
Authors: Zoss Gaspard , Chandran Prashanth , Sifakis Eftychios , Gross Markus , Gotardo Paulo , Bradley Derek
Published: November 2022

Photorealistic digital re-aging of faces in video is becoming increasingly common in entertainment and advertising. But the predominant 2D painting workflow often requires frame-by-frame manual work that can take days to accomplish, even by skilled artists. Although research on facial image re-aging has attempted to automate and solve this problem, current techniques are of little practical use as they typically suffer from facial identity loss, poor resolution, and unstable results across subsequent video frames. In this paper, we present the first practical, fully-automatic and production-ready method for re-aging faces in video images. Our first key insight is in addressing the problem of collecting longitudinal training data for learning to re-age faces over extended periods of time, a task that is nearly impossible to accomplish for a large number of real people. We show how such a longitudinal dataset can be constructed by leveraging the current state-of-the-art in facial re-aging that, although failing on real images, does provide photoreal re-aging results on synthetic faces. Our second key insight is then to leverage such synthetic data and formulate facial re-aging as a practical image-to-image translation task that can be performed by training a well-understood U-Net architecture, without the need for more complex network designs. We demonstrate how the simple U-Net, surprisingly, allows us to advance the state of the art for re-aging real faces on video, with unprecedented temporal stability and preservation of facial identity across variable expressions, viewpoints, and lighting conditions. Finally, our new face re-aging network (FRAN) incorporates simple and intuitive mechanisms that provides artists with localized control and creative freedom to direct and fine-tune the re-aging effect, a feature that is largely important in real production pipelines and often overlooked in related research work.
Getting Authoring Right: How to innovate for meaningful improvement
Authors: Leigh Alice , Crowley Shaun
Published: November 2022

With so much technology-enabled innovation in assessment, one area has often had too little attention--the exam development phase, often called the "hardest job in publishing." Writing exams is so challenging and so important, not just for the validity of tests but also for the ability of exam providers to innovate their services and operate efficiently. With genuinely enterprise scale solutions finally becoming available in this area, the authors offer a checklist guide for assessment providers to review their authoring processes. They also explain why the combination of modular systems architectures and standards-based approaches to content will liberate assessment owners in tackling this key area in their businesses.
OCCAM-v2: Combining Static and Dynamic Analysis for Effective and Efficient Whole-program Specialization
Authors: Navas Jorge A. , Gehani Ashish
Published: November 2022

OCCAM-v2 leverages scalable pointer analysis, value analysis, and dynamic analysis to create an effective and efficient tool for specializing LLVM bitcode. The extent of the code-size reduction achieved depends on the specific deployment configuration. Each application that is to be specialized is accompanied by a manifest that specifies concrete arguments that are known a priori, as well as a count of residual arguments that will be provided at runtime. The best case for partial evaluation occurs when the arguments are completely concretely specified. OCCAM-v2 uses a pointer analysis to devirtualize calls, allowing it to eliminate the entire body of functions that are not reachable by any direct calls. The hybrid analysis feature can handle cases that are challenging for static analysis, such as input loops, string processing, and external data (in files, for example). On the suite of evaluated programs, OCCAM-v2 was able to reduce the instruction count by 40.6 percent on average, taking a median of 2.4 seconds.
Club Cell Secretory Protein in Lung Disease: Emerging Concepts and Potential Therapeutics
Authors: Martinu Tereza , Todd Jamie L. , Gelman Andrew E. , Guerra Stefano , Palmer Scott M.
Published: November 2022

Club cell secretory protein (CCSP), also known as secretoglobin 1A1 (gene name SCGB1A1), is one of the most abundant proteins in the lung, primarily produced by club cells of the distal airway epithelium. At baseline, CCSP is found in large concentrations in lung fluid specimens and can also be detected in the blood and urine. Obstructive lung diseases are generally associated with reduced CCSP levels, thought to be due to decreased CCSP production or club cell depletion. Conversely, several restrictive lung diseases have been found to have increased CCSP levels both in the lung and in the circulation, likely related to club cell dysregulation as well as increasedlung permeability. Recent studies demonstrate multiple mechanisms by which CCSP dampens acute and chronic lung inflammation. Given these anti-inflammatory effects, CCSP represents a novel potential therapeutic modality in lung disease.
Changes in Perspective and Perspectives on Change: Reflections on a Career
Authors: Judge Timothy A.
Published: November 2022

