Machine Learning Meets Physics-based Modeling: A Mass-spring System to Predict Protein-ligand Binding Affinity.

BACKGROUND: Computational assessment of the energetics of protein-ligand complexes is a challenge in the early stages of drug discovery. Previous comparative studies on computational methods to calculate the binding affinity showed that targeted scoring functions outperform universal models. OBJECTIVE: The goal here is to review the application of a simple physics-based model to estimate the binding. The focus is on a mass-spring system developed to predict binding affinity against cyclin-dependent kinase. METHOD: Publications in PubMed were searched to find mass-spring models to predict binding affinity. Crystal structures of cyclin-dependent kinases found in the protein data bank and two web servers to calculate affinity based on the atomic coordinates were employed. RESULTS: One recent study showed how a simple physics-based scoring function (named Taba) could contribute to the analysis of protein-ligand interactions. Taba methodology outperforms robust physics-based models implemented in docking programs such as AutoDock4 and Molegro Virtual Docker. Predictive metrics of 27 scoring functions and energy terms highlight the superior performance of the Taba scoring function for cyclin- dependent kinase. CONCLUSION: The recent progress of machine learning methods and the availability of these techniques through free libraries boosted the development of more accurate models to address protein-ligand interactions. Combining a naïve mass-spring system with machine-learning techniques generated a targeted scoring function with superior predictive performance to estimate pKi.

Eight challenges in developing theory of intelligence.

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

Capillary effect-based selective sealing strategy for increasing piezoelectric MEMS speaker performance.

To address the serious acoustic performance deterioration induced by air leakage in the low-frequency range and the asynchronous vibration in electroacoustic transduction structures near the resonant frequency, a novel sealing strategy is proposed that targets one of the most widely reported piezoelectric MEMS speaker designs. This design consists of multiple cantilever beams, in which the air gaps between cantilevers are automatically and selectively filled with liquid polydimethylsiloxane (PDMS) via the capillary effect, followed by curing. In the proof-of-concept demonstration, the sound pressure level (SPL) within the frequency range lower than 100 Hz markedly increased after sealing in an experiment using an IEC ear simulator. Specifically, the SPL is increased by 4.9 dB at 20 Hz for a 40 Vpp driving voltage. Moreover, the deteriorated SPL response near the resonant frequencies of the cantilever beams (18 kHz-19 kHz) caused by their asynchronous vibration induced by the fabrication process nonuniformity also significantly improved, which successfully increased the SPL to approximately 17.5 dB. Moreover, sealed devices feature nearly the same SPL response as the initial counterpart in the frequency band from 100 Hz to 16 kHz and a total harmonic distortion (THD) of 0.728% at 1 kHz for a 40 Vpp driving voltage. Compared with existing sealing methods, the current approach offers easy operation, low damage risk, excellent repeatability/reliability and excellent robustness advantages and provides a promising technical solution for MEMS acoustic devices.

Combining mutation and recombination statistics to infer clonal families in antibody repertoires.

B-cell repertoires are characterized by a diverse set of receptors of distinct specificities generated through two processes of somatic diversification: V(D)J recombination and somatic hypermutations. B cell clonal families stem from the same V(D)J recombination event, but differ in their hypermutations. Clonal families identification is key to understanding B-cell repertoire function, evolution and dynamics. We present HILARy (High-precision Inference of Lineages in Antibody Repertoires), an efficient, fast and precise method to identify clonal families from single- or paired-chain repertoire sequencing datasets. HILARy combines probabilistic models that capture the receptor generation and selection statistics with adapted clustering methods to achieve consistently high inference accuracy. It automatically leverages the phylogenetic signal of shared mutations in difficult repertoire subsets. Exploiting the high sensitivity of the method, we find the statistics of evolutionary properties such as the site frequency spectrum and 𝑑𝑁∕𝑑𝑆 ratio do not depend on the junction length. We also identify a broad range of selection pressures spanning two orders of magnitude.

A multi-orbital Hund´s rules-based Ionic Hamiltonian for transition metal atoms: High-order Equation of Motion Method approach and Kondo resonances.

