Search Results (20)

Magnetic wall-climbing robots have great potential applications for the maintenance and inspection of large steel structures. However, they are susceptible to overturning when climbing over obstacles on vertical walls, primarily due to localized failures in the adhesion and shifts in the center of gravity. To address this issue, this paper presents an improved robot design featuring a passive adaptive tracked mobility module and a link-spring anti-overturning module. The adaptive tracked mobility module, incorporating spring tensioning mechanisms and belt press wheels, enables dynamic conformity to uneven walls and maintains stable magnetic adhesion. The link-spring anti-overturning module converts the front-end lift during obstacle crossing into an anti-overturning moment applied to the rear end of the robot. Notably, there is no need for additional drivers or control units. The structural design and three-dimensional modeling of the robot are carried out. Its working principle is analyzed, and parametric modeling and simulation analysis are performed. A physical prototype is developed and obstacle-crossing experiments are conducted on a vertical wall. The results demonstrate that the adaptive tracked mobility module and the anti-overturning module can successfully assist the robot in climbing over an obstacle with a maximum height of 23 mm, and the robot exhibits excellent stability while climbing over continuous obstacles and moving on uneven vertical walls. Full article

Sustainable manufacturing requires modern intelligent approaches to monitoring products of the manufacturing process. An integral part of intelligent manufacturing is the measurement of geometric parameters of products, which allows diagnosing the state of the manufacturing process, optimizing it and predicting its further development. For these reasons, it is necessary to monitor the condition of measuring instruments, as decision-making is based on the data provided by them. Calibration intervals of measuring instruments are commonly defined using fixed time-based rules that are not explicitly linked to measurement uncertainty growth or conformity risk. This practice may lead to either unnecessary recalibration or an increased probability of using out-of-tolerance instruments. In this study, a Monte Carlo-based methodology for determining recalibration intervals is proposed, in which recalibration decisions are derived from the probabilistic evolution of measurement error over time. Measurement uncertainty is modeled as a time-dependent stochastic process combining calibration uncertainty, drift behavior, and repeatability. Monte Carlo simulation is used to propagate uncertainty and to estimate both the expanded uncertainty and the probability that the measurement error exceeds the maximum permissible error (MPE). The recalibration interval is defined as the earliest time at which this probability exceeds a predefined acceptable risk threshold. A numerical experiment using realistic synthetic data representative of a typical dimensional measuring instrument demonstrates that probability-based and uncertainty-based criteria may lead to substantially different recalibration intervals. The results confirm that risk-informed recalibration intervals provide a more transparent and metrologically justified alternative to fixed schedules while remaining fully compatible with ISO/IEC 17025 and GUM principles. The proposed approach is instrument-agnostic and readily applicable in calibration laboratories and industrial measurement systems. Full article

Additive manufacturing offers significant design freedom for injection mold tooling, particularly in optimizing cooling performance and reducing mass. This study presents a holistic framework for the topology optimization of mold inserts considering design for additive manufacturing principles, integrating essential boundary conditions from the mold making, injection molding process, and post-processing operations. A slider component with conformal cooling channels serves as the case study. Using simulation-driven design and finite element analysis, two design variants, based on conventional and modified design spaces, were evaluated. Mechanical loads from clamping and the injection process were considered, with safety factors applied to reflect industrial misuse scenarios. The topology optimization process was implemented using Altair OptiStruct and validated through displacement and stress analyses. The results show savings in both mass and costs of up to 60% while maintaining structural integrity under operational and misuse conditions. The maximum displacements—only a 4 µm increase compared to the reference—remained within DIN ISO 20457 tolerances, and stresses did not exceed 170 MPa under operational conditions, confirming industrial applicability. This study concludes with a proposed framework for integrating topology optimization into mold design workflows. Full article

Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical reactivity. In this work, we present GAELLE, a cheminformatics tool that combines conformer generation with quantum reactivity descriptors to identify the most reactive structure of a molecule in solution. GAELLE integrates an evolutionary algorithm with fast semiempirical quantum chemical calculations (xTB), enabling the automated ranking of conformers based on HOMO–LUMO gap minimization (Pearson’s principle of maximum hardness) and electrophilicity index (Parr’s electrophilicity scale). Solvent effects are accounted for via implicit solvation models (GBSA/ALPB) to ensure realistic evaluation of reactivity in solution. The method is fully SMILES-driven, open-source, and scalable to medium-sized drug-like molecules. Applications to reactive intermediates, bioactive conformations, and pre-reactive complexes demonstrate the method’s relevance for mechanism elucidation, molecular design, and in silico screening. GAELLE is publicly available and offers a reactivity-focused alternative to traditional energy-minimization tools in conformational analysis. Full article

