Face mask wearing is a protective health behaviour that helps mitigate the spread of infectious diseases such as influenza and COVID-19. Understanding predictors of face mask wearing can help refine public health messaging and policy in future pandemics. Government mandates influence face mask wearing, but how mandates change predictors of face mask wearing has not been explored. We investigate how mandates changed predictors of face mask wearing and general protective behaviours within Australia during the COVID-19 pandemic using cross-sectional survey data. We compared four machine learning models to predict face mask wearing and general protective behaviours before and after mandates started in Australia; ensemble, tree-based models (XGBoost and random forests) performed best. Other than state, common predictors before and after mandates included age, survey week, average number of contacts, wellbeing, and perception of illness threat. Predictors that only appeared in the top ten before mandates included trust in government, and employment status; and after mandates were willingness to isolate. These distinct predictors are possible targets for future public health messaging at different stages of a new pandemic.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Accurate extraction of key information from 2D engineering drawings is crucial for high-precision manufacturing. Manual extraction is slow and labor-intensive, while traditional Optical Character Recognition (OCR) techniques often struggle with complex layouts and overlapping symbols, resulting in unstructured outputs. To address these challenges, this paper proposes a novel hybrid deep learning framework for structured information extraction by integrating an Oriented Bounding Box (OBB) detection model with a transformer-based document parsing model (Donut). An in-house annotated dataset is used to train YOLOv11 for detecting nine key categories: Geometric Dimensioning and Tolerancing (GD&T), General Tolerances, Measures, Materials, Notes, Radii, Surface Roughness, Threads, and Title Blocks. Detected OBBs are cropped into images and labeled to fine-tune Donut for structured JSON output. Fine-tuning strategies include a single model trained across all categories and category-specific models. Results show that the single model consistently outperforms category-specific ones across all evaluation metrics, achieving higher precision (94.77% for GD&T), recall (100% for most categories), and F1 score (97.3%), while reducing hallucinations (5.23%). The proposed framework improves accuracy, reduces manual effort, and supports scalable deployment in precision-driven industries.
Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a wider range of socio-technical activities and concerns to make it work in practice. This paper contributes to the emerging visions by: (a) recommending an expansion of its scope beyond code, toward a 'whole of process' vision, grounding it in SE foundations and evolution and emerging agentic SE frameworks, (b) proposing a preliminary set of values and principles to guide community efforts, and (c) sharing guidance on designing and using well-defined vocabulary for agentic SE. It is hoped that these ideas will encourage collaborations and steer the SE community toward laying strong foundations of agentic SE so it is not limited to enabling coding acceleration but becomes the next process-level paradigm shift.
Muhammad Tayyab Khan, Zane Yong, Lequn Chen
et al.
Engineering drawings are fundamental to manufacturing communication, serving as the primary medium for conveying design intent, tolerances, and production details. However, interpreting complex multi-view drawings with dense annotations remains challenging using manual methods, generic optical character recognition (OCR) systems, or traditional deep learning approaches, due to varied layouts, orientations, and mixed symbolic-textual content. To address these challenges, this paper proposes a three-stage hybrid framework for the automated interpretation of 2D multi-view engineering drawings using modern detection and vision language models (VLMs). In the first stage, YOLOv11-det performs layout segmentation to localize key regions such as views, title blocks, and notes. The second stage uses YOLOv11-obb for orientation-aware, fine-grained detection of annotations, including measures, GD&T symbols, and surface roughness indicators. The third stage employs two Donut-based, OCR-free VLMs for semantic content parsing: the Alphabetical VLM extracts textual and categorical information from title blocks and notes, while the Numerical VLM interprets quantitative data such as measures, GD&T frames, and surface roughness. Two specialized datasets were developed to ensure robustness and generalization: 1,000 drawings for layout detection and 1,406 for annotation-level training. The Alphabetical VLM achieved an overall F1 score of 0.672, while the Numerical VLM reached 0.963, demonstrating strong performance in textual and quantitative interpretation, respectively. The unified JSON output enables seamless integration with CAD and manufacturing databases, providing a scalable solution for intelligent engineering drawing analysis.