This article describes my journey as an organizational behavior scholar, including reflections on the state of the field of organizational scholarship. I organize the article into two main sections. First, I provide my autobiographical review, beginning with my early years and ending with the five universities where I have been employed in my career. Second, I provide a set of observations about the state of the organizational sciences, focusing specifically on the two areas of my most significant focus—personality and leadership—as well as offering some general observations about the field. The organizational sciences have seen many positive advances: Research is more rigorous theoretically and methodologically, and more concern is devoted to replication and research ethics. However, partly owing to prioritizing these advancements over other concerns, new problems have developed, and other long-standing concerns have been exacerbated. I discuss my own changing perspectives on these issues and present some thoughts on how they might be addressed.
A Microfluidic pH Sensor with Temperature Compensation Based on Extended-Gate Field-Effect Transistor
Authors: Zou Qiang , Wang Xiaole , Su Qi , Xue Tao , Lan Kuibo
Published: November 2022

PH sensors are widely used in food processing, health monitoring, water quality monitoring, and many other fields. The pH sensor applied in the health monitoring field can realize the real-time measurement of human health information and timely prevent the occurrence of diseases by improving people’s health levels. However, these sensors have some challenges in body fluid collection and temperature compensation. Here, we developed a pH sensor based on an extended-gate field-effect transistor (EGFET) with microfluidic channels and a temperature sensor to overcome these challenges. The sensor is prepared using a low-cost, solution-based process in which a temperature sensor is used for body temperature measurement and temperature compensation, and a microfluidic channel is used for body fluid collection. This pH sensor can measure both body temperature and pH of body fluid, where the temperature coefficient of resistance (TCR) of the temperature sensor is up to 5.17%, and the measurement result of body temperature is only 0.17 ℃ different from that of the gold standard. The pH sensitivity is 58.41 mV pH−1, and the pH value is only 0.066 deviating from the standard pH value. The sensor is expected to be used on a large scale in the applications of wearable health monitoring.
Impact of Porous Microstructure on Performance of Redox Flow Batteries: A Modeling Study
Authors: Shokri Javad , Niblett Daniel , Babaei Masoud , Niasar Vahid
Published: November 2022

Electrodes are essential components of redox flow batteries (RFBs) that determine performance. Given the advances in 3D printing technologies, bottom-up design and manufacturing electrodes with controllable and reproducible properties can be attainable. In this study, pore network modeling is utilized to understand the coupled transport and reaction processes in Hydrogen-Bromine (H2/Br2) flow batteries. This study highlights the importance of the microstructure while designing a 3D printable microstructure. The 3D pore-scale model is validated against the experimental measurements using an unstructured pore network, extracted from a tomography scan of a commercial carbon paper. A printable ordered cubic lattice with the same permeability as carbon paper increases the maximum power density by 35% due to lower tortuosity and higher mass transport. A parametric study is carried out to understand the role of microstructure, such as pore size, throat size, anisotropy, and lattice unit size, for flow-through and interdigitated flow fields. It is shown that a smaller lattice unit, increases the reactive surface area, lowers the activation and mass transport overpotentials, and consequently enhances the performance of the battery. An improved microstructure design for both flow fields is proposed that intensifies the performance of the battery via engineering the flow path of the electrolyte.
Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images
Authors: Gupta Anand Kumar , Srinivasulu Asadi , Hiran Kamal Kant , Sreenivasulu Goddindla , Rajeyyagari Sivaram , Subramanyam Madhusudhana
Published: November 2022

COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.
Research on Abnormal Behavior Prediction by Integrating Multiple Indexes of Student Behavior and Text Information in Big Data Environment
Authors: Wang Yubiao , Wen Junhao , Zhou Wei , Wu Quanwang , Wei Yingchun , Li Heng , Tao Bamei
Published: November 2022

With the wide application of information technologies such as big data, the Internet of Things, and cloud computing, college students have accumulated a large amount of personal information and daily behavior data in their daily studies and life. How to dynamically integrate multidimensional information of students to build accurate student portraits, using multi-indicator data of student behavior and comment texts, and finding out students with abnormal behavior from among many students has become an important problem to be solved. This paper proposes an abnormal behavior prediction method integrating multiple indicators of student behavior and text information (ABPM-IMISBTI) for the problem of abnormal behavior prediction of college students in the big data environment. First, given the problems of multidimensionality, timeliness, and dynamics of student behavior information fusion in the construction of student behavior portraits, by integrating students’ objective tags and subjective tags, an optimized K-means algorithm based on a cloud platform environment is proposed. Second, aiming at the problem of insufficient text information analysis in the analysis of students’ abnormal behavior, the ABPM-IMISBTI method is proposed to solve the prediction of students’ abnormal behavior through long and short-term memory networks (LSTM) combined with student behavior multi-index data and text information. Finally, this paper takes student achievement prediction as an example for verification. The experimental results show that, compared with other prediction methods, the ABPM-IMISBTI method proposed in this paper can improve the accuracy of student behavior prediction, and then quickly determine the abnormal behavior of students, to improve the level of education management in universities and promote the development of safe campuses, smart campuses, and smart education.
Systemic Bevacizumab for Severe Recurrent Respiratory Papillomatosis
Authors: Tatineni Swetha , Warren Zachary , Applebaum Mark A. , Baroody Fuad M.
Published: November 2022

