bluuebunny/crossref_metadata_2025 huggingface dataset:
Unbiased inverse volume rendering with differential trackers
Authors: Nimier-David Merlin , Müller Thomas , Keller Alexander , Jakob Wenzel
Published: July 2022

Volumetric representations are popular in inverse rendering because they have a simple parameterization, are smoothly varying, and transparently handle topology changes. However, incorporating the full volumetric transport of light is costly and challenging, often leading practitioners to implement simplified models, such as purely emissive and absorbing volumes with "baked" lighting. One such challenge is the efficient estimation of the gradients of the volume's appearance with respect to its scattering and absorption parameters. We show that the straightforward approach---differentiating a volumetric free-flight sampler---can lead to biased and high-variance gradients, hindering optimization. Instead, we propose using a new sampling strategy:
differential ratio tracking
, which is unbiased, yields low-variance gradients, and runs in linear time. Differential ratio tracking combines ratio tracking and reservoir sampling to estimate gradients by sampling distances proportional to the unweighted transmittance rather than the usual extinction-weighted transmittance. In addition, we observe local minima when optimizing scattering parameters to reproduce dense volumes or surfaces. We show that these local minima can be overcome by bootstrapping the optimization from nonphysical emissive volumes that are easily optimized.
A unified newton barrier method for multibody dynamics
Authors: Chen Yunuo , Li Minchen , Lan Lei , Su Hao , Yang Yin , Jiang Chenfanfu
Published: July 2022

We present a simulation framework for multibody dynamics via a universal variational integration. Our method naturally supports mixed rigid-deformables and mixed codimensional geometries, while providing guaranteed numerical convergence and accurate resolution of contact, friction, and a wide range of articulation constraints. We unify (1) the treatment of simulation degrees of freedom for rigid and soft bodies by formulating them both in terms of Lagrangian nodal displacements, (2) the handling of general linear equality joint constraints through an efficient change-of-variable strategy, (3) the enforcement of nonlinear articulation constraints based on novel distance potential energies, (4) the resolution of frictional contact between mixed dimensions and bodies with a variational Incremental Potential Contact formulation, and (5) the modeling of generalized restitution through semi-implicit Rayleigh damping. We conduct extensive unit tests and benchmark studies to demonstrate the efficacy of our method.
Efficient estimation of boundary integrals for path-space differentiable rendering
Authors: Yan Kai , Lassner Christoph , Budge Brian , Dong Zhao , Zhao Shuang
Published: July 2022

Boundary integrals are unique to physics-based differentiable rendering and crucial for differentiating with respect to object geometry. Under the differential path integral framework---which has enabled the development of sophisticated differentiable rendering algorithms---the boundary components are themselves path integrals. Previously, although the mathematical formulation of boundary path integrals have been established, efficient estimation of these integrals remains challenging.

In this paper, we introduce a new technique to efficiently estimate boundary path integrals. A key component of our technique is a primary-sample-space guiding step for importance sampling of boundary segments. Additionally, we show multiple importance sampling can be used to combine multiple guided samplings. Lastly, we introduce an optional edge sorting step to further improve the runtime performance. We evaluate the effectiveness of our method using several differentiable-rendering and inverse-rendering examples and provide comparisons with existing methods for reconstruction as well as gradient quality.
Umbrella meshes
Authors: Ren Yingying , Kusupati Uday , Panetta Julian , Isvoranu Florin , Pellis Davide , Chen Tian , Pauly Mark
Published: July 2022

We present a computational inverse design framework for a new class of volumetric deployable structures that have compact rest states and deploy into bending-active 3D target surfaces.
Umbrella meshes
consist of elastic beams, rigid plates, and hinge joints that can be directly printed or assembled in a zero-energy fabrication state. During deployment, as the elastic beams of varying heights rotate from vertical to horizontal configurations, the entire structure transforms from a compact block into a target curved surface. Umbrella Meshes encode both intrinsic and extrinsic curvature of the target surface and in principle are free from the area expansion ratio bounds of past auxetic material systems.

We build a reduced physics-based simulation framework to accurately and efficiently model the complex interaction between the elastically deforming components. To determine the mesh topology and optimal shape parameters for approximating a given target surface, we propose an inverse design optimization algorithm initialized with conformal flattening. Our algorithm minimizes the structure's strain energy in its deployed state and optimizes actuation forces so that the final deployed structure is in stable equilibrium close to the desired surface with few or no external constraints. We validate our approach by fabricating a series of physical models at various scales using different manufacturing techniques.
Neural dual contouring
Authors: Chen Zhiqin , Tagliasacchi Andrea , Funkhouser Thomas , Zhang Hao
Published: July 2022

