Search Results (2,435)

This paper presents a novel mechanically beam-steered antenna system for 10 GHz applications, enabled by multi-material 3D-printing technology. The proposed design eliminates the need for complex electronic circuitry by integrating a mechanically rotatable, 3D-printed hemispherical lens with a conventional rectangular patch antenna. The system comprises three main components: a 10-GHz patch antenna, a precision-fabricated hemispherical dielectric lens produced via stereolithography (SLA), and a structurally robust rotation assembly fabricated using fused deposition modeling (FDM). The mechanical rotation of the lens enables discrete beam-steering from −45° to +45° in 5° steps. Experimental results demonstrate a gain improvement from 6.21 dBi (standalone patch) to 10.47 dBi with the integrated lens, with minimal degradation across steering angles (down to 9.59 dBi). Simulations and measurements show strong agreement, with the complete system achieving 94% accuracy in beam direction. This work confirms the feasibility of integrating additive manufacturing with passive beam-steering structures to deliver a low-cost, scalable, and high-performance alternative to electronically scanned arrays. Moreover, the design is readily adaptable for motorized actuation and closed-loop control via embedded systems, enabling future development of real-time, programmable beam-steering platforms. Full article

The accurate assessment of Chinese bayberry (Myrica rubra) maturity is critical for intelligent harvesting. This study proposes a novel cascaded framework combining instance segmentation and multi-feature regression for accurate maturity detection. First, a lightweight SOLOv2-Light network is employed to segment each fruit individually, which significantly reduces computational costs with only a marginal drop in accuracy. Then, a multi-feature extraction network is developed to fuse deep semantic, color (LAB space), and multi-scale texture features, enhanced by a channel attention mechanism for adaptive weighting. The maturity ground truth is defined using the a*/b* ratio measured by a colorimeter, which correlates strongly with anthocyanin accumulation and visual ripeness. Experimental results demonstrated that the proposed method achieves a mask mAP of 0.788 on the instance segmentation task, outperforming Mask R-CNN and YOLACT. For maturity prediction, a mean absolute error of 3.946% is attained, which is a significant improvement over the baseline. When the data are discretized into three maturity categories, the overall accuracy reaches 95.51%, surpassing YOLOX-s and Faster R-CNN by a considerable margin while reducing processing time by approximately 46%. The modular design facilitates easy adaptation to new varieties. This research provides a robust and efficient solution for in-field bayberry maturity detection, offering substantial value for the development of automated harvesting systems. Full article

A Nitinol rod-driven discrete continuum robot with two sections and eight units was developed to support clinicians in performing transoral surgery. The robot measures 120 mm in length, with each unit having a diameter of 15 mm and a height of 20 mm. The distal and proximal sections are designed to bend independently, each with two degrees of freedom (DOF) actuated by four Nitinol rods. To validate the independent controllability of the two sections, two-dimensional bending tests and ANSYS simulations were conducted. For the assessment of clinical feasibility, head and neck CT images from ten patients were manually segmented to reconstruct three-dimensional oral cavity models. Ten fictitious reference passages were generated from the lips to the oropharynx, and planar path-planning simulations were performed using these passages. Verification experiments were carried out on three reference passages employing experimentally derived inverse kinematics. The simulation results demonstrated an average reference path-following error within a root mean square (RMS) of 1.9705 mm at maximum insertion length. Experimental path-planning results showed average absolute angular differences of 5.6 degrees in the distal section and 4.1 degrees in the proximal section when compared with the simulations. Full article

This paper presents a systematic methodology for identifying optimal scaling regions in segment-based box-counting fractal dimension calculations through a three-phase algorithmic framework combining grid offset optimization, boundary artifact detection, and sliding window optimization. Unlike traditional pixelated approaches that suffer from rasterization artifacts, the method used directly analyzes geometric line segments, providing superior accuracy for mathematical fractals and other computational applications. The three-phase optimization algorithm automatically determines optimal scaling regions and minimizes discretization bias without manual parameter tuning, achieving significant error reduction compared to traditional methods. Validation across the Koch curve, Sierpinski triangle, Minkowski sausage, Hilbert curve, and Dragon curve demonstrates substantial improvements: excellent accuracy for the Koch curve (0.11% error) and significant error reduction for the Hilbert curve. All optimized results achieve $R^2\ge 0.9988$. Iteration analysis establishes minimum requirements for reliable measurement, with convergence by level 6+ for the Koch curve and level 3+ for the Sierpinski triangle. Each fractal type exhibits optimal iteration ranges where authentic scaling behavior emerges before discretization artifacts dominate, challenging the assumption that higher iteration levels imply more accurate results. Application to a Rayleigh–Taylor instability interface (D = 1.835 ± 0.0037) demonstrates effectiveness for physical fractal systems where theoretical dimensions are unknown. This work provides objective, automated fractal dimension measurement with comprehensive validation establishing practical guidelines for mathematical and real-world fractal analysis. The sliding window approach eliminates subjective scaling region selection through systematic evaluation of all possible linear regression windows, enabling measurements suitable for automated analysis workflows. Full article

