Simple Model Research Articles

Quantum many-body scars in few-body dipole-dipole interactions

We simulate the dynamics of Rydberg atoms resonantly exchanging energy via two-, three-, and four-body dipole-dipole interactions in a one-dimensional array. Using simplified models of a realistic experimental system, we study the initial-state survival probability, mean level spacing, spread of entanglement, and properties of the energy eigenstates. By exploring a range of disorders and interaction strengths, we find regions in parameter space where the three- and four-body dynamics either fail to thermalize or do so slowly. The interplay between the stronger hopping and weaker field-tuned interactions gives rise to quantum many-body scar states, which play a critical role in slowing the dynamics of the three- and four-body interactions. Published by the American Physical Society 2024

Enhancing commercial building resiliency through microgrids with distributed energy sources and battery energy storage systems

Resilience analysis is gaining focus, but no extensive research exists for commercial buildings. This research presents the results of a novel analysis of the resiliency in commercial buildings by examining the relationship between electric microgrids, Distributed Energy Resources (DERs), and Battery Energy Storage Systems (BESS). As energy systems face increasing challenges, including extreme weather events and grid vulnerabilities, integrating microgrids, DERs, and BESS has emerged as a promising solution to strengthen the resilience of commercial buildings. Microgrids can harness renewable energy sources and reduce environmental impacts when integrated with DERs. Most literature studies focus on residential or commercial buildings with peak-valley tariffs and simplified electrical market models. In contrast, this study focuses on BESS’ pivotal role in DER output and ensuring uninterrupted power during grid disruptions and presents an innovative approach to analyzing resilience in commercial building microgrids and an economic optimization of commercial building microgrids with Time of Use tariffs utilizing DERS and BESS. The analysis includes the technical aspects of BESS integration and control strategies that optimize their operation. It also studies the economic and environmental benefits of microgrids, DERs, and BESS, focusing on cost savings, greenhouse gas emission reductions, and grid support services.

Integrating temporal deep learning models for predicting screen-out risk levels in hydraulic fracturing

Amid the transformative shift in global energy structures, the exploitation and utilization of shale gas, an essential unconventional natural gas resource, have drawn widespread attention from both industrial and academic circles. However, screen-out incidents during hydraulic fracturing operations pose significant obstacles to extraction efficiency and safety. Traditional prediction methods, which rely on empirical estimations and simplified models, are deficient in accuracy and real-time applicability. Addressing this, our study introduces a novel deep learning ensemble integrating Gated Recurrent Units (GRU), Transformer, and One-Dimensional Convolutional Neural Networks (1D-CNN) for precise screen-out prediction. This approach markedly improves predictive accuracy by efficiently processing time-series data and capturing the complex dynamics of fracturing processes. Furthermore, the application of the correlation coefficient method and random forest algorithm for feature selection optimizes model input and further enhances prediction accuracy and operational efficiency. Our comparative analysis demonstrates the model’s superiority, achieving an F1 score of 0.951 and a loss of 0.430, clearly surpassing traditional and other deep learning methods. This integration of advanced neural architectures and feature selection techniques not only advances screen-out prediction but also yields practical insights for optimizing shale gas extraction strategies and enhancing safety.

Functional Assessment of a Bioprinted Immuno-Mimetic Peyer's Patch Recapitulating Gut-Associated Lymphoid Tissue.

Gut immune models have attracted much interest in better understanding the microbiome in the human gastrointestinal tract. The gut-associated lymphoid tissue (GALT) has complex structures that interact with microorganisms, including the intestinal monolayer as a physiological barrier and the Peyer's patch (PP) involved in the immune system. Although essential for studying GALT and microbiome interactions, current research often uses simplified models that only recapitulate some components. In this study, GALT is recapitulated to consider the morphology and function of lymphocyte-containing PP beneath the intestinal monolayer and to analyze microbiome interaction. Using the bioprinting technique, a dome-shaped structure array for the PP is fabricated, and epithelial cells are cocultured to form the intestinal monolayer. The developed GALT model shows stable cell differentiation on the hydrogel while exhibiting durability against lipopolysaccharides. It also exhibits increased responsiveness to Escherichia coli, as indicated by elevated nitric oxide levels. In addition, the model underscores the critical role of GALT in maintaining bacterial coexistence and in facilitating immune defense against foreign antigens through the secretion of immunoglobulin A by lymphocyte spheroids. The proposed GALT model is expected to provide significant insights into studying the gut-immune system complexity and microbiome.