We introduce
neural dual contouring
(NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC). Like traditional DC, it produces exactly one vertex per grid cell and one quad for each grid edge intersection, a natural and efficient structure for reproducing sharp features. However, rather than computing vertex locations and edge crossings with hand-crafted functions that depend directly on difficult-to-obtain surface gradients, NDC uses a neural network to predict them. As a result, NDC can be trained to produce meshes from signed or unsigned distance fields, binary voxel grids, or point clouds (with or without normals); and it can produce open surfaces in cases where the input represents a sheet or partial surface. During experiments with five prominent datasets, we find that NDC, when trained on one of the datasets, generalizes well to the others. Furthermore, NDC provides better surface reconstruction accuracy, feature preservation, output complexity, triangle quality, and inference time in comparison to previous learned (e.g., neural marching cubes, convolutional occupancy networks) and traditional (e.g., Poisson) methods. Code and data are available at https://github.com/czq142857/NDC.
True seams
Authors: Rodríguez Alejandro , Cirio Gabriel
Published: July 2022

Seams play a fundamental role in the way a garment looks, fits, feels and behaves. Seams can have very different shapes and mechanical properties depending on how fabric is overlapped, folded and stitched together, with garment designers often choosing specific seam and stitch type combinations depending on the appearance and behavior they want for the garment. Yet, virtually all 3D CAD tools for fashion and visual effects ignore most of the visual and mechanical complexity of seams, and just treat them as joint edges, their simplest possible form, drastically limiting the fidelity of digital garments. In this paper, we present a method that models seams following their true, real-life construction. Each seam brings together and overlaps the fabric pieces to be sewn, folds the fabric according to the type of seam, and stitches the resulting assembly following the type of stitch. To avoid dealing with the complexities of folding in 3D space, we cast the problem into a sequence of simpler 2D problems where we can easily shape the seam and produce a result free of self-intersections, before lifting the folded geometry back to 3D space. We run a series of constrained optimizations to enforce spatial properties in these 2D settings, allowing us to treat asymmetric seams, gatherings and overlapping construction orders. Using a variety of common seams and stitches, we show how our approach substantially improves the visual appearance of full garments, for a better and more predictive digital replica.
WallPlan
Authors: Sun Jiahui , Wu Wenming , Liu Ligang , Min Wenjie , Zhang Gaofeng , Zheng Liping
Published: July 2022

Floorplan generation has drawn widespread interest in the community. Recent learning-based methods for generating realistic floorplans have made significant progress while a complex heuristic post-processing is still necessary to obtain desired results. In this paper, we propose a novel wall-oriented method, called
WallPlan
, for automatically and efficiently generating plausible floorplans from various design constraints. We pioneer the representation of the floorplan as a wall graph with room labels and consider the floorplan generation as a graph generation. Given the boundary as input, we first initialize the boundary with windows predicted by WinNet. Then a graph generation network GraphNet and semantics prediction network LabelNet are coupled to generate the wall graph progressively by imitating graph traversal.
WallPlan
can be applied for practical architectural designs, especially the wall-based constraints. We conduct ablation experiments, qualitative evaluations, quantitative comparisons, and perceptual studies to evaluate our method's feasibility, efficacy, and versatility. Intensive experiments demonstrate our method requires no post-processing, producing higher quality floorplans than state-of-the-art techniques.
DEF
Authors: Matveev Albert , Rakhimov Ruslan , Artemov Alexey , Bobrovskikh Gleb , Egiazarian Vage , Bogomolov Emil , Panozzo Daniele , Zorin Denis , Burnaev Evgeny
Published: July 2022

We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven methods, which reduce this problem to feature classification, we propose to
regress a scalar field
representing the distance from point samples to the closest feature line on
local patches.
Our approach is the first that scales to massive point clouds by fusing distance-to-feature estimates obtained on individual patches.

We extensively evaluate our approach against related state-of-the-art methods on newly proposed synthetic and real-world 3D CAD model benchmarks. Our approach not only outperforms these (with improvements in Recall and False Positives Rates), but generalizes to real-world scans after training our model on synthetic data and fine-tuning it on a small dataset of scanned data.

We demonstrate a downstream application, where we reconstruct an explicit representation of straight and curved sharp feature lines from range scan data.

