Physical Model Research Articles

Predicting spatial curvature ΩK in globally CPT -symmetric universes

Boyle and Turok’s CPT-symmetric universe model posits that the Universe was symmetric at the big bang, addressing numerous problems in both cosmology and the Standard Model of particle physics. We extend this model by incorporating the symmetric conditions at the end of the Universe, which impose constraints on the allowed perturbation modes. These constrained modes conflict with the integer wave vectors required by the global spatial geometry in a closed universe. To resolve this conflict, only specific values of curvature are permissible, and in particular the curvature density is constrained to be ΩK∈{−0.016,−0.011,−0.004,…}, consistent with observations. Published by the American Physical Society 2024

Abstract A031: ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer

Abstract Cancer progression poses a clinical hurdle due to tumor heterogeneity that contributes to treatment resistance and immune evasion. While the adaptive immune system can effectively eliminate cancer in certain instances, tumor escape remains challenging. The immune microenvironment plays an important role in this process. One unanswered question is the role of ECM geometry, also known as Tumor Associated Collagen Signatures (TACS), and its effects on tumor immune recognition. In solid malignancies, ECM geometry has been associated with disease stage and T cell infiltration. Specifically, empirically observed ECM topologies are frequently categorized based on fiber arrangement: random fibers (TACS1), circumferentially aligned fibers (TACS2), and radially arranged fibers (TACS3). Even though a clear negative correlation between TACS and patient survival has been established, the specific roles and extent of TACS in T cell-driven cancer evolution remain uncharacterized. The precise mechanisms underlying how TACS influences cell movement are still not fully elucidated, with divergent opinions on whether and how the ECM mediates immune cell infiltration. At present, we currently lack a physical model relating the impact of TACS on the spatial co-evolution between an adaptive immune repertoire and a heterogeneous population of evading cancer cells. Here, we developed the EVO-ACT (EVOlutionary Agent-based Cancer T cell interaction) model, which is based on the Gillespie algorithm, to study the effects of TACS on tumor evolution and dynamical tumor-T cell interactions. Our analysis of tumor-T cell interactions across three TACS types reveals distinct migration patterns, with TACS3 showing the highest efficiency, TACS1 intermediate, and TACS2 the lowest. TACS impacts T cells more than tumors, with stronger chemokine gradients aiding T cell infiltration. Our model suggests that despite TACS2's limited efficiency, it cannot fully impede T cell infiltration. Varied migration efficiencies lead to diverse T cell infiltration patterns and immunoediting levels. TACS3 shows a benefit to immune recognition and higher survival, contrary to lower survival in TACS3 patients observed clinically in breast cancer. Our results indicate that only after introducing phenotypic changes, such as Epithelial-Mesenchymal Transition (EMT), and noting that EMT occurs exclusively after the TACS2-TACS3 progression, can our findings successfully explain the clinically observed trends. Considering mesenchymal tumor cells' ability to upregulate PD-1/PD-L1, we explore TACS-specific tumor evolution's impact on PD-1/PD-L1 inhibitor responses and patient survival. We observe higher tumor escape rates and lower survival in TACS3 compared to TACS2. Thus, TACS3 alone does not account for lower survival in breast cancer; TACS3-associated late-stage cancer byproducts like EMT and elevated checkpoint expression significantly decrease survival. Citation Format: Yijia Fan, Jason Tomas George. ECM architecture drives distinct evolutionary patterns of tumor immune escape and elimination in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A031.

Multimodal classifier of medical risk based on a multielectrode bioimpedance converter

The purpose of the research. The aim of the study is Russian and foreign studies have shown that malignant breast tumors have significantly different impedance from normal tissues. However, bioimpedance analysis has limitations in resolution, as well as in the imperfection of bioimpedance models required to generate input vectors for machine learning systems.Methods. The presented study proposes a multimodal classifier, the raw data for which are obtained through an electrode matrix. It also has three channels for processing the results of bioimpedance analysis, with subsequent aggregation of their solutions. An impedance model of biomaterial is proposed, which allows forming descriptors for medical risk classifiers.Results. Hardware and software for bioimpedance studies have been developed, which include a data collection device for bioimpedance spectroscopy based on an electrode matrix, a device for communication with the object of study, and a device for bioimpedance spectroscopy using an electrode matrix. The software includes interface windows for setting up the bioimpedance research program and training and testing fully connected neural networks. An experimental study of the multimodal classifier on a physical model was conducted using inclusions of higher conductivity (tumor imitation) of various types and sizes in the conductivity range from 1.1 to 1.9 of the background. Based on the obtained images in the two-level neural network of the first channel, the integral risk of breast cancer was determined for all pixels of the image. Statistical studies (ROC analysis) showed sufficient sensitivity and specificity for the screening method - > 0.75. Conclusion. Thus, a new model of intelligent support for medical decision-making has been created, integrating the capabilities of bioimpedance spectroscopy, convolutional neural networks and expert assessment of images generated by bioimpedance mapping. However, current data confirming the possibility of separating benign and malignant breast tumors using bioimpedancemetry methods are very limited, which requires further research in this direction.