Advanced piezoelectric fluid energy harvesters by monolithic fluid–structure–piezoelectric coupling: A full-scale finite element model

A full-scale finite element model is presented for monolithic fluid–structure interaction (FSI) simulations of thin-walled piezoelectric fluid energy harvesters (PFEHs). Unlike widely used beam/plate-based models, our model employs a solid finite element discretization to precisely represent the complex PFEH designs involving microstructured transducers and non-uniform cantilevers. These features, plus the local FSI effects, are often ignored by simplified models. We applied the Galerkin method to formulate the weak form of the mixed equation system, integrating the flow dynamics, the geometrically nonlinear cantilever, the piezoelectric components, the electrode, and the output circuit within a closed-circuit electro-mechanical coupled system. The coupling of the multiple domains is achieved through boundary-fitted discretization within a monolithic scheme, using shifted-Crank–Nicolson temporal integration. This work explored implementing piezoelectric FSI systems within the FEniCS-based TurtleFSI library, and experimented techniques such as employing penalty functions for achieving electrode components with uniform electric potentials. We investigated various advanced PFEH features, including the baseplate design, the arrangement and microstructure of the piezoelectric components, and their influence on the system's dynamic and energy output behavior. The results confirmed the model's key advantages: full-scale modeling allows the integration of complex base structures and transducer microstructures in PFEH design. Combined with monolithic FSI coupling, it offers greater versatility, supporting a wider range of fluid environments and configurations in both wind and hydropower harvesting. Additionally, the modeling strategy can be intended not only to enhance power output, but also to minimize material usage, reduce mechanical fatigue, and extend the operational lifespan of PFEH systems.

“Halfway to Rayleigh” and Other Insights into the Rossby Wave Instability

The Rossby wave instability (RWI) is the fundamental nonaxisymmetric radial shear instability in disks. The RWI can facilitate disk accretion, set the shape of planetary gaps, and produce large vortices. It arises from density and/or temperature features, such as radial gaps, bumps, or steps. A general, sufficient condition to trigger the RWI is lacking, which we address by studying the linear RWI in a suite of simplified models, including incompressible and compressible shearing sheets and global, cylindrical disks. We focus on enthalpy amplitude and width as the fundamental properties of disk features with various shapes. We find analytic results for the RWI boundary and growth rates across a wide parameter space, in some cases with exact derivations and in others as a description of numerical results. Features wider than a scale height generally become unstable about halfway to Rayleigh instability, i.e., when the squared epicyclic frequency is about half the Keplerian value, reinforcing our previous finding. RWI growth rates approximately scale as enthalpy amplitude to the 1/3 power, with a weak dependence on width, across much of the parameter space. Global disk curvature affects wide planetary gaps, making the outer gap edge more susceptible to the RWI. Our simplified models are barotropic and height integrated, but the main results should carry over to more complex and realistic scenarios.

Floating wave energy farms: How energy calculations shape economic feasibility?

The aim of this study is to use two different methodologies for calculating the energy produced by a total wave energy farm in the Cantabrian Sea (North of Spain). First method studies the energy produced by the farm taking into account the wave energy converter power matrix and the probability matrix of sea conditions for a particular location. On the other hand, second method is a more simplified methodology in which the wave energy converter power matrix is not taken into account, but it considers the density of the water, gravity, period and height of the wave, as well as the percentage of efficiency, among others. Finally, the energy generated by the farm and the economic parameters that allow calculating its economic feasibility are studied, such as Levelized Cost of Energy, Net Present Value and Internal Rate of Return. Results indicate the importance of using a particular method for calculating the energy produced by a wave energy farm in terms of the variation of its economic feasibility. In this sense, it is shown that despite the fact that the first method is more detailed, the variations between both methods are very small, therefore the simplest model can be used in the future studies, reducing the calculation process in the future.