Recurrent respiratory papillomatosis (RRP) is the most common benign pediatric laryngeal neoplasm. Various adjuvant medical therapies have failed to reliably decrease surgical frequency in this challenging airway disease. Recently, systemic bevacizumab has shown promise in advanced, treatment-resistant papillomatosis. We describe the use of systemic bevacizumab in two children with severe RRP unresponsive to other therapies. Voice and breathing improved dramatically in both patients with minimal side effects. Both patients have not required surgery in 24 months and 16 months, respectively. Systemic bevacizumab is a promising long-term treatment for severe RRP, with oncology playing an important role in patient care.
Magneto-Rotational Augmentation of Bioconvective Transport in Plasma-Nanofluid Flowing through a Penetrable Spinning Disc
Authors: Ige Ebenezer Olubunmi , Odesola Isaac Folorunso , Ayedun Sikiru Babatunde , Ilori Olusegun M.
Published: November 2022

The phenomenon of bioconvective transport through the manipulation of motile microorganisms is considered a promising process control technique in several biological processes and microdevices. Inducing convective transport in self-propelling microbes could be tailored to improve mixing, reaction propensity, and concentration transport within the media. This paper examined the combined effect of magnetic and rotational fields on the argumentation of bioconvective transport in the nanofluid-mediated plasma flow. A detailed analysis of the transport and dynamics of reactive forces during bioconvection in a rotary disc-like microchannel is presented. The physics of the problem was described by coupled nonlinear ordinary differential equations, which were numerically computed using the spectral relaxation scheme of the spectral homotopy analysis method. It was observed that the imposition of a magnetic field constituted viscous drag in the plasma-nanofluid media, which consequently increases the thermophoretic parameter in the bioconvective flow. It was ascertained that coupled magnetic and rotational effects significantly augmented the motility of microorganisms and translated to growth in momentum and concentration fields which is noticeable in the generation of stretching effect on the bacterium-containing plasma-nanofluid flow. The findings of this study could provide an essential basis for the design of bioreactors, centrifugal microfluidics technologies, and microdevices for use in a broad spectrum of biotechnology.
Effects of Injection Pressure and Duration on Alternate High-Pressure Water-Gas Sequestration of Coalbed Methane
Authors: Li Ke , Zhu Chuanjie , Liu Siyuan , Chen Deliang , Cai Guanghui
Published: November 2022

To solve the problems of high ground stress, low permeability and low coalbed methane recovery, this paper proposes an optimization technique for improving coal seam permeability, i.e., water-gas alternating displacement enhances coalbed methane recovery (WGA-ECBM). The seepage field of gas-water two-phase flow is derived and a multiphase fluid-solid coupling model between physical parameters is established. This paper studies the effects of three displacement methods (water injection, air injection and water-air alternating injection) on methane recovery. The influence of injection pressure and injection duration on the WGA-ECBM technique is also studied. The

 

V

C

H

4

(methane volume fraction) of the three methods decrease by 17.84%, 12.72% and 44.62% in the borehole, respectively. The influence ranges (diameter) are 24.68 m, 149.73 m and 135.21 m, respectively. Pore pressures and displacement area also increase as injection pressure increases in a 5 MPa gradient. And

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C

H

4

in the borehole decrease by 35.57%, 43.82%, 46.78% and 51.72% from 10 MPa to 25 MPa, respectively. With the increase of injection duration, the influence range expands significantly but the pore pressure distribution changes little. And

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C

H

4

decreases slightly in the displaced area and the displacement velocity gradually slows down. This technique promotes multiphase flow migration in coal seam, and increases the displacement range and methane recovery efficiency.
The Role of Optical Coherence Tomography Angiography in Optic Nerve Head Edema: A Narrative Review
Authors: Karrabi Narges , Hooshmandi Sadid , Amirabadi Alireza , Roshandel Danial , Hassanpour Kiana , Pakravan Mohammad
Published: November 2022

