bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural determinants of spike infectivity in bat SARS-like coronaviruses RsSHC014 and WIV1
Authors: Qiao Shuyuan , Wang Xinquan
Published: July 2024

The recurrent spillovers of coronaviruses (CoVs) have posed severe threats to public health and the global economy. Bat severe acute respiratory syndrome (SARS)-like CoVs RsSHC014 and WIV1, currently circulating in bat populations, are poised for human emergence. The trimeric spike (S) glycoprotein, responsible for receptor recognition and membrane fusion, plays a critical role in cross-species transmission and infection. Here, we determined the cryo-electron microscopy (EM) structures of the RsSHC014 S protein in the closed state at 2.9 Å, the WIV1 S protein in the closed state at 2.8 Å, and the intermediate state at 4.0 Å. In the intermediate state, one receptor-binding domain (RBD) is in the “down” position, while the other two RBDs exhibit poor density. We also resolved the complex structure of the WIV1 S protein bound to human ACE2 (hACE2) at 4.5 Å, which provides structural basis for the future emergence of WIV1 in humans. Through biochemical experiments, we found that despite strong binding affinities between the RBDs and both human and civet ACE2, the pseudoviruses of RsSHC014, but not WIV1, failed to infect 293T cells overexpressing either human or civet ACE2. Mutagenesis analysis revealed that the Y623H substitution, located in the SD2 region, significantly improved the cell entry efficiency of RsSHC014 pseudoviruses, which is likely accomplished by promoting the open conformation of spike glycoproteins. Our findings emphasize the necessity of both efficient RBD lifting and tight RBD-hACE2 binding for viral infection and underscore the significance of the 623 site of the spike glycoprotein for the infectivity of bat SARS-like CoVs.

IMPORTANCE

The bat SARS-like CoVs RsSHC014 and WIV1 can use hACE2 for cell entry without further adaptation, indicating their potential risk of emergence in human populations. The S glycoprotein, responsible for receptor recognition and membrane fusion, plays a crucial role in cross-species transmission and infection. In this study, we determined the cryo-EM structures of the S glycoproteins of RsSHC014 and WIV1. Detailed comparisons revealed dynamic structural variations within spike proteins. We also elucidated the complex structure of WIV1 S-hACE2, providing structural evidence for the potential emergence of WIV1 in humans. Although RsSHC014 and WIV1 had similar hACE2-binding affinities, they exhibited distinct pseudovirus cell entry behavior. Through mutagenesis and cryo-EM analysis, we revealed that besides the structural variations, the 623 site in the SD2 region is another important structural determinant of spike infectivity.
Physicians’ perspectives on COVID-19 vaccinations for children: a qualitative exploration in Ontario, Canada
Authors: Hodwitz Kathryn , Wigle Jannah , Juando-Prats Clara , Allan Kate , Li Xuedi , Fallon Barbara , Birken Catherine S. , Maguire Jonathon L. , Parsons Janet A.
Published: July 2024

Objectives

Parents’ decisions to vaccinate their children against COVID-19 are complex and often informed by discussions with primary care physicians. However, little is known about physicians’ perspectives on COVID-19 vaccinations for children or their experiences counselling parents in their decision-making. We explored physicians’ experiences providing COVID-19 vaccination recommendations to parents and their reflections on the contextual factors that shaped these experiences.

Design

We conducted an interpretive qualitative study using in-depth interviews. We analyzed the data using reflexive thematic analysis and a socioecological framework.

Setting

This study involved primary care practices associated with The Applied Research Group for Kids (TARGet Kids!) primary care research network in the Greater Toronto Area, Ontario, Canada.

Participants

Participants were 10 primary care physicians, including family physicians, paediatricians and paediatric subspecialists.

Results

Participants discussed elements at the individual level (their identity, role, and knowledge), the interpersonal level (their relationships with families, responsiveness to parents’ concerns, and efforts to build trust) and structural level (contextual factors related to the evolving COVID-19 climate, health system pandemic response, and constraints on care delivery) that influenced their experiences providing recommendations to parents. Our findings illustrated that physicians’ interactions with families were shaped by a confluence of their own perspectives, their responses to parents’ perspectives, and the evolving landscape of the broader pandemic.

Conclusions

Our study underscores the social and relational nature of vaccination decision-making and highlights the multiple influences on primary care physicians’ experiences providing COVID-19 vaccination recommendations to parents. Our findings offer suggestions for future COVID-19 vaccination programmes for children. Delivery of new COVID-19 vaccinations for children may be well suited within primary care offices, where trusting relationships are established, but physicians need support in staying knowledgeable about emerging information, communicating available evidence to parents to inform their decision-making and dedicating time for vaccination counselling.
Predicting postacute phase anaemia after aneurysmal subarachnoid haemorrhage: nomogram development and validation
Authors: Wu Fan , Chen Huai , Liu Zongchi , Ye Di , Wang Xiaoyi , Zhou Lihui , Xu Zihan , Wang Duanbu , Shen Jian , Zhan Renya , Zhu Yu
Published: July 2024

Background

Anaemia is a severe and common complication in patients with aneurysmal subarachnoid haemorrhage (aSAH). Early intervention for at-risk patients before anaemia occurs is indicated as potentially beneficial, but no validated method synthesises patients’ complicated clinical features into an instrument. The purpose of the current study was to develop and externally validate a nomogram that predicted postacute phase anaemia after aSAH.

