bluuebunny/crossref_metadata_2025 huggingface dataset:
High-Order Moment-Encoded Kinetic Simulation of Turbulent Flows
Authors: Li Wei , Wang Tongtong , Pan Zherong , Gao Xifeng , Wu Kui , Desbrun Mathieu
Published: December 2023

Kinetic solvers for incompressible fluid simulation were designed to run efficiently on massively parallel architectures such as GPUs. While these lattice Boltzmann solvers have recently proven much faster and more accurate than the macroscopic Navier-Stokes-based solvers traditionally used in graphics, it systematically comes at the price of a very large memory requirement: a mesoscopic discretization of statistical mechanics requires over an order of magnitude more variables per grid node than most fluid solvers in graphics. In order to open up kinetic simulation to gaming and simulation software packages on commodity hardware, we propose a HighOrder Moment-Encoded Lattice-Boltzmann-Method solver which we coined HOME-LBM, requiring only the storage of a few moments per grid node, with little to no loss of accuracy in the typical simulation scenarios encountered in graphics. We show that our lightweight and lightspeed fluid solver requires three times less memory and runs ten times faster than state-of-the-art kinetic solvers, for a nearly-identical visual output.
ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
Authors: Zhang Yuxin , Dong Weiming , Tang Fan , Huang Nisha , Huang Haibin , Ma Chongyang , Lee Tong-Yee , Deussen Oliver , Xu Changsheng
Published: December 2023

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, and layout remains a challenge, leading to a lack of disentanglement and editability. To address this problem, we propose a novel approach that leverages the step-by-step generation process of diffusion models, which generate images from low to high frequency information, providing a new perspective on representing, generating, and editing images. We develop the Prompt Spectrum Space P*, an expanded textual conditioning space, and a new image representation method called
ProSpect. ProSpect
represents an image as a collection of inverted textual token embeddings encoded from per-stage prompts, where each prompt corresponds to a specific generation stage (i.e., a group of consecutive steps) of the diffusion model. Experimental results demonstrate that P* and
ProSpect
offer better disentanglement and controllability compared to existing methods. We apply
ProSpect
in various personalized attribute-aware image generation applications, such as image-guided or text-driven manipulations of materials, style, and layout, achieving previously unattainable results from a single image input without fine-tuning the diffusion models. Our source code is available at https://github.com/zyxElsa/ProSpect.
Power Plastics: A Hybrid Lagrangian/Eulerian Solver for Mesoscale Inelastic Flows
Authors: Qu Ziyin , Li Minchen , Yang Yin , Jiang Chenfanfu , De Goes Fernando
Published: December 2023

We present a novel hybrid Lagrangian/Eulerian method for simulating inelastic flows that generates high-quality particle distributions with adaptive volumes. At its core, our approach integrates an updated Lagrangian time discretization of continuum mechanics with the Power Particle-In-Cell geometric representation of deformable materials. As a result, we obtain material points described by optimized density kernels that precisely track the varying particle volumes both spatially and temporally. For efficient CFL-rate simulations, we also propose an implicit time integration for our system using a non-linear Gauss-Seidel solver inspired by X-PBD, viewing Eulerian nodal velocities as primal variables. We demonstrate the versatility of our method with simulations of mesoscale bubbles, sands, liquid, and foams.
Topology Guaranteed B-Spline Surface/Surface Intersection
Authors: Yang Jieyin , Jia Xiaohong , Yan Dong-Ming
Published: December 2023

The surface/surface intersection technique serves as one of the most fundamental functions in modern Computer Aided Design (CAD) systems. Despite the long research history and successful applications of surface intersection algorithms in various CAD industrial software, challenges still exist in balancing computational efficiency, accuracy, as well as topology correctness. Specifically, most practical intersection algorithms fail to guarantee the correct topology of the intersection curve(s) when two surfaces are in near-critical positions, which brings instability to CAD systems. Even in one of the most successfully used commercial geometry engines ACIS, such complicated intersection topology can still be a tough nut to crack.

In this paper, we present a practical topology guaranteed algorithm for computing the intersection loci of two B-spline surfaces. Our algorithm well treats all types of common and complicated intersection topology with practical efficiency, including those intersections with multiple branches or cross singularities, contacts in several isolated singular points or highorder contacts along a curve, as well as intersections along boundary curves. We present representative examples of these hard topology situations that challenge not only the open-source geometry engine OCCT but also the commercial engine ACIS. We compare our algorithm in both efficiency and topology correctness on plenty of common and complicated models with the open-source intersection package in SISL, OCCT, and the commercial engine ACIS.
MetaLayer: A Meta-Learned BSDF Model for Layered Materials
Authors: Guo Jie , Li Zeru , He Xueyan , Wang Beibei , Li Wenbin , Guo Yanwen , Yan Ling-Qi
Published: December 2023

