A Comprehensive Evaluation of the DFP Method for Geometric Constraint Solving Algorithm Using PlaneGCS

The development of open-source geometric constraint solvers is a pressing research topic, as commercially available solvers may not meet the research requirements. In this paper, we examine the use of numerical methods in PlaneGCS, an open-source geometric constraint solver within the FreeCAD CAD software. Our study focuses on PlaneGCS\u27s constraint solving algorithms and the three built-in single-subsystem solving methods: BFGS, LM, and Dogleg. Based on our research results, the DFP method was implemented in PlaneGCS and was successfully verified in FreeCAD. To evaluate the performance of the algorithms, we used the solving state of the constraint system as a test criterion, and analysed their solving time, adaptability, and number of iterations. Our results highlight the performance differences between the algorithms and provide empirical guidance for selection of constraint solving algorithms and research based on open-source geometric constraint solvers

Towards an Open-Source Industry CAD: A Review of System Development Methods

Due to the industry knowledge barrier, general computer aided design (CAD) software cannot do everything in digital manufacturing by itself, and industry CAD, therefore, occupies a crucial position in the CAD industry. To develop industry CAD smoothly, open-source is the best choice. We analyzed recent examples of industry CAD development and divided the development methods into four types: development based on the graphics development environment, development based on geometric modelling kernel, secondary development based on general CAD, and hybrid development. We analyzed the characteristics of various methods and believe that the method based on the hybrid development of the geometric modelling kernel and the graphics development environment is the best open-source industry CAD development method. We proposed a system architecture of open-source industry CAD for reference and conducted a preliminary exploration of the reference architecture to verify its feasibility

Editorial: Microorganisms and their derivatives for cancer therapy

[Execerpt] Cancer remains an unsolved and challenging problem. In 1890, Dr. William Bradley Coley attempted to use a mixture of dead microbes to treat cancers (Dobosz and Dzieciatkowski, 2019; Liu et al., 2022), establishing the foundation of bacteria-mediated cancer therapy. Given the recent advances in the study of the human microbiome that revealed its crucial role in tumorigenesis, development, therapy, and prognostic evaluation, additional research efforts on cancer microbial therapies have been conducted (Kurtz et al., 2019; Feng et al., 2022), with new findings supporting the potential role of bacteriolytic therapy in cancer. Our Special Research Topic aimed at exploring the trends and recent advances on the use of microorganisms and their derivatives for cancer therapy, on new anticancer agents, new genetic engineering techniques, and synthetic or new identified bacteria, which could be used for cancer monotherapy or adjuvant therapy, as well as understanding the mechanisms underlying their anticancer effects. [...]We appreciate the editorial staff and the contributors who made the Special Research Topic possible. We acknowledge the support by the National Natural Science Foundation of China (Grant Nos. 81971726, 32101218) and the State Key Laboratory of Oncogenes and Related Genes (No. KF2111).info:eu-repo/semantics/publishedVersio

Image Denoising via L

The L0 gradient minimization (LGM) method has been proposed for image smoothing very recently. As an improvement of the total variation (TV) model which employs the L1 norm of the gradient, the LGM model yields much better results for the piecewise constant image. However, just as the TV model, the LGM model also suffers, even more seriously, from the staircasing effect and the inefficiency in preserving the texture in image. In order to overcome these drawbacks, in this paper, we propose to introduce an effective fidelity term into the LGM model. The fidelity term is an exemplar of the moving least square method using steering kernel. Under this framework, these two methods benefit from each other and can produce better results. Experimental results show that the proposed scheme is promising as compared with the state-of-the-art methods

cuML-DSA: Optimized Signing Procedure and Server-Oriented GPU Design for ML-DSA

The threat posed by quantum computing has precipitated an urgent need for post-quantum cryptography. Recently, the post-quantum digital signature draft FIPS 204 has been published, delineating the details of the ML-DSA, which is derived from the CRYSTALS-Dilithium. Despite these advancements, server environments, especially those equipped with GPU devices necessitating high-throughput signing, remain entrenched in classical schemes. A conspicuous void exists in the realm of GPU implementation or server-specific designs for ML-DSA. In this paper, we propose the first server-oriented GPU design tailored for the ML-DSA signing procedure in high-throughput servers. We introduce several innovative theoretical optimizations to bolster performance, including depth-prior sparse ternary polynomial multiplication, the branch elimination method, and the rejection-prioritized checking order. Furthermore, exploiting server-oriented features, we propose a comprehensive GPU hardware design, augmented by a suite of GPU implementation optimizations to further amplify performance. Additionally, we present variants for sampling sparse polynomials, thereby streamlining our design. The deployment of our implementation on both server-grade and commercial GPUs demonstrates significant speedups, ranging from 170.7× to 294.2× against the CPU baseline, and an improvement of up to 60.9% compared to related work, affirming the effectiveness and efficiency of the proposed GPU architecture for ML-DSA signing procedure

2.45 GHz power and data transmission for a low-power autonomous sensors platform

This paper describes a power conversion and data recovery system for a microwave powered sensor platform. A patch microwave antenna, a matching filter and a rectifier make the system front-end and implement the RF-to-DC conversion of power carrier. The efficiency of the power conversion is as high as 47 % with an input power level 250 W at 2.45 GHz. Then, a 0.18 m CMOS integrated circuit extracts the clock and the digital data. A modified pulse amplitude modulation scheme is used to modulate the data on the 2.45 GHz carrier frequency for combined data and power transmission; this scheme allows very low power consumption of the entire IC to be less than 10 W and making the system suitable for an autonomous wireless connected sensor module

A new framework for RFID privacy

A*Star SERC; Singapore Management Universit

Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)