Methods

We developed a novel nomogram for aSAH patients to predict postacute phase anaemia (3 days after occurrence of aSAH, prior to discharge) on the basis of demographic information, imaging, type of treatment, aneurysm features, blood tests and clinical characteristics. We designed the model from a development cohort and tested the nomogram in external and prospective validation cohorts. We included 456 aSAH patients from The First Affiliated Hospital for the development, 220 from Sanmen People’s Hospital for external validation and a prospective validation cohort that included 13 patients from Hangzhou Red Cross Hospital. We assessed the performance of the nomogram via concordance statistics and evaluated the calibration of predicted anaemia outcome with observed anaemia occurrence.

Results

Variables included in the nomogram were age, treatment method (open surgery or endovascular therapy), baseline haemoglobin level, fasting blood glucose level, systemic inflammatory response syndrome score on admission, Glasgow Coma Scale score, aneurysm size, prothrombin time and heart rate. In the validation cohort, the model for prediction of postacute phase anaemia had a c-statistic of 0.910, with satisfactory calibration (judged by eye) for the predicted and reported anaemia outcome. Among forward-looking forecasts, our predictive model achieved an 84% success rate, which showed that it has some clinical practicability.

Conclusions

The developed and validated nomogram can be used to calculate individualised anaemia risk and has the potential to serve as a practical tool for clinicians in devising improved treatment strategies for aSAH.
Examining the impact of age on chemotherapy completion in epithelial ovarian, fallopian tube and primary peritoneal cancer: a retrospective cohort study in Thailand
Authors: Assavapokee Nicha , Santibenchakul Somsook , Ratree Sasivimon , Lertkhachonsuk Ruangsak , Phoolcharoen Natacha
Published: July 2024

Objective

To explore the difference in chemotherapy completion and reasons for discontinuation between older (≥70 years) and younger (<70 years) patients.

Design

Retrospective cohort study.

Setting

Single tertiary centre in Thailand.

Participants

The patients who received chemotherapy from 1 January 2009 to 30 June 2021 were included and followed up until 30 June 2022. Of the 757 patients with epithelial ovarian, fallopian tube and primary peritoneal cancer (EOC), 108 were in the older group and 649 were in the younger group.

Primary and secondary outcome measures

The difference in chemotherapy completion, the association between younger and older patients and early discontinuation of chemotherapy.

Results

The proportion of chemotherapy completion was significantly lower in older versus younger patients (84.3% versus 92.6%, p=0.007). Excluding discontinuation due to disease progression, the chemotherapy completion was comparable (93.5 versus 95.7%, p=0.456). Dose reduction and grade 3–4 hematotoxicity occurred more often in the older group. The univariable logistic regression model showed that older age (≥70 years) was significantly associated with early chemotherapy discontinuation (OR 2.39; 95% CI 1.29–4.24). However, after adjusting for potential confounders, age was not significantly associated with early discontinuation (OR 1.20; 95% CI 0.54–2.66). Multiple comorbidities and types of surgery were identified as independent risk factors for chemotherapy discontinuation.

Conclusion

The completion of chemotherapy was observed in a majority of older adults with EOC. Age is not the only determinant of chemotherapy completion. Comorbidity and disease status are crucial for determining chemotherapy discontinuation.
Long-term follow-up in outpatients with mildly elevated pulmonary artery systolic pressure on echocardiography: a single-centre retrospective cohort study in Shanghai, China
Authors: Weng Zilong , Zhang Lei , Lin Dawei , Yang Lifan , Jin Qi , Long Yuliang , Li Wei , Pan Wenzhi , Shu Xianhong , Zhou Daxin , Ge Junbo
Published: July 2024

Objective

To investigate the correlation between mildly elevated pulmonary artery systolic pressure (PASP) on echocardiography and mortality, as well as long-term changes in PASP.

Design

Retrospective cohort study.

Setting

Shanghai, China, a single centre.

Participants

A total of 910 patients were enrolled in this study. From January to June 2016, 1869 patients underwent echocardiography at the Zhongshan Hospital affiliated with Fudan University. Patients with malignant tumours, previous heart or other solid organ transplantation, previous or scheduled ventricular assist device implantation, severe kidney dysfunction (uraemia and patients on dialysis) and a life expectancy of less than 1 year for any medical condition were excluded.

Interventions

No interventions were done.

Primary and secondary outcome measures

The predictors of death in patients with mild echocardiographic pulmonary hypertension were analysed using univariate and multivariate Cox regression analyses. Paired t-tests were used to calculate changes in the PASP values at baseline and follow-up for different patient groups.