Reproducing the appearance of arbitrary layered materials has long been a critical challenge in computer graphics, with regard to the demanding requirements of both physical accuracy and low computation cost. Recent studies have demonstrated promising results by learning-based representations that implicitly encode the appearance of complex (layered) materials by neural networks. However, existing generally-learned models often struggle between strong representation ability and high runtime performance, and also lack physical parameters for material editing. To address these concerns, we introduce
MetaLayer
, a new methodology leveraging meta-learning for modeling and rendering layered materials. MetaLayer contains two networks: a
BSDFNet
that compactly encodes layered materials into implicit neural representations, and a
MetaNet
that establishes the mapping between the physical parameters of each material and the weights of its corresponding implicit neural representation. A new positional encoding method and a well-designed training strategy are employed to improve the performance and quality of the neural model. As a new learning-based representation, the proposed MetaLayer model provides both fast responses to material editing and high-quality results for a wide range of layered materials, outperforming existing layered BSDF models.
C-Shells: Deployable Gridshells with Curved Beams
Authors: Becker Quentin , Suzuki Seiichi , Ren Yingying , Pellis Davide , Panetta Julian , Pauly Mark
Published: December 2023

We introduce a computational pipeline for simulating and designing
C-shells
, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-free planar configuration. When actuated, the elastic beams deform and the assembly deploys towards the target 3D shape.

We propose two alternative computational design approaches for C-shells: (i) Forward exploration simulates the deployed shape from a planar beam layout provided by the user. Once a satisfactory overall shape is found, a subsequent design optimization adapts the beam geometry to reduce the elastic energy of the linkage while preserving the target shape. (ii) Inverse design is facilitated by a new geometric flattening method that takes a design surface as input and computes an initial layout of piecewise straight linkage beams. Our design optimization algorithm then calculates the smooth curved beams to best reproduce the target shape at minimal elastic energy.

We find that C-shells offer a rich space for design and show several studies that highlight new shape topologies that cannot be achieved with existing deployable linkage structures.
AvatarStudio: Text-Driven Editing of 3D Dynamic Human Head Avatars
Authors: Mendiratta Mohit , Pan Xingang , Elgharib Mohamed , Teotia Kartik , R Mallikarjun B , Tewari Ayush , Golyanik Vladislav , Kortylewski Adam , Theobalt Christian
Published: December 2023

Capturing and editing full-head performances enables the creation of virtual characters with various applications such as extended reality and media production. The past few years witnessed a steep rise in the photorealism of human head avatars. Such avatars can be controlled through different input data modalities, including RGB, audio, depth, IMUs, and others. While these data modalities provide effective means of control, they mostly focus on editing the head movements such as the facial expressions, head pose, and/or camera viewpoint. In this paper, we propose AvatarStudio, a text-based method for editing the appearance of a dynamic full head avatar. Our approach builds on existing work to capture dynamic performances of human heads using Neural Radiance Field (NeRF) and edits this representation with a text-to-image diffusion model. Specifically, we introduce an optimization strategy for incorporating multiple keyframes representing different camera viewpoints and time stamps of a video performance into a single diffusion model. Using this personalized diffusion model, we edit the dynamic NeRF by introducing view-and-time-aware Score Distillation Sampling (VT-SDS) following a model-based guidance approach. Our method edits the full head in a canonical space and then propagates these edits to the remaining time steps via a pre-trained deformation network. We evaluate our method visually and numerically via a user study, and results show that our method outperforms existing approaches. Our experiments validate the design choices of our method and highlight that our edits are genuine, personalized, as well as 3D- and time-consistent.
AdaptNet: Policy Adaptation for Physics-Based Character Control
Authors: Xu Pei , Xie Kaixiang , Andrews Sheldon , Kry Paul G. , Neff Michael , Mcguire Morgan , Karamouzas Ioannis , Zordan Victor
Published: December 2023

Motivated by humans' ability to adapt skills in the learning of new ones, this paper presents AdaptNet, an approach for modifying the latent space of existing policies to allow new behaviors to be quickly learned from like tasks in comparison to learning from scratch. Building on top of a given reinforcement learning controller, AdaptNet uses a two-tier hierarchy that augments the original state embedding to support modest changes in a behavior and further modifies the policy network layers to make more substantive changes. The technique is shown to be effective for adapting existing physics-based controllers to a wide range of new styles for locomotion, new task targets, changes in character morphology and extensive changes in environment. Furthermore, it exhibits significant increase in learning efficiency, as indicated by greatly reduced training times when compared to training from scratch or using other approaches that modify existing policies. Code is available at
https://motion-lab.github.io/AdaptNet
.
From Skin to Skeleton: Towards Biomechanically Accurate 3D Digital Humans
Authors: Keller Marilyn , Werling Keenon , Shin Soyong , Delp Scott , Pujades Sergi , Liu C. Karen , Black Michael J.
Published: December 2023