We make code, pre-trained models, and our training and evaluation datasets available at https://github.com/artonson/def.
Neural jacobian fields
Authors: Aigerman Noam , Gupta Kunal , Kim Vladimir G. , Chaudhuri Siddhartha , Saito Jun , Groueix Thibault
Published: July 2022

This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art. The framework is based on reducing the neural aspect to a prediction of a matrix for a single given point, conditioned on a global shape descriptor. The field of matrices is then projected onto the tangent bundle of the given mesh, and used as candidate jacobians for the predicted map. The map is computed by a standard Poisson solve, implemented as a differentiable layer with cached pre-factorization for efficient training. This construction is agnostic to the triangulation of the input, thereby enabling applications on datasets with varying triangulations. At the same time, by operating in the intrinsic gradient domain of each individual mesh, it allows the framework to predict highly-accurate mappings. We validate these properties by conducting experiments over a broad range of scenarios, from semantic ones such as morphing, registration, and deformation transfer, to optimization-based ones, such as emulating elastic deformations and contact correction, as well as being the first work, to our knowledge, to tackle the task of learning to compute UV parameterizations of arbitrary meshes. The results exhibit the high accuracy of the method as well as its versatility, as it is readily applied to the above scenarios without any changes to the framework.
Accommodative holography
Authors: Kim Dongyeon , Nam Seung-Woo , Lee Byounghyo , Seo Jong-Mo , Lee Byoungho
Published: July 2022

Holographic displays have gained unprecedented attention as next-generation virtual and augmented reality applications with recent achievements in the realization of a high-contrast image through computer-generated holograms (CGHs). However, these holograms show a high energy concentration in a limited angular spectrum, whereas the holograms with uniformly distributed angular spectrum suffer from a severe speckle noise in the reconstructed images. In this study, we claim that these two physical phenomena attributed to the existing CGHs significantly limit the support of accommodation cues, which is known as one of the biggest advantages of holographic displays. To support the statement, we analyze and evaluate various CGH algorithms with contrast gradients - a change of contrast over the change of the focal diopter of the eye - simulated based on the optical configuration of the display system and human visual perception models. We first introduce two approaches to improve monocular accommodation response in holographic viewing experience; optical and computational approaches to provide holographic images with sufficient contrast gradients. We design and conduct user experiments with our prototype of holographic near-eye displays, validating the deficient support of accommodation cues in the existing CGH algorithms and demonstrating the feasibility of the proposed solutions with significant improvements on accommodative gains.
Computational design of passive grippers
Authors: Kodnongbua Milin , Good Ian , Lou Yu , Lipton Jeffrey , Schulz Adriana
Published: July 2022

This work proposes a novel generative design tool for passive grippers---robot end effectors that have no additional actuation and instead leverage the existing degrees of freedom in a robotic arm to perform grasping tasks. Passive grippers are used because they offer interesting trade-offs between cost and capabilities. However, existing designs are limited in the types of shapes that can be grasped. This work proposes to use rapid-manufacturing and design optimization to expand the space of shapes that can be passively grasped. Our novel generative design algorithm takes in an object and its positioning with respect to a robotic arm and generates a 3D printable passive gripper that can stably pick the object up. To achieve this, we address the key challenge of jointly optimizing the shape and the insert trajectory to ensure a passively stable grasp. We evaluate our method on a testing suite of 22 objects (23 experiments), all of which were evaluated with physical experiments to bridge the virtual-to-real gap. Code and data are at https://homes.cs.washington.edu/~milink/passive-gripper/
Disentangling random and cyclic effects in time-lapse sequences
Authors: Härkönen Erik , Aittala Miika , Kynkäänniemi Tuomas , Laine Samuli , Aila Timo , Lehtinen Jaakko
Published: July 2022

Time-lapse image sequences offer visually compelling insights into dynamic processes that are too slow to observe in real time. However, playing a long time-lapse sequence back as a video often results in distracting flicker due to random effects, such as weather, as well as cyclic effects, such as the day-night cycle. We introduce the problem of disentangling time-lapse sequences in a way that allows separate, after-the-fact control of overall trends, cyclic effects, and random effects in the images, and describe a technique based on data-driven generative models that achieves this goal. This enables us to "re-render" the sequences in ways that would not be possible with the input images alone. For example, we can stabilize a long sequence to focus on plant growth over many months, under selectable, consistent weather.

Our approach is based on Generative Adversarial Networks (GAN) that are conditioned with the time coordinate of the time-lapse sequence. Our architecture and training procedure are designed so that the networks learn to model random variations, such as weather, using the GAN's latent space, and to disentangle overall trends and cyclic variations by feeding the conditioning time label to the model using Fourier features with specific frequencies.

We show that our models are robust to defects in the training data, enabling us to amend some of the practical difficulties in capturing long time-lapse sequences, such as temporary occlusions, uneven frame spacing, and missing frames.
Contact-centric deformation learning
Authors: Romero Cristian , Casas Dan , Chiaramonte Maurizio M. , Otaduy Miguel A.
Published: July 2022

We propose a novel method to machine-learn highly detailed, nonlinear contact deformations for real-time dynamic simulation. We depart from previous deformation-learning strategies, and model contact deformations in a contact-centric manner. This strategy shows excellent generalization with respect to the object's configuration space, and it allows for simple and accurate learning. We complement the contact-centric learning strategy with two additional key ingredients: learning a continuous vector field of contact deformations, instead of a discrete approximation; and sparsifying the mapping between the contact configuration and contact deformations. These two ingredients further contribute to the accuracy, efficiency, and generalization of the method. We integrate our learning-based contact deformation model with subspace dynamics, showing real-time dynamic simulations with fine contact deformation detail.
NeuralSound
Authors: Jin Xutong , Li Sheng , Wang Guoping , Manocha Dinesh
Published: July 2022