Correction: Physical modelling techniques for the dynamical characterization and sound synthesis of historical bells

Correction: Physical modelling techniques for the dynamical characterization and sound synthesis of historical bells

Research on Typical Decay-like Fracture Defects of Composite Insulators Based on Electro-Thermal Coupling

In response to the typical decay and fracture defects of composite insulators, a three-dimensional electrically and thermally coupled simulation physical model was constructed based on the finite element method, and the local electric field distortion and temperature rise were analyzed. The study confirms that the insulator interface’s axial electric and thermal fields show a U-shaped curve; the interface field strength is the largest. There is an electric field gradient difference between the mandrel and the sheath, and the thermal field is concentrated at the mandrel and the interface. The field strength at the edge of the defect is the largest, the aberrant electric field at the defect shows a sawtooth shape, and the temperature rise is concentrated in the defect area. The degradation is fast in the air gap, the etching hole diameter direction, and the carbonation channel axial direction. The larger the defect volume, the larger the aberration in the electric field and temperature rise. Water vapor air gaps, breakdown holes, and carbonized channels have the most pronounced electric field and temperature changes. The functional relationship between electric field aberration, temperature rise, and defect volume is established. The results provide a basis for the protection of insulator decay-like fracture.

An aftermath analysis of caving characteristics and movement of overlying strata in fully mechanized longwall gob.

The large-scale collapse of overlying strata in the gob directly affect the safe production of coal mines; they are also the major causes of geological disasters, such as ground cracks, surface subsidence, and ground collapse. In this paper, the movement and caved characteristics of overlying strata during coal seam excavation are studied by conducting a physical model experiment. Results show that overlying strata have different movement and caved laws during the initial, intermediate, and later mining stages. During the initial mining stage, overlying strata do not collapse, and the subsidence is extremely small. During the intermediate mining stage, overlying strata cave along the vertical direction, and caved height gradually increases. Large numbers of cavities, abscission layers, and fractures exist between caved strata. The fractured area gradually increases upward, and the subsidence increases considerably. During the later mining stage, overlying strata cave along the horizontal direction. The abscission layers between the caved strata of the central are compacted. The compacted area is surrounded by a fractured area. The compacted and fractured areas increase along the horizontal direction. The subsidence curves exhibit a horizontal variation. Overlying strata evolve from the self-equilibrium stage to the vertical collapse stage, and finally, the horizontal collapse stage. The fractured area changes from a no fractured area to a fractured area, increases vertically, and finally, increases horizontally. The subsidence curve changes from extremely small to large, and finally, changes horizontally.

A Low-Cost Workflow to Generate Virtual and Physical Three-Dimensional Models of Cardiac Structures.

Three-dimensional modeling and printing (3DMP) of anatomical structures from cross-sectional imaging data can enhance the understanding of spatial relationships in complex congenital heart defects. Partially due to the substantial financial, material and personnel resources required, 3DMP is not yet universally used. Here, we describe a workflow that addresses and eliminates these drawbacks. The workflow utilizes the open-source software "3D Slicer" (The Slicer Community) and "Blender" (Blender Foundation) for segmentation and post-editing of datasets. This approach enables the generation of virtual or physical 3D models. The physical models are printed using a standard fused deposition modeling printer. The financial challenges that likely constrain the wider use of 3DMP are largely addressed by this approach. However, the workflow still requires a considerable amount of time to manually segment the imaging data. Three-dimensional modeling and printing might improve planning and safety of congenital cardiac surgical treatment. Furthermore, it is a useful tool for education of parents and medical professionals. This workflow increases its suitability for routine use also in regions with low economic resources.

