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 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 withN, N+ 1 andN- 1 electrons, and discuss its Kondo properties. The shellsd1,d2andd3are analyzed in detail and Kondo energies are deduced in all these cases showing good agreement with the conventional known results.

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.

Physics-informed neural networks for parameter estimation in blood flow models.

BACKGROUND: Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available. This is especially useful in hemodynamics since the boundary information is often difficult to model, and high-quality blood flow measurements are generally hard to obtain. METHODS: In this work, we use the PINNs methodology for estimating reduced-order model parameters and the full velocity field from scatter 2D noisy measurements in the aorta. Two different flow regimes, stationary and transient were studied. RESULTS: We show robust and relatively accurate parameter estimations when using the method with simulated data, while the velocity reconstruction accuracy shows dependence on the measurement quality and the flow pattern complexity. Comparison with a Kalman filter approach shows similar results when the number of parameters to be estimated is low to medium. For a higher number of parameters, only PINNs were capable of achieving good results. CONCLUSION: The method opens a door to deep-learning-driven methods in the simulations of complex coupled physical systems.

Adsorptive features of cyclohexane carboxylic naphthenic acid on a novel cross-linked polymer developed from spent coffee grounds.

Naphthenic acids (NA) are organic compounds commonly found in crude oil and produced water, known for their recalcitrance and toxicity. This study introduces a new adsorbent, a polymer derived from spent coffee grounds (SCGs), through a straightforward cross-linking method for removing cyclohexane carboxylic acid as representative NA. The adsorption kinetics followed a pseudo-second-order model for the data (0.007 g min-1 mg-1), while the equilibrium data fitted the Sips model ( q m = 140.55 mg g-1). The process's thermodynamics indicated that the target NA's adsorption was spontaneous and exothermic. The localized sterical and energetic aspects were investigated through statistical physical modeling, which corroborated that the adsorption occurred indeed in monolayer, as suggested by the Sips model, but revealed the contribution of two energies per site ( n 1 ; n 2 ). The number of molecules adsorbed per site ( n ) was highly influenced by the temperature as n 1 decreased with increasing temperature and n 2 increased. These results were experimentally demonstrated within the pH range between 4 and 6, where both C6H11COO-(aq.) and C6H11COOH(aq.) species coexisted and were adsorbed by different energy sites. The polymer produced was naturally porous and amorphous, with a low surface area of 20 to 30 m2 g-1 that presented more energetically accessible sites than other adsorbents with much higher surface areas. Thus, this study shows that the relation between surface area and high adsorption efficiency depends on the compatibility between the energetic states of the receptor sites, the speciation of the adsorbate molecules, and the temperature range studied.

Improving engineering students' understanding of classical physics through visuo-haptic simulations.

Introduction: The teaching process plays a crucial role in the training of professionals. Traditional classroom-based teaching methods, while foundational, often struggle to effectively motivate students. The integration of interactive learning experiences, such as visuo-haptic simulators, presents an opportunity to enhance both student engagement and comprehension. Methods: In this study, three simulators were developed to explore the impact of visuo-haptic simulations on engineering students' engagement and their perceptions of learning basic physics concepts. The study used an adapted end-user computing satisfaction questionnaire to assess students' experiences and perceptions of the simulators' usability and its utility in learning. Results: Feedback from participants suggests a positive reception towards the use of visuo-haptic simulators, highlighting their usefulness in improving the understanding of complex physics principles. Discussion: Results suggest that incorporating visuo-haptic simulations into educational contexts may offer significant benefits, particularly in STEM courses, where traditional methods may be limited. The positive responses from participants underscore the potential of computer simulations to innovate pedagogical strategies. Future research will focus on assessing the effectiveness of these simulators in enhancing students' learning and understanding of these concepts in higher-education physics courses.

Ultrasound principles and instrumentation.

Ultrasound (US) is a fundamental and inexpensive tool both for the prompt diagnosis and for the study of diverse medical conditions. Its widespread use is partly due to the availability of US devices in the daily practice of physicians. US can be performed in real-time and is instrumental in the generation of clinical algorithms for the management of situations like trauma. It also constitutes a primary approach for the study of oncological diseases, and a guidance tool for interventions such as percutaneous drainages. In addition, and specifically for HPB surgeons, US is an essential tool in the operating room: intraoperative (either open or laparoscopic) US is necessary for the accurate determination of the stage, location, number, and margins of tumors within the liver, pancreas, or biliary tree. On another note, reading and understanding US images are skills that require time and training, which should be taught during surgical residencies. However, this is not customary in most residencies globally. This chapter offers a concise yet comprehensive elucidation of the basic principles of ultrasonography, the instruments required to perform an ultrasonic assessment of a patient, and the basic ultrasound controls.