A multi-orbital ionic Hamiltonian is presented to analyze the many-body properties of the d-transition metal atoms. This Hamiltonian considers all the atomic states obeying the first Hund´s rule and also includes all orbital degeneracy, as well as the interaction of the atom with a metal. We analyze the solution of this ionic Hamiltonian by means of the Equation of Motion (EOM) method up to the fourth order, V4, in the atom-metal interaction. Equations for the appropriate Green-functions for analyzing the chemical and transport properties of the system are given for different atom occupancies. In particular, we introduce a full analysis of the multi-orbital Hamiltonian including atomic configurations with N, N+1 and N-1 electrons, and discuss its Kondo properties. The shells d1, d2 and d3 are analyzed in detail and Kondo energies are deduced in all these cases showing good agreement with the conventional known results. .

Cytochrome c prompts the recruitment of its nuclear partners SET/TAF-Iß and NPM1 into biomolecular condensates.

Compartmentalization of proteins by liquid-liquid phase separation (LLPS) is used by cells to control biochemical reactions spatially and temporally. Among them, the recruitment of proteins to DNA foci and nucleolar trafficking occur by biomolecular condensation. Within this frame, the oncoprotein SET/TAF-Iß plays a key role in both chromatin remodeling and DNA damage response, as does nucleophosmin (NPM1) which indeed participates in nucleolar ribosome synthesis. Whereas phase separation by NPM1 is widely characterized, little is known about that undergone by SET/TAF-Iß. Here, we show that SET/TAF-Iß experiences phase separation together with respiratory cytochrome c (Cc), which translocates to the nucleus upon DNA damage. Here we report the molecular mechanisms governing Cc-induced phase separation of SET/TAF-Iß and NPM1, where two lysine-rich clusters of Cc are essential to recognize molecular surfaces on both partners in a specific and coordinated manner. Cc thus emerges as a small, globular protein with sequence-encoded heterotypic phase-separation properties.

Production of Martian fiber by in-situ resource utilization strategy.

Many countries and commercial organizations have shown great interest in constructing a Martian base. In situ resource utilization (ISRU) provides a cost-effective way to achieve this ambitious goal. In this article, we proposed to use Martian soil simulant to produce a fiber to satisfy material requirement for the construction of Martian base. The composition, melting behavior, and fiber forming process of the soil simulant was studied, and continuous fiber with maximum strength of 1320 MPa and elastic modulus of 99 GPa was obtained on a spinning facility. The findings of this study demonstrate the feasibility of ISRU to prepare Martian fiber from the soil on the Mars, offering a new way to obtain key materials for the construction of a Martian base.

Quality assurance for cancer patient safety: Clinical assessment for precise angles in linac during radiation therapy.

PURPOSE: Quality assurance for stereotactic body radiation treatment requires that isocentric verification be ensured during gantry rotation at various angles. This study examined statistical parameters on Winston-Lutz tests to distinguish the deviation of angles from isocenter during gantry rotation using machine learning. METHOD: The Varian TrueBeam linac was aligned with the marked lines on the Ruby phantom. Eight images were captured while the gantry was rotating at a 45° shift. The statistical features were derived from IsoCheck EPID software. The decision tree model was applied to these Winston-Lutz tests to cluster data into two groups: precise and error angles. RESULTS: At 90° and 270° angles, the gantry exhibits isocentric stability compared to other angles. In these angles, the most statistical features were inside the range. Most variations were observed at 0° and 180° angles. In most tests, the angles 45°, 135°, 225°, and 315° showed reasonable performance and with less variation. CONCLUSION: The comprehensive statistical analyses for gantry rotation of angles assists expert radiotherapists in determining the contribution of each feature that highly affects gantry movement at specific angles. Misalignment between radiation isocenter and imaging isocenter, tuning of the beam at each angle, or a slight change in the position of the Ruby phantom can further improve the inaccuracy that causes the most variations. Better precision can effectively increase patient safety and quality during cancer treatment.

Identifying Heterogeneous Micromechanical Properties of Biological Tissues via Physics-Informed Neural Networks.