Results

The 5-year survival of patients was 93.2%. Patients were grouped according to whether they had combined organic heart disease (OHD). The PASP value was an independent predictor of all-cause mortality in patients with OHD, with each 1 mm Hg increase associated with an HR of 1.02 (95% CI: 1.01–1.03, p=0.038) but not in patients without OHD. Of the total, 46% (419/910) of the patients with 5–6 years of echocardiography were investigated for changes in the PASP value. We found significant PASP reduction in patients without OHD (42.8±2.4 mm Hg vs 39.3±8.2 mm Hg, p<0.001), but no significant change was observed for patients with OHD (42.8±2.5 mm Hg vs 42.4±8.8 mm Hg, p=0.339).

Conclusions

The PASP was associated with all-cause mortality in patients with OHD and mildly elevated PASP compared with patients without OHD. After 5–6 years of follow-up, the PASP on echocardiography was not further elevated in patients without OHD.
California steelhead populations were regionally buffered and individually resistant to a severe multi-year drought
Authors: Ohms Haley A. , Palkovacs Eric P. , Boughton David A.
Published: July 2024

Weather extremes, such as drought, are predicted to be a strong determinant of species persistence under climate change. Yet predictions often fail to consider that variation in streamflow responses, variation in population dynamics, or adaptations to drought could buffer species against extremes. In this study, we examined the responses of eight California (USA) steelhead populations to a severe drought from 2012 to 2016. We observed that streamflows were highly synchronous across the region in all seasons and did not appear to buffer drought impacts. Population dynamics were variable across the region and did appear to buffer the region from drought impacts. Some populations had very low productivity for 4 years associated with the drought, while others had slightly below-average productivity for only 2 years. Population synchrony was associated with spring-smolt flow, temperature, and drought over time, but was not associated with winter-spawner or summer-juvenile drought, suggesting populations may be adapted to drought conditions. Our results highlight how regional buffering and adaptation can be important mechanisms against climate extremes both now and into the future.
Assessing soil microbial catabolic diversity in alder and oak plantations at varying developmental stages
Authors: Parsapour Mohammad Kazem , Kooch Yahya , Hosseini Seyed Mohsen , Alavi Seyed Jalil
Published: July 2024

To evaluate the impact of forest plantations on soil microbial catabolic diversity, this study investigated the microbial response to the addition of various carbon-rich substrates in different ages of forest plantations and compared catabolic diversity between a nitrogen-fixing tree species (Alder, Alnus subcordata C. A. M.) and a non-nitrogen-fixing one (Oak, Quercus castaneifolia C. A. M.). The study also analyzed the effects of carbon substrates found in association with roots, such as carbohydrates, carboxylic acids, amino acids, and amine groups, on substrate-induced respiration. The diversity indices (Simpson and Shannon diversity indicies) and Shannon evenness index demonstrated higher values in the oldest plantations than the youngest ones (with an order of 25 > 20 > 15 years). In younger plantations (15 and 20 years old), oak stands exhibited higher microbial diversity compared to alder stands. However, at 25 years old, alder plantation displayed greater diversity than the oak plantation, as indicated by higher values for the Shannon diversity index (2.77), Simpson diversity index (0.93), and Shannon evenness index (0.89). Forest plantations with alder and oak species significantly enhanced catabolic evenness and diversity, thereby increasing the soil capacity to decompose organic matter and the resilience of the soil to disturbance. These findings suggest that both alder and oak plantations can promote soil microbial diversity, but the influence might differ depending on the specific age of the trees. In conclusion, this study highlights the influence of forest plantations on soil microbial catabolic diversity, demonstrating the potential of diverse tree species to promote soil health and resilience through enhanced functional complexity.
Improvement of vortex shedding control and drag reduction on a square cylinder using twin plates
Authors: Abbasi Waqas Sarwar , Nadeem Sumaira , Saleem Amina , Rahman Hamid
Published: July 2024

This study aims to numerically investigate the optimal conditions for fluid flow control around a single square cylinder with the help of a pair of attached flat plates. It is comparatively a new approach for controlling fluid flows as compared to the traditional solo plate flow control devices. The plates are attached adjacent to the both rear corners of the cylinder and their length (l) is varied from [Formula: see text] to 4 times size of main cylinder while fixing the height (h) at [Formula: see text]. By varying the length, the plates manage to control the flow gradually. This study discusses how a steady wake can be achieved through control plates. Results indicate that the flow regime changes from unsteady to transitional at [Formula: see text] while for [Formula: see text] the steady flow appears. The streamlines visualizations reveal different flow structures termed as the oval-eye vortex, chain necklace vortex, sphere vortex, hair pin vortex and wooden eyes vortex-like structures. Among these the oval-eye vortex structure is found to have higher flow induced forces and shedding frequencies while the wooden eyes vortex structure is found to have minimal flow induced forces and shedding frequencies. After [Formula: see text], the plates’ efficacy is proven by a 100[Formula: see text] reduction in Strouhal number, root-mean-square values of lift coefficient and amplitudes of lift and drag coefficients. This study reveals that [Formula: see text] is the best optimal value of plates length for complete wake and fluid forces control.
Theoretical point of view on Cabibbo angle anomaly
Authors: Kitahara Teppei
Published: July 2024