Great progress has been made in estimating 3D human pose and shape from images and video by training neural networks to directly regress the parameters of parametric human models like SMPL. However, existing body models have simplified kinematic structures that do not correspond to the true joint locations and articulations in the human skeletal system, limiting their potential use in biomechanics. On the other hand, methods for estimating biomechanically accurate skeletal motion typically rely on complex motion capture systems and expensive optimization methods. What is needed is a parametric 3D human model with a biomechanically accurate skeletal structure that can be easily posed. To that end, we develop SKEL, which re-rigs the SMPL body model with a biomechanics skeleton. To enable this, we need training data of skeletons inside SMPL meshes in diverse poses. We build such a dataset by optimizing biomechanically accurate skeletons inside SMPL meshes from AMASS sequences. We then learn a regressor from SMPL mesh vertices to the optimized joint locations and bone rotations. Finally, we re-parametrize the SMPL mesh with the new kinematic parameters. The resulting SKEL model is animatable like SMPL but with fewer, and biomechanically-realistic, degrees of freedom. We show that SKEL has more biomechanically accurate joint locations than SMPL, and the bones fit inside the body surface better than previous methods. By fitting SKEL to SMPL meshes we are able to "upgrade" existing human pose and shape datasets to include biomechanical parameters. SKEL provides a new tool to enable biomechanics in the wild, while also providing vision and graphics researchers with a better constrained and more realistic model of human articulation. The model, code, and data are available for research at https://skel.is.tue.mpg.de.
Variational Barycentric Coordinates
Authors: Dodik Ana , Stein Oded , Sitzmann Vincent , Solomon Justin
Published: December 2023

We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, in practice limiting the choice of objective function. In contrast, we directly parameterize the continuous function that maps any coordinate in a polytope's interior to its barycentric coordinates using a neural field. This formulation is enabled by our theoretical characterization of barycentric coordinates, which allows us to construct neural fields that parameterize the entire function class of valid coordinates. We demonstrate the flexibility of our model using a variety of objective functions, including multiple smoothness and deformation-aware energies; as a side contribution, we also present mathematically-justified means of measuring and minimizing objectives like total variation on discontinuous neural fields. We offer a practical acceleration strategy, present a thorough validation of our algorithm, and demonstrate several applications.
Kirchhoff-Love Shells with Arbitrary Hyperelastic Materials
Authors: Wen Jiahao , Barbič Jernej
Published: December 2023

Kirchhoff-Love shells are commonly used in many branches of engineering, including in computer graphics, but have so far been simulated only under limited nonlinear material options. We derive the Kirchhoff-Love thin-shell mechanical energy for an arbitrary 3D volumetric hyperelastic material, including isotropic materials, anisotropic materials, and materials whereby the energy includes both even and odd powers of the principal stretches. We do this by starting with any 3D hyperelastic material, and then analytically computing the corresponding thin-shell energy limit. This explicitly identifies and separates in-plane stretching and bending terms, and avoids numerical quadrature. Thus, in-plane stretching and bending are shown to originate from one and the same process (volumetric elasticity of thin objects), as opposed to from two separate processes as done traditionally in cloth simulation. Because we can simulate materials that include both even and odd powers of stretches, we can accommodate standard mesh distortion energies previously employed for 3D solid simulations, such as Symmetric ARAP and Co-rotational materials. We relate the terms of our energy to those of prior work on Kirchhoff-Love thin-shells in computer graphics that assumed small in-plane stretches, and demonstrate the visual difference due to the presence of our exact stretching and bending terms. Furthermore, our formulation allows us to categorize all distinct hyperelastic Kirchhoff-Love thin-shell energies. Specifically, we prove that for Kirchhoff-Love thin-shells, the space of all hyperelastic materials collapses to two-dimensional hyperelastic materials. This observation enables us to create an interface for the design of thin-shell Kirchhoff-Love mechanical energies, which in turn enables us to create thin-shell materials that exhibit arbitrary stiffness profiles under large deformations.
Capturing Animation-Ready Isotropic Materials Using Systematic Poking
Authors: Chen Huanyu , Zhao Danyong , Barbič Jernej
Published: December 2023

Capturing material properties of real-world elastic solids is both challenging and highly relevant to many applications in computer graphics, robotics and related fields. We give a non-intrusive, in-situ and inexpensive approach to measure the nonlinear elastic energy density function of man-made materials and biological tissues. We poke the elastic object with 3d-printed rigid cylinders of known radii, and use a precision force meter to record the contact force as a function of the indentation depth, which we measure using a force meter stand, or a novel unconstrained laser setup. We model the 3D elastic solid using the Finite Element Method (FEM), and elastic energy using a compressible Valanis-Landel material that generalizes Neo-Hookean materials by permitting arbitrary tensile behavior under large deformations. We then use optimization to fit the nonlinear isotropic elastic energy so that the FEM contact forces and indentations match their measured real-world counterparts. Because we use carefully designed cubic splines, our materials are accurate in a large range of stretches and robust to inversions, and are therefore "animation-ready" for computer graphics applications. We demonstrate how to exploit radial symmetry to convert the 3D elastostatic contact problem to the mathematically equivalent 2D problem, which vastly accelerates optimization. We also greatly improve the theory and robustness of stretch-based elastic materials, by giving a simple and elegant formula to compute the tangent stiffness matrix, with rigorous proofs and singularity handling. We also contribute the observation that volume compressibility can be estimated by poking with rigid cylinders of different radii, which avoids optical cameras and greatly simplifies experiments. We validate our method by performing full 3D simulations using the optimized materials and confirming that they match real-world forces, indentations and real deformed 3D shapes. We also validate it using a "Shore 00" durometer, a standard device for measuring material hardness.
Reliability Criteria for News Websites
Authors: Heuer Hendrik , Glassman Elena L.
Published: December 2023