We present a novel learning-based modal sound synthesis approach that includes a mixed vibration solver for modal analysis and a radiation network for acoustic transfer. Our mixed vibration solver consists of a 3D sparse convolution network and a Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) module for iterative optimization. Moreover, we highlight the correlation between a standard numerical vibration solver and our network architecture. Our radiation network predicts the Far-Field Acoustic Transfer maps (FFAT Maps) from the surface vibration of the object. The overall running time of our learning-based approach for most new objects is less than one second on a RTX 3080 Ti GPU while maintaining a high sound quality close to the ground truth solved by standard numerical methods. We also evaluate the numerical and perceptual accuracy of our approach on different objects with various shapes and materials.
Which cross fields can be quadrangulated?
Authors: Shen Hanxiao , Zhu Leyi , Capouellez Ryan , Panozzo Daniele , Campen Marcel , Zorin Denis
Published: July 2022

We describe a method for the generation of seamless surface parametrizations with guaranteed local injectivity and full control over holonomy. Previous methods guarantee only one of the two. Local injectivity is required to enable these parametrizations' use in applications such as surface quadrangulation and spline construction. Holonomy control is crucial to enable guidance or prescription of the parametrization's isocurves based on directional information, in particular from cross-fields or feature curves, and more generally to constrain the parametrization topologically. To this end we investigate the relation between cross-field topology and seamless parametrization topology. Leveraging previous results on locally injective parametrization and combining them with insights on this relation in terms of holonomy, we propose an algorithm that meets these requirements. A key component relies on the insight that arbitrary surface cut graphs, as required for global parametrization, can be homeomorphically modified to assume almost any set of turning numbers with respect to a given target cross-field.
AI education matters
Authors: Neller Todd W.
Published: July 2022

In this column, we describe the Model AI Assignment "FairKalah: Fair Mancala Competition". After introducing the rules of Mancala (a.k.a. Kalah), we discuss the primary difficulty that its unfairness causes for AI competition assessment, and present a solution along with a description of a set of resources to aid in assignment adoption.
Semantic Embedding Guided Attention with Explicit Visual Feature Fusion for Video Captioning
Authors: Dong Shanshan , Niu Tianzi , Luo Xin , Liu Wu , Xu Xinshun
Published: July 2022

Video captioning, which bridges vision and language, is a fundamental yet challenging task in computer vision. To generate accurate and comprehensive sentences, both visual and semantic information is quite important. However, most existing methods simply concatenate different types of features and ignore the interactions between them. In addition, there is a large semantic gap between visual feature space and semantic embedding space, making the task very challenging. To address these issues, we propose a framework named semantic embedding guided attention with
Explicit visual Feature Fusion for vidEo CapTioning, EFFECT
for short, in which we design an
explicit visual-feature fusion (EVF)
scheme to capture the pairwise interactions between multiple visual modalities and fuse multimodal visual features of videos in an explicit way. Furthermore, we propose a novel attention mechanism called
semantic embedding guided attention (SEGA
), which cooperates with the temporal attention to generate a joint attention map. Specifically, in SEGA, the semantic word embedding information is leveraged to guide the model to pay more attention to the most correlated visual features at each decoding stage. In this way, the semantic gap between visual and semantic space is alleviated to some extent. To evaluate the proposed model, we conduct extensive experiments on two widely used datasets, i.e., MSVD and MSR-VTT. The experimental results demonstrate that our approach achieves state-of-the-art results in terms of four evaluation metrics.
The Ocean Carbon Cycle
Authors: DeVries Tim
Published: July 2022

The ocean holds vast quantities of carbon that it continually exchanges with the atmosphere through the air-sea interface. Because of its enormous size and relatively rapid exchange of carbon with the atmosphere, the ocean controls atmospheric CO2 concentration and thereby Earth's climate on timescales of tens to thousands of years. This review examines the basic functions of the ocean's carbon cycle, demonstrating that the ocean carbon inventory is determined primarily by the mass of the ocean, by the chemical speciation of CO2 in seawater, and by the action of the solubility and biological pumps that draw carbon into the ocean's deeper layers, where it can be sequestered for decades to millennia. The ocean also plays a critical role in moderating the impacts of climate change by absorbing an amount of carbon equivalent to about 25% of anthropogenic CO2 emissions over the past several decades. However, this also leads to ocean acidification and reduces the chemical buffering capacity of the ocean and its future ability to take up CO2. This review closes with a look at the uncertain future of the ocean carbon cycle and the scientific challenges that this uncertainty brings.
Dietary fructose as a metabolic risk factor
Authors: Sindhunata Daniko P. , Meijnikman A. Stijn , Gerdes Victor E. A. , Nieuwdorp Max
Published: July 2022