The heterogeneous micromechanical properties of biological tissues have profound implications across diverse medical and engineering domains. However, identifying full-field heterogeneous elastic properties of soft materials using traditional engineering approaches is fundamentally challenging due to difficulties in estimating local stress fields. Recently, there has been a growing interest in data-driven models for learning full-field mechanical responses, such as displacement and strain, from experimental or synthetic data. However, research studies on inferring full-field elastic properties of materials, a more challenging problem, are scarce, particularly for large deformation, hyperelastic materials. Here, a physics-informed machine learning approach is proposed to identify the elasticity map in nonlinear, large deformation hyperelastic materials. This study reports the prediction accuracies and computational efficiency of physics-informed neural networks (PINNs) in inferring the heterogeneous elasticity maps across materials with structural complexity that closely resemble real tissue microstructure, such as brain, tricuspid valve, and breast cancer tissues. Further, the improved architecture is applied to three hyperelastic constitutive models: Neo-Hookean, Mooney Rivlin, and Gent. The improved network architecture consistently produces accurate estimations of heterogeneous elasticity maps, even when there is up to 10% noise present in the training data.

Insights Into the Research Landscape of Biomedical Physics: A Bibliometric Inquiry.

Biomedical physics is the interdisciplinary field that links the scientific concepts in physics to the practice of medicine and biology, in an effort to understand biological processes, help in the development of medical technologies, to improve human health. This bibliometric study investigates the interdisciplinary field of biomedical physics, which integrates the principles of physics with biological and medical sciences to develop innovative diagnostic and therapeutic technologies. Utilizing the Web of Science database for bibliographic data collection, the analysis employs advanced bibliometric software tools, including Biblioshiny and VOSviewer, to comprehensively map the research landscape. Our findings delineate the annual scientific production, highlighting growth trends and identifying the most influential authors and key publication venues in the field. A thematic analysis reveals prevailing research topics and the evolution of scientific interests over time, providing insights into the shifting focus areas within biomedical physics. The factorial analysis goes further to clarify the conceptual structure of the discipline by providing a topological image of how the different research areas are involved. It helps to recognize topical fields and the possibility of the topicalization of other subjects. Keyword co-occurrence assumes the leading themes and measures the value of the topology. Meanwhile, bibliographical information defines the authors' network, and co-citation analysis identifies the critical authors' pool. The last points to the topic dependence and the network of research collaboration on a global scale. As a result, a survey identifies the deficits and rules of recommendations for the further development of research. It adds practical implications that are necessary for the development and identifies influences for popularization that it might have in the future.

Multi-Sensor monitoring dataset for milling process with varied parameters and materials.

Real-time monitoring of milling parameters is essential to improve machining efficiency and quality, especially for the workpieces with complex geometry. Its main task is to build the relationship between the parameters and the monitoring data. As the relationship is challenging to be established solely through mechanism-driven or data-driven methods, the physics informed method, based on prior physical laws between physical signals and milling parameters, becomes the optimal method. However, this method is limited due to the lack of a high-quality dataset. Therefore, a multi-sensor monitoring dataset for the milling process with various milling parameters and milling materials is built. The variables include cutting depth, cutting width, feed rate, spindle speed and workpiece materials (aluminium alloy 7030 and CK45 steel). The multi-sensor includes force, vibration, noise, and current. A dataset comprising 115 samples is built, including 100 samples collected using the 'all factors' method, and 15 slot milling samples using two different workpiece materials. The 15 slot milling samples are used to calibrate mechanical milling force coefficients, which is beneficial for developing a physics-informed machine learning algorithm.

Physics-informed neural networks for high-resolution weather reconstruction from sparse weather stations.

Background: The accurate provision of weather information holds immense significance to many disciplines. One example corresponds to the field of air traffic management, in which one basis for weather detection is set upon recordings from sparse weather stations on ground. The scarcity of data and their lack of precision poses significant challenges to achieve a detailed description of the atmosphere state at a certain moment in time. Methods: In this article, we foster the use of physics-informed neural networks (PINNs), a type of machine learning (ML) architecture which embeds mathematically accurate physics models, to generate high-quality weather information subject to the regularization provided by the Navier-Stokes equations. Results: The application of PINNs is oriented to the reconstruction of dense and precise wind and pressure fields in areas where only a few local measurements provided by weather stations are available. Our model does not only disclose and regularize such data, which are potentially corrupted by noise, but is also able to precisely compute wind and pressure in target areas. Conclusions: The effect of time and spatial resolution over the capability of the PINN to accurately reconstruct fluid phenomena is thoroughly discussed through a parametric study, concluding that a proper tuning of the neural network's loss function during training is of utmost importance.