Misinformation poses a threat to democracy and to people’s health. Reliability criteria for news websites can help people identify misinformation. But despite their importance, there has been no empirically substantiated list of criteria for distinguishing reliable from unreliable news websites. We identify reliability criteria, describe how they are applied in practice, and compare them to prior work. Based on our analysis, we distinguish between manipulable and less manipulable criteria and compare politically diverse laypeople as end-users and journalists as expert users. We discuss 11 widely recognized criteria, including the following 6 criteria that are difficult to manipulate: content, political alignment, authors, professional standards, what sources are used, and a website’s reputation. Finally, we describe how technology may be able to support people in applying these criteria in practice to assess the reliability of websites.
Towards Energy-Efficient Spiking Neural Networks: A Robust Hybrid CMOS-Memristive Accelerator
Authors: Nowshin Fabiha , An Hongyu , Yi Yang
Published: December 2023

Spiking Neural Networks (SNNs) are energy-efficient artificial neural network models that can carry out data-intensive applications. Energy consumption, latency, and memory bottleneck are some of the major issues that arise in machine learning applications due to their data-demanding nature. Memristor-enabled Computing-In-Memory (CIM) architectures have been able to tackle the memory wall issue, eliminating the energy and time-consuming movement of data. In this work we develop a scalable CIM-based SNN architecture with our fabricated two-layer memristor crossbar array. In addition to having an enhanced heat dissipation capability, our memristor exhibits substantial enhancement of 10% to 66% in design area, power and latency compared to state-of-the-art memristors. This design incorporates an inter-spike interval (ISI) encoding scheme due to its high information density to convert the incoming input signals into spikes. Furthermore, we include a time-to-first-spike (TTFS) based output processing stage for its energy-efficiency to carry out the final classification. With the combination of ISI, CIM and TTFS, this network has a competitive inference speed of 2μs/image and can successfully classify handwritten digits with 2.9mW of power and 2.51pJ energy per spike. The proposed architecture with the ISI encoding scheme can achieve ∼10% higher accuracy than those of other encoding schemes in the MNIST dataset.
Knowledge Equivalence in Digital Twins of Intelligent Systems
Authors: Zhang Nan , Bahsoon Rami , Tziritas Nikos , Theodoropoulos Georgios
Published: December 2023

A digital twin contains up-to-date data-driven models of the physical world being studied and can use simulation to optimise the physical world. However, the analysis made by the digital twin is valid and reliable only when the model is equivalent to the physical world. Maintaining such an equivalent model is challenging, especially when the physical systems being modelled are intelligent and autonomous. The article focuses in particular on digital twin models of intelligent systems where the systems are knowledge-aware but with limited capability. The digital twin improves the acting of the physical system at a meta-level by accumulating more knowledge in the simulated environment. The modelling of such an intelligent physical system requires replicating the knowledge-awareness capability in the virtual space. Novel equivalence maintaining techniques are needed, especially in synchronising the knowledge between the model and the physical system. This article proposes the notion of knowledge equivalence and an equivalence maintaining approach by knowledge comparison and updates. A quantitative analysis of the proposed approach confirms that compared to state equivalence, knowledge equivalence maintenance can tolerate deviation thus reducing unnecessary updates and achieve more Pareto efficient solutions for the tradeoff between update overhead and simulation reliability.
Causality-driven Testing of Autonomous Driving Systems
Authors: Giamattei Luca , Guerriero Antonio , Pietrantuono Roberto , Russo Stefano
Published: December 2023

Testing Autonomous Driving Systems (ADS) is essential for safe development of self-driving cars. For thorough and realistic testing, ADS are usually embedded in a simulator and tested in interaction with the simulated environment. However, their high complexity and the multiple safety requirements lead to costly and ineffective testing. Recent techniques exploit many-objective strategies and ML to efficiently search the huge input space. Despite the indubitable advances, the need for smartening the search keep being pressing. This article presents CART (
CAusal-Reasoning-driven Testing
), a new technique that formulates testing as a causal reasoning task. Learning causation, unlike correlation, allows assessing the effect of actively changing an input on the output, net of possible confounding variables. CART first infers the causal relations between test inputs and outputs, then looks for promising tests by querying the learnt model. Only tests suggested by the model are run on the simulator. An extensive empirical evaluation, using Pylot as ADS and CARLA as simulator, compares CART with state-of-the-art algorithms used recently on ADS. CART shows a significant gain in exposing more safety violations and does so more efficiently. More broadly, the work opens to a wider exploitation of causal learning beside (or on top of) ML for testing-related tasks.
Experimental Investigation of a Lead-Acid Battery Regeneration Technique for Improved Energy Efficiency
Authors: Degla Aicha , Aoudjit Mounir , Guemraoui Kamel , Guemraoui Adel
Published: December 2023