Thiago Barradas, Aline Paes, Vânia de Oliveira Neves
The effective execution of tests for REST APIs remains a considerable challenge for development teams, driven by the inherent complexity of distributed systems, the multitude of possible scenarios, and the limited time available for test design. Exhaustive testing of all input combinations is impractical, often resulting in undetected failures, high manual effort, and limited test coverage. To address these issues, we introduce RestTSLLM, an approach that uses Test Specification Language (TSL) in conjunction with Large Language Models (LLMs) to automate the generation of test cases for REST APIs. The approach targets two core challenges: the creation of test scenarios and the definition of appropriate input data. The proposed solution integrates prompt engineering techniques with an automated pipeline to evaluate various LLMs on their ability to generate tests from OpenAPI specifications. The evaluation focused on metrics such as success rate, test coverage, and mutation score, enabling a systematic comparison of model performance. The results indicate that the best-performing LLMs - Claude 3.5 Sonnet (Anthropic), Deepseek R1 (Deepseek), Qwen 2.5 32b (Alibaba), and Sabia 3 (Maritaca) - consistently produced robust and contextually coherent REST API tests. Among them, Claude 3.5 Sonnet outperformed all other models across every metric, emerging in this study as the most suitable model for this task. These findings highlight the potential of LLMs to automate the generation of tests based on API specifications.
The phenomenon of high-temperature oxidation in magnesium alloys constitutes a significant obstacle to their application in the aerospace field. However, the incorporation of active elements such as alloys and rare earth elements into magnesium alloys alters the organization and properties of the oxide film, resulting in an enhancement of their antioxidation capabilities. This paper comprehensively reviews the impact of alloying elements, solubility, intermetallic compounds (second phase), and multiple rare earth elements on the antioxidation and flame-retardant effects of magnesium alloys. The research progress of flame-retardant magnesium alloys containing multiple rare earth elements is summarized from two aspects: the oxide film and the matrix structure. Additionally, the existing flame-retardancy models for magnesium alloys and the flame-retardant mechanisms of various flame-retardant elements are discussed. The results indicate that the oxidation of rare earth magnesium alloys is a complex process determined by internal properties such as the structure and properties of the oxide film, the type and amount of rare earth elements added, the proportion of multiple rare earth elements, synergistic element effects, as well as external properties like heat treatment, oxygen concentration, and partial pressure. Finally, some issues in the development of multi-rare earth magnesium alloys are raised and the potential directions for the future development of rare earth flame-retardant magnesium alloys are discussed. This paper aims to promote an understanding of the oxidation behavior of flame-retardant magnesium alloys and provide references for the development of rare earth flame-retardant magnesium alloys with excellent comprehensive performance.
ABSTRACT: China established Xiong'an New Area in Hebei Province in 2017, which is planned to accommodate about 5 million people, aiming to relieve Beijing City of the functions non-essential to its role as China's capital and to expedite the coordinated development of the Beijing-Tianjin-Hebei region. From 2017 to 2021, the China Geological Survey (CGS) took the lead in multi-factor urban geological surveys involving space, resources, environments, and disasters according to the general requirements of “global vision, international standards, distinctive Chinese features, and future-oriented goals” in Xiong'an New Area, identifying the engineering geologic conditions and geologic environmental challenges of this area. The achievements also include a 3D engineering geological structure model for the whole area, along with “one city proper and five clusters”, insights into the ecology and the background endowment of natural resources like land, geothermal resources, groundwater, and wetland of the area before engineering construction, a comprehensive monitoring network of resources and environments in the area, and the “Transparent Xiong'an” geological information platform that is open, shared, dynamically updated, and three-dimensionally visualized. China's geologists and urban geology have played a significant role in the urban planning and construction of Xiong'an New Area, providing whole-process geological solutions for urban planning, construction, operation and management. The future urban construction of Xiong'an New Area will necessitate the theoretical and technical support of earth system science (ESS) from various aspects, and the purpose is to enhance the resilience of the new type of city and to provide support for the green, low-carbon, and sustainable development of this area.
Abstract Extreme winds associated with tropical cyclones (TCs) can cause significant loss of life and economic damage globally, highlighting the need for accurate, high‐resolution modeling and forecasting for wind. However, due to their coarse horizontal resolution, most global climate and weather models suffer from chronic underprediction of TC wind speeds, limiting their use for impact analysis and energy modeling. In this study, we introduce a cascading deep learning framework designed to downscale high‐resolution TC wind fields given low‐resolution data. Our approach maps 85 TC events from ERA5 data (0.25° resolution) to high‐resolution (0.05° resolution) observations at 6‐hr intervals. The initial component is a debiasing neural network designed to model accurate wind speed observations using ERA5 data. The second component employs a generative super‐resolution strategy based on a conditional denoising diffusion probabilistic model (DDPM) to enhance the spatial resolution and to produce ensemble estimates. The model is able to accurately model intensity and produce realistic radial profiles and fine‐scale spatial structures of wind fields, with a percentage mean bias of −3.74% compared to the high‐resolution observations. Our downscaling framework enables the prediction of high‐resolution wind fields using widely available low‐resolution and intensity wind data, allowing for the modeling of past events and the assessment of future TC risks.
