Scale Factor Research Articles

Higgs mechanism for the Ashtekar self-dual connection

Abstract We introduce the Higgs mechanism for the self-dual spin connection (also known as the Ashtekar connection), using the Plebański formulation of gravity. We develop our formalism within the framework of the chiral action and derive the equations of motion of the theory. One particular test model is explored: since anisotropy is an intrinsic property of the theory, a modified version of the spatially flat Bianchi I model with two different scale factors is considered. We apply our formalism and derive the Friedmann equations which regulate the scale factors and the Higgs field. We also present a Proca-like term for the connection, which when reduced to minisuperspace with a positive $$\Lambda $$ Λ yields a De Sitter universe with an effective cosmological constant that depends on the mass of the gauge fields. We finally investigate the effect of these mass terms on gravitational waves and find that the wave equation remains unchanged relatively to GR; however the Weyl tensor is scaled by a constant which depends on the mass of the connection components.

Locally rendered high-resolution Land Use/Land Cover Bitmaps from OpenStreetMap Data for geospecific 3D Simulation

Abstract. This paper describes a method for creating land use/land cover (LULC) bitmaps from OpenStreetMap data for virtually any part of the world at any resolution on a standard personal computer. The automatic workflow utilizes free software and open formats, does not require web services nor graphical GIS applications and can be run by novice cartographers. In the process, offline vector data sets from the collaborative mapping project are prepared into self-contained transferable database files that arrange relevant geometric features and attributes as spatially indexed tables for fast access. The database gets rendered into a georeferenced bitmap within minutes by the same engine as for the original OpenStreetMap tiles. In contrast to digital maps, the appearance of the output is altered with XML-based style sheets implementing filter rules to picture land cover classes according to various nomenclatures such as CORINE and Virtual Battlespace 4 (VBS4). For linear features like roads and railway tracks lacking a second dimension, the width is scaled to match real-world proportions. The scale factor passed to the style sheets is determined from the extent of the region to be rendered, the requested ground sampling distance, database hints and the latitude of the conformal target projection. Results for scenes around the world are visually analyzed, and a comparison to validated LULC data is conducted. As a showcase targeting security agencies, highly detailed land cover is integrated into a scene for the VBS4 3D simulator to model geospecific landscapes in real time.

Revisiting the Relationship Between the Scale Factor (a(t)) and Cosmic Time (t) Using Numerical Analysis

Background: Current cosmological fits typically assume a direct relation between cosmic time (t) and the scale factor (a(t)), yet this ansatz remains largely untested across diverse observations. Objectives: We (i) test whether a single power-law scaling (a(t)∝tα) can reproduce late- and early-time cosmological data and (ii) explore whether a dynamically evolving (α(t)), modeled as a scalar–tensor field, naturally induces directional asymmetry in cosmic evolution. Methods: We fit a constant-α model to four independent datasets: 1701 Pantheon+SH0ES supernovae, 162 gamma-ray bursts, 32 cosmic chronometers, and the Planck 2018 TT spectrum (2507 points). The CMB angular spectrum is mapped onto a logarithmic distance-like scale (μ=log10Dℓ), allowing for unified likelihood analysis. Each dataset yields slightly different preferred values for H0 and α; therefore, we also perform a global combined fit. For scalar–tensor dynamics, we integrate α(t) under three potentials—quadratic, cosine, and parity breaking (α3sinα)—and quantify directionality via forward/backward evolution and Lyapunov exponents. Results: (1) The constant-α model achieves good fits across all datasets. In combined analysis, it yields H0≃70kms−1Mpc−1 and α≃1.06, outperforming ΛCDM globally (ΔAIC≃401254), though ΛCDM remains favored for some low-redshift chronometer data. High-redshift GRB and CMB data drive the improved fit. Numerical likelihood evaluations are approximately three times faster than for ΛCDM. (2) Dynamical α(t) models exhibit time-directional behavior: under asymmetric potentials, forward evolution displays finite Lyapunov exponents (λL∼10−3), while backward trajectories remain confined (λL<0), realizing classical arrow-of-time emergence without entropy or quantum input. Limitations: This study addresses only homogeneous background evolution; perturbations and physical derivations of potentials remain open questions. Conclusions: The time-scaling approach offers a computationally efficient control scenario in cosmological model testing. Scalar–tensor extensions naturally introduce classical time asymmetry that is numerically accessible and observationally testable within current datasets. Code and full data are available.