Application of physical and mathematical modeling in the educational process by the example of power plant simulators

This article discusses the application of digital twin technology in thermal power engineering. Special attention is paid to the influence of digital models of the existing equipment of the powerplant on the educational process in higher educational institutions and professional development of service personnel. The article summarizes the results of the introduction of power plant simulators into the curriculum, the methodology of laboratory work and the organization of training sessions. It is also shown that the introduction of physical and mathematical modeling in education and practice contributes to improving the safety and efficiency of work in the energy sector. The results of the introduction of plant simulators into the curriculum are summarized. The software of computer equipment provided for classes at a higher educational institution is considered as a tool for practicing operation skills and creating digital models. When working in a software package, the user is given the opportunity to control units of equipment individually, collectively as a whole unit, or create computational models consisting of simple blocks of equipment. The proposed mutual cooperation between software developers and universities can lead to the creation of digital models to enable retraining of specialists in the workplace, as well as increasing the level of knowledge of graduates. The result of the introduction of a virtual testing ground in a higher educational institution in order to verify the knowledge of students is considered. Thus, the introduction of digital twins into the educational process and practice of work in the thermal power industry is a promising direction for the development of the industry. It contributes to improving the level of training of specialists, ensuring the safety and efficiency of equipment operation, as well as reducing the risks of emergency situations

Host-Guest Binding Free Energies à la Carte: An Automated OneOPES Protocol.

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but the agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than systematic optimization.

Lie symmetries, exact solution and conservation laws of (2 + 1)-dimensional time fractional Kadomtsev–Petviashvili system

Abstract In this paper, Lie symmetry analysis method is applied to the ( 2 + 1 ) (2+1) -dimensional time fractional Kadomtsev–Petviashvili (KP) system, which is an important model in mathematical physics. We obtain all the Lie symmetries admitted by the KP system and use them to reduce the ( 2 + 1 ) (2+1) -dimensional fractional partial differential equations with Riemann–Liouville fractional derivative to some ( 1 + 1 ) (1+1) -dimensional fractional partial differential equations with Erdélyi–Kober fractional derivative or Riemann–Liouville fractional derivative, thereby getting some exact solutions of the reduced equations. In addition, the new conservation theorem and the generalization of Noether operators are developed to construct the conservation laws for the system studied.

Amp-Holo-Net enables speckle-free and high-quality coaxial amplitude-only holographic display

At present, amplitude-only holograms (AOH) are widely applied because of the high response speed and small pitch of amplitude spatial light modulators. However, AOHs are generally generated by traditional diffraction theories, and there are no effective deep learning algorithms to generate high-quality coaxial AOHs, which makes it difficult to achieve speckle-free, high-quality holographic displays using amplitude spatial light modulators. Thereby, we propose two cascaded deep neural networks, embedding physical models of the coaxial AOH to generate speckle-free and high-quality coaxial AOHs within an acceptable time, named Amp-Holo-Net, which is the first work that introduces deep learning to generate coaxial AOHs. We demonstrate the superiority of the proposed method through simulation, achieving an 81% reduction in speckle contrast of two-dimensional (2D) monochrome reconstruction and a 60% improvement in peak signal-to-noise ratio of 2D color reconstruction compared with point source method, and optical experiments are conducted to verify the validity of the proposed method. We believe our method fills the blank of AOH algorithms and paves the way for holographic displays based on amplitude spatial light modulators.

Physical education teaching scheduling technology based on chaotic genetic algorithm.

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28s, and when the number of iterations is 175-200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling.

Process–Material–Performance Trade-off Exploration of Materials Sintering with Machine Learning Models

AbstractProcess-induced porosity, defects, and residual stresses lead to mechanical performance degradation in fiber-reinforced composite and other heterogeneous structures. Physical and chemical processes create complex process–material–performance relationships. Predicting porosity and residual stresses in this context requires computationally burdensome forward simulations and obtaining optimal process settings and calibrating properties of new materials requires solving inverse problems with predictions from the forward simulations. In this paper, we parameterized the process–material–performance space and created a dataset based on physics models that are valid for sintering ceramic powders. The dataset was used to train several machine learning models that captured the process–material–performance relationships. The trained ML models were applied in process optimization, calibration of properties for new material systems, and estimating performance for a given process and material. Support vector regression (SVR), convolutional neural networks (CNNs), and a Gaussian mixture model (GMM) called REPAIRS were selected, and their prediction accuracy was determined. While the SVR and CNN models require training several models, we show that the GMM model captures the process–material–performance relationships with a single machine-learned model and partial system completion methods. The paper describes root-mean-square error and mean absolute percentage errors of the inferences from the models on a validation dataset.