Dynamic Rupture Modeling of Large Earthquake Scenarios at the Hellenic Arc Toward Physics‐Based Seismic and Tsunami Hazard Assessment

AbstractThe Mediterranean Hellenic Arc subduction zone (HASZ) has generated several 8 earthquakes and tsunamis. Seismic‐probabilistic tsunami hazard assessment typically utilizes uniform or stochastic earthquake models, which may not represent dynamic rupture and tsunami generation complexity. We present an ensemble of ten 3D dynamic rupture earthquake scenarios for the HASZ, utilizing a realistic slab geometry. Our simplest models use uniform along‐arc pre‐stresses or a single circular initial stress asperity. We then introduce progressively more complex models varying initial shear stress along‐arc, multiple asperities based on scale‐dependent critical slip weakening distance, and a most complex model blending all aforementioned heterogeneities. Thereby, regional initial conditions are constrained without relying on detailed geodetic locking models. Varying epicentral locations in the simplest, homogeneous model leads to different rupture speeds and moment magnitudes. We observe dynamic fault slip penetrating the shallow slip‐strengthening region and affecting seafloor uplift. Off‐fault plastic deformation can double vertical seafloor uplift. A single‐asperity model generates a 8 scenario resembling the 1303 Crete earthquake. Using along‐strike varying initial stresses results in 8.0–8.5 dynamic rupture scenarios with diverse slip rates and uplift patterns. The model with the most heterogeneous initial conditions yields a 7.5 scenario. Dynamic rupture complexity in prestress and fracture energy tends to lower earthquake magnitude but enhances tsunamigenic displacements. Our results offer insights into the dynamics of potential large Hellenic Arc megathrust earthquakes and may inform future physics‐based joint seismic and tsunami hazard assessments.

Thermal pressure on ultrarelativistic bubbles from a semiclassical formalism

We study a planar bubble wall that istravelingat an ultrarelativistic speed through a thermal plasma. This situation may arise during a first-order electroweak phase transition in the early universe. As particles cross the wall, it is assumed that their mass grows from m a to m b , and they are decelerated causing them to emit massless radiation (m c = 0). We are interested in the momentum transfer to the wall, the thermal pressure felt by the wall, and the resultant terminal velocity of the wall. We employ the semiclassical current radiation (SCR) formalism to perform these calculations. An incident-charged particle is treated as a point-like classical electromagnetic current, and the spectrum of quantum electromagnetic radiation (photons) is derived by calculating appropriate matrix elements. To understand how the spectrum depends on the thickness of the wall, we explore simplified models for the current corresponding to an abrupt and a gradual deceleration. For the model of abrupt deceleration, we find that the SCR formalism can reproduce the P therm ∝ γ 0 w scaling found in earlier work by assuming that the emission is soft, but if the emission is not soft the SCR formalism can be used to obtain P therm ∝ γ 2 w instead. For the model of gradual deceleration, we find that the wall thickness L w enters to cutoff the otherwise log-flat radiation spectrum above a momentum of ∼ γ 2 w / L w , and we discuss the connections with classical electromagnetic bremsstrahlung.

Geometric tortuosity at invaginating rod synapses slows glutamate diffusion and shapes synaptic responses: insights from anatomically realistic Monte Carlo simulations.

