4D millimeter-wave radar is a promising sensing modality for autonomous driving, yet effective 3D object detection from 4D radar and monocular images remains challenging. Existing fusion approaches either rely on instance proposals lacking global context or dense BEV grids constrained by rigid structures, lacking a flexible and adaptive representation for diverse scenes. To address this, we propose RaGS, the first framework that leverages 3D Gaussian Splatting (GS) to fuse 4D radar and monocular cues for 3D object detection. 3D GS models the scene as a continuous field of Gaussians, enabling dynamic resource allocation to foreground objects while maintaining flexibility and efficiency. Moreover, the velocity dimension of 4D radar provides motion cues that help anchor and refine the spatial distribution of Gaussians. Specifically, RaGS adopts a cascaded pipeline to construct and progressively refine the Gaussian field. It begins with Frustum-based Localization Initiation (FLI), which unprojects foreground pixels to initialize coarse Gaussian centers. Then, Iterative Multimodal Aggregation (IMA) explicitly exploits image semantics and implicitly integrates 4D radar velocity geometry to refine the Gaussians within regions of interest. Finally, Multi-level Gaussian Fusion (MGF) renders the Gaussian field into hierarchical BEV features for 3D object detection. By dynamically focusing on sparse and informative regions, RaGS achieves object-centric precision and comprehensive scene perception. Extensive experiments on View-of-Delft, TJ4DRadSet, and OmniHD-Scenes demonstrate its state-of-the-art performance. Code will be released.
We introduce a general, easy-to-implement AI-based modeling technique for analyzing human behavior. A key feature of this approach, which contrasts with existing modeling techniques, is that it combines the flexibility and interpretability of natural language with a mathematical structure that can be fitted to data and easily analyzed. We assign a large language model a vector of trait intensities-a type vector-and then ask it to choose actions across settings in which we observe human choices. For instance, the type vector (2,4) could correspond to "You are a player characterized by the following profile: Altruism: 2 out of 5, Risk Aversion: 4 out of 5," after which it is asked to make choices. We can then vary the traits (e.g., Altruism, Fairness, Trust,...) and values (e.g., 1-5) to minimize distance to human choices. We illustrate the method by applying it to model 119,147 decisions made by 78,657 subjects from more than 35 countries across 10 classic economic game roles. We find that human behavior can be closely matched using three dimensions: Risk Aversion, Strategic Sophistication, and Trust. The type vectors needed to fit individuals across games cluster into fewer than a dozen groups, with substantial variation in fit across subjects. Moreover, the individual type vectors can predict behavior in held-out games with different rules and available actions. More broadly, this new modeling method is highly generalizable and interpretable: we can input any vector of traits and use them to model behavior across any setting
Objectives:We conducted a Health Labour Market Analysis (HLMA) to evaluate the alignment of health workforce supply with population health needs and fiscal sustainability through 2030. Study design:A quantitative study design using secondary data was based on the World Health Organization (WHO) HLMA Framework and the 2021 WHO HLMA Guidebook. Methods:Quantitative data were gathered from the Human Resources Information System (HRIS), professional associations, training institutions, and national accounts, supplemented by grey literature and stakeholder consultations. Workforce supply and demand projections for 2024-2030 considered an attrition rate of 3.5%, a 20% unemployment rate for new graduates, and an 80% absorption rate. Financial analyses were aligned with Gross Dometic Product (GDP) and fiscal projections from the World Bank, International Monetary Fund (IMF), and the National Bank of Ethiopia. Data quality assurance included multi-level validation using a standardized Ministry of Health (MOH) tool, with outlier checks and stakeholder verification. Results:The supply of health professionals is projected to increase steadily, reaching approximately 74,693 nurses, 30,980 midwives, and 25,576 general practitioners by 2030. Despite these gains, significant shortages persist relative to Essential Health Services Package (EHSP)-based requirements, particularly among medical specialists, nurses, anesthetists, and laboratory professionals. Financial analysis indicates that cumulative fiscal space is projected at USD 945 million by 2030, while the cost of employing the available workforce is estimated at USD 1.08 billion, and the EHSP-aligned requirement at USD 1.8 billion. This results in an annual financing gap of USD 20-30 million for workforce absorption and over USD 800 million relative to service needs. Conclusions:Ethiopia's HLMA highlights gaps between workforce supply, health needs, and fiscal capacity. Despite an increase in graduates, unemployment persists. Improving Human Resource for Health (HRH) governance, expanding fiscal resources, and ensuring fair deployment are vital for effective workforce utilization and advancing universal health coverage.
Background: The reported accuracy of pertechnetate scintigraphy (Meckel scan) for evaluating children with Meckel's diverticulum has steadily increased since its introduction into clinical practice in 1970. In our study, we retrospectively analyze the accuracy of Tc-99 m pertechnetate scintigraphy in NINMAS over 01 year. Objective: This study aimed to evaluate the accuracy of pertechnetate scintigraphy performed at the National Institute of Nuclear Medicine & Allied Sciences (NINMAS) in pediatric patients with suspected Meckel's Diverticulum. Methods: A retrospective study was conducted of 60 pediatric patients who presented with symptoms suggestive of MD and underwent 99mTc-pertechnetate scintigraphy at NINMAS over a 1-year period. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall diagnostic accuracy were calculated by comparing scintigraphic results with surgical and histopathological findings. Result: Six patients were diagnosed with MD on 99mTc-pertechnetate scintigraphy, all confirmed histopathologically as true positives. Among 54 patients with negative scans, one was a false negative, and 53 were confirmed as true negatives through clinical, imaging, endoscopic, and laparoscopic evaluation. Accordingly, 99mTc-pertechnetate scintigraphy showed a sensitivity of 85.7% (6/7), specificity of 100.0% (53/53), positive predictive value of 100.0% (6/6), negative predictive value of 98.1% (53/54), and overall diagnostic accuracy of 98.3% (59/60) for MD. Receiver operating characteristic analysis revealed an area under the curve of 0.929 (standard error, 0.079; p < 0.001; 95% confidence interval, 0.775–1.082), indicating excellent diagnostic performance. Conclusion: We find that Meckel scanning is a non-invasive procedure with low radiation exposure. It is a highly specific and accurate method for diagnosing MD in pediatric patients. Bangladesh J. Nuclear Med. 28(2): 329-335, July 2025
Le terme « moulages » est d’un usage courant pour désigner des objets en plâtre (ou aujourd’hui en résine) reproduisant des oeuvres d’art, dans un but muséographique et/ou pédagogique. Ce terme demeure cependant ambigu car moulage désigne à la fois un processus et son résultat matériel. Cette introduction revient sur le vocabulaire communément employé par les spécialistes (moulage, tirage, épreuve, oeuvre) pour mieux en préciser les contextes d’usages appropriés.