Over the past decades, the role of the intestinal microbiota in metabolic diseases has come forward. In this regard, both composition and function of our intestinal microbiota is highly variable and influenced by multiple factors, of which diet is one of the major elements. Between 1970 and 1990, diet composition has changed and consumption of dietary sugars has increased, of which fructose intake rose by more than 10-fold. This increased intake of sugars and fructose is considered as one of the major risk factors in the developments of obesity and several metabolic disturbances. In this review, we describe the association of dietary fructose intake with insulin resistance, nonalcoholic fatty liver disease (NAFLD) and lipid metabolism. Moreover, we will focus on the potential causality of this altered gut microbiota using fecal transplantation studies in human metabolic disease and whether fecal microbial transplant can reverse this phenotype.
Risk Analysis of A-H Share Connect Market Based on Deep Learning and BP Neural Network
Authors: Cui Rumeng , Chen Wen
Published: July 2022

China’s Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs make it possible for investors to trade stocks within specified limits through the two stock exchanges. The A-H share exchange stock market is crucial to the opening of the Mainland market, but few studies have paid attention to the market risks of such stocks. Using deep learning and BP neural network algorithm, this study constructs a three-dimensional A-H share interconnection market risk prediction index system including stock price fundamental indicators, technical indicators, and macro indicators based on the CES300 Index. Taking the CES300 Index return as the output layer indicator, a BP neural network with a 21-10-1 structure is constructed, and the tan-sigmoid transfer function and the LM optimization algorithm training function are used for network training to predict the return of the A-H share interconnected stock market. The mean square error (MSE) converges to 10−6, and the goodness of fit R reaches 0.9928 and validates the prediction accuracy of the BP neural network model. It provides an efficient and accurate risk prediction model for the A-H share interconnected market, which facilitates the interactive development of the Mainland and Hong Kong markets.
Application of an Optical Tracking System for Motor Skill Assessment in Laparoscopic Surgery
Authors: Yang Lixiao , Lyu Kunyong , Song Chengli
Published: July 2022

Objective. Motion analysis of surgical instruments can be used to evaluate laparoscopic surgical skills, and this study assessed the validity of an optical tracking system for the assessment of laparoscopic surgical motor skills. Methods. Ten experienced surgeons and ten novices were recruited to complete the transferring tasks on a laparoscopic simulator. An optical tracking system, Micron Tracker, was used to capture the marker points on each instrument and to obtain the coordinates of the marker points and the corresponding instrument tip coordinates. The data are processed to create a coordinate system based on the laparoscopic simulator and to calculate the movement parameters of the instruments, such as operating time, path length, speed, acceleration, and smoothness. At the same time, the range of motion of the instrument (insertion depth and pivoting angle) is also calculated. Results. The position that the tip of the instrument can reach is a small, irregularly shaped spatial area. Significant differences (

p
<
0.05

) were found between the surgeon and novice groups in parameters such as operating time, path length, mean speed, mean acceleration, and mean smoothness. The range of insertion depth of the instruments was approximately 150 mm to 240 mm, and the pivoting angles of the left and right instruments were 30.9° and 46.6° up and down and 28.0° and 35.0° left and right, respectively. Conclusions. The optical tracking system was effective in subjectively evaluating laparoscopic surgical skills, with significant differences between the surgeon and novice groups in terms of movement parameters, but not in terms of range of motion.
Traditional Village Landscape Identification and Remodeling Strategy: Taking the Radish Village as an Example
Authors: Huang Dongsheng , Zhang Ning , Zhang Yaou
Published: July 2022

As an important relic of traditional Chinese culture, traditional villages have important cultural values. With the continuous deepening of modern urbanization and the development of rural tourism, the village landscape is also facing profound challenges. In the context of rural revitalization and tourism development, it is necessary to strengthen the landscape identity of traditional villages. Based on the background of rural revitalization, this article reviews and discusses the related concepts and research status of traditional village landscapes, the identity of village landscapes and existing problems in landscapes, and remodeling strategies by sorting out relevant research literature at home and abroad in recent years. People’s awareness of local landscape identity reshapes the landscape uniqueness of traditional villages so that the local culture and foreign culture can reach a state of balance and integration. The village landscape identity and the impact of digital technology and self-media platforms on landscape remodeling are reviewed and discussed. The study found that the landscape identity of traditional villages is reflected in the activity places with local regional cultural characteristics and relies on the spiritual emotions of the villagers. For the existing problems in the landscape, a landscape remodeling strategy is proposed to restore people’s awareness of local landscape identity and reshape the landscape uniqueness of traditional villages.
Investigation of Mechanical Behavior and Microstructure Analysis of AA7075/SiC/B4C-Based Aluminium Hybrid Composites
Authors: Mahmoud HassabAlla M. A. , Satishkumar P. , Rao Yenda Srinivasa , Chebolu Rohinikumar , Capangpangan Rey Y. , Alguno Arnold C. , Gopal Mahesh , Firos A. , Saravana Murthi C.
Published: July 2022