Igneous processes in the small bodies of the Solar System II: Small satellites and dwarf planets.

Evidence of hot and cold igneous processes has been reported in small satellites and dwarf planets of the Solar System. Olivine and pyroxenes were detected in the spectral bands of both small satellites and dwarf planets. The aqueously altered form of olivine and serpentine has been detected in the spectrums of Ceres and Miranda hinting at possible hydrothermal processes in their interiors. Once more, the ubiquitous distribution of 26Al in the planetary nebula, then evolving in the protoplanetary disk, contributed to the primordial widespread heating. Volcanism, or cryovolcanism, then developed only in those bodies where long-lived radiogenic elements, and/or tidal processes, were available.

Enhancing Robotic Perception through Synchronized Simulation and Physical Common-Sense Reasoning.

We introduce both conceptual and empirical findings arising from the amalgamation of a robotics cognitive architecture with an embedded physics simulator, aligning with the principles outlined in the intuitive physics literature. The employed robotic cognitive architecture, named CORTEX, leverages a highly efficient distributed working memory known as deep state representation. This working memory inherently encompasses a fundamental ontology, state persistency, geometric and logical relationships among elements, and tools for reading, updating, and reasoning about its contents. Our primary objective is to investigate the hypothesis that the integration of a physics simulator into the architecture streamlines the implementation of various functionalities that would otherwise necessitate extensive coding and debugging efforts. Furthermore, we categorize these enhanced functionalities into broad types based on the nature of the problems they address. These include addressing challenges related to occlusion, model-based perception, self-calibration, scene structural stability, and human activity interpretation. To demonstrate the outcomes of our experiments, we employ CoppeliaSim as the embedded simulator and both a Kinova Gen3 robotic arm and the Open-Manipulator-P as the real-world scenarios. Synchronization is maintained between the simulator and the stream of real events. Depending on the ongoing task, numerous queries are computed, and the results are projected into the working memory. Participating agents can then leverage this information to enhance overall performance.

On geometrical aspects of the graph approach to contextuality.

The connection between contextuality and graph theory has paved the way for numerous advancements in the field. One notable development is the realization that sets of probability distributions in many contextuality scenarios can be effectively described using well-established convex sets from graph theory. This geometric approach allows for a beautiful characterization of these sets. The application of geometry is not limited to the description of contextuality sets alone; it also plays a crucial role in defining contextuality quantifiers based on geometric distances. These quantifiers are particularly significant in the context of the resource theory of contextuality, which emerged following the recognition of contextuality as a valuable resource for quantum computation. In this paper, we provide a comprehensive review of the geometric aspects of contextuality. Additionally, we use this geometry to define several quantifiers, offering the advantage of applicability to other approaches to contextuality where previously defined quantifiers may not be suitable. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

Non-destructive latent fingerprint development and background elimination by Mueller Polarimetry.

In this article we have explored the use of Mueller polarimetry for the simultaneous and non-destructive latent fingerprint development and background elimination. This proposal not only expands previously reported uses of the polarization state of light for fingerprint development, but offers some advantages. Samples of a few donors, taken on different heavily back grounded substrates, were measured and processed. Several strategies for background removal were applied. The results are very promising, since the background was successfully erased in all cases and the quality of the developed fingerprints was excellent for most substrates and donors. Considering that the method is non-destructive and that the proposed measurement instrument is inexpensive and portable the method could be tried in real cases with no risk for the evidence. In addition, the success of the procedure for background removal raises the possibility of exploring if it would work for the enhancement of fingerprints developed with other methods.

No-Tillage System Can Improve Soybean Grain Production More Than Conventional Tillage System.

Soil management systems can directly interfere with crop yield via changes in the soil's physical and hydraulic properties. However, short- to medium-term experiments of conduction do not always demonstrate the modifications of the management systems in these properties. Thus, the aim of this study was to evaluate the physical properties of the soil in a long-term management system and to relate it to the storage and availability of water to plants, verifying its effect on soybean yield. The experiment was conducted in randomized blocks in a split-plot scheme with four replications. Plots were composed by soil management (conventional tillage and no-tillage), and subplots represented three soil depths (0.0-0.1, 0.1-0.2, and 0.2-0.4 m). The soil's physical and hydraulic properties, root development, and soybean yield were evaluated. The no-tillage system not only presented higher bulk density and soil resistance to compaction up to a depth of 0.2 m but also greater root development. This management also did not affect the process of water infiltration in the soil and presented an increase in soybean grain yield by 6.5%. The long-term no-tillage system (33 years) offers less risk of water stress to soybean plants; it contributes to greater grain yield of this crop when compared to the conventional tillage system.

Current status and development of neutron radiation for biophysical applications in Colombia.