In this paper, we present the current situation of the determinations of the first-row CKM components and show the Cabibbo angle anomaly corresponding to a deficit in the first-row CKM unitarity condition at the [Formula: see text] level. In this contribution, we show two new physics interpretations: heavy vector-like quark models and a MeV scale sterile neutrino scenario. The super tau-charm facility will directly probe the other CKM unitarity conditions related to [Formula: see text].
Acoustic analogue for quantum field theory with a source term
Authors: Pandey Akshat
Published: July 2024

We propose a nonrelativistic fluid analogue model for a scalar field coupled to a classical source. The generic analogue gravity model involves the phonon-field which is coupled to the acoustic metric. We work in the special relativity limit of the acoustic analogue. By assuming a time-dependent external potential on the fluid system, we are able to model a source term for the scalar field. Upon quantization, phonon creation due to the source is studied.
Toward Proactive Maintenance: A Multi-Tiered Architecture for Industrial Equipment Health Monitoring and Remaining Useful Life Prediction
Authors: Gultekin Emrullah , Aktas Mehmet S.
Published: July 2024

This research paper introduces a comprehensive proactive maintenance architecture designed for large-scale industrial machinery systems. The proposed architectural framework integrates supervised and unsupervised machine learning business processes in order to enhance maintenance capabilities. The primary objective of this architecture is to enhance operational efficiency and reduce the occurrence of problems in industrial equipment. The collection of data on the state of industrial machinery is conducted through the utilization of sensors that are attached to it. The recommended framework offers modules that might potentially implement capabilities such as immediate anomaly detection, pre-failure status prediction, and assessment of remaining usable life. We offer a prototype implementation to verify the appropriateness of the proposed framework for testing purposes. The prototype utilizes a simulation framework, Cooja, to model a sensor network. The concept entails the collection of status data from industrial machinery by each sensor. The prototype utilizes a machine learning library for data streams, the MOA framework, to design and implement a business process for anomaly detection using unsupervised machine learning, as well as a business process for early machine fault prediction using supervised machine learning. In addition, deep learning libraries are employed to construct a business process that predicts the remaining operational lifespan of industrial machinery that is anticipated to experience failure. Furthermore, we examined the efficacy of the prototype’s integrated business protocols in this investigation. The proposed framework aligns effectively with software architectures designed to offer maintenance functionalities for industrial machinery, as indicated by our research findings.
Toward Domain Adaptive Learning-Based Variation Autoencoder Emotional Analysis in English Teaching
Authors: Wang Xue
Published: July 2024

Speech emotion analysis plays an important role in English teaching by analyzing the reading state of students. Teachers can dynamically adjust the teaching content according to the emotional feedback of students and improve the teaching quality of the school. Due to unstable student emotions and background noise, the accuracy of speech emotion recognition is constrained. Although multimodal data can alleviate the deficiency of a single modality, collecting and annotating multimodal samples requires a significant amount of resources. To resolve this issue, this paper proposes a novel multimodal sentiment analysis framework based on domain adaptive learning mechanisms to assist English teaching. We construct a novel multi-task variation autoencoder framework in which we simultaneously complete reconstruction and classification tasks. To improve speech emotion recognition performance, we introduce domain adaptive learning based on the Wasserstein distance between two variational hidden layers from the video domain (source domain) and speech domain (target domain). To validate the effectiveness of our proposed model, we conducted extensive comparative experiments on two public datasets and a self-built English oral dataset. All experimental results indicate that domain adaptation learning mechanisms can effectively improve the recognition performance of the target domain. On the self-built dataset for English teaching, the proposed model achieves higher performance compared to other deep models.
Quantum symmetry in multigraphs (part II)
Authors: Goswami Debashish , Hossain Sk Asfaq
Published: July 2024

This paper is a continuation of Ref. 7. In this paper, we give an explicit construction of a non-Bichon type co-action on a multigraph that is, it preserves quantum symmetry of [Formula: see text] in our sense but not always in Bichon’s sense.2 This construction itself is motivated from automorphisms of quantum graphs.
Post-Buckling Behavior of Sandwich Composite Beams Made of FGM Faces and FGP Core Under High-Temperature Loads
Authors: Vessakosol Passakorn , Eiadtrong Suppakit , Thai Son , Wattanasakulpong Nuttawit
Published: July 2024

The objective of this study is to investigate the post-buckling of sandwich beams possessing functionally graded material (FGM) faces and functionally graded porous (FGP) cores in thermal environment. Thus, the critical buckling temperature and the deformation of such sandwich beams are determined and discussed in detail for three main types of temperature distributions which are uniform, linear and nonlinear temperature rises. An improved third-order shear deformable theory based on more rigorous kinematics of displacements is employed with von Kármán nonlinearity for constructing the energy equations of the problem. A Jacobi–Ritz method cooperating with the direct iteration procedure and Newton–Raphson technique is utilized to carry out the solutions of the sandwich beams associated with several parameters of material composition, porous coefficient, geometrical ratio and others. Based on the results, it can be disclosed that the beams can withstand the deformation due to thermal loadings if they have more pores inside the core. The resistance to deformation brought on by thermal loadings is significantly improved by increasing the sandwich thickness ratio.
A Model of Product Carbon Intensity Based on Life Cycle Assessment Process
Authors: Wang Bin , Wang Min-Jie , Wang Hao , Wu Yuan-Bo , Wang Shi-Ming
Published: July 2024