At the first synapse in the vertebrate retina, rod photoreceptor terminals form deep invaginations occupied by multiple second-order rod bipolar and horizontal cell (RBP and HC) dendrites. Synaptic vesicles are released into this invagination at multiple sites beneath an elongated presynaptic ribbon. We investigated the impact of this complex architecture on the diffusion of synaptic glutamate and activity of postsynaptic receptors. We obtained serial electron micrographs of mouse retina and reconstructed four rod terminals along with their postsynaptic RBP and HC dendrites. We incorporated these structures into an anatomically realistic Monte Carlo simulation of neurotransmitter diffusion and receptor activation. We compared passive diffusion of glutamate in these realistic structures to existing, geometrically simplified models of the synapse and found that glutamate exits anatomically realistic synapses ten times more slowly than previously predicted. By comparing simulations with electrophysiological recordings, we modeled synaptic activation of EAAT5 glutamate transporters in rods, AMPA receptors on HC dendrites, and metabotropic glutamate receptors (mGluR6) on RRBP dendrites. Our simulations suggested that ~3,000 EAAT5 transporters populate the rod presynaptic membrane and that, while uptake by surrounding glial Müller cells retrieves much of the glutamate released by rods, binding and uptake by EAAT5 influences RBP response kinetics. The relatively long lifetime of glutamate within the cleft allows mGluR6 on RBP dendrites to temporally integrate the steady stream of vesicles released at this synapse in darkness. Glutamate's tortuous diffusional path through realistic synaptic geometry confers quantal variability, as release from nearby ribbon sites exerts larger effects on RBP and HC receptors than release from more distant sites. While greater integration may allow slower sustained release rates, added quantal variability complicates the challenging task of detecting brief decreases in release produced by rod light responses at scotopic threshold.

Monte Carlo damage models of different complexity levels predict similar trends in radiation induced DNA damage

Ion therapies have an increased relative biological effectiveness (RBE) compared to X-rays, but this remains poorly quantified across different radiation qualities. Mechanistic models that simulate DNA damage and repair after irradiation could be used to help better quantify RBE. However, there is large variation in model design with the simulation detail and number of parameters required to accurately predict key biological endpoints remaining unclear. This work investigated damage models with varying detail to determine how different model features impact the predicted DNA damage.

Methods: Damage models of reducing detail were designed in TOPAS-nBio and Medras investigating the inclusion of chemistry, realistic nuclear geometries, single strand break damage, and track structure. The nucleus models were irradiated with 1 Gy of protons across a range of linear energy transfers (LETs). Damage parameters in the models with reduced levels of simulation detail were fit to proton double strand break (DSB) yield predicted by the most detailed model. Irradiation of the optimised models with a range of radiation qualities was then simulated, before undergoing repair in the Medras biological response model.

Results: Simplified damage models optimised to proton exposures predicted similar trends in DNA damage across radiation qualities. On average across radiation qualities, the simplified models experienced an 8% variation in double strand break (DSB) yield but a larger 28% variation in chromosome aberrations. Aberration differences became more prominent at higher LETs, with model features having an increasing impact on the distribution and therefore misrepair of DSBs. However, overall trends remained similar with better agreement likely achievable through repair model optimisation. 

Conclusion: Several model simplifications could be made without compromising key damage yield predictions, although changes in damage complexity and distribution were observed. This suggests simpler, more efficient models may be sufficient for initial radiation damage comparisons, if validated against experimental data.&#xD.

Enhancing Heart Disease Prediction through Machine Learning: A Comparative Analysis of Algorithm Generalization across Datasets with Various Distributions

Heart disease, due to its high prevalence and mortality, remains a key area of global research. Although traditional diagnostic methods are effective, they are often invasive and time-consuming, highlighting the need for non-invasive, AI-based approaches. A significant challenge in real-world applications is ensuring model generalization across different datasets, particularly when the datasets are small. In this study, the performance of machine learning models, including Decision Tree, Random Forest, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), was evaluated on two distinct heart disease datasets with different distributions and relatively small sizes. Two datasets with varying distributions were used for training and testing, with the primary focus on assessing model generalization in crossdataset applications. It is shown in the results that, while the Decision Tree model performed best after hyperparameter tuning, the improvements in Random Forest and MLP were limited, and SVM exhibited a decline in performance after tuning in the cross-dataset task. It was found that grid search tuning has limitations in cross-dataset scenarios, especially with small datasets, where complex models are prone to overfitting. The study demonstrates that, with smaller datasets, simpler models like Decision Trees often adapt better to different datasets. Furthermore, transfer learning and domain adaptation techniques are suggested as crucial for improving model generalization. Future research should focus on employing these techniques to enhance the robustness and accuracy of heart disease prediction models across diverse datasets.