In Colombia, medical physics started formally about 3 decades ago. Two master's programs in medical physics initiated activities at two different universities. In particular, the master's program at the Pontificia Universidad Javeriana has been underway since 2012, and taking into account its projections, a team was established in 2015 in collaboration with the Universidad Distrital Francisco José de Caldas to conduct basic research on cancer treatment using neutron capture therapy (NCT). The primary goal of our initiative is to create the infrastructure required to adapt new technologies in our universities in the future. The long-term objective is to use neutron radiation to study not only NCT but also biomolecules, membranes, and materials. This will require the commissioning of an actual nuclear facility. Our group has been exclusively focused on carrying out calculations with GEANT4 because of its characteristics as open-source software, its accessibility, and its ample worldwide use and validation in the particle physics, nuclear physics, and medical physics communities. In this work, we present some results of our preliminary design for the ion accelerator column of a compact neutron generator. Also, we present the characterization of the kinematical and dose distributions of boron neutron capture processes using Geant4.

Igneous processes in the small bodies of the Solar System I. Asteroids and comets.

Igneous processes were quite widespread in the small bodies of the Solar System (SBSS) and were initially fueled by short-lived radioisotopes, the proto-Sun, impact heating, and differentiation heating. Once they finished, long-lived radioisotopes continued to warm the active bodies of the Earth, (possibly) Venus, and the cryovolcanism of Enceladus. The widespread presence of olivine and pyroxenes in planets and also in SBSS suggests that they were not necessarily the product of igneous processes and they might have been recycled from previous nebular processes or entrained in comets from interstellar space. The difference in temperature between the inner and the outer Solar System has clearly favored thermal annealing of the olivine close to the proto-Sun. Transport of olivine within the Solar System probably occurred also due to protostellar jets and winds but the entrainment in SBSS from interstellar space would overcome the requirement of initial turbulent regime in the protoplanetary nebula.

On identity, indiscernibility and (non-)individuality in the quantum domain.

The topics of identity, individuality and indiscernibility had already entered the domain of the philosophy of physics, mainly in what respects the philosophy of quantum theories. In this paper, I present the main ideas of an underlying metaphysics that considers the basic entities of quantum physics as non-individuals, that is, as entities to which the standard theory of identity of classical logic and standard mathematics (set theory) does not apply. Two things must be emphasized: the first is that we pursue Erwin Schrödinger's ideas that the notion of identity (or sameness) does not apply to quantum things; the second is to follow Heinz Post's claim that the non-individuality of quantum entities 'must be introduced right at the start' and not by taking individuals and making them pretend to be non-individuals 'by hand', say by confining them to non-rigid structures. Thus, this paper should be regarded as an attempt to justify the metaphysics of non-individuals and the logical systems that cope with them, although these systems are here only referred to. We also answer some criticisms that have been directed to such a view. The paper is directed to an audience of both physicists interested in foundations and philosophers of physics. This article is part of the theme issue 'Identity, individuality and indistinguishability in physics and mathematics'.

The Scientific Contribution of the Kaniadakis Entropy to Nuclear Reactor Physics: A Brief Review.

In nuclear reactors, tracking the loss and production of neutrons is crucial for the safe operation of such devices. In this regard, the microscopic cross section with the Doppler broadening function is a way to represent the thermal agitation movement in a reactor core. This function usually considers the Maxwell-Boltzmann statistics for the velocity distribution. However, this distribution cannot be applied on every occasion, i.e., in conditions outside the thermal equilibrium. In order to overcome this potential limitation, Kaniadakis entropy has been used over the last seven years to generate generalised nuclear data. This short review article summarises what has been conducted so far and what has to be conducted yet.

Hospital-based proton therapy implementation during the COVID pandemic: early clinical and research experience in a European academic institution.