Optic nerve head (ONH) edema is a clinical manifestation of many ocular and systemic disorders. Ocular and central nervous system imaging has been used to differentiate the underlying cause of ONH edema and monitor the disease course. ONH vessel abnormalities are among the earliest signs of impaired axonal transportation. Optical coherence tomography angiography (OCTA) is a noninvasive method for imaging ONH and peripapillary vessels and has been used extensively for studying vascular changes in ONH disorders, including ONH edema. In this narrative review, we describe OCTA findings of the most common causes of ONH edema and its differential diagnoses including ONH drusen.
Survival Outcomes, Digital TILs, and On-treatment PET/CT During Neoadjuvant Therapy for HER2-positive Breast Cancer: Results from the Randomized PREDIX HER2 Trial
Authors: Matikas Alexios , Johansson Hemming , Grybäck Per , Bjöhle Judith , Acs Balazs , Boyaci Ceren , Lekberg Tobias , Fredholm Hanna , Elinder Ellinor , Margolin Sara , Isaksson-Friman Erika , Bosch Ana , Lindman Henrik , Adra Jamila , Andersson Anne , Agartz Susanne , Hellström Mats , Zerdes Ioannis , Hartman Johan , Bergh Jonas , Hatschek Thomas , Foukakis Theodoros
Published: November 2022

Purpose:

PREDIX HER2 is a randomized Phase II trial that compared neoadjuvant docetaxel, trastuzumab, and pertuzumab (THP) with trastuzumab emtansine (T-DM1) for HER2-positive breast cancer. Rates of pathologic complete response (pCR) did not differ between the two groups. Here, we present the survival outcomes from PREDIX HER2 and investigate metabolic response and tumor-infiltrating lymphocytes (TIL) as prognostic factors.

Patients and Methods:

In total, 202 patients with HER2-positive breast cancer were enrolled and 197 patients received six cycles of either THP or T-DM1. Secondary endpoints included event-free survival (EFS), recurrence-free survival (RFS), and overall survival (OS). Assessment with PET/CT was performed at baseline, after two and six treatment cycles. TILs were assessed manually at baseline biopsies, while image-based evaluation of TILs [digital TILs (DTIL)] was performed in digitized full-face sections.

Results:

After a median follow-up of 5.21 years, there was no difference between the two treatment groups in terms of EFS [HR = 1.26; 95% confidence interval (CI), 0.54–2.91], RFS (HR = 0.69; 95% CI, 0.24–1.93), or OS (HR = 0.52; 95% CI, 0.09–2.82). Higher SUVmax at cycle 2 (C2) predicted lower pCR (ORadj = 0.65; 95% CI, 0.48–0.87; P = 0.005) and worse EFS (HRadj = 1.27; 95% CI, 1.12–1.41; P < 0.001). Baseline TILs and DTILs provided additional prognostic information to clinical parameters and C2 SUVmax.

Conclusions:

Long-term outcomes following neoadjuvant T-DM1 were similar to neoadjuvant THP. SUVmax after two cycles of neoadjuvant therapy for HER2-positive breast cancer may be an independent predictor of both short- and long-term outcomes. Combined assessment with TILs may facilitate early selection of poor responders for alternative treatment strategies.
Agreement between Noninvasive Hemoglobin and Laboratory Hemoglobin Measurements in Neonates: A Systematic Review and Meta-Analysis
Authors: Panda Santosh Kumar , Mishra Alpana , Jena Pratap Kumar
Published: November 2022

Background and Objective: The noninvasive hemoglobin (NHb) devices are recently evaluated as an alternative to laboratory hemoglobin (LHb) in neonates. This systematic review explores the diagnostic accuracy of NHb devices for neonatal hemoglobin measurement. Methods: Literature related to the comparison of NHb device with LHb in neonates was searched from Medline, PubMed Central, PubMed, Web of Science, Google Scholar, and Scopus databases after PROSPERO registration. The quality of included publications was assessed by QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). The pooled correlation coefficient and bias (precision) in Bland-Altman difference plots were used for summary statistics using MetaXL 5.0 software. Results: A total of 1,477 paired NHb-LHb observations were analyzed from 1,047 neonates in 10 studies. Massimo radical-pulse co-oximetry (8 studies) and Mediscan-2000 (2 studies) were used for NHb estimation. The pooled correlation coefficient between NHb and LHb was r = 0.94 (95% CI: 0.83–0.98, p < 0.001), and the pooled bias (precision) was −0.013 (1.4) gm/dL between NHb and LHb measurements in Bland-Altman analysis. NHb device had better precision in stable neonates (0.91gm/dL) over sick neonates (1.66 gm/dL). Conclusions: Hemoglobin measurement by NHb is excellently correlated with LHb measurement with a minimal average difference. It may be used as a screening tool for hemoglobin measurement in neonates to avoid frequent phlebotomy.
From Antiquity to Modern Times: A History of Diabetes Mellitus and Its Treatments
Authors: March Christine A. , Libman Ingrid M. , Becker Dorothy J. , Levitsky Lynne L.
Published: November 2022