Carbon accounting plays a pivotal role in the manufacturing industry, being crucial for achieving low-carbon transformation and lean carbon management. A thorough understanding of carbon emissions during the manufacturing process is essential to account for the carbon footprint of products accurately. By analyzing the impact of supplies consumption (SC), energy penalty (EP), and equipment wear and tear (EWT) on carbon emissions in each process, a mathematical model of carbon consumption of the production process is proposed. The aim of the model is to help make implicit carbon emissions explicit across the entire product lifecycle. A lifecycle assessment (LCA) mathematical model of product carbon intensity is developed on this basis. Finally, the LCA mathematical model of product carbon intensity is applied to the ball valve manufacturing case, and the values of SC, EP, and EWT carbon emissions are acquired. The key factors influencing the carbon intensity of the ball valve are analyzed. The accuracy, effectiveness, and feasibility of the proposed model and calculation methods are validated, which provides a theoretical basis for carbon accounting and the establishment of carbon labels for industrial products.
An Improved Nonprobabilistic Monte Carlo Simulation Method for Structural Reliability Analysis Based on Convex Models
Authors: Qiao Xinzhou , Zhao Yuetong , Fang Xiurong , Elishakoff Isaac
Published: July 2024

This paper proposes an improved nonprobabilistic Monte Carlo simulation method for structural reliability analysis based on convex models. The novelty of the proposed method is to use a unit hyper-sphere sampling technique to ensure the validity of all samples, and to restrict the sampling domain to the evaluated domain which is geometrically the intersection of a hyper-spherical shell and a hyper-spherical sector and generally has a smaller volume than the whole ellipsoid. The nonprobabilistic characteristics and application to the interval model of the proposed method are also discussed. Seven numerical examples are provided to demonstrate the efficacy of the proposed methodology.
A low-cost indoor IoT object localization method based on monocular cameras at the edge device
Authors: Bai Yunmiao , Gao Yutong
Published: July 2024

With the rapid development of Internet of Things (IoT) and edge computing, a lot of edge devices are connected into the network, including the cameras that are used and applied in many scenarios, for example, the indoor environment. Now, in order to solve the problem of fast location of object indoors with low cost, the monocular vision based object location method is proposed in this paper. First of all, the pixel coordinates of the objects are obtained by target matching, and then the area of the object on the captured image is calculated for rough matching. Finally, the precise coordinates of the objects to be located are obtained through their center values and recognized by the fine matching. Experimental results indicate that the proposed method has better performance.
Alocasia Indica Assisted Green Synthesis of Metallic Cu Nanoparticles for Enhanced Antimicrobial Activities
Authors: Mim Juhi Jannat , Islam Md. Aminul , Arup Md. Mostafizur Rahman , Al Mahmud Md. Zobair , Hossain Amran , Mobarak Md. Hosne , Hossain Nayem
Published: July 2024

In the past several years, the widespread utility of copper nanoparticles (CuNPs) has spurred global interest in streamlining synthesis methodologies. Notably, this study introduces an environmentally conscious strategy for synthesizing CuNPs using the extract of Alocasia indica. Employing a chemical reduction process, A. indica, abundant in phenolics and flavonoids, acts as a potent reducing agent. The nanoparticles (NPs) synthesized by A. indica have been carefully characterized by Ultraviolet–Visible (UV–Viz) spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray (EDX), Transmission Electron Microscopy (TEM), X-ray Diffraction (XRD) analysis and antimicrobial analysis. UV testing, spanning wave length, elucidates light absorption attributed to the newly formed CuNPs by showcasing a surface plasmonic resonance peak at 204 nm. The evolution of the FTIR spectrum of the produced CuNPs confirms the presence of diverse biomolecules. TEM delineates the synthesis of irregularly shaped particles. Elemental composition analysis via EDX identifies the presence of various elements, including C, O, Al, Cl, K and Cu, with detailed insights into their mass percentages, standard deviations, atom percentages and characteristic X-ray energy levels. The successful synthesis of CuNPs is validated by XRD, which reveals characteristic copper peaks at 2[Formula: see text] = 43.375[Formula: see text], 50.520[Formula: see text] and 74.165[Formula: see text]. The synthesized NP showed 98.99% inhibition against Staphylococcus aureus and 99.99% inhibition against Escherichia coli. This research presents an easy, economical and ecologically friendly process for producing CuNPs utilizing A. indica extracts.
Soft-Voting Ensemble Model: An Efficient Learning Approach for Predictive Prostate Cancer Risk
Authors: Cao-Van Kien , Minh Tran Cao , Minh Le Gia , Quyen Tran Thi Be , Tan Ha Minh
Published: July 2024

