Search Results (5,215)

Competency in musculoskeletal (MSK) ultrasound requires learners to relate probe manipulation to spatial reasoning, image projection, and the appearance of characteristic artefacts, which remains challenging during early training due to the limited spatial context provided by conventional instructional resources. This study investigates whether reconstructing real MSK ultrasound examinations in an immersive extended reality (XR) environment is perceived as useful for early familiarisation with probe handling and image interpretation. The proposed system reproduces ultrasound acquisitions using synchronised ultrasound video, six-degree-of-freedom probe tracking, and surface scans acquired from cadaveric specimens, enabling the reconstruction of spatially accurate probe trajectories with each ultrasound frame linked to a corresponding position and orientation. Within the XR environment, users can interactively explore these trajectories or observe automated playback in which the recorded probe motion is presented together with the corresponding ultrasound sequence. An exploratory evaluation with healthcare professionals was conducted to assess perceived usefulness and clarity of spatial relationships. The results indicate that participants perceived spatially coherent playback of real ultrasound examinations in XR as a potentially useful aid for understanding probe–image relationships. These findings suggest the feasibility of this approach as a complementary resource for introductory MSK ultrasound training. Full article

The present work reveals a direct correspondence between modified theories of gravity (cosmology) and entropic cosmology based on the thermodynamics of apparent horizon. It turns out that due to the total differentiable property of entropy, the usual thermodynamic law (used for Einstein gravity) needs to be generalized for modified gravity theories having more than one thermodynamic degree of freedom (d.o.f.). For the modified theories having n number of thermodynamic d.o.f., the corresponding horizon entropy is given by $S_{\mathrm{h}}\sim S_{\mathrm{BH}}+$ terms containing the time derivatives of $S_{\mathrm{BH}}$ up to $(n-1)$-th order, and moreover, the coefficient(s) of the derivative term(s) are proportional to the modification parameter of the gravity theory (compared to the Einstein gravity; $S_{\mathrm{BH}}$ is the Bekenstein–Hawking entropy). By identifying the independent thermodynamic variables from the first law of thermodynamics, we show that the equivalent thermodynamic description of modified gravity naturally allows the time derivative of the Bekenstein–Hawking entropy in the horizon entropy. Full article

This article presents an approach to extend the capabilities of vat photopolymerization (VPP) 3D printing using a robotic arm, with a focus on robust path planning. The robotic cell consists of a Mecademic Meca500 six-axis robot mounted on a Zaber X-LRQ300AP linear guide. The kinematic chain is inverted to reflect the logic of VPP: the world reference frame is fixed to the robot’s tool (the build plate), while the tool frame is attached to the polymerization zone. A virtual degree of freedom for screen image rotation is introduced, bringing the system to eight degrees of freedom. Inverse kinematics are solved under constraints (pose tolerance, joint limits, collision avoidance, and continuity) and evaluated using multi-criteria metrics: manipulability, normalized joint-limit margin, and positional/angular sensitivity. The algorithm follows a deterministic coarse-to-fine search procedure: discrete sweeping of global part orientations, initial sampling with Halton sequences, abd feasibility filtering on a sparsified trajectory, followed by refinement and multi-criteria ranking. The pipeline successfully discarded infeasible orientations and identified feasible printing trajectories for six of the seven benchmark parts, while the remaining case highlights a limitation that may be addressed in future improvements. Full article

Existing methods for selecting the number of teeth in complex planetary gear systems are often methodologically demanding. They do not always ensure all conditions required for proper operation and assembly. This paper presents an analytical methodology for determining gear tooth numbers. The method is demonstrated on a Ravigneaux planetary gear set with seven kinematic links and two degrees of freedom. It ensures the simultaneous satisfaction of all meshing and assembly conditions. Starting from the known transmission ratios, the number of teeth of one central gear, and the selected angular displacement of the outer planet gear, analytical relationships are derived. These allow the determination of the tooth numbers of all remaining gear elements. The procedure is implemented in Python 3.13. This enables a systematic evaluation of predefined input ranges and an automatic verification of geometric constraints, including interference and undercutting conditions. The proposed method yields six feasible configurations. Compared with the Borg-Warner M85 automatic transmission, deviations in individual gear ratios reach up to 10%. Significantly lower tooth numbers are achieved for several gears. These results suggest that the proposed methodology can achieve comparable kinematic performance while offering more compact gear designs and a potential weight reduction. The developed model also provides a basis for extension to more complex configurations and integration with optimisation and dynamic criteria in planetary gear synthesis. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature–like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature–like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩~r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Compact stars serve as natural systems where matter exists at densities far beyond those achievable in laboratory experiments. Among them, magnetars are expected to possess interior magnetic fields that may reach values of the order of ${10}^{17}$–${10}^{18}$ G. These extreme conditions are expected to alter the microscopic and macroscopic properties of dense matter. In this review, we examine how strong magnetic fields affect fermionic matter through mechanisms such as Landau quantization and anomalous magnetic moment interactions. We further discuss the behavior of magnetized hadronic matter within relativistic mean-field approaches and consider the possible emergence of additional degrees of freedom, including hyperons, $\Delta$ resonances, meson condensates, and quark matter. The consequences of these effects for neutron star structure and observational constraints are also briefly outlined. Full article