The past 200 years have brought an understanding of diabetes and its pathogenesis, as well as the development of treatments that could not have been predicted when the disorder was first clinically described 2000 years ago. Beginning in the late 19th century, the initial descriptions of the microscopic anatomy of the pancreatic islets by Langerhans led to recognition of pancreatic endocrine function. Many investigators attempted to isolate the hypoglycemic factor produced by the pancreas, but Banting, Best, Macleod, and Collip were able to extract and purify “isletin” to treat human diabetes in 1921. Rapid scientific progress over the next 100 years led to an understanding of insulin synthesis, structure and function, production of modified synthetic insulins, and the physiopathology that permitted classification of diabetes subtypes. Improvements in control of diabetes have reduced the risks of complications. In less than two hundred years, we have gone from being unable to measure glucose in blood to being able to offer people with diabetes continuous blood glucose monitoring, linked to continuous subcutaneous insulin infusion. We come ever closer with new drugs and treatments to repair the biochemical defects in type 2 diabetes and to biologically replace islets and their function in type 1 diabetes. This review addresses the history of continuing progress in diabetes care.
History of Puberty: Normal and Precocious
Authors: Fuqua John S. , Eugster Erica A.
Published: November 2022

Spanning from bench to bedside, the history of normal and precocious puberty is characterized by a series of remarkable advances that have illuminated reproductive physiology and profoundly impacted clinical care. Early recognition of the hypothalamic and pituitary control of ovarian and testicular function led to the identification of GnRH as the key driver of pubertal onset. Decades later, discovery of the kisspeptin system further refined our understanding of human reproductive neuroendocrinology. Development of long-acting analogs of GnRH revolutionized the treatment of precocious puberty worldwide and ushered in the current era of an ever-expanding therapeutic armamentarium. Identification of monogenic etiologies of precocious puberty has further illustrated the exquisite complexity that comprises neurosecretory modulation of the hypothalamic GnRH neuron and may well lead to exciting novel targeted therapies.
The History of Obesity Research
Authors: Lustig Robert H. , Fennoy Ilene
Published: November 2022

Perhaps the most unexpected development in pediatric endocrinology in the past 50 years has been the recognition of obesity as an endocrine/metabolic disorder rather than a life choice or moral failing. The history of obesity research is disjointed, having followed two separate paths in the 20th century, based on two independent yet overlapping paradigms. Proponents of the “Energy Storage” hypothesis point to data implicating monogenetic disorders, the ventromedial hypothalamus, insulin, cortisol, and the adipocyte itself in the pathogenesis of obesity. Alternatively, proponents of the “Energy Balance” hypothesis point to data implicating increased caloric intake, decreased caloric expenditure, gastrointestinal hormones, and microbiome changes as being critical for obesity. These two separate lines of research merged somewhat with the discovery of leptin in 1994, as leptin established a major hormonal role in weight control. Leptin has explained some of the dichotomy and has proved essential in understanding the importance of developmental programming and epigenetics. However, the mystery of leptin resistance remains unsolved. Despite all our collective knowledge, we appear no closer in solving the obesity puzzle today than we were 50 years ago.
Protective Effects and Mechanisms of Flavonoids in Renal Ischemia-Reperfusion Injury
Authors: Peng Peng , Zou Junrong , Zhong Bin , Zhang Guoxi , Zou Xiaofeng , Xie Tianpeng
Published: November 2022

Background: Acute kidney injury (AKI) is a common and potentially fatal complication encountered during a variety of kidney surgeries. Renal ischemia/reperfusion (I/R) injury is the predominant mechanism of AKI in this setting. Hence, controlling I/R injury is a key research imperative as it is directly related to the prognosis of patients. Summary: In the last decade, studies in vitro and in animal models have demonstrated that flavonoids can significantly alleviate I/R-induced AKI through a variety of pathways, including anti-oxidative stress, anti-inflammation, anti-cell death, inhibition of endoplasmic reticulum stress, and alleviation of mitochondrial dysfunction. Based on the extensive role of flavonoids in ischemia-reperfusion injury, the lack of drugs entering the clinic so far is a question worthy of consideration. Key Messages: This review summarizes the available evidence pertaining to the protective effect of flavonoids against renal I/R injury and discusses their potential clinical application in renal I/R injury.