INTRODUCTION: A rapid deploy of unexpected early impact of the COVID pandemic in Spain was described in 2020. Oncology practice was revised to facilitate decision-making regarding multimodal therapy for prevalent cancer types amenable to multidisciplinary treatment in which the radiotherapy component searched more efficient options in the setting of the COVID-19 pandemic, minimizing the risks to patients whilst aiming to guarantee cancer outcomes. METHODS: A novel Proton Beam Therapy (PBT), Unit activity was analyzed in the period of March 2020 to March 2021. Institutional urgent, strict and mandatory clinical care standards for early diagnosis and treatment of COVID-19 infection were stablished in the hospital following national health-authorities' recommendations. The temporary trends of patients care and research projects proposals were registered. RESULTS: 3 out of 14 members of the professional staff involved in the PBR intra-hospital process had a positive test for COVID infection. Also, 4 out of 100 patients had positive tests before initiating PBT, and 7 out of 100 developed positive tests along the weekly mandatory special checkup performed during PBT to all patients. An update of clinical performance at the PBT Unit at CUN Madrid in the initial 500 patients treated with PBT in the period from March 2020 to November 2022 registers a distribution of 131 (26%) pediatric patients, 63 (12%) head and neck cancer and central nervous system neoplasms and 123 (24%) re-irradiation indications. In November 2022, the activity reached a plateau in terms of patients under treatment and the impact of COVID pandemic became sporadic and controlled by minor medical actions. At present, the clinical data are consistent with an academic practice prospectively (NCT05151952). Research projects and scientific production was adapted to the pandemic evolution and its influence upon professional time availability. Seven research projects based in public funding were activated in this period and preliminary data on molecular imaging guided proton therapy in brain tumors and post-irradiation patterns of blood biomarkers are reported. CONCLUSIONS: Hospital-based PBT in European academic institutions was impacted by COVID-19 pandemic, although clinical and research activities were developed and sustained. In the post-pandemic era, the benefits of online learning will shape the future of proton therapy education.

Perception and Recognition of Clinical Medical Physicist Roles and Responsibilities by Specialist Physician Staff: The First Mexican Survey.

Introduction: Although medical physics as a profession is recognized as part of the health-care professional workforce by the International Labor Organization, in the Mexican context, the figure of the medical physicist (MP) is often inappropriately associated solely with technical work, leading to perception, recognition, and salary implications. The aim of this study was to explore the perception of medical specialists regarding the role and responsibilities of MPs in clinical practice in Mexico. Methods: A national survey was answered by medical personnel, ranging from residents to qualified specialists in November 2019. The questionnaire consisted of ten questions related to perception of MPs. The survey was open to all medical specialists regardless of their involvement in the use of ionizing radiations or otherwise. Results: It was shown that approximately two-thirds of specialists know and recognize the medical physics profession in hospitals and the roles and responsibilities of MPs. However, 19% of medical specialists considered the standard of service as inadequate. Conclusion: MPs must exert greater efforts to promote their status and enhance the recognition of their contribution to health care. The low level of recognition in diagnostic and interventional radiology and in nuclear medicine in Mexico might be related to nonexistent or unclear documentation and inadequate regulations, policies, or directives promoted by the health-care authorities.

First-Principle Validation of Fourier's Law: One-Dimensional Classical Inertial Heisenberg Model.

The thermal conductance of a one-dimensional classical inertial Heisenberg model of linear size L is computed, considering the first and last particles in thermal contact with heat baths at higher and lower temperatures, Th and Tl (Th>Tl), respectively. These particles at the extremities of the chain are subjected to standard Langevin dynamics, whereas all remaining rotators (i=2,⋯,L-1) interact by means of nearest-neighbor ferromagnetic couplings and evolve in time following their own equations of motion, being investigated numerically through molecular-dynamics numerical simulations. Fourier's law for the heat flux is verified numerically, with the thermal conductivity becoming independent of the lattice size in the limit L→∞, scaling with the temperature, as κ(T)∼T-2.25, where T=(Th+Tl)/2. Moreover, the thermal conductance, σ(L,T)≡κ(T)/L, is well-fitted by a function, which is typical of nonextensive statistical mechanics, according to σ(L,T)=Aexpq(-Bxη), where A and B are constants, x=L0.475T, q=2.28±0.04, and η=2.88±0.04.

Predictive Modeling of Soft Stretchable Nanocomposites Using Recurrent Neural Networks.

Human skin is characterized by rough, elastic, and uneven features that are difficult to recreate using conventional manufacturing technologies and rigid materials. The use of soft materials is a promising alternative to produce devices that mimic the tactile capabilities of biological tissues. Although previous studies have revealed the potential of fillers to modify the properties of composite materials, there is still a gap in modeling the conductivity and mechanical properties of these types of materials. While traditional Finite Element approximations can be used, these methodologies tend to be highly demanding of time and processing power. Instead of this approach, a data-driven learning-based approximation strategy can be used to generate prediction models via neural networks. This paper explores the fabrication of flexible nanocomposites using polydimethylsiloxane (PDMS) with different single-walled carbon nanotubes (SWCNTs) loadings (0.5, 1, and 1.5 wt.%). Simple Recurrent Neural Networks (SRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU) models were formulated, trained, and tested to obtain the predictive sequence data of out-of-plane quasistatic mechanical tests. Finally, the model learned is applied to a dynamic system using the Kelvin-Voight model and the phenomenon known as the bouncing ball. The best predictive results were achieved using a nonlinear activation function in the SRNN model implementing two units and 4000 epochs. These results suggest the feasibility of a hybrid approach of analogy-based learning and data-driven learning for the design and computational analysis of soft and stretchable nanocomposite materials.