Hydraulics of Time-Variable Water Surface Slope in Rivers Observed by Satellite Altimetry

The ICESat-2 and SWOT satellite earth observation missions have provided highly accurate water surface slope (WSS) observations in global rivers for the first time. While water surface slope is expected to remain constant in time for approximately uniform flow conditions, we observe time varying water surface slope in many river reaches around the globe in the ICESat-2 record. Here, we investigate the causes of time variability of WSSs using simplified river hydraulic models based on the theory of steady, gradually varied flow. We identify bed slope or cross section shape changes, river confluences, flood waves, and backwater effects from lakes, reservoirs, or the ocean as the main non-uniform hydraulic situations in natural rivers that cause time changes of WSSs. We illustrate these phenomena at selected river sites around the world, using ICESat-2 data and river discharge estimates. The analysis shows that WSS observations from space can provide new insights into river hydraulics and can enable the estimation of river discharge from combined observations of water surface elevation and WSSs at sites with complex hydraulic characteristics.

Optimizing the transition pathway of a steel plant towards hydrogen-based steelmaking

Direct reduction of iron ore using hydrogen is a promising alternative to traditional coke-based reduction that could greatly reduce CO2 emissions from the steelmaking industry. In this alternative route, hydrogen and iron oxides react in a shaft furnace to produce direct reduced iron that is further processed in electric arc furnaces into liquid steel. The total process is greatly dependent on electricity, both for producing hydrogen and for operating the electric arc furnace. When considering a possible transformation of a traditional steel plant to hydrogen-based direct reduction, there are several open questions regarding appropriate configurations and dimensions of new units, scheduling of investments, integrated operation of old and new units, etc. Furthermore, the entire transformation process could span several decades, during which market conditions may change substantially. To help planning such a transition, this paper presents a steel plant optimization model that minimizes accumulated costs or emissions of investments and operation over a selected time horizon, during which new units can be constructed and old units possibly be decommissioned. The task is formulated as a mixed-integer quadratically constrained programming problem, with simplified regression models of blast furnaces and shaft furnaces based on more detailed models. Model performance is illustrated with an example case in which the possible transition of a steel plant with two blast furnaces that are expected to be decommissioned within the next twenty years is analyzed, demonstrating that the model is a flexible planning tool for comparing and assessing different future scenarios for decarbonizing the steel industry.

Incorporating soil‐structure interaction into simplified numerical models for fragility analysis of RC structures

AbstractSimplified building models are a valuable option for seismic assessment at the regional scale. These models often use calibrated springs to model column behaviour, and recent advances have made them suitable for capturing torsional response in Reinforced‐Concrete Moment‐Resisting‐Frames. Nevertheless, their validation is typically achieved using fixed‐base models, which do not include the influence of soil‐structure interaction (SSI). This study introduces a novel approach to quantify the accuracy of a recently developed simplified model while accounting for dynamic SSI, using a newly implemented, refined 3D Finite Element non‐linear soil model in OpenSees. The accuracy of the simplified structural model is assessed by comparing the results of non‐linear dynamic analyses with those of a refined model in terms of (i) a peak structural demand parameter such as the interstorey‐drift ratio and (ii) fragility curves computed from cloud analysis and accounting for collapse cases. The study presents details of the proposed refined approach for 3D soil modelling in OpenSees, focusing on implementing free‐field boundary conditions and structure‐to‐soil connections. Results show that the accuracy of the simplified model is maintained, even in the presence of SSI, and it successfully captures the overall structural response measured at peak demand. For the proposed case study, the difference between the simplified and refined models’ fragility curves’ medians is 4% and 2% for fixed and SSI models, respectively. The simplified structural model, combined with the refined soil model for SSI effects, presents an innovative and conservative, yet computationally efficient, alternative for seismic risk analysis, even in the presence of structural irregularity.