Currently, extensive advancements have been made in battery technology. After years of study and development, researchers have been examining batteries with a primary focus on finding ways to enhance their efficiency, dependability, and lifespan. This research aims to effectively extend the lifespan of lead-acid batteries by using appropriate methodology. The investigations demonstrate that injecting electric pulses during the charging process improves battery capacity and cycle life. The enhanced efficiency is a result of the elimination of lead sulphate buildup on the battery’s electrodes. These tests ensure successful regeneration of the battery’s capacity and optimise the regeneration process. Using electric pulses during the charging process can help minimise the formation of lead sulphate on the electrodes. The proposed technique covers five cycles of charge and discharge, leading to improved battery performance with a 23% increase in the state of charge (SoC) at the end of the charge mode. Furthermore, the suggested method enhances battery performance by elevating the recovered charge capacity from 60% to an impressive 90%. Extensive tests and experiments have confirmed the accuracy of these findings. Consequently, the improved lead-acid battery has a remarkable efficiency improvement of approximately 34%, making it useful and highly practical.
miR-146a regulates emphysema formation and abnormal inflammation in the lungs of two mouse models
Authors: Yoshikawa Hitomi , Sato Tadashi , Horikoshi Kimiko , Komura Moegi , Nitta Naoko Arano , Mitsui Aki , Koike Kengo , Kodama Yuzo , Takahashi Kazuhisa
Published: December 2023

This study demonstrates that miR-146a expression is upregulated in mouse lungs because of elastase- and CS-induced emphysema and that the inflammatory response by elastase or CS is enhanced in the lungs of miR-146a-KO mice than in those of control mice, resulting in the promotion of emphysema. This is the first study to evaluate the protective role of miR-146a in emphysema formation and the associated abnormal inflammatory response in different in vivo models.
Preparation and Property Study of PA56/POE-g-MAH-GMA/PPO Ternary Alloy
Authors: Chong Yunsheng , Wang Liyan , Xu Xiangming , Zhuang Xiao , Zheng Rongrong , Cao Di , Chen Yanming
Published: December 2023

Bio-based PA56 is as matrix resin, maleic anhydride (MAH), and glycidyl methacrylate (GMA) dual monomer grafted POE (POE-g-MAH/GMA) polyphenyl ether(PPO) are simultaneous as toughening agents. The PA56/POE-g-MAH-GMA/PPO ternary alloys containing 10 wt% POE-g-MAH-GMA with different PPO content prepared by the melt blending method through twin screw extruder. The structure and property of PA56/POE-g-MAH-GMA/PPO ternary alloys were studied. The results showed that the microscopic morphology of each alloy showed an obvious “sea-island” structure. PA56 is a continuous sea phase, and the island phase assumes the “core–shell structure,” the etched POE-g-MAH-GMA particles are as the nucleus, and PPO are as the shell. When the PPO mass fraction is 30%, the relative crystallinity of the alloy is 11.56%, which is 1.68 times lower compared to the pure PA56, the alloy notch impact strength is 42.1 kJ/m2, and reaches 11.7 times of pure PA56; the water absorption rate of 1.22%, which is 3.22 times lower than pure PA56.
Diversity, Abundance, and Distribution of Avifauna in District Jhang, Pakistan
Authors: Zaman Ahmad , Rafique Azhar , Ashraf Asma , Mahmood Muhammad Shahid , Nahid Nazia , Jabeen Farhat , Sultana Salma , Sultana Tayyaba , Ali Sultan , Zihad S. M. Neamul Kabir , Uddin Shaikh Jamal
Published: December 2023

The present study was conducted from March 2021 to February 2022 in the Thal desert and Trimmu barrage of district Jhang located in the Punjab province of Pakistan to find out the diversity, abundance, and distribution of birds. The point count method was used for the Trimmu barrage, and the line transect method was used for the collection of data in the Thal desert area. We recorded 31,696 individuals belonging to 67 species of birds from two distinct types of habitats (Thal desert and Trimmu barrage) of district Jhang. Among these species, 39 species were residents, 18 species were winter visitors, 9 species were summer breeders, and a single species (Terek sandpiper) was a passage migrant. Highly significant differences (

X

2

=
14979.7

,

df
=
11

, and

P
<
0.01

) were observed in the abundance of birds between both habitats on every month. From the Thal desert area, 12,905 individuals belonging to 45 species were identified while 18,791 individuals from 58 bird species were observed in the Trimmu barrage, with 36 bird species among 67 species being common at both habitats. The most dominant species of the Trimmu barrage were the common pochard, little grebe, common coot, cattle egret, gadwall, little egret, red-wattled lapwing, and common teal. On the other hand, the Indian roller, common quail, cattle egret, common myna, and Indian kite were found to be the most common species in the Thal desert. The most notable feature of the present study is the identification of Xenus cinereus (Terek sandpiper) and Ammomanes phoenicura (rufous-tailed lark) in district Jhang as both species had no previous record in the study area. The Trimmu barrage was the more diverse and abundant site compared to the Thal desert as it provides both terrestrial and aquatic habitats for birds. The study determined that more varieties in the habitat and thermal changes affect the diversity, abundance, and distribution of birds.
Optimizing Electric Truck Routing and Charging with Soft Time Windows Using Vehicle-to-Grid Technology
Authors: Ryu Jong-Hyun , Hwang Seong Wook
Published: December 2023