There is currently a great interest in the many uses of artificial intelligence (AI) and how it is affecting our daily lives. From the robotics field to the use of language recognition to interact with different users, we are experiencing how machine intelligence is increasing day by day. In this article, we delve into one of the many applications of artificial intelligence: weather reconstruction. The ability to accurately determine weather conditions is believed to have an impact on various disciplines, e.g. reducing costs at airports due to delays, cancellations and associated compensations. In this particular example, a precise description of the status of the atmosphere is therefore necessary if countermeasures are to be executed. However, conventional weather recording with on-ground stations is often limited to a few sparse locations. Following that line of thought, it is not only necessary to estimate the weather in areas surrounding stations, but also on other target areas which may be subject to lack of weather information. Our strategy is based on the application of neural networks, a type of AI architecture, to infer data based on the underlying physics that drive the measured weather phenomena. For that purpose, we make use of neural networks which are consistent with physics laws, the so-called physics-informed neural networks (PINNs). This article deals with their adoption to weather pattern reconstruction, with the objective of further increasing the precision and availability of information given scarce reference measurements.

Advancements in Interventional Pulmonology: Harnessing Ultrasound Techniques for Precision Diagnosis and Treatment.

Medical ultrasound has emerged as an indispensable tool within interventional pulmonology, revolutionizing diagnostic and procedural practices through its non-invasive nature and real-time visualization capabilities. By harnessing the principles of sound waves and employing a variety of transducer types, ultrasound facilitates enhanced accuracy and safety in procedures such as transthoracic needle aspiration and pleural effusion drainage, consequently leading to improved patient outcomes. Understanding the fundamentals of ultrasound physics is paramount for clinicians, as it forms the basis for interpreting imaging results and optimizing interventions. Thoracic ultrasound plays a pivotal role in diagnosing conditions like pleural effusions and pneumothorax, while also optimizing procedures such as thoracentesis and biopsy by providing precise guidance. Advanced ultrasound techniques, including endobronchial ultrasound, has transformed the evaluation and biopsy of lymph nodes, bolstered by innovative features like elastography, which contribute to increased procedural efficacy and patient safety. Peripheral ultrasound techniques, notably radial endobronchial ultrasound (rEBUS), have become essential for assessing pulmonary nodules and evaluating airway structures, offering clinicians valuable insights into disease localization and severity. Neck ultrasound serves as a crucial tool in guiding supraclavicular lymph node biopsy and percutaneous dilatational tracheostomy procedures, ensuring safe placement and minimizing associated complications. Ultrasound technology is suited for further advancement through the integration of artificial intelligence, miniaturization, and the development of portable devices. These advancements hold the promise of not only improving diagnostic accuracy but also enhancing the accessibility of ultrasound imaging in diverse healthcare settings, ultimately expanding its utility and impact on patient care. Additionally, the integration of enhanced techniques such as contrast-enhanced ultrasound and 3D imaging is anticipated to revolutionize personalized medicine by providing clinicians with a more comprehensive understanding of anatomical structures and pathological processes. The transformative potential of medical ultrasound in interventional pulmonology extends beyond mere technological advancements; it represents a paradigm shift in healthcare delivery, empowering clinicians with unprecedented capabilities to diagnose and treat pulmonary conditions with precision and efficacy. By leveraging the latest innovations in ultrasound technology, clinicians can navigate complex anatomical structures with confidence, leading to more informed decision-making and ultimately improving patient outcomes. Moreover, the portability and versatility of modern ultrasound devices enable their deployment in various clinical settings, from traditional hospital environments to remote or resource-limited areas, thereby bridging gaps in healthcare access and equity.