The microstructure and mechanical properties of an MMC based on AA 7075 and strengthened through silicon carbide (SiC) as well as boron carbide (B4C) elements were studied. The (SiC + B4C) combination was used in various weight percentages of 4, 8, 12, and 16% to create the hybrid composites utilizing the traditional stir casting procedure. XRD and SEM measurements were used to investigate the dispersion of the reinforced particles. For example, microhardness, impact strength, and ultimate tensile strength were measured on hybrid composites at room temperature. The density and porosity of the materials were also studied. The researchers found that increasing the weight percentage of the (SiC + B4C) mixture resulted in a small drop in % elongation. However, hybrid composites comprising 16% (SiC + B4C) weight reduction showed some decrease in hardness and tensile strength. Equated to unreinforced alloys, the hardness and tensile strength of hybrid composites rise by 8% and 21%, respectively. Reinforcement also resulted in a decrease in impact strength and density, as well as an increase in porosity.
Stress Estimation Model for the Sustainable Health of Cancer Patients
Authors: Adeel Muhammad , Mehmood Zahid , Ullah Amin
Published: July 2022

Good health is the most important and very necessary characteristic for stress-free, skillful, and hardworking people with a cooperative environment to create a sustainable society. Validating two algorithms, namely, sequential minimal optimization for regression (SMOreg) using vector machine and linear regression (LR) and using their predicted cancer patients’ cases, this study presents a patient’s stress estimation model (PSEM) to forecast their families’ stress for patients’ sustainable health and better care with early management by under-study cancer hospitals. The year-wise predictions (1998-2010) by LR and SMOreg are verified by comparing with observed values. The statistical difference between the predictions (2021-2030) by these models is analyzed using a statistical

t

-test. From the data of 217067 patients, patients’ stress-impacting factors are extracted to be used in the proposed PSEM. By considering the total population of under-study areas and getting the predicted population (2021-2030) of each area, the proposed PSEM forecasts overall stress for expected cancer patients (2021-2030). Root mean square error (RMSE) (1076.15.46) for LR is less than RSME for SMOreg (1223.75); hence, LR remains better than SMOreg in forecasting (2011-2020). There is no significant statistical difference between values (2021-2030) predicted by LR and SMOreg (

p

value =

0.767
>
0.05

). The average stress for a family member of a cancer patient is 72.71%. It is concluded that under-study areas face a minimum of 2.18% stress, on average 30.98% stress, and a maximum of 94.81% overall stress because of 179561 expected cancer patients of all major types from 2021 to 2030.
A Novel Approach to the Sintering Schedule of Ba (Co0.7Zn0.3)1/3Nb2/3O3 Dielectric Ceramics for Microwave Applications
Authors: Mouyane Mohamed , Itaalit Brahim , Bernard Jérôme , Houivet David
Published: July 2022

The present research is devoted to the optimization of the sintering schedule of Ba (Co0.7Zn0.3)1/3Nb2/3O3 (BCZN) dielectric ceramics for microwaves applications. A novel approach to the heat treatment of these ceramics based on the rapid-rate sintering (RRS) technique followed by a lower temperature annealing cycle has been developed. The relationships among the heat treatment process optimization, the structural, microstructural characteristics, and the microwave dielectric properties of the BCZN ceramics were investigated using X-ray diffraction, scanning electron microscopy, energy dispersion analysis, and vector network analysis. The RRS-technique shortens substantially the time required for the elaboration of these components in comparison with conventional sintering techniques and prevents simultaneously the formation of secondary phases as Ba5Nb4O15 and Ba8(Co, Zn) 1Nb6O24 on the surface of the ceramics. All of the sintered and annealed ceramics exhibit a high quality factor QF close to 110 000 GHz at 6 GHz. The high dielectric constant εr of ∼34.5 and a temperature coefficient of the resonant frequency τf of ∼0 ppm.C-1 were obtained in all annealed ceramics.
HRLR-LOGISTIC: A Factor Selection Machine Learning Method Coupled with Binary Logistic Regression
Authors: Xie Haoyan , Sadiq Maryam , Huang Hai , Sarwar Sughra
Published: July 2022