Due to the rapid increase in the use of electric vehicles and instability in energy supply, the application of vehicle-to-grid (V2G) technology has gained attention in the freight transportation sector. V2G has the potential to increase the efficiency of power grid and make additional profits by utilizing surplus power from electric vehicle batteries. This paper proposes an optimization model for electric trucks (ETs) to provide operational decision-making support for the freight transportation sector. The objective of the model is to minimize the total net cost, which includes charging cost, discharging reward, and time penalties, while considering changes in ET charging cost and the system marginal price. Furthermore, we conduct sensitivity analysis in the vehicle routing problem with soft time windows using ETs in the V2G system.
Evaluation of Antimicrobial Activity and Cytotoxicity of an Epoxy Resin-Based Endodontic Sealer Containing Nanoparticles Amorphous Calcium Phosphate
Authors: Ibrahim Bilal H. , Al-Huwaizi Hussain
Published: December 2023

Background. The main cause of posttreatment disease in endodontics is the remaining of microorganisms within the root canal walls after endodontic therapy. Therefore, it is essential to use root canal sealers with potent antibacterial properties. These sealers play a vital role in eradicating any remaining microorganisms and preventing recontamination, especially in situations where there might be microleakage. Aim. The objective of this research was to examine the antimicrobial properties of epoxy root canal sealers containing nanoparticle amorphous calcium phosphate (NACP) against planktonic Enterococcus faecalis in a controlled laboratory environment. Furthermore, the study aimed to assess the potential cytotoxic effects of these sealers. Method. In order to determine the antimicrobial activity, the epoxy resin sealer (AH Plus, Dentsply, Germany) was supplemented with NACP from Sigma–Aldrich, at a concentration of 3wt.%, as per the previous flowability tests. The agar well diffusion assay method was employed to evaluate the antibacterial efficacy. For this, cultured plates (n = 8) were prepared, with each plate containing three wells: one with AH Plus, one with AH Plus + NACP, and one with NACP alone. Subsequently, the plates were sited at 37°C in an incubator and allowed to incubate overnight. The width of the inhibition zones was then analyzed and recorded by the SPSS statistical software package (Version 20.0 for Windows, SPSS, Chicago, IL, USA). The cytotoxicity of the NACP incorporated AH Plus and AH Plus sealers were tested indirectly by MTT assay and directly by the multiparametric high content screening toxicology assay using fibroblast-like cells as cell lines. Results. AH Plus + NACP showed a higher antimicrobial activity than AH Plus with significant difference (

P
<
0.0001

). Both materials showed nonsignificant difference compared to negative control, which indicated lower cytotoxicities. For AH Plus,

P
=
0.3599
,
0.5959
,
 
and
 
0.4071

, with time intervals 24, 72, and 168 hr, respectively. For AH Plus + NACP,

P
=
0.4386
,
0.6182
,
 
and
 
0.2080

, with 24, 72, and 168 hr, respectively. Conclusions. NACP incorporation with epoxy resin sealer AH PLUS had a higher antimicrobial activity with lower cytotoxic effect indicating their potential therapeutic values.
The Role of Nuclear Energy in Reducing Greenhouse Gas (GHG) Emissions and Energy Security: A Systematic Review
Authors: Addo Emmanuel Kusi , Kabo-bah Amos Tiereyangn , Diawuo Felix Amankwah , Debrah Seth Kofi
Published: December 2023

The energy sector accounts for about two-thirds of all human-related greenhouse gas (GHG) emissions due to the reliance on fossil-based fuels. This is a significant concern as it can have dire consequences on the survival of humankind and disrupt other natural processes. The research indicated that some mitigation measures to curb GHG emissions are to increase energy from low-carbon sources such as nuclear. However, due to the continuous adverse climate change impact, a comprehensive systematic review of research in this area must be conducted to inform policy practice and future studies. This study attempts to address this gap by mapping the global reflections on the potential of nuclear technology to mitigate GHG through a bibliometric review process. A total of 741 studies were retrieved from the Scopus database and a few from Google Scholar, spanning from 1962 to 2022, and analyzed using a science mapping tool—VOSviewer. The study confirmed that fossil fuels are a significant source of greenhouse gas emissions and contributor to greenhouse emissions. Those authors concluded that promoting clean and alternative energy sources to fossil fuels would help reduce carbon emissions. Although renewable energy has proven to be very efficient among pollution and climate change mitigation sources, nuclear energy is the most dependable option for meeting national and regional CO2 emission targets while meeting energy supply needs. The bibliometric analysis with VOSviewer suggested that only five African countries, including Ghana, have contributed to the research area with limited collaboration. As a result, it calls for stakeholders to make informed decisions to invest resources in research to address the challenge on the continent. The MESSAGE planning model is recommended for the study.
A Systems Biology Approach to Understand the Racial Disparities in Colorectal Cancer
Authors: Nwaokorie Annabelle , Kolch Walter , Fey Dirk
Published: December 2023