In this paper, we clarify a serious misinterpretation and consequent misuse of the Principle of Maximum Conformality (PMC), which also can serve as a mini-review of PMC. In a recently published article, P. M. Stevenson has claimed that “the PMC is ineffective and does nothing to resolve the renormalization-scheme-dependence problem”, concluding incorrectly that the success of PMC predictions is due to the PMC being a “laborious, ad hoc, and back-door” version of the Principle of Minimal Sensitivity (PMS). We show that such conclusions are incorrect, deriving from a misinterpretation of the PMC and an overestimation of the applicability of the PMS. The purpose of the PMC is to achieve precise fixed-order pQCD predictions, free from conventional renormalization schemes and scale ambiguities. We demonstrate that the PMC predictions satisfy all the self-consistency conditions of the renormalization group and standard renormalization-group invariance; the PMC predictions are thus independent of any initial choice of renormalization scheme and scale. The scheme independence of the PMC is also ensured by commensurate scale relations, which relate different observables to each other. Moreover, in the Abelian limit, the PMC dovetails into the well-known Gell-Mann–Low framework, a method universally revered for its precision in QED calculations. Due to the elimination of factorially divergent renormalon terms, the PMC series not only attains a convergence behavior far superior to that of its conventional counterparts but also deftly curtails any residual scale dependence caused by the unknown higher-order terms. This refined convergence, coupled with its robust suppression of residual uncertainties, furnishes a sound and reliable foundation for estimating the contributions from unknown higher-order terms. Anchored in the bedrock of standard renormalization-group invariance, the PMC simultaneously eradicates the factorial divergences and eliminates superfluous systematic errors, which inversely provides a good foundation for achieving high-precision pQCD predictions. Consequently, owing to its rigorous theoretical underpinnings, the PMC is eminently applicable to virtually all high-energy hadronic processes. Full article

The precision and predictive power of perturbative QCD (pQCD) prediction depends on both a precise, convergent, fixed-order series and a reliable way of estimating the contributions of unknown higher-order (UHO) terms. It has been shown that by applying the principle of maximum conformality (PMC), which applies the renormalization group equation recursively to set the effective magnitude of ${\alpha }_s$ of the process, the remaining conformal coefficients will be well matched with the corresponding ${\alpha }_s$ at each order, leading to a scheme-and-scale invariant and more convergent perturbative series. The PMC series, being satisfied with the standard renormalization group invariance, has a rigorous foundation. Thus it not only can be widely applied to virtually all high-energy hadronic processes, but also can be a reliable platform for estimating UHO contributions. In this paper, by using the total decay width $\Gamma (H\to \gamma \gamma )$ which has been calculated up to N⁴LO QCD corrections, we first derive its PMC series by using the PMC single-scale setting approach and then estimate its unknown N⁵LO contributions by using a Bayesian analysis. The newly suggested Bayesian-based approach estimates the magnitude of the UHO contributions based on an optimized analysis of the probability density distribution, and the predicted UHO contribution becomes more accurate when more loop terms have been known to tame the probability density function. Using the top-quark pole mass $M_t$ = 172.69 GeV and the Higgs mass $M_H$ = 125.25 GeV as inputs, we obtain $\Gamma (H\to \gamma \gamma )=9.56504\mathrm{keV}$, and the estimated N⁵LO contribution to the total decay width is $\Delta {\Gamma }_H=\pm 1.65\times {10}^{-4}\mathrm{keV}$ for the smallest credible interval of $95.5\%$ degree of belief. Full article

Exoskeletal robots are of critical importance in the domain of mechanical boosting. The pneumatic artificial muscle (PAM) is commonly used as a flexible actuator in exoskeletal robots designed for upper limbs due to its high power-to-weight ratio, conformability, and safety. This study establishes a new model based on the existing model to improve its control precision by implementing elastic and frictional forces and empirical coefficients, battling against the time-variant hysteresis that PAM’s output force exhibits. In the meantime, a BP neural network is employed in reverse modeling, followed by the adoption of the least-square-based particle swarm optimization algorithm in order to determine the optimized parameter values. PAM provides the Upper Limb Exoskeletal Robot with appropriate auxiliary power, which can be adjusted to accommodate variations in posture change during the lifting process. PAM is also capable of handling variable loads based on the principle of torque balance, constructing a control system according to the inverse dynamics of exoskeletal robots accompanied by an inverse model of PAM’s output force, and finally, rendering tracking control of the elbow angle during the auxiliary process possible. Finally, the tracking error results are calculated and shown; the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and IEAT value is 4.6426 rad. This control method is able to improve the precision of tracking control of the elbow angle of the upper limb–exoskeleton coupled system during the process of lifting goods. Full article