Prostate cancer is increasingly common among men. However, the process of diagnosing malignant disease is relatively complicated and time-consuming. Identifying benign or malignant tumors early can assist medical professionals in choosing appropriate treatment methods. Consequently, we introduce a soft-voting ensemble model comprising several single machine learning models such as Logistic Regression, Random Forest, XGBoost, LGBM, and Support Vector Machine for the classification task with the prostate cancer dataset. The dataset was divided into two parts for training and testing with a ratio of 67:33. The confusion matrix was used to evaluate the performance of both the individual and ensemble models. Experimental results show that ensemble models achieve performance ranging from 87.88% to 96.97%, which is 3% to 9% better than individual models, surpassing recent research. Integrating the strengths of individual models helps minimize errors, resulting in optimal classification with high accuracy and overall performance in the field of machine learning.
Optimization of machining for the maximal productivity rate of the drilling operations
Authors: Usubamatov Ryspek , Abdiraimov Abdusamad
Published: July 2024

Machining processes focused primarily on the quality of surfaces and sizes of machined workpieces, as well as minimizing costs and tool wear. Existing publications related to maximizing the productivity of machining processes, present results that are in fact sub-optimal. The increase in machining regimes improves productivity but may reduce the quality of machined products. Mathematical optimization methods help find a balance between the benefits and drawbacks of machining processes. The tool life of cutters largely influences machining productivity and sets limits on machining speed. The normative tool life equations developed in research labs do not take into account the reliability of machine units and the specificities of technological processes. The normative machining speeds are suboptimal for manufacturing processes and may not maximize productivity. This paper presents a mathematical model for optimizing the cutting speed of a drilling machine tool to achieve its maximum productivity under realistic manufacturing conditions.
SketchDream: Sketch-based Text-To-3D Generation and Editing
Authors: Liu Feng-Lin , Fu Hongbo , Lai Yu-Kun , Gao Lin
Published: July 2024

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing texture-less mesh models within predefined categories. Integrating sketch and text simultaneously for 3D generation promises enhanced control over geometry and appearance but faces challenges from 2D-to-3D translation ambiguity and multi-modal condition integration. Moreover, further editing of 3D models in arbitrary views will give users more freedom to customize their models. However, it is difficult to achieve high generation quality, preserve unedited regions, and manage proper interactions between shape components. To solve the above issues, we propose a text-driven 3D content generation and editing method, SketchDream, which supports NeRF generation from given hand-drawn sketches and achieves free-view sketch-based local editing. To tackle the 2D-to-3D ambiguity challenge, we introduce a sketch-based multi-view image generation diffusion model, which leverages depth guidance to establish spatial correspondence. A 3D ControlNet with a 3D attention module is utilized to control multi-view images and ensure their 3D consistency. To support local editing, we further propose a coarse-to-fine editing approach: the coarse phase analyzes component interactions and provides 3D masks to label edited regions, while the fine stage generates realistic results with refined details by local enhancement. Extensive experiments validate that our method generates higher-quality results compared with a combination of 2D ControlNet and image-to-3D generation techniques and achieves detailed control compared with existing diffusion-based 3D editing approaches.
PEA-PODs: Perceptual Evaluation of Algorithms for Power Optimization in XR Displays
Authors: Chen Kenneth , Wan Thomas , Matsuda Nathan , Ninan Ajit , Chapiro Alexandre , Sun Qi
Published: July 2024

Display power consumption is an emerging concern for untethered devices. This goes double for augmented and virtual extended reality (XR) displays, which target high refresh rates and high resolutions while conforming to an ergonomically light form factor. A number of image mapping techniques have been proposed to extend battery usage. However, there is currently no comprehensive quantitative understanding of how the power savings provided by these methods compare to their impact on visual quality. We set out to answer this question.

To this end, we present a perceptual evaluation of algorithms (PEA) for power optimization in XR displays (PODs). Consolidating a portfolio of six power-saving display mapping approaches, we begin by performing a large-scale perceptual study to understand the impact of each method on perceived quality in the wild. This results in a unified quality score for each technique, scaled in just-objectionable-difference (JOD) units. In parallel, each technique is analyzed using hardware-accurate power models.

The resulting JOD-to-Milliwatt transfer function provides a first-of-its-kind look into tradeoffs offered by display mapping techniques, and can be directly employed to make architectural decisions for power budgets on XR displays. Finally, we leverage our study data and power models to address important display power applications like the choice of display primary, power implications of eye tracking, and more
1
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Automatic Digital Garment Initialization from Sewing Patterns
Authors: Liu Chen , Xu Weiwei , Yang Yin , Wang Huamin
Published: July 2024