The selection of influential predictor factors with maximum model accuracy is the main goal of the regression domain. The present study is conducted to integrate an innovative method, that is, “a hybrid of relaxed lasso and ridge regression,” with a logistic regression model in the context of dichotomous factors. The efficacy of the proposed approach is illustrated using both simulated and real-life data. The results suggested that HRLR-logistic selected the best subset compared to standard logistic, Lasso, and Ridge regression. Based on the Akaike information criterion (3065.85) and the Bayesian information criterion (3151.46), the proposed approach is proved to have the highest efficiency for cesarean section data. In addition, the study identified the elements that contribute to the cesarean section in Pakistan. It is evidenced that woman’s literacy level (β = 0.5828), place of delivery (β = 0.8990), availability of nurse as an assistant (β = 0.7370), and care during the first two days of delivery (β = 0.7837) are remarkable factors associated with cesarean section.
Deconstruction and Realization of Sports Entity Simulation Based on Fish Swarm Algorithm
Authors: Wu Kai
Published: July 2022

Due to the limitation of sports movement, the current simulation technology of sports entities is prone to deficiencies in capturing dynamic motion figures and is prone to lack of accuracy. It is also affected by external noise and brightness. To solve these problems, this paper proposes a sports entity simulation based on the fish swarm algorithm and compares the figure effectiveness, figure segmentation, core point, and noise reduction effect of the two in the shooting figure. Through the comparison, it is found that the figure is more appropriate to the real moving figure, the motion capture is more accurate, and the number of core points is related to the accuracy of motion capture. The more core points, the more accurate the motion capture, and the noise reduction effect is also increased by 20.3%, which reduces the impact of brightness on the motion simulation. The difference in the effect of the traditional simulation technology (particle swarm algorithm) and the entity simulation based on the fish swarm algorithm was also compared. The combination with the artificial fish swarm algorithm is to simulate the moving entity and learn from some reference data. By comparing the data between the two after the experiment, it is concluded that the fish swarm algorithm is more effective in the simulation of sports entities.
Influence of Stone Shape Factor on the Pull-Out Resistance of Geogrid in the Soil-Rock Mixture
Authors: Sun Yongshuai , Li Xuefeng , Wang Guihe
Published: July 2022

In this paper, the particle shape factor of the stone in the soil-rock mixture was used as the specific research object of the geogrid pull-out test, combining the random generation method and evaluation criteria of rock blocks in the soil-rock mixture, and a feasible method for classifying stones according to their shape factors is presented. By comparing the mechanical behavior of soil-rock mixture composed of rock blocks with different rock material shape factors in the geogrid pull-out tests under different working conditions, we studied its changing laws. The research shows that with the increase of the shape factor of the stone, the pull-out resistance of the geogrid gradually decreases. When the stone paved in the model box changes from a single layer to a double layer, the pull-out resistance of the geogrid increases, and there is a disturbance range in the contact interface between the geogrid and the stone; stone blocks 5 cm away from the geogrid surface can still have an indirect effect on the pull-out resistance of geogrids; the pull-out resistance of geogrid in clay is greater than that in the sand; and an empirical expression for predicting the pull-out resistance of geogrids is obtained. This empirical expression helps to explain the influence mechanism of stone shape on drawing resistance of geogrid.
Competitiveness Evaluation and Obstacle Factor Analysis of Urban Green and Low-Carbon Development in Beijing-Tianjin-Hebei Cities
Authors: Yuan Kai , Hu Biao , Niu Tingyun , Zhu Binglong , Zhang Liang , Guan Yuqiong
Published: July 2022

Based on the PSR conceptual evaluation model of urban green and low-carbon development competitiveness, this paper evaluates the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 and reveals their spatial effects and obstacle factors combined with the spatial autocorrelation and obstacle model. The results show the following: (1) In terms of the overall situation, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, the comprehensive evaluation indexes of Beijing, Zhangjiakou, and Tianjin are very competitive, while Hengshui, Handan, and Xingtai are relatively weak. (2) In terms of spatial data analysis, the global and local Moran`s I of Beijing-Tianjin-Hebei cities from 2010 to 2019 were positive and passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. On the whole, it presents a spatial agglomeration pattern of “high in the north, partial jump, and low in the south”. (3) In the aspect of obstacle factor analysis, the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan of GDP, the urban green technology innovation level, and industrial soot emission of ten thousand yuan of GDP are the main obstacle factors restricting the improvement of urban green low-carbon development competitiveness in Beijing-Tianjin-Hebei cities.
Single-Layer Planar Monopole Antenna-Based Artificial Magnetic Conductor (AMC)
Authors: Abdulbari Ali Abdulateef , Abdul Rahim Sharul Kamal , Abedi Firas , Soh Ping Jack , Hashim Ali , Qays Rami , Ahmad Sarosh , Zeain Mohammed Yousif
Published: July 2022