Racial disparities between Black/African Americans (AA) and White patients in colorectal cancer are an ever-growing area of concern. Black/AA show the highest incidence and have the highest mortality among major U.S. racial groups. There is no definite cause other than possible sociodemographic, socioeconomic, education, nutrition, delivery of healthcare, screening, and cultural factors. A primary limitation in this field is the lack of and small sample size of Black/AA studies. Thus, this study aimed to investigate whether differences in gene expression contribute to this ongoing unanswered racial disparity issue. In this study, we examined transcriptomic data of Black/AA and White patient cohorts using a bioinformatic and systems biology approach. We performed a Kaplan–Meier overall survival analysis between both patient cohorts across critical colorectal cancer signal transduction networks (STN), to determine the differences in significant genes across each cohort. Other bioinformatic analyses performed included PROGENy (pathway responsive genes for activity inference), RNA sequencing differential expression using DESeq2, multivariable-adjusted regression, and other associated Kaplan–Meier analyses. These analyses identified novel prognostic genes independent from each cohort, 176 differentially expressed genes, and specific patient cohort STN survival associations. Despite the overarching limitation, the results revealed several novel differences in gene expression between the colorectal cancer Black/AA and White patient cohorts, which allows one to dive deeper into and understand the behavior on a systems level of what could be driving this racial difference across colorectal cancer. Concretely, this information can guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.

Significance:

The purpose of this work is to investigate the racial disparities in colorectal cancer between Black/AA and White patient cohorts using a systems biology and bioinformatic approach. Our study investigates the underlying biology of each patient cohort. Concretely, the findings of this study include disparity-associated genes and pathways, which provide a tangible starting point to guide precision medicine approaches tailored specifically for colorectal cancer racial disparities.
Discovery of KRB-456, a KRAS G12D Switch-I/II Allosteric Pocket Binder That Inhibits the Growth of Pancreatic Cancer Patient-derived Tumors
Authors: Kazi Aslamuzzaman , Ranjan Alok , Kumar M.V. Vasantha , Agianian Bogos , Garcia Chavez Martin , Vudatha Vignesh , Wang Rui , Vangipurapu Rajanikanth , Chen Liwei , Kennedy Perry , Subramanian Karthikeyan , Quirke Jonathan C.K. , Beato Francisca , Underwood Patrick W. , Fleming Jason B. , Trevino Jose , Hergenrother Paul J. , Gavathiotis Evripidis , Sebti Said M.
Published: December 2023

Currently, there are no clinically approved drugs that directly thwart mutant KRAS G12D, a major driver of human cancer. Here, we report on the discovery of a small molecule, KRB-456, that binds KRAS G12D and inhibits the growth of pancreatic cancer patient-derived tumors. Protein nuclear magnetic resonance studies revealed that KRB-456 binds the GDP-bound and GCP-bound conformation of KRAS G12D by forming interactions with a dynamic allosteric binding pocket within the switch-I/II region. Isothermal titration calorimetry demonstrated that KRB-456 binds potently to KRAS G12D with 1.5-, 2-, and 6-fold higher affinity than to KRAS G12V, KRAS wild-type, and KRAS G12C, respectively. KRB-456 potently inhibits the binding of KRAS G12D to the RAS-binding domain (RBD) of RAF1 as demonstrated by GST-RBD pulldown and AlphaScreen assays. Treatment of KRAS G12D-harboring human pancreatic cancer cells with KRB-456 suppresses the cellular levels of KRAS bound to GTP and inhibits the binding of KRAS to RAF1. Importantly, KRB-456 inhibits P-MEK, P-AKT, and P-S6 levels in vivo and inhibits the growth of subcutaneous and orthotopic xenografts derived from patients with pancreatic cancer whose tumors harbor KRAS G12D and KRAS G12V and who relapsed after chemotherapy and radiotherapy. These results warrant further development of KRB-456 for pancreatic cancer.

Significance:

There are no clinically approved drugs directly abrogating mutant KRAS G12D. Here, we discovered a small molecule, KRB-456, that binds a dynamic allosteric binding pocket within the switch-I/II region of KRAS G12D. KRB-456 inhibits P-MEK, P-AKT, and P-S6 levels in vivo and inhibits the growth of subcutaneous and orthotopic xenografts derived from patients with pancreatic cancer. This discovery warrants further advanced preclinical and clinical studies in pancreatic cancer.
Intrauterine Detection of Ureaplasma Species after Vaginal Colonization in Pregnancy and Neonatal Outcome
Authors: Rittenschober-Boehm Judith , Fuiko Renate , Farr Alex , Willinger Birgit , Berger Angelika , Goeral Katharina
Published: December 2023