The rapid advancement of digital fashion and generative AI technology calls for an automated approach to transform digital sewing patterns into well-fitted garments on human avatars. When given a sewing pattern with its associated sewing relationships, the primary challenge is to establish an initial arrangement of sewing pieces that is free from folding and intersections. This setup enables a physics-based simulator to seamlessly stitch them into a digital garment, avoiding undesirable local minima. To achieve this, we harness AI classification, heuristics, and numerical optimization. This has led to the development of an innovative hybrid system that minimizes the need for user intervention in the initialization of garment pieces. The seeding process of our system involves the training of a classification network for selecting seed pieces, followed by solving an optimization problem to determine their positions and shapes. Subsequently, an iterative selection-arrangement procedure automates the selection of pattern pieces and employs a phased initialization approach to mitigate local minima associated with numerical optimization. Our experiments confirm the reliability, efficiency, and scalability of our system when handling intricate garments with multiple layers and numerous pieces. According to our findings, 68 percent of garments can be initialized with zero user intervention, while the remaining garments can be easily corrected through user operations.
Stochastic Computation of Barycentric Coordinates
Authors: de Goes Fernando , Desbrun Mathieu
Published: July 2024

This paper presents a practical and general approach for computing barycentric coordinates through stochastic sampling. Our key insight is a reformulation of the kernel integral defining barycentric coordinates into a weighted least-squares minimization that enables Monte Carlo integration without sacrificing linear precision. Our method can thus compute barycentric coordinates directly at the points of interest, both inside and outside the cage, using just proximity queries to the cage such as closest points and ray intersections. As a result, we can evaluate barycentric coordinates for a large variety of cage representations (from quadrangulated surface meshes to parametric curves) seamlessly, bypassing any volumetric discretization or custom solves. To address the archetypal noise induced by sample-based estimates, we also introduce a denoising scheme tailored to barycentric coordinates. We demonstrate the efficiency and flexibility of our formulation by implementing a stochastic generation of harmonic coordinates, mean-value coordinates, and positive mean-value coordinates.
Specular Polynomials
Authors: Fan Zhimin , Guo Jie , Wang Yiming , Xiao Tianyu , Zhang Hao , Zhou Chenxi , Chen Zhenyu , Hong Pengpei , Guo Yanwen , Yan Ling-Qi
Published: July 2024

Finding valid light paths that involve specular vertices in Monte Carlo rendering requires solving many non-linear, transcendental equations in high-dimensional space. Existing approaches heavily rely on Newton iterations in path space, which are limited to obtaining at most a single solution each time and easily diverge when initialized with improper seeds.

We propose
specular polynomials
, a Newton iteration-free methodology for finding a complete set of admissible specular paths connecting two arbitrary endpoints in a scene. The core is a reformulation of specular constraints into polynomial systems, which makes it possible to reduce the task to a univariate root-finding problem. We first derive bivariate systems utilizing rational coordinate mapping between the coordinates of consecutive vertices. Subsequently, we adopt the hidden variable resultant method for variable elimination, converting the problem into finding zeros of the determinant of univariate matrix polynomials. This can be effectively solved through Laplacian expansion for one bounce and a bisection solver for more bounces.

Our solution is generic, completely deterministic, accurate for the case of one bounce, and GPU-friendly. We develop efficient CPU and GPU implementations and apply them to challenging glints and caustic rendering. Experiments on various scenarios demonstrate the superiority of specular polynomial-based solutions compared to Newton iteration-based counterparts. Our implementation is available at https://github.com/mollnn/spoly.
Semantic Gesticulator: Semantics-Aware Co-Speech Gesture Synthesis
Authors: Zhang Zeyi , Ao Tenglong , Zhang Yuyao , Gao Qingzhe , Lin Chuan , Chen Baoquan , Liu Libin
Published: July 2024

In this work, we present
Semantic Gesticulator
, a novel framework designed to synthesize realistic gestures accompanying speech with strong semantic correspondence. Semantically meaningful gestures are crucial for effective non-verbal communication, but such gestures often fall within the long tail of the distribution of natural human motion. The sparsity of these movements makes it challenging for deep learning-based systems, trained on moderately sized datasets, to capture the relationship between the movements and the corresponding speech semantics. To address this challenge, we develop a generative retrieval framework based on a large language model. This framework efficiently retrieves suitable semantic gesture candidates from a motion library in response to the input speech. To construct this motion library, we summarize a comprehensive list of commonly used semantic gestures based on findings in linguistics, and we collect a high-quality motion dataset encompassing both body and hand movements. We also design a novel GPT-based model with strong generalization capabilities to audio, capable of generating high-quality gestures that match the rhythm of speech. Furthermore, we propose a semantic alignment mechanism to efficiently align the retrieved semantic gestures with the GPT's output, ensuring the naturalness of the final animation. Our system demonstrates robustness in generating gestures that are rhythmically coherent and semantically explicit, as evidenced by a comprehensive collection of examples. User studies confirm the quality and human-likeness of our results, and show that our system outperforms state-of-the-art systems in terms of semantic appropriateness by a clear margin. We will release the code and dataset for academic research.
A Neural Network Model for Efficient Musculoskeletal-Driven Skin Deformation
Authors: Han Yushan , Chen Yizhou , Ong Carmichael , Chen Jingyu , Hicks Jennifer , Teran Joseph
Published: July 2024