Modeling ring gyroscopes with higher-order curved beam elements

Abstract Microring gyroscopes are promising candidates for inertial navigation systems due to their exceptional performance, robustness in harsh environments, and relatively low cost, which makes them well suited for applications such as self-driving car navigation systems. Previous models of ring gyroscopes often relied on the assumed mode method and low-order multi-elements finite element analysis (FEA) to predict gyroscope performance, while simplifying the geometry for ease of computation. In this paper, a modeling approach that uses higher-order curved beam finite elements is proposed. This approach leads to a clear relation between the gyroscope’s dynamic performance and its geometric parameters. Consequently, this approach allows the gyroscope designer to decide the performance of the gyroscope by using one geometric parameter and it leaves a room for him to select material properties and other dimensions based on other design considerations. The dynamic performance is measured by the scale factor, which is directly proportional to the Coriolis constant. The proposed approach is validated by comparing its results with finite element simulation software, demonstrating an estimation error of less than 5% over a specific design range. The proposed method not only improves the accuracy of gyroscope modeling but also offers a straightforward approach to modeling more complex gyroscopes, such as multi-ring gyroscopes, without significantly increasing formulation complexity.

Scaling Urban Methane Emissions: Utility of Single-Site Measurements in Five Urban Domains.

Urban methane (CH4) missions remain poorly understood due to limited observational constraints. Most estimates rely on bottom-up inventories based on assumed emission factors and activity data or downscaling methods, which often underestimate emissions, sometimes by a factor of 2 or more in United States and European cities. While satellite and mobile observations can improve understanding, they face limitations in spatial resolution, coverage, and frequency. In contrast, fixed in situ measurements calibrated to World Meteorological Organization standards offer high precision continuous data, although with limited spatial coverage due to logistical constraints. This study uses in situ observations from single tower sites in five northeastern United States cities to estimate total urban CH4 emissions using a Bayesian scaling factor framework. Despite limited spatial sampling, the approach yields robust emission estimates consistent with other studies. To explore drivers of variability, the analysis examines correlations between inferred emissions and urban characteristics including population, residential gas usage, and infrastructure. Results show that residential building volume outperforms population as a predictor in some regions, highlighting the importance of infrastructure-specific factors. By demonstrating a scalable observation-based approach using minimal sites, this work addresses key gaps in urban CH4 monitoring and emphasizes the value of robust measurements and tailored proxies for improving emission estimates in diverse urban settings.

Specialized modification to the G4 composite method for computing formation enthalpies of nitrophenyl and nitrogen-heterocycle organic compounds.

We propose modifications to the G4 composite method, which allows us to continue using the method as a black box for computing gas-phase formation enthalpies of closed-shell organic molecules, but with the following additional features. (a) The resultant predictions are drastically improved when the molecules contain the nitrophenyl and nitrogen-heterocycle moieties, relative to the original G4 recipe. (b) The proposed modified G4 recipes retain a similar predictive capability for computing formation enthalpies of small closed-shell organic molecules, which were part of the original training set of the G4 recipe. The modifications consist of re-parameterizing two empirical parameters related to the high-level correction term and the frequency scaling factor. The latter is related to both the zero point energy correction as well as to some anharmonic effects that impact the vibrational partition function. We also show that the method used to optimize geometries and to calculate frequencies does not significantly impact the quality of the predictions (after the respective reparameterization). Here, the functionals ωB97XD and M06-2X as well as the MP2 method were tested. In contrast, we show that the chemical information contained in the set used to determine the empirical parameters plays a major role in the overall agreement with experimental formation enthalpies. Molecules that are not closed-shell organic are not studied.