In order to protect the high-sensitivity optical lens of the “magnetic field and velocity field imager” in extreme deep space environments, this paper proposes a new type of dual redundant planetary differential lens cover drive mechanism. In view of the critical vulnerability that traditional single-motor direct drive is prone to sudden mechanical jamming and catastrophic single-point failure (SPF) in severe tasks such as Jupiter exploration, this study constructs a “dual input single output (DISO)” rigid decoupling architecture from the perspective of physical topology. Through theoretical analysis and kinematic modeling, the adaptive decoupling mechanism of the two-degree-of-freedom (2-DOF) system under unilateral mechanical stalling is revealed. Dynamic analysis shows that in the nominal dual-motor synergy mode, the system shows a significant “kinematic load-sharing effect”, thus greatly reducing the sliding friction and gear wear rate. In addition, under the severe dynamic fault injection scenario (maximum gravity deviation and sudden jam superposition of a single motor), the cold standby motor is activated and the dynamic takeover is quickly performed. The high-fidelity transient simulation based on ADAMS verifies that although the fault will produce transient global torque spikes and pulsed internal gear contact forces at the moment, all extreme dynamic loads remain well within the structural safety margin. The output successfully achieved a smooth transition, which is characterized by a non-zero-crossing velocity recovery. This research provides an innovative theoretical basis and a practical engineering paradigm for the design of high-reliability fault-tolerant mechanisms in deep space exploration. Full article

This study investigates the internal structure and functional consistency of a brief scale designed to assess the social regulation of learning in collaborative higher education environments. Social regulation is essential to understanding how students coordinate cognitive and socio-emotional processes during group work, but brief and valid instruments remain limited. A total of 973 undergraduate students responded to seven items on a seven-point Likert scale. Exploratory and confirmatory factor analyses were performed to evaluate the dimensionality of the instrument. The results supported a two-factor structure comprising coordination regulation and collective engagement regulation. Standardized loadings ranged from 0.772 to 0.935 and the factors showed a high latent correlation (r = 0.792), indicating that they are distinct yet strongly interdependent. The model demonstrated excellent fit according to incremental indices (CFI = 0.992, TLI = 0.988) and acceptable residual fit (SRMR = 0.064). Although the RMSEA value exceeded conventional thresholds (RMSEA = 0.137, this result should be interpreted with caution due to the limited number of items and degrees of freedom, as documented in prior methodological research), these findings highlight how shared planning, monitoring, and socio-emotional alignment function as interconnected processes that support effective collaboration in academic teams. Overall, the study provides empirical evidence that a parsimonious two-dimensional model can capture key regulatory dynamics relevant to fostering sustainable collaborative practices in higher education. Future research should examine measurement invariance across contexts and explore associations with student performance, engagement, and well-being. Full article

The multi-finger robotic hand offers great potential for precise control due to its high degrees of freedom. Yet, manipulating objects forms a closed-chain kinematic system, which compounds the dimensionality and computational complexity of trajectory tracking. To tackle this challenge, and inspired by the widespread application of the zeroing neurodynamics (ZND) in robotic control, this study proposes a novel direct-discrete robust neurodynamics (DDRN) algorithm. The proposed algorithm advances the ZND methodology by employing a direct discretization design strategy. This strategy is crucial for two reasons. First, it fits naturally with the discrete-time nature of digital systems, enabling practical implementation. Second, it enhances precision by avoiding the integration errors inherent in continuous-to-discrete transformations. By simultaneously integrating this direct discretization with explicit noise suppression mechanisms, the DDRN algorithm efficiently solves the high-dimensional tracking problem formulated as a constrained time-varying quadratic programming (CTVQP) problem. Theoretical analyses demonstrate that under various noise environments, the steady-state residuals (SSRs) achieve global convergence, guaranteeing the algorithm’s strong robustness and high accuracy. Furthermore, comprehensive numerical simulations substantiate its superior performance. Practically, this DDRN algorithm enables more reliable and precise real-time control of dexterous robotic hands, with potential benefits for advanced manufacturing, prosthetic hands, and automated assembly where accurate trajectory tracking under sensor noise is critical. Full article