Dynamics of the Collision of Two Nearly Equal Solitary Waves for the Zakharov–Kuznetsov Equation

AbstractWe study the dynamics of the collision of two solitary waves for the 2 and 3-dimensional Zakharov–Kuznetsov equation, a high-dimensional non-integrable version of the Korteweg-de Vries equation that appears as an asymptotic model in plasma physics. We describe the evolution of the solution behaving as a sum of 2-solitary waves of nearly equal speeds at time $$t=-\infty $$ t = - ∞ up to time $$t=+\infty $$ t = + ∞ . We show that this solution behaves as the sum of two modulated solitary waves and an error term which is small in $$H^1$$ H 1 for all time $$t \in {\mathbb {R}}$$ t ∈ R . Finally, we also prove the stability of this solution for large times around the collision. The proofs are a non-trivial extension of the ones of Martel and Merle for the quartic generalized Korteweg-de Vries equation to higher dimensions. First, despite the non-explicit nature of the solitary wave, we construct an approximate solution in an intrinsic way by canceling the error to the equation only in the natural directions of scaling and translation. Then, to control the difference between a solution and the approximate solution, we use a modified energy functional and a refined modulation estimate in the transverse variable. Moreover, we rely on the Hamiltonian structure of the ODE governing the distance between the waves, which cannot be approximated by explicit solutions, to close the bootstrap estimates on the parameters. We hope that the techniques introduced here are robust and will prove useful in studying the collision phenomena for other focusing non-linear dispersive equations with non-explicit solitary waves.

Quantization and the Preservation of Structure across Theory Change

Abstract This article presents a novel sense in which theoretical structure has been preserved across the transition from classical to quantum physics. I import mathematical tools from category theory that have been used for structural comparisons in the context of theoretical equivalence and apply these tools to new situations involving theory change. The structural preservation takes the form of a categorical equivalence between categories of models of classical and quantum physics. I situate the significance of this structural preservation in terms of prospects for theory construction in quantum physics.

Physical and occupational therapy service delivery models for populations identified as hard-to-reach: A scoping review.

The delivery of rehabilitation services for hard-to-reach populations (e.g., refugees) is highly complex. There is a need for evidence-based approaches to deliver physiotherapy (PT) or occupational therapy (OT) services to this underserved group. The purpose of this scoping review was to identify PT and OT service delivery models that have been implemented, for populations typically identified as hard-to-reach and their associated health outcomes. Articles were eligible if they described PT and/or OT services for hard-to-reach populations. There were no restrictions on study design. Six electronic databases (AMED, CINAHL, MEDLINE, EMBASE, Healthstar, and PsycINFO) were searched from January 2000 to June 2023. Articles were screened in duplicate by two independent reviewers, and conflicts were resolved by consensus. Twenty-one articles with variable sample sizes (min, max n = 3 to 237) were included and detailed PT and/or OT services for immigrants/migrants, refugees, hard-to-reach veterans, people experiencing homelessness, lower incomes, trauma/torture, and those living in rehabilitation-deficient areas. Common rehabilitation needs (e.g., clinician to client connectivity), barriers (e.g., high transportation costs) and facilitators (e.g., encouragement) were identified among the various populations, mainly due to intersecting identities such as those who are both traumatized and refugees. Unique factors pertaining to the PT and OT services were also identified in some groups, including access to child and family services for people experiencing homelessness. Despite common and individual needs, barriers, and facilitators in hard-to-reach groups in the literature, there is a need for studies with larger sample sizes, rigorous methodology and a conscious effort to publish the results of interventions to generate stronger recommendations for practice.

X-ray fluoroscopy guided localization and steering of miniature robots using virtual reality enhancement

In developing medical interventions using untethered milli- and microrobots, ensuring safety and effectiveness relies on robust methods for real-time robot detection, tracking, and precise localization within the body. The inherent non-transparency of human tissues significantly challenges these efforts, as traditional imaging systems like fluoroscopy often lack crucial anatomical details, potentially compromising intervention safety and efficacy. To address this technological gap, in this study, we build a virtual reality environment housing an exact digital replica (digital twin) of the operational workspace and a robot avatar. We synchronize the virtual and real workspaces and continuously send the robot position data derived from the image stream into the digital twin with short average delay time around 20–25 ms. This allows the operator to steer the robot by tracking its avatar within the digital twin with near real-time temporal resolution. We demonstrate the feasibility of this approach with millirobots steered in confined phantoms. Our concept demonstration herein can pave the way for not only improved procedural safety by complementing fluoroscopic guidance with virtual reality enhancement, but also provides a platform for incorporating various additional real-time derivative data, e.g., instantaneous robot velocity, intraoperative physiological data obtained from the patient, e.g., blood flow rate, and pre-operative physical simulation models, e.g., periodic body motions, to further refine robot control capacity.