Smart adaptive learning and optimized feature clustering for enhanced image retrieval

In recent years, Content-Based Image Retrieval (CBIR) has faced increasing challenges due to the rapid growth of multimedia content on the Internet. One of the primary difficulties in CBIR lies in identifying dissimilarities between various objects. This paper introduces a novel approach called SEGJO (Scaling Factor and Elite Opposition Learning-based Golden Jackal Optimization) for effective clustering of extracted features in CBIR. The inclusion of a Scaling Factor (SF) and Elite Opposition Learning (EOL) in SEGJO enhances its search capabilities and helps prevent premature convergence during the clustering process. CBIR extracts key features related to texture, shape, and color using techniques such as Local Binary Pattern, Zernike Moments, and Color Moments. Additionally, an Entropy-based Divergence (ED) function is incorporated into a Convolutional Neural Network (CNN) named EDCNN to improve matching accuracy by reducing redundant activation in hidden layers. The proposed SEGJO-EDCNN method is evaluated on the Corel 5 K and Oxford Flower datasets, and its performance is measured using precision, recall, F-score, mean average precision, and average recall. Comparative analysis with existing methods—ELNDP, SVM-CBIR, SPDNN, and DNN-SAR—demonstrates that SEGJO-EDCNN achieves a higher mean average precision of 97.595% on the Corel 5 K dataset, outperforming both ELNDP and DNN-SAR. Next, the MAP of SEGJO-EDCNN for the Oxford flower dataset is 99.239% which is higher compared to the SVM-CBIR.

Psychometric Properties of the Brazilian Version of Environmental Protectors Against Hospital Work Stress.

Objective: To analyse the psychometric properties of the Brazilian version of the Environmental Protectors Against Hospital Work Stress (ENPROS). Methods: A cross-sectional methodological study was conducted in two public hospitals in São Paulo state, Brazil, involving 431 doctors and nursing staff. Exploratory and confirmatory factor analyses were performed to assess the construct validity of the instrument. To evaluate internal consistency and measurement stability, Cronbach's alpha and the intraclass correlation coefficient (ICC) were used. Convergent validity was tested by comparison with the Job Stress Scale. Results: Exploratory factor analysis indicated a structure comprising four factors, with satisfactory factor loadings and commonalities ranging from 0.26 to 0.75. Confirmatory factor analysis suggested a model with good fit (CFI = 0.988; TLI = 0.988; RMSEA = 0.064; SRMR = 0.067). Cronbach's alpha was 0.95, and the intraclass correlation coefficient (ICC) was 0.45. Convergent validity showed significant correlations between the ENPROS domains and the Job Stress Scale factors, particularly in the social support domain. Conclusions: The Brazilian Portuguese version of the Environmental Protectors Against Hospital Work Stress scale has a four-dimensional structure, adequate reliability, and a good fit to the proposed factorial model.

Prediction of Monoclonal Antibody Pharmacokinetics in Pediatric Populations Using PBPK Modeling and Simulation

Background: Accurately determining pediatric dosing is essential prior to initiating clinical trials or administering medications in routine clinical settings. In children, ethical considerations demand careful evaluation of both safety and effectiveness. Typically, dosing recommendations for therapeutic proteins, such as monoclonal antibodies (mAbs), are derived from adult dosages using body weight as a scaling factor. However, this method overlooks key physiological and biochemical distinctions between pediatric and adult patients. Therefore, this could lead to the underexposure of mAbs, limiting their efficacy in this population. Additional methods are necessary to predict pediatric doses mechanistically. For small molecules, physiologically based pharmacokinetic (PBPK) models have been extensively used to predict pediatric doses based on physiological age-related changes and enzymes/transporters ontogeny. This study aims to evaluate the ability of PBPK models to predict mAbs’ pediatric exposure. Methods: Three mAbs were used for model development and validation: bevacizumab, infliximab, and atezolizumab. The PBPK models were built using GastroPlus© Biologics module. For each mAb, the PBPK model was developed based on observed data in healthy and/or patient adults. Then, the physiological parameters were scaled to describe the pediatric physiology to predict exposure to the pediatric populations. Predicted plasma concentration–time courses were overlaid with reported observed data to assess the ability of the PBPK model to predict pediatric exposure. Results: Results showed that PBPK models accurately predicted pediatric pharmacokinetics for mAbs. Conclusions: This research marks a significant step in validating mechanistic extrapolation methods for biologics exposure prediction in children using PBPK models.