Frequency diverse array multiple-input multiple-output (FDA-MIMO) radar offers significant advantages in mainlobe deceptive interference suppression, as its transmit steering vector contains both angle and range information, providing additional degrees of freedom beyond the angular dimension. However, conventional FDA-MIMO radar suffers from insufficient angle-range resolution, which limits its ability to suppress interferences located close to the target. Moreover, it lacks robustness under limited snapshots and parameter mismatch conditions. To address these issues, this paper proposes a robust beamforming method based on the FDA-MIMO radar model. A collocated sparse array with a sinusoidal element spacing offset and a logarithmic frequency offset is adopted to enhance beam resolution and resolve the periodic angle-range ambiguity problem. Based on this model, the interference-plus-noise covariance matrix is reconstructed using two-dimensional Capon spatial spectrum, and the steering vector is corrected via a joint objective function that combines MUSIC orthogonality and the flatness of the covariance residual spectrum. Simulation results demonstrate that, under conditions of near-target interferences, random range-angle errors, and frequency offset errors, the proposed method achieves a signal-to-interference-plus-noise ratio (SINR) close to the ideal value, exhibiting excellent mainlobe interference suppression performance and robustness. Full article

We analyze the Taylor expansion of the metric $f\left(R\right)$ gravity in the Jordan frame around the General Relativity limit, expanding in the small deviation $(\varphi -{\varphi }_0)$ with ${\varphi }_0=1$. By relating the scalar–tensor representation to the original $f\left(R\right)$ formulation, we derive constraints on the expansion parameters from the observed value of the present-day $\Lambda$CDM ($\Lambda$ Cold Dark Matter) deceleration parameter and from cosmological bounds on the variation of Newton’s constant. We show that these requirements imply that the scalar degree of freedom must have a mass exceeding the Hubble scale by several orders of magnitude. This result challenges the common assumption that the scalar mode can drive cosmological dynamics with a mass of order of the Hubble constant $H_0$. We provide a dynamical interpretation of this hierarchy by emphasizing that a proper definition of the scalar mass, in a field-theoretical sense, requires an adiabatic separation between background evolution and perturbations, which naturally leads to a super-Hubble mass scale. Full article

To address the bottlenecks of accuracy in head pose estimation caused by occlusion and rotational representation ambiguities, we propose Deep6DHead, a 6-degree-of-freedom (6DoF) head pose estimation method based on deep feature enhancement. This method innovatively integrates RGB and depth information to construct a four-channel input and achieves feature fusion of RGB-D through a dual-branch network. First, a Squeeze-and-Excitation (SE) module adaptively weights the depth geometric features of key anatomical regions to achieve channel recalibration. Second, based on the 6DoF rotation representation framework, we introduce an anatomical constraint loss using the nasal bridge normal. This constraint corrects rotation deviations caused by noise by enforcing consistency in local geometric orientation. Finally, the model outputs the rotation matrix end-to-end for final pose estimation. Experiments on the 300W-LP, BIWI, and AFLW2000 datasets demonstrate that our method significantly improves robustness and accuracy, particularly under extreme head poses. Notably, it achieves state-of-the-art performance on the roll axis (lowest error: 2.05) and a competitive overall MAE of 3.45, providing an effective solution for head pose estimation in complex real-world scenarios including extreme viewing angles. Full article

The fuel consumption of power-split hybrid electric vehicles (PSHEVs) can be effectively reduced via mode transition that includes the engine process. However, factors such as engine torque ripple, system parameter uncertainties, and variations in torsional vibration characteristics can easily induce drivetrain vibration. These factors not only degrade ride comfort but also lead to a fundamental control challenge. The inherent trade-off between rapid response and stability is difficult to reconcile. In addition, the lack of adaptive mechanisms further limits consistent performance under varying conditions. To tackle these problems, a scenario-adaptive composite robust control (SACRC) strategy is proposed. The strategy consists of a UIO (unknown input observer)-based torque observation module, an adaptive VSS-LMS approach, and an H∞ controller with self-tuning parameters. Firstly, a six-degree-of-freedom dynamic model of the PSHEV transmission system is established with excitation sources, considering the characteristics of dual elastic elements. Secondly, a UIO-based torque observer is designed using a simplified dual-elastic-element model. By using engine speed and output shaft speed, the observer can accurately identify the torque transmitted by the torsional damper and drive shaft. Then, an adaptive VSS-LMS and H∞ controller with self-tuning parameters is constructed to ensure a balanced performance between fast torsional vibration suppression and control stability. Finally, simulation and experimental results demonstrate that the proposed strategy provides favorable adaptability to complex scenarios, and unifies the performance goals of rapidity, stability, and robustness. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article