In this paper, a coplanar waveguide (CPW)-fed patch antenna is fabricated on a layer of metasurface to increase gain. The antenna is fabrication on Roger substrate with a thickness of 0.25 mm, with the overall dimension of the proposed design being 45 × 30 × 0.25 mm3. The size of the patch antenna is 24 × 14 × 0.25 mm3, and the AMC unit cell is 22 × 22 × 0.25 mm3. This metasurface is designed based on the split-ring resonator unit cells forming an array of the artificial magnetic conductor (AMC). Meanwhile, the antenna operation on 3.5 GHz is enabled by etching a split-ring resonator slot on the ground plane with a small gap to enhance antenna gain and improve impedance bandwidth when integrated with a metasurface. This simulation planer monopole antenna is applied for 5G application. The experimenter test is applied for the antenna performance in terms of return loss, gain, and radiation patterns. The operating frequency range with and without MTM is from 3.41 to 3.68 GHz (270 MHz) and 3.37 to 3.55 GHz (180 MHz), respectively, with gain improvements of about 2.7 dB (without MTM) to 6.0 dB (with MTM) at 3.5 GHz. The maximum improvement of the gain is about 42% when integrated with the AMC. The AMC has solved several issues to overcome the typical limitation in conventional antenna design. A circuit model is also proposed to simplify the estimation of the performance of this antenna at the desired frequency band. The proposed design is simulated by CST microwave studio. Finally, the antenna is fabricated and measured. Result comparison between simulations and measurements indicates a good agreement between them.
Rotating Machinery Fault Identification via Adaptive Convolutional Neural Network
Authors: Zhang Luke , Liu Jia , Su Shu , Lu Tong , Xue Chunrong , Wang Yinjun , Ding Xiaoxi , Shao Yimin
Published: July 2022

Rotating machinery plays an important role in transportation, petrochemical industry, industrial production, national defence equipment, and other fields. With the development of artificial intelligence, the equipment condition monitoring especially needs an intelligent fault identification method to solve the problem of high false alarm rate under complex working conditions. At present, intelligent recognition models mostly increase the complexity of the network to achieve the purpose of high recognition rate. This method often needs better hardware support and increases the operation time. Therefore, this paper proposes an adaptive convolutional neural network (ACNN) by combining ensemble learning and simple convolutional neural network (CNN). ACNN model consists of input layer, subnetwork unit, fusion unit, and output layer. The input of the model is one-dimensional (1D) vibration signal sample, and the subnetwork unit consists of several simple CNNs, and the fusion unit weights the output of the subnetwork units through the weight matrix. ACNN recognizes the self-adaptive of weight factors through the fusion unit. The adaptive performance and robustness of ACNN for sample recognition under variable working conditions are verified by gear and bearing experiments.
Analysis of Strategies and Skills of English Translation Based on Coverage Mechanism
Authors: Liu Bin , Wang Jing
Published: July 2022

In order to alleviate the problem of over translation and missing translation in NMT, based on the consistency and complementarity of information stored in different covering models, a multicoverage fusion model is proposed, which uses coverage vector and coverage score to guide the attention mechanism at the same time. First, the concept level definitions of words are covered. Then, two kinds of translation history information stored in the cover vector and cover score are used to guide the calculation of the attention score at the same time. Finally, the dual attention decoding method based on the fusion coverage mechanism is adopted. The experimental results show that the multicoverage fusion model can improve the translation quality of NMT.
Pretransplant Evaluation and Liver Transplantation Outcome in PBC Patients
Authors: Mijic Maja , Saric Ivona , Delija Bozena , Lalovac Milos , Sobocan Nikola , Radetic Eva , Martincevic Dora , Filipec Kanizaj Tajana
Published: July 2022

Primary biliary cholangitis (PBC) is an autoimmune chronic cholestatic liver disease characterized by progressive cholangiocyte and bile duct destruction leading to fibrosis and finally to liver cirrhosis. The presence of disease-specific serological antimitochondrial antibody (AMA) together with elevated alkaline phosphatase (ALP) as a biomarker of cholestasis is sufficient for diagnosis. Ursodeoxycholic acid (UDCA) is the first treatment option for PBC. Up to 40% of patients have an incomplete response to therapy, and over time disease progresses to liver cirrhosis. Several risk scores are proposed for better evaluation of patients before and during treatment to stratify patients at increased risk of disease progression. GLOBE score and UK PBC risk score are used for the evaluation of UDCA treatment and Mayo risk score for transplant-free survival. Liver transplantation (LT) is the only treatment option for end-stage liver disease. More than 10 years after LT, 40% of patients experience recurrence of the disease. A liver biopsy is required to establish rPBC (recurrent primary biliary cholangitis). The only treatment option for rPBC is UDCA, and data show biochemical and clinical improvement, plus potential beneficial effects for use after transplantation for the prevention of rPBC development. Additional studies are required to assess the full impact of rPBC on graft and recipient survival and for treatment options for rPBC.