Introduction: Intrauterine infection with Ureaplasma species (U.spp.) is mostly a result of vaginal colonization with subsequent ascending infection and is associated with adverse pregnancy outcome. Little is known about rates and risk factors for ascending infection. Aim of the current study was to analyse the frequency of ascending U.spp. infection in vaginally colonized pregnant women delivering preterm and subsequent short- and long-term outcome of infants. Methods: Women delivering ≤32 weeks of gestation with available data on vaginal U.spp. colonization in early pregnancy as well as amniotic and placental colonization screening during caesarean section were included. Neonatal short- and long-term outcome was analysed depending on vaginal and intrauterine colonization. Results: Seventy-two women giving birth to 104 preterm infants were included. Intrauterine microbial invasion was found in 23/72 (31.9%) pregnancies. The most commonly detected organisms were U.spp. (52.2%), followed by E. coli (21.7%) and Enterococcus faecalis (17.4%). Intrauterine growth of U.spp. occurred exclusively after previous vaginal colonization in early pregnancy (42/72; 58.3%) and was found in 12/42 (28.6%) cases. Ascending U.spp. infection mainly occurred in pregnancies delivering <28 weeks after preterm rupture of membranes or preterm labour (9/17, 52.3%). Intrauterine detection of U.spp., but not vaginal colonization, was associated with a significantly higher rate of severe intraventricular haemorrhage, retinopathy of prematurity, bronchopulmonary dysplasia, and unfavourable psychomotor outcome. Conclusion: Ascending U.spp. infection after previous vaginal colonization occurred in almost one-third of pregnancies delivering ≤32 weeks, with particularly high rates in those <28 weeks, and was associated with adverse outcome of preterm infants.
Nine-Year Trend in the Prevalence of Allergic Diseases and Their Associated Factors in Young Adults
Authors: Matsumoto Munehiro , Kimura Hirokazu , Shimizu Kaoruko , Suzuki Masaru , Asakura Satoshi , Hashino Satoshi , Konno Satoshi
Published: December 2023

Introduction: Notably, few studies have evaluated the recent changes in the prevalence of allergic diseases in young adults. Studies examining the risk of allergy in two populations with similar social backgrounds, other than the region in which they live, are rare. Methods: First-year students from Hokkaido University were enrolled in this study between 2011 and 2019. A questionnaire survey was conducted to determine the annual prevalence of current wheeze, seasonal allergic rhinitis (SAR), and perennial allergic rhinitis (PAR) in nonsmoking young adults. Trends in the presence of these disease conditions were evaluated based on their hometowns (Hokkaido and outside Hokkaido separately) due to the low prevalence of cedar pollen allergies in Hokkaido. The association between these disease conditions and body mass index (BMI) was also assessed. Results: The prevalence of current wheeze and PAR food allergies did not change in both regions. SAR showed a significantly increasing trend; however, the prevalence of SAR was higher among those whose place of origin was not Hokkaido. Current wheeze was positively associated with obesity (p < 0.05), whereas the high prevalence of SAR was not associated with body weight. In contrast, a lean body type was significantly associated with a higher prevalence of PAR (p < 0.05). Discussion/Conclusion: The prevalence of current wheeze was stable and that of PAR has decreased over the past 9 years. However, the prevalence of SAR in Hokkaido has been increasing in Japanese young adults. A differential association between current wheeze and BMI was observed when comparing PAR and SAR.
Relationship between Brain Function and Microstructural Brain Maturation in Preterm Infants
Authors: Winkler Ira , Sappler Maria , Gizewski Elke Ruth , Kiechl-Kohlendorfer Ursula , Neubauer Vera , Griesmaier Elke
Published: December 2023

Introduction: Preterm infants are at risk for impairment in brain maturation at term equivalent age (TEA). Diffusion tensor imaging (DTI) is a powerful magnetic resonance imaging (MRI) technique, quantitatively reflecting microstructural brain development of white matter regions with parameters such as fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Amplitude-integrated electroencephalography (aEEG) assesses electrocortical activity and brain function. Methods: Aim of this study was to investigate a possible correlation between functional and microstructural brain maturation using neonatal aEEG and DTI-MRI at TEA. The study was conducted as a retrospective single-center study in 446 infants born below 32 gestational weeks. Spearman rank’s correlation coefficients were calculated between aEEG (total maturation score) and FA/ADC value. To compare aEEG and DTI-MRI to neurodevelopmental outcome at 24 months of corrected age, we performed a multivariate linear regression analysis. Results: Analysis showed an all-time significant correlation between total maturation score and FA/ADC values of the corpus callosum at TEA with the strongest correlation at day 2, day 3, week 3, and week 4. After including perinatal variables in the model, this correlation remained highly significant at day 2 and 3. When comparing the association of aEEG and DTI-MRI to outcome, both the total maturation score at day 2, day 3, and FA/ADC of the splenium of the corpus callosum showed a significant correlation. Conclusion: This study indicates that early monitoring of functional brain maturation may predict later assessment of microstructural brain development of corpus callosum in preterm infants with a relation to neurodevelopmental outcome.