Soliton structures of fractional coupled Drinfel'd-Sokolov-Wilson equation arising in water wave mechanics.

This article delves into the dynamic constructions of distinctive traveling wave solutions for wave circulation in shallow water mechanics, specifically addressing the time-fractional couple Drinfel'd-Sokolov-Wilson (DSW) equation. Introducing the previously untapped e x p ( - ϕ ( ξ ) ) -expansion method, we successfully generate a diverse set of analytic solutions expressed in hyperbolic, trigonometric, and rational functions, each with permitted parameters. Visualization through three-dimensional (3D) as well two-dimensional (2D) plots, including contour plots, reveals inherent wave phenomena in the DSW equation. These newly obtained wave solutions serve as a catalyst for refining theories in applied science, offering a means to validate mathematical simulations for the proliferation of waves in shallow water as well as other nonlinear scenarios. Obtained wave solutions demonstrate the bright soliton, periodic, multiple soliton, and kink soliton shape. The simplicity and efficacy of the implemented methods are demonstrated, providing a valuable tool for approximating the considered equation. All figures are devoted to demonstrate the complete wave futures of the attained solutions to the studied equation with the collaboration of specific selections of the chosen parameters. Moreover, it may have summarized that the attained wave solutions and their physical phenomena might be useful to comprehend the various kind of wave propagation in mathematical physics and engineering.

Large-scale flood modeling and forecasting with FloodCast.

Large-scale hydrodynamic models generally rely on fixed-resolution spatial grids and model parameters as well as incurring a high computational cost. This limits their ability to accurately forecast flood crests and issue time-critical hazard warnings. In this work, we build a fast, stable, accurate, resolution-invariant, and geometry-adaptive flood modeling and forecasting framework that can perform at large scales, namely FloodCast. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the multi-satellite observation module, a real-time unsupervised change detection method and a rainfall processing and analysis tool are proposed to harness the full potential of multi-satellite observations in large-scale flood prediction. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced, benefiting from the absence of a requirement for training data in physics-informed neural networks (PINNs) and featuring a fast, accurate, and resolution-invariant architecture with Fourier neural operators. To adapt to complex river geometries, we reformulate PINNs in a geometry-adaptive space. GeoPINS demonstrates impressive performance on popular partial differential equations across regular and irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. This model employs sequence-to-sequence learning and hard-encoding of boundary conditions. Next, we establish a benchmark dataset in the 2022 Pakistan flood using a widely accepted finite difference numerical solution to assess various flood simulation methods. Finally, we validate the model in three dimensions - flood inundation range, depth, and transferability of spatiotemporal downscaling - utilizing SAR-based flood data, traditional hydrodynamic benchmarks, and concurrent optical remote sensing images. Traditional hydrodynamics and sequence-to-sequence GeoPINS exhibit exceptional agreement during high water levels, while comparative assessments with SAR-based flood depth data show that sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with smaller simulation errors. The experimental results for the 2022 Pakistan flood demonstrate that the proposed method enables high-precision, large-scale flood modeling with an average MAPE of 14.93 % and an average Mean Absolute Error (MAE) of 0.0610 m for 14-day water depth simulations while facilitating real-time flood hazard forecasting using reliable precipitation data.

An integrated micromachined flexible ultrasonic-inductive sensor for pipe contaminant multiparameter detection.

Pipe contaminant detection holds considerable importance within various industries, such as the aviation, maritime, medicine, and other pertinent fields. This capability is beneficial for forecasting equipment potential failures, ascertaining operational situations, timely maintenance, and lifespan prediction. However, the majority of existing methods operate offline, and the detectable parameters online are relatively singular. This constraint hampers real-time on-site detection and comprehensive assessments of equipment status. To address these challenges, this paper proposes a sensing method that integrates an ultrasonic unit and an electromagnetic inductive unit for the real-time detection of diverse contaminants and flow rates within a pipeline. The ultrasonic unit comprises a flexible transducer patch fabricated through micromachining technology, which can not only make installation easier but also focus the sound field. Moreover, the sensing unit incorporates three symmetrical solenoid coils. Through a comprehensive analysis of ultrasonic and induction signals, the proposed method can be used to effectively discriminate magnetic metal particles (e.g., iron), nonmagnetic metal particles (e.g., copper), nonmetallic particles (e.g., ceramics), and bubbles. This inclusive categorization encompasses nearly all types of contaminants that may be present in a pipeline. Furthermore, the fluid velocity can be determined through the ultrasonic Doppler frequency shift. The efficacy of the proposed detection principle has been validated by mathematical models and finite element simulations. Various contaminants with diverse velocities were systematically tested within a 14 mm diameter pipe. The experimental results demonstrate that the proposed sensor can effectively detect contaminants within the 0.5-3 mm range, accurately distinguish contaminant types, and measure flow velocity.