Drilling fluid loss is a common and complex downhole problem that occurs during drilling in deep fractured formations, which has a significant negative impact on the exploration and development of oil and gas resources. Establishing a drilling fluid loss model for the quantitative analysis of drilling fluid loss is the most effective method for the diagnosis of drilling fluid loss, which provides a favorable basis for the formulation of drilling fluid loss control measures, including the information on thief zone location, loss type, and the size of loss channels. The previous loss model assumes that the drilling fluid is driven by constant flow or pressure at the fracture inlet. However, drilling fluid loss is a complex physical process in the coupled wellbore circulation system. The lost drilling fluid is driven by dynamic bottomhole pressure (BHP) during the drilling process. The use of a single-phase model to describe drilling fluids ignores the influence of solid-phase particles in the drilling fluid system on its rheological properties. This paper aims to model drilling fluid loss in the coupled wellbore–-fracture system based on the two-phase flow model. It focuses on the effects of well depth, drilling pumping rate, drilling fluid density, viscosity, fracture geometric parameters, and their morphology on loss during the drilling fluid circulation process. Numerical discrete equations are derived using the finite volume method and the “upwind” scheme. The correctness of the model is verified by published literature data and experimental data. The results show that the loss model without considering the circulation of drilling fluid underestimates the extent of drilling fluid loss. The presence of annular pressure loss in the circulation of drilling fluid will lead to an increase in BHP, resulting in more serious loss. Full article

To address the issues of quadrature component attenuation and signal-to-noise ratio (SNR) degradation caused by carrier phase delay in Phase-Generated Carrier (PGC) demodulation, this paper proposes a phase delay compensation method based on sampling-point shift pre-calibration. By establishing a discrete phase offset model, we derive the mathematical relationship between sampling point shift and carrier cycle duration, and introduce a compensation mechanism that adjusts the starting point of the sampling sequence to achieve carrier phase pre-alignment. Theoretical analysis demonstrates that this method restricts the residual phase error to within Δθ_max = πf₀/f_s, thereby fundamentally avoiding the denominator-zero problem inherent in traditional compensation algorithms when θ approaches 45°. Experimental validation using an Extrinsic Fabry–Perot Interferometric (EFPI) ultrasonic sensor shows that, at a sampling rate of 10 MS/s, the proposed pre-alignment algorithm improves the minimum demodulation SNR by 35 dB and reduces phase fluctuation error to 2% of that of conventional methods. Notably, in 1100 consecutive measurements, the proposed method eliminates demodulation failures at critical phase points (e.g., π/4, π/2), which are commonly problematic in traditional techniques. By performing phase pre-compensation at the signal acquisition level, this method significantly enhances the long-term measurement stability of interferometric fiber-optic sensors in complex environments while maintaining the existing PGC demodulation architecture. Full article

The discrete element method (DEM) is widely employed in various fields for analyzing rock and soil movement. However, the traditional DEM involves a large number of calculations, which leads to reduced computational efficiency. Deep-learning presents a promising solution to this issue by utilizing neural networks to approximate DEM calculations. Moreover, the consistency between the arrangement of discrete particles and the structure presented in graph neural networks further reinforces the validity of this approach. In this study, we propose a novel model called Graph-DEM based on graph neural networks, which significantly enhances the speed of DEM calculations. Meanwhile, our model demonstrates the capability of adaptive learning across various constitutive relationships. To evaluate the model’s performance, we measure particle-trajectory prediction accuracy on three scenario datasets (dynamic, static, and principle experiments) and on two public datasets. In addition, the computational efficiency of the Graph-DEM model are compared against the traditional DEM. The experimental results demonstrate the superiority of the model in terms of accuracy, universality, and computational efficiency. Full article

The presence of discontinuities in fractured reservoirs, their mechanical and physical characteristics, and fluid flow through them are important factors influencing their effective large-scale properties. In this paper, the variation of elastic moduli in a block measuring 100 × 100 × 100 m³ that hosts a discrete fracture network (DFN) is evaluated using the discrete element method (DEM). Fractures are characterised by (1) constant, (2) interlocked, and (3) mismatched stiffness properties. First, three uniaxial verification tests were performed on a block (1 × 1 × 2 m³) containing a circular finite fracture (diameter = 0.5 m) to validate the developed numerical algorithm that implements the three fracture stiffnesses mentioned above. The validated algorithms were generalised to fractures in a DFN embedded in a 100 × 100 × 100 m³ rock block that reproduces in situ conditions at various depths (4.7 km, 2.3 km, and 0.5 km) of the Soultz-sous-Forêts geothermal site. The effective elastic moduli of this large-scale rock mass were then numerically evaluated through a triaxial loading scenario by comparing to the numerically evaluated stress field using the DFN, with the stress field computed using an effective homogeneous elastic block. Based on the results obtained, we evaluate the influence of fracture interaction and stress perturbation around fractures on the effective elastic moduli and subsequently on the large-scale P-wave velocity. The numerical results differ from the elastic moduli of the rock matrix at higher fracture densities, unlike the other methods. Additionally, the effect of nonlinear fracture stiffness is reduced by increasing the depth or stress level in both the numerical and semi-analytical methods. Full article

We propose a general and experimentally accessible framework to quantify transition timing in discrete quantum systems via the time-of-flow (TF) distribution. Defined from the rate of population change in a target state, the TF distribution can be reconstructed through repeated projective measurements at discrete times on independently prepared systems, thus avoiding Zeno inhibition. In monotonic regimes, it admits a clear interpretation as a time-of-arrival (TOA) or time-of-departure (TOD) distribution. We apply this approach to optimize time-dependent Hamiltonians, analyze shortcut-to-adiabaticity (STA) protocols, study non-adiabatic features in the dynamics of a three-level time-dependent detuning model, and derive a transition-based quantum speed limit (TF-QSL) for both closed and open quantum systems. We also establish a lower bound on temporal uncertainty and examine decoherence effects, demonstrating the versatility of the TF framework for quantum control and diagnostics. This method provides both a conceptual tool and an experimental protocol for probing and engineering quantum dynamics in discrete-state platforms. Full article

Compared with a traditional distribution transformer with silicon steel sheet as the core material, the no-load loss of