We present a comprehensive neural network to model the deformation of human soft tissues including muscle, tendon, fat and skin. Our approach provides kinematic and active correctives to linear blend skinning [Magnenat-Thalmann et al. 1989] that enhance the realism of soft tissue deformation at modest computational cost. Our network accounts for deformations induced by changes in the underlying skeletal joint state as well as the active contractile state of relevant muscles. Training is done to approximate quasistatic equilibria produced from physics-based simulation of hyperelastic soft tissues in close contact. We use a layered approach to equilibrium data generation where deformation of muscle is computed first, followed by an inner skin/fascia layer, and lastly a fat layer between the fascia and outer skin. We show that a simple network model which decouples the dependence on skeletal kinematics and muscle activation state can produce compelling behaviors with modest training data burden. Active contraction of muscles is estimated using inverse dynamics where muscle moment arms are accurately predicted using the neural network to model kinematic musculotendon geometry. Results demonstrate the ability to accurately replicate compelling musculoskeletal and skin deformation behaviors over a representative range of motions, including the effects of added weights in body building motions.
Interactive Character Control with Auto-Regressive Motion Diffusion Models
Authors: Shi Yi , Wang Jingbo , Jiang Xuekun , Lin Bingkun , Dai Bo , Peng Xue Bin
Published: July 2024

Real-time character control is an essential component for interactive experiences, with a broad range of applications, including physics simulations, video games, and virtual reality. The success of diffusion models for image synthesis has led to the use of these models for motion synthesis. However, the majority of these motion diffusion models are primarily designed for offline applications, where space-time models are used to synthesize an entire sequence of frames simultaneously with a pre-specified length. To enable real-time motion synthesis with diffusion model that allows time-varying controls, we propose A-MDM (Auto-regressive Motion Diffusion Model). Our conditional diffusion model takes an initial pose as input, and auto-regressively generates successive motion frames conditioned on the previous frame. Despite its streamlined network architecture, which uses simple MLPs, our framework is capable of generating diverse, long-horizon, and high-fidelity motion sequences. Furthermore, we introduce a suite of techniques for incorporating interactive controls into A-MDM, such as task-oriented sampling, in-painting, and hierarchical reinforcement learning (See Figure 1). These techniques enable a pre-trained A-MDM to be efficiently adapted for a variety of new downstream tasks. We conduct a comprehensive suite of experiments to demonstrate the effectiveness of A-MDM, and compare its performance against state-of-the-art auto-regressive methods.
Differentiable Voronoi Diagrams for Simulation of Cell-Based Mechanical Systems
Authors: Numerow Logan , Li Yue , Coros Stelian , Thomaszewski Bernhard
Published: July 2024

Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at the cellular level, explicit network representations struggle with topology changes, and per-cell representations are computationally too demanding for large-scale simulations. To address these challenges, we propose a novel cell-centered approach based on differentiable Voronoi diagrams. Representing each cell with a Voronoi site, our method defines shape and topology of the interface network implicitly. In this way, we substantially reduce the number of problem variables, eliminate the need for explicit contact handling, and ensure continuous geometry changes during topological transitions. Closed-form derivatives of network positions facilitate simulation with Newton-type methods for a wide range of per-cell energies. Finally, we extend our differentiable Voronoi diagrams to enable coupling with arbitrary rigid and deformable boundaries. We apply our approach to a diverse set of examples, highlighting splitting and merging of cells as well as neighborhood changes. We illustrate applications to inverse problems by matching soap foam simulations to real-world images. Comparative analysis with explicit cell models reveals that our method achieves qualitatively comparable results at significantly faster computation times.
Position-Based Nonlinear Gauss-Seidel for Quasistatic Hyperelasticity
Authors: Chen Yizhou , Han Yushan , Chen Jingyu , Zhang Zhan , Mcadams Alex , Teran Joseph
Published: July 2024

Position based dynamics [Müller et al. 2007] is a powerful technique for simulating a variety of materials. Its primary strength is its robustness when run with limited computational budget. Even though PBD is based on the projection of static constraints, it does not work well for quasistatic problems. This is particularly relevant since the efficient creation of large data sets of plausible, but not necessarily accurate elastic equilibria is of increasing importance with the emergence of quasistatic neural networks [Bailey et al. 2018; Chentanez et al. 2020; Jin et al. 2022; Luo et al. 2020]. Recent work [Macklin et al. 2016] has shown that PBD can be related to the Gauss-Seidel approximation of a Lagrange multiplier formulation of backward Euler time stepping, where each constraint is solved/projected independently of the others in an iterative fashion. We show that a position-based, rather than constraint-based nonlinear Gauss-Seidel approach resolves a number of issues with PBD, particularly in the quasistatic setting. Our approach retains the essential PBD feature of stable behavior with constrained computational budgets, but also allows for convergent behavior with expanded budgets. We demonstrate the efficacy of our method on a variety of representative hyperelastic problems and show that both successive over relaxation (SOR), Chebyshev and multiresolution-based acceleration can be easily applied.