In this paper, we investigate properties of solutions to a space-time fractional variable-order conformable nonlinear differential equation with a generalized tempered fractional Laplace operatorby using the maximum principle. We first establish some new important fractional various-order conformable inequalities. With these inequalities, we prove a new maximum principle with space-time fractional variable-order conformable derivatives and a generalized tempered fractional Laplace operator. Moreover, we discuss some results about comparison principles and properties of solutions for a family of space-time fractional variable-order conformable nonlinear differential equations with a generalized tempered fractional Laplace operator by maximum principle. Full article

The setting of the renormalization scale (${\mu }_r$) in the perturbative QCD (pQCD) is one of the crucial problems for achieving precise fixed-order pQCD predictions. The conventional prescription is to take its value as the typical momentum transfer Q in a given process, and theoretical uncertainties are then evaluated by varying it over an arbitrary range. The conventional scale-setting procedure introduces arbitrary scheme-and-scale ambiguities in fixed-order pQCD predictions. The principle of maximum conformality (PMC) provides a systematic way to eliminate the renormalization scheme-and-scale ambiguities. The PMC method has rigorous theoretical foundations; it satisfies the renormalization group invariance (RGI) and all of the self-consistency conditions derived from the renormalization group. The PMC has now been successfully applied to many physical processes. In this paper, we summarize recent PMC applications, including event shape observables and heavy quark pair production near the threshold region in $e^{+}{e}^{-}$ annihilation and top-quark decay at hadronic colliders. In addition, estimating the contributions related to the uncalculated higher-order terms is also summarized. These results show that the major theoretical uncertainties caused by different choices of ${\mu }_r$ are eliminated, and the improved pQCD predictions are thus obtained, demonstrating the generality and applicability of the PMC. Full article

Hypotrochoidal profile contours have been produced in industrial applications in recent years using two-spindle processes, and they are considered effective high-quality solutions for form-fit shaft and hub connections. This study mainly concerns analytical approaches to determine the stresses and deformations in hypotrochoidal profile shafts due to pure bending loads. The formulation was developed according to bending principles using the mathematical theory of elasticity and conformal mappings. The loading was further used to investigate the rotating bending behaviour. The stress factors for the classical calculation of maximum bending stresses were also determined for all those profiles presented and compiled in the German standard DIN3689-1 for practical applications. The results were also compared with the corresponding numerical and experimental results, and very good agreement was observed. Additionally, based on previous work, the stress factor was determined for the case of torsional loading to calculate the maximum torsional stresses in the standardised profiles, and the results are listed in a table. This study contributes to the further refinement of the current DIN3689 standard. Full article

This paper presents a wearable metasurface multiple-input multiple-output (MIMO) antenna for biomedical applications in a 5 GHz wireless body area network (WBAN) with broadband, circular polarization (CP), and high gain. The physical properties of the MIMO antenna element and the principles of polarization conversion are analyzed in-depth using characteristic mode analysis. For the proposed MIMO antenna, the measured −10 dB impedance bandwidth is 34.87% (4.76–6.77 GHz), and the 3 dB axial ratio bandwidth is 22.94% (4.9–6.17 GHz). By adding an isolation strip, the measured isolation of the two antenna elements is greater than 19.85 dB. The overall size of the MIMO antenna is 1.67λ₀ × 0.81λ₀ × 0.07λ₀ at 5.6 GHz, and the maximum gain is 7.95 dBic. The envelope correlation coefficient (ECC) is less than 0.007, with the maximum diversity gain greater than 9.98 dB, and the channel capacity loss is less than 0.29 b/s/Hz. The specific absorption rate (SAR) of the wearable MIMO antenna is simulated by the human tissue model, which proves that the proposed antenna conforms to international standards and is harmless to humans. The proposed wearable metasurface MIMO antenna has CP, broadband, high gain, low ECC, and low SAR, which can be used in wearable devices for biomedical applications. Full article