Geophysics. Cosmic physics, Information technology
In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing.
Generative LLMs, such as GPT, have the potential to revolutionize Requirements Engineering (RE) by automating tasks in new ways. This column explores the novelties and introduces the importance of precise prompts for effective interactions. Human evaluation and prompt engineering are essential in leveraging LLM capabilities.
Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.
In the present study, the Nusselt number, exergy efficiency, entropy generation and themal performance factor of nanodiamond-Fe3O4/water hybrid nanofluids in a tube with twisted tape inserts have been conducted experimentally. The experiments have been performed under turbulent flow regime in different Reynolds number ranging from 2000 to 22000, volume loadings from 0 to 0.2 %, and twisted tape inserts, H/D of 5, 10, and 15, respectively. It is found that, compared to water plain tube, the Nusselt number and exergy efficiency of 0.2 % vol. of nanofluid in a tube is raised by 29.55 % and 18.13 % with twisted tape insert, H/D of 5, the Nusselt number and exergy efficiency is further raised by 73.04 % and 161.31 % at a Reynolds number of 20,095 with a maximum friction factor penalty of 61.93 %. The thermal entropy generation is dropped by 30.72 % for 0.2 % vol. of nanofluid in a tube and with twisted tape insert, H/D of 5, the thermal entropy generation is further dropped by 48.58 % compared to water. Maximum increase of frictional entropy generation is about 74.81 % at 0.2 % vol. of nanofluid with twisted tape insert of H/D = 5 over base fluid. New Nusselt number and friction factor correlations were developed.
Fast tool servo (FTS) in ultra-precision diamond turning is an efficient technique for high-precision fabrication of freeform optics. However, the currently adopted constant scheme for control point sampling takes no account of the shape variation of the desired surface, which might lose some micro features and result in low form accuracy and non-uniform surface quality. Facing this issue, this manuscript proposes a novel adaptive control points sampling strategy, which improves the form accuracy and keeps as many as the micro surface features. In the optimization method, the sampling stepovers between two adjacent control points are actively adjusted to adapt to the surface profile variation. By adopting this method, the control point sampling induced interpolation error is constrained within the desired tolerance and eliminates the lack/over-definition of control points in the machining area. The feasibility of the proposed optimization method is demonstrated by both theoretical simulations and fabrication experiments of sinusoid freeform surfaces. Compared with the constant sampling method, both the theoretical predicted and experimental measured form error of the proposed method is remarkably reduced by about 35 % with the same amount of control points. This technique provides a new route to allocating control points in FTS diamond turning to achieve high form accuracy and machining efficiency in the fabrication of freeform optics.
Engineering machinery, tools, and implements, Mechanical engineering and machinery
In the standard Einstein's theory the exterior gravitational field of any static and axially symmetric stellar object can be described by means of a single function from which we obtain a metric into a four-dimensional space-time. In this work we present a generalization of those so called Weyl solutions to a space-time-matter metric in a five-dimensional manifold within a non-compactified Kaluza-Klein theory of gravity. The arising field equations reduce to those of vacuum Einstein's gravity when the metric function associated to the fifth dimension is considered to be constant. The calculation of the geodesics allows to identify the existence or not of different behaviours of test particles, in orbits on a constant plane, between the two metrics. In addition, static solutions on the hypersurface orthogonal to the added dimension but with time dependence in the five-dimensional metric are also obtained. The consequences on the variation of the rest mass, if the fifth dimension is identified with it, are studied.
Matthew Tyler, Hengyun Zhou, Leigh S. Martin
et al.
We introduce a framework for designing Hamiltonian engineering pulse sequences that systematically accounts for the effects of higher-order contributions to the Floquet-Magnus expansion. Our techniques result in simple, intuitive decoupling rules, despite the higher-order contributions naively involving complicated, non-local-in-time commutators. We illustrate how these rules can be used to efficiently design improved Hamiltonian engineering pulse sequences for a wide variety of tasks, such as dynamical decoupling, quantum sensing, and quantum simulation.