Shear wave propagation in a sandwich layered piezoelectric microbeam: A Zener model and sliding theory approach

This study investigates a theory of shear horizontal surface wave propagating in a sandwich structure composed of a piezoelectric layer with micro-scale features and two identical viscoelastic layers under sliding contacts. The friction at the common interface is modeled using a rate and state-dependent law, capturing sliding velocity dependence and rate-weakening behavior under electrical conditions. The physical properties of the viscoelastic layer are described using the Zener model, and Maxwell’s equations are used to derive the governing equations for wave propagation. An analytical expression for the complex dispersion relation is derived using appropriate boundary conditions. A graphical representation of parameters such as the shear moduli ratio, sliding parameter, micro-length scale, attenuation coefficient, layer thickness, thickness ratio, and loss factor on shear wave velocity is presented. The results reveal the significant influence of shear moduli ratio and viscoelastic loss factor on shear wave dispersion, highlighting a hardening or softening effects in advanced sandwich structures. These findings have potential applications in soft biomaterials, nondestructive testing, and tissue analysis.

Super-resolution tactile sensor arrays with sparse units enabled by deep learning.

High-resolution tactile perception is essential for humanoid robots to perform contact-based interaction tasks. However, enhancing resolution is typically accompanied by increasing the density of sensing nodes, large numbers of interconnecting wires, and complex signal processing modules. This work presents super-resolution (SR) tactile sensor arrays with sparsely distributed taxels powered by a universal intelligent framework. Such smart sensor systems involve a general topological optimization strategy for taxel layout design and a deep learning model called self-attention-assisted tactile SR. Driven by the proposed model, they can dynamically distinguish high-density pressure stimuli by generating 2700 virtual taxels from only 23 physical taxels. An SR scale factor of more than 115 and an average localization error of 0.73millimeters are achieved, approximating human fingertip accuracy and surpassing current state-of-the-art solutions. This framework enhances flexible sensors with SR capabilities in a facile and energy-efficient manner, illustrating the potential to equip robots with embodied tactile perceptions.

Electron Impact Ionization and Partial Ionization Cross Sections of Plasma-Relevant SiClx (x = 1–3) Molecules

The electron-impact ionization and partial ionization cross sections are reported for few silicon-chlorine molecules using semi-empirical methods. The partial ionization cross sections are determined using a modified version of the binary-encounter-Bethe model. In this approach, the binary-encounter-Bethe model is modified through a two-step process, namely, transforming the binding energies of the occupied orbitals and introducing a scaling factor. The scaling can be done using either the mass spectrometry data or experimental values of cross sections. It correctly adjusts the scaling term of the BEB model so that the order of magnitude of resulting partial ionization cross sections is the same as that of experimental values. Further, the use of the experimental value of ionization and appearance energy values ensures that the cross sections have a correct threshold. This further mitigates the dependence of cross sections on energy at low values. The role of the scaling factor and the behavior of branching ratios is also examined at different energies. The species whose partial ionization cross sections are reported are highly relevant in plasma processing. However, the proposed model can be extended to any multi-centerd molecular structures comprising a large number of atoms or electrons, except in cases where resonance effects or additional ionization channels become significant. The mass spectrometry data is of utmost importance in computing partial ionization cross sections in order to obtain reliable results.

Thermodynamics of an universe with decaying cold dark matter

In this work we focus on the thermodynamics consistency of a new set of solutions emerging from a cosmology in which dark matter is able to decay into relativistic particles within the dark sector. It is important to stress that the lifetime of dark matter is larger than the age of the universe in order to be consistent with observations. Given that the corresponding decay rate is small, this one can be used as a perturbative parameter and it is possible to construct analytic solutions from a perturbative analysis for the densities of the species and the scale factor. The decay of dark matter is an irreversible process since it occurs out of chemical equilibrium and therefore the entropy per comoving volume increases considerably, as a consequence the temperature does not scale as a-1 in contrast to an adiabatic expansion. We take into account two scenarios: a) The case in which both species making up the fluid end up in thermal equilibrium and therefore their temperature is the same. b) A second instance in which the species do not reach thermal equilibrium and therefore they have different temperatures. We verify that the second law of thermodynamics is satisfied in any case.