Sensitivity analysis and parameter optimization of stand models can improve their efficiency and accuracy, and increase their applicability. In this study, the sensitivity analysis, screening, and optimization of 63 model parameters of the Physiological Principles in Predicting Growth (3PG) model were performed by combining a sensitivity analysis method and the Markov chain Monte Carlo (MCMC) method of Bayesian posterior estimation theory. Additionally, a nine-year observational dataset of Chinese fir trees felled in the Shunchang Forest Farm, Nanping, was used to analyze, screen, and optimize the 63 model parameters of the 3PG model. The results showed the following: (1) The parameters that are most sensitive to stand stocking and diameter at breast height (DBH) are nWs(power in stem mass vs. diameter relationship), aWs(constant in stem mass vs. diameter relationship), alphaCx(maximum canopy quantum efficiency), k(extinction coefficient for PAR absorption by canopy), pRx(maximum fraction of NPP to roots), pRn(minimum fraction of NPP to roots), and CoeffCond(defines stomatal response to VPD); (2) MCMC can be used to optimize the parameters of the 3PG model, in which the posterior probability distributions of nWs, aWs, alphaCx, pRx, pRn, and CoeffCond conform to approximately normal or skewed distributions, and the peak value is prominent; and (3) compared with the accuracy before sensitivity analysis and a Bayesian method, the biomass simulation accuracy of the stand model was increased by 13.92%, and all indicators show that the accuracy of the improved model is superior. This method can be used to calibrate the parameters and analyze the uncertainty of multi-parameter complex stand growth models, which are important for the improvement of parameter estimation and simulation accuracy. Full article

Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of a value function expressed as a numeric table or a function approximator. The learned behavior is then derived using a greedy policy with respect to this value function. Nevertheless, sometimes the learned policy does not meet expectations, and the task of authoring is difficult and unsafe because the modification of one value or parameter in the learned value function has unpredictable consequences in the space of the policies it represents. This invalidates direct manipulation of the learned value function as a method to modify the derived behaviors. In this paper, we propose the use of Inverse Reinforcement Learning to incorporate real behavior traces in the learning process to shape the learned behaviors, thus increasing their trustworthiness (in terms of conformance to reality). To do so, we adapt the Inverse Reinforcement Learning framework to the navigation problem domain. Specifically, we use Soft Q-learning, an algorithm based on the maximum causal entropy principle, with MARL-Ped (a Reinforcement Learning-based pedestrian simulator) to include information from trajectories of real pedestrians in the process of learning how to navigate inside a virtual 3D space that represents the real environment. A comparison with the behaviors learned using a Reinforcement Learning classic algorithm (Sarsa($\lambda$)) shows that the Inverse Reinforcement Learning behaviors adjust significantly better to the real trajectories. Full article

Magnetized particle motion around black holes in conformal gravity immersed in asymptotically uniform magnetic field has been studied. We have also analyzed the behavior of magnetic fields near the horizon of the black hole in conformal gravity and shown that with the increase of conformal parameters L and N the value of angular component of magnetic field at the stellar surface decreases. The maximum value of the effective potential corresponding to circular motion of the magnetized particle increases with the increase of conformal parameters. It is shown that in all cases of neutral, charged and magnetized particle collisions in the black hole environment the center-of-mass energy decreases with the increase of conformal parameters L and N. In the case of the magnetized and negatively charged particle collisions, the innermost collision point with the maximum center-of-mass energy comes closer to the central object due to the effects of the parameters of the conformal gravity. We have applied the results to the real astrophysical scenario when a pulsar treated as a magnetized particle is orbiting the super massive black hole (SMBH) Sgr A* in the center of our galaxy in order to obtain the estimation of magnetized compact object’s orbital parameter. The possible detection of pulsar in Sgr A* close environment can provide constraints on black hole parameters. Here we have shown that there is degeneracy between spin of SMBH and ambient magnetic field and consequently the interaction of magnetic field $\sim {10}^2$ Gauss with magnetic moment of magnetized neutron star can in principle mimic spin of Kerr black holes up to $0.6$ . Full article

Any physical system can be regarded on different levels of description varying by how detailed the description is. We propose a method called Dynamic MaxEnt (DynMaxEnt) that provides a passage from the more detailed evolution equations to equations for the less detailed state variables. The method is based on explicit recognition of the state and conjugate variables, which can relax towards the respective quasi-equilibria in different ways. Detailed state variables are reduced using the usual principle of maximum entropy (MaxEnt), whereas relaxation of conjugate variables guarantees that the reduced equations are closed. Moreover, an infinite chain of consecutive DynMaxEnt approximations can be constructed. The method is demonstrated on a particle with friction, complex fluids (equipped with conformation and Reynolds stress tensors), hyperbolic heat conduction and magnetohydrodynamics. Full article