Serum metabolomics improve risk stratification for incident heart failure

Abstract Background The prediction and early detection of heart failure (HF) is vital to reduce its substantial impact on quality of life, survival and healthcare expenditure. Current incident HF risk stratification strategies achieve only modest performance. Moreover, state-of-the-art risk scores focus on commonly acknowledged clinical risk factors, thus necessitating the integration of heterogenous data sources and prioritizing individuals with obvious risk constellations. Purpose Here, we explored the predictive value of serum metabolomics (168 metabolites detected by proton nuclear magnetic resonance (1H-NMR) spectroscopy) for incident HF. Methods Leveraging data of n = 68,311 individuals and > 0.8 million person-years of follow-up from the UK Biobank (UKB) cohort, we (I) evaluated the association of individual metabolites with incident HF via fitting of per-metabolite COX proportional hazards models and (II) trained and validated elastic net (EN) models to predict incident HF using the serum metabolome. Discriminative performance was benchmarked against a comprehensive, well-validated clinical risk score (Pooled Cohort Equations to Prevent HF, PCP-HF). External validation was conducted in the independent FINRISK study. Results Several metabolites were independently associated with incident HF (90/168 adjusting for age and sex, 48/168 adjusting for PCP-HF). Performance-optimized risk models effectively retained key predictors representing highly correlated clusters (≈ 80 % feature reduction). The addition of metabolomics to PCP-HF improved predictive performance (Harrel’s C: 0.768 vs. 0.755, ΔC = 0.013 (95% confidence interval (CI) 0.004 – 0.022), continuous net reclassification improvement (NRI) = 0.287 (95% CI 0.200 – 0.367), relative integrated discrimination improvement (IDI): 17.47 % (95% CI 9.463 - 27.825)). Simplified models only including age, sex and metabolomics performed almost as well as the PCP-HF model (Harrel’s C: 0.745 vs. 0.755, ΔC = 0.010 (95% CI -0.004 - 0.027), continuous NRI: 0.097 (95% CI -0.025 - 0.217), relative IDI: 13.445 % (95% CI -10.608 - 41.454)). Risk and survival stratification was improved by integrating metabolomics. External validation within FINRISK revealed similar patterns using the original model coefficients. Conclusions Serum metabolomics improve the prediction of incident HF risk. Scores obtained from the combination of age, sex and metabolomics exhibit similar predictive power as clinical risk models. Offering serum metabolomics to a middle-aged cohort might significantly improve HF risk stratification, thus facilitating preventive efforts. Via a single blood draw, serum metabolomics displays a highly cost- and time-effective, standardizable, and scalable alternative to clinical risk scores involving physical measurements and history taking in addition to clinical chemistry.Relative performance (test split)ROC & DCA plots (test split)

Feature importance explanation of prosthetic valve endocarditis through machine learning via SHAP