Differential Evolution-Based Transient Search Optimizer for Image Multi-Thresholding Problem

A significant number of image processing and evaluation processes are presented and applied, due to their practical significance, in a variety of disciplines in the literature related to image processing and computer vision. One pre-processing approach that is frequently used to improve the information in a class of images is thresholding. By grouping correlated pixels according to the selected thresholds, the thresholding approach improves the image. For the benchmark image suite in this study, an entropy-based threshold is put into place. This study aims to investigate the thresholding performance of well-recognized fitness function called Kapur’s entropy, for a selected threshold. To facilitate the automatic identification of the optimum threshold (Th) on the benchmark images for a specified threshold value (Th=3,5,7), there is a need for an optimization algorithm that determines the optimum threshold values for the given image under study. This research proposed a self-adaptive hybrid differential evolution (DE) based transient search optimizer (TSO) called DETSO. DETSO uses the search equations of DE algorithm to improve the exploration capability of original TSO. Additionally, self-adaptivity has been included by modifying scaling factor and crossover rate using exponential decreasing and logarithmic decreasing mutation operator, respectively. The CEC 2019 numerical test problems have been used to confirm the working efficiency of DETSO. The experimental investigation confirms that utilizing a benchmark image test suite with varying dimensions, the DETSO helps to get superior results in achieving better threshold values in terms of performance metrics, such as PSNR, SSIM, FSIM, MSE, etc., compared to the other competitive heuristic algorithms.

A refined lion optimizer for deep learning

Optimization algorithms play a fundamental role in training neural networks. The optimizer focuses on the updating weights of momentum and velocity on learning rates and losses, furthermore the complexity of the optimizer and the quantity of updated parameters are considered. The Lion optimizer, proposed by Google, is an excellent optimizer known for its faster training speed and more efficient memory usage. However, due to the discreteness of the sign function, the optimizer’s parameter updates may fail to adapt dynamically with momentum in some models, leading to non-convergence issues. In this paper, a Refined Lion Optimizer(RLion) introduce a novel update rule for the optimizer by leveraging a non-linear continuous bounded function that maps the product of momentum and a scaling factor. This design enables the optimization parameters to adaptively adjust according to both the magnitude of momentum and the scaling factor. Theoretical analysis demonstrates that the RLion is able to smooth out the fluctuations, converge faster and more reliable. The FasterNet, EfficientNetV2 and the YOLOV8 on ImageNet1k dataset are trained without warm up for classification by leveraging the RLion optimizer. The YOLOV8, YOLOV11 on VOC2012 data and Object detection with Vision Transformers on Caltech 101 dataset are trained for Object detection. The DeepLabV3+ on instance-level human parsing dataset, TwinLiteNet on BDD100K, UNet on part of the CARLA self-driving dataset are trained for semantic segmentation. Compared to the AdamW and Lion optimizer, the loss and accuracy present the RLion can promote the classification validation accuracy about higher than AdamW on many models even the learning rate is as high as AdamW. For object detection and semantic segmentation, RLion achieves or approaches the performance of the adamw, but overcomes the Lion gradient’s explosive or disappear susceptibility. The RLion has better convergence performance and versatility.

How to measure the motivations of sports volunteers in Poland? First stage of the study with the Polish adaptation and validation of the VMS-ISE

BackgroundThe study aimed to adapt to Polish conditions and validate the seven-factor Volunteer Motivation Scale for International Sporting Events (VMS-ISE) as one of the tools used to measure volunteer motivation at international sports events.MethodsThe study used a diagnostic survey method. The sample consisted of 78 volunteers– 36 women and 43 men with a mean age of 24.8 years (± 5.6 years). To assess the reliability of the Polish version of the questionnaire, measurements were conducted in a two-week test-retest.ResultsThe CFA result showed that the chi-square test was not significant (χ2 = 8.541, p = 0.231), indicating an acceptable fit of the items in each factor. The reliability of the internal consistency of the factors was tested by examining Cronbach’s alpha scores for each motivation dimension, which ranged from 0.72 to 0.86, with a mean value of 0.77, meeting the recommended criterion. Construct validity was confirmed for all factors– AVEs ranged from 0.58 to 0.84, with a mean value of 0.70, exceeding the cut-off point. Of all the scale factors, all correlations except Interpersonal Contacts and Patriotism showed a statistically significant relationship (p < 0.05), indicating that the factors were satisfactorily distinct from each other and represented correctly prepared constructs. In addition, all factors except Love of Sport showed statistically significant correlations in terms of the studied socio-demographic characteristics (p < 0.05).ConclusionsThe obtained result indicates that the Polish adaptation of the questionnaire (called VMS-ISE.PL), after applying the necessary procedures, is characterized by satisfactory psychometric indices and can be used to study the motivation of sports volunteers in Poland.