How to make land use policy decisions: Integrating science and economics to deliver connected climate, biodiversity, and food objectives

Land use change is crucial to addressing the existential threats of climate change and biodiversity loss while enhancing food security [M. Zurek et al., Science 376, 1416-1421 (2022)]. The interconnected and spatially varying nature of the impacts of land use change means that these challenges must be addressed simultaneously [H.-O. Pörtner et al., Science 380, eabl4881 (2023)]. However, governments commonly focus on single issues, incentivizing land use change via "Flat-Rate" subsidies offering constant per hectare payments, uptake of which is determined by the economic circumstances of landowners rather than the integrated environmental outcomes that will be delivered [G. Q. Bull et al., Forest Policy Econ. 9, 13-31 (2006)]. Here, we compare Flat-Rate subsidies to two alternatives: "Land Use Scenario" allocation of subsidies through consultation across stakeholders and interested parties; and a "Natural Capital" approach which targets subsidies according to expected ecosystem service response. This comparison is achieved by developing a comprehensive decision support system, integrating new and existing natural, physical, and economic science models to quantify environmental, agricultural, and economic outcomes. Applying this system to the United Kingdom's net zero commitment to increase carbon storage via afforestation, we show that the three approaches result in significantly different outcomes in terms of where planting occurs, their environmental consequences, and economic costs and benefits. The Flat-Rate approach actually increases net carbon emissions while Land Use Scenario allocation yields poor economic outcomes. The Natural Capital targeted approach outperforms both alternatives, providing the highest possible social values while satisfying net zero commitments.

Bioinspired intratumoral infusion port catheter improves local drug delivery in the liver.

Tumor immune modulation can be achieved using intratumoral injection of different immunomodulators during different phases of the cancer-immunity cycle. Intratumoral infusion catheters have been used in brain tumors, but these are not suitable outside the brain, where breathing motion results in catheter migration. Here, we use microstereolithography to manufacture a barbed sidehole catheter, modeled after the barbs in a bee stinger, where the barbs maintain the catheter position in the tumor, and sideholes within the barbs infuse drug into tumor tissue. In pig liver, we demonstrated 183-fold higher local drug concentration using the barbed sidehole catheter, compared to intravenous injection of water-soluble drug. High resistance sideholes and pulsatile injection both generate higher pressure in the catheter, which overcomes the tissue pressure, resulting in more drug delivery into tumor. A physical model of intratumoral infusion catheters accurately predicts the observed drug delivery results. Our catheter design is retained in the liver (and does not migrate out with breathing motion), and it preferentially infuses the drug into tumor tissue (not intratumoral vessels).

From STEM-EDXS data to phase separation and quantification using physics-guided NMF

Abstract We present the development of a new algorithm which combines state-of-the-art energy-dispersive X-ray (EDX) spectroscopy theory and a suitable machine learning formulation for the hyperspectral unmixing of scanning transmission electron microscope EDX spectrum images. The algorithm is based on non-negative matrix factorization (NMF) incorporating a physics-guided factorization model. It optimizes a Poisson likelihood, under additional simplex constraint together with user-chosen sparsity-inducing and smoothing regularizations, and is based on iterative multiplicative updates. The fluorescence of X-rays is fully modeled thanks to state-of-the-art theoretical work. It is shown that the output of the algorithm can be used for a direct chemical quantification. With this approach, it is straightforward to include a priori knowledge on the specimen such as the presence or absence of certain chemical elements in some of its phases. This work is implemented within two open-source Python packages, espm and emtables, which are used here for data simulation, data analysis and quantification. Using simulated data, we demonstrate that incorporating physical modeling in the decomposition helps retrieve meaningful components from spatially and spectrally mixed phases, even when the data are very noisy. For synthetic data with a higher signal, the regularizations yield a tenfold increase in the quality of the reconstructed abundance maps compared to standard NMF. Our approach is further validated on experimental data with a known ground truth, where state-of-the art results are achieved by using prior knowledge about the sample. Our model can be generalized to any other scanning spectroscopy techniques where underlying physical modeling can be linearized.