Abstract Introduction Diagnosing prosthetic valve endocarditis poses a significant challenge in daily clinical practice. Accurately identifying these patients is vital due to its treatment implications. Clinical and imaging assessment of suspected cases yields a substantial number of interrelated variables, which can be analyzed through machine learning techniques in order to improve diagnostic accuracy. Feature ranking facilitates identification and interpretability of relevant clinical and imaging variables in this task. Purpose To utilize machine learning feature ranking and modeling in order to improve interpretable identification of patients with prosthetic valve endocarditis. Methods 89 cases defined by the endocarditis team and 173 controls were analyzed. We implemented the SHapley Additive exPlanations (SHAP) method to evaluate feature importance. 5-fold cross-validated Xgboost models were trained and independently tested with additive clinical, echocardiographic and 18F-FDG PET/CT data. Performance was evaluated through confusion matrices, F1-scores and AUCs. These were compared against conventional logistic regression of the Duke Criteria classification. Results Mean age was 59.6 ± 15.7, with 29.7% women. 14 clinical (demographic, laboratory and cultures), 2 echocardiographic, and 5 PET/CT features were included. The XGBoost model trained with all additive features demonstrated an AUC of 0.91±0.1 and outperformed simpler models as well as the Duke classification model (AUC=0.85). The clinical variables alone achieved an AUC of 0.85±0.11, while integration with echocardiography enhanced its performance to 0.90±0.04 (Fig. 1). Feature ranking demonstrated that antibiotic treatment duration, positive blood cultures, valve type, weight and CRP were the most relevant clinical variables, while the presence of vegetation or abscess, and abnormal uptake and uptake focality on PET/CT represented the most relevant imaging variables (Fig. 2). Conclusion Machine learning-based feature ranking and modeling may significantly enhance identification of patients with prosthetic valve endocarditis beyond standard clinical criteria. Feature selection offers interpretable performance in identifying these cases, model testing in clinical decision support may be warranted.Fig. 1:Comparative AUC Analysis.Fig. 2:SHAP feature importance.

Two-Step Iterative Medical Microwave Tomography.

In the field of medical imaging, microwave tomography (MWT) is based on the scattering and absorption characteristics of different tissues to microwaves and can reconstruct the electromagnetic property distribution of biological tissues non-invasively and without ionizing radiation. However, due to the inherently nonlinear and ill-posed characteristics of MWT calculations, actual imaging is prone to overfitting or artifacts. To address this, this paper proposes a two-step iterative imaging approach for rapid medical microwave tomography. This method establishes corresponding objective functions for microwave imaging across multiple frequencies and conducts iterative calculations on images at varying resolutions. This effectively enhances image clarity and accuracy while alleviating the issue of prolonged computational time associated with imaging complex structures at high resolution due to insufficient prior information during iterative processes. In the electromagnetic simulation section, we simulated a three-layer brain model and conducted imaging experiments. The results demonstrate that the algorithm significantly enhances imaging resolution, accurately pinpointing cerebral hemorrhages at different locations using an eight-antenna array and successfully reconstructs tomography images with a hemorrhage area radius of 1 cm. Lastly, experiments were conducted using a medical microwave tomography platform and four simplified human brain models, achieving millimeter-level accuracy in MWT.

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

A codifiable methodology for estimating pallet sliding displacements on steel racking systems

AbstractTo facilitate the logistic processes within a modern warehouse, the contents of steel racking systems are not mechanically connected to the supporting beams, allowing a content‐sliding mechanism to develop when static friction is exceeded. Given that the mass of contents is dominant, this is beneficial for limiting the apparent inertia. On the other hand, pallet fall‐off may occur during strong seismic excitations, which is a failure mode that is not addressed by current seismic design processes and guidelines for racks. Along these lines, a codifiable methodology is proposed for estimating sliding displacements on steel racking systems, based on the statistical interpretation of a large set of response history analyses, using different rack configurations and ground motions. Firstly, a multi‐parametric analysis is conducted using simplified rack models to (i) select the intensity measure and engineering demand parameter that can best describe the problem of pallet sliding and (ii) identify the salient rack characteristics that dominate sliding behavior. Thereafter, a series of multi‐stripe analyses are performed using 180 rack realizations with different feature combinations to derive a so‐called, Empirical Sliding Prediction Equation (ESPE) by following a three‐step procedure: (a) perform regression on maximum sliding, (b) perform regression on the normalized sliding profile, and (c) combine steps a‐b to derive the denormalized profile at a given confidence level. The proposed empirical relationships are then validated through a comparison between the observed and fitted sliding displacements in three rack case studies.