Size Reduction in Micro Gas Turbines Using Silicon Carbide

Micro gas turbines serve small-scale generation where swift response and low emissions are highly valued, and they are commonly fuelled by natural gas. True to their ‘micro’ designation, their size is indeed compact; however, a noteworthy portion of the enclosure is devoted to power electronics components. This article considers whether these components can be made even smaller by substituting their conventional silicon switches with switches fashioned from silicon carbide. The wider bandgap of silicon carbide permits stronger electric fields and reliable operation at higher temperatures, which together promise lower switching losses, less heat, and simpler cooling arrangements. This study rests on a simple volumetric model. Two data sets feed the model. First come the manufacturer specifications for a pair of converter modules (one silicon, the other silicon carbide) with identical operation ratings. Second are the operating data and dimensions of a commercial 100 kW micro gas turbine. The model splits the converter into two parts: the semiconductor package and its cooling hardware. It then applies scaling factors that capture the higher density of silicon carbide and its lower switching losses. Lower switching losses reduce generated heat, so heatsinks, fans, or coolant channels can be slimmer. Together these effects shrink the cooling section and, therefore, the entire converter. The findings show that a micro gas turbine inverter built with silicon carbide occupies about one fifth less space and delivers more than a quarter higher power density than its silicon counterpart.

Self-optimization composite dynamic control for trajectory tracking of robot manipulators via deep reinforcement learning

In practical engineering applications, robot manipulators frequently encounter varying payloads, posing significant challenges for fixed-parameter dynamic controllers to maintain optimal performance across diverse operating conditions. To address this issue, this paper proposes a learning-based parameter self-optimization method to enhance the adaptability of the dynamic controller. Specifically, a non-smooth composite control framework is initially designed, leveraging its fractional power factor and scaling factor to improve robustness compared to traditional smooth controllers. A Markov decision process (MDP) is subsequently formulated, integrating fundamental control objectives such as minimizing tracking error and reducing control energy consumption. By utilizing the soft actor-critic (SAC) algorithm, an adaptive parameter self-optimization scheme is developed to optimize transient performance and enhance system adaptability. Training results demonstrate that compared with those feasible fixed-parameter controllers, the proposed method achieves up to 80.0% reduction in root mean square error (RMSE) of the compared with the classical computed torque control (CTC) method while maintaining smooth control inputs. Experimental validation under varying payloads and velocities highlights the effectiveness of the proposed method.

Prediction of CYP3A-mediated drug-drug interactions using endogenous biomarkers 6β-hydroxycortisol and cortisol.

Prediction of CYP3A-mediated drug-drug interactions using endogenous biomarkers 6β-hydroxycortisol and cortisol.

An improved vector tracking method based on robust adaptive navigation filter for complex environments

Abstract Compared with the traditional scalar tracking loop of the GNSS receiver, the vector tracking loop has higher tracking sensitivity in weak signal scenarios. However, due to the interdependence of the tracking channels, the vector loop is highly susceptible to exhibit vulnerability to low-quality signals in occluded environments such as boulevards or high-rise buildings. To solve this problem, an adaptive vector tracking loop with KF+EKF architecture is proposed in this paper. The Kalman filter is used as a local pre-filtering and the robust adaptive extended Kalman filter as a global fusion navigation filter. In the navigation filter, the scaling factor and the adaptive factor are utilized to adjust the measurement noise covariance matrix and the state prediction covariance matrix, respectively, so that the two covariance matrices are constantly changing in response to the complex environment to reduce the influence of low-quality signals. Compared with the conventional VTL, this method can effectively suppress the influence of measurement abnormalities and modeling errors on system parameter estimation. The experimental results show that in a complex occlusion environment, this method has better tracking and positioning performance than the conventional VTL method.&amp;#xD;