The effect of using simulator "evolution of electrical systems'' in electricity lessons on students' motivation and academic performance.

The emergence of simulators and their integration into teaching practice in the world of education have offered us technological opportunities to enhance and promote learning. Science students' abilities to observe, measure, predict, control variables, formulate hypotheses, and interpret data can all be activated by including simulations into the curriculum. The aim of this work is to study the effects of integrating an "evolution of electrical systems" simulator in improving students' motivation, participation and school results in learning and teaching electricity lessons in Moroccan secondary schools. Two study groups of 34 and 35 students were chosen to examine the research hypothesis. They both meet the standards for this research (same teacher, same school level, coming from the same socio-economic environment, and almost similar results in their school careers). Before beginning the process of incorporating simulation sequences in teaching, a diagnostic test was administered to both groups to assess the prerequisites for the RC and RL dipoles, and the results were evaluated. Then we designated one of the two groups as the test group, which received instruction using simulation sequences, and the other group as the control group, which received traditional teaching. Both groups took an Achievement test to evaluate the impact of this integration on the learning of physics. After examining the test data (Charts Comparison and Student's t-test), we came to the conclusion that the use of simulation sequences in the classroom produced significantly more positive and satisfactory results than the traditional approach (Mt = 12,09 for the test group and Mc = 9,69 for the control group). We saw during the sessions that the experimental class students were more motivated and engaged in their learning than the control group. We collected this data by closely observing behavioral shifts, participation rates, and student involvement in the design of the course. These new techniques contribute at improving the experimental part of electricity in secondary schools.

One Flow to Correct Them all: Improving Simulations in High-Energy Physics with a Single Normalising Flow and a Switch.

Simulated events are key ingredients in almost all high-energy physics analyses. However, imperfections in the simulation can lead to sizeable differences between the observed data and simulated events. The effects of such mismodelling on relevant observables must be corrected either effectively via scale factors, with weights or by modifying the distributions of the observables and their correlations. We introduce a correction method that transforms one multidimensional distribution (simulation) into another one (data) using a simple architecture based on a single normalising flow with a boolean condition. We demonstrate the effectiveness of the method on a physics-inspired toy dataset with non-trivial mismodelling of several observables and their correlations.

Developing and implementing a mentoring and enabling programme within EFOMP.

To ensure the continued advancement of the medical physics profession, the European Federation of Organisations for Medical Physics (EFOMP) has designed a mentorship programme. This programme aims to support Early Career Medical Physicists by providing them with the guidance needed for both professional and personal development to meet the continually evolving demands of the medical physics field within their working environments. The EFOMP mentorship programme is an important step forward in supporting the next generation of medical physicists. This article provides an overview of the history, framework, goals, and implementation strategy of this programme. The programme will have two main orientations: mentoring, which will help mentees to improve their scientific, professional and soft skills, and enabling, which aims to prepare a sufficient number of early career professionals to get involved within EFOMP's activities, join the EFOMP structures and represent the European Medical Physics community to National and International Organisations. Each year a survey from mentors and mentees will be conducted and analysed by the European and International Matters Committee and the Early Career Special Interest Group to identify areas for improvement and to evaluate the degree of satisfaction and achievements of the participants. By providing structured mentoring, fostering professional development, and promoting sustainability, EFOMP aims to ensure that early career medical physicists are well-prepared to meet the challenges of the future and continue to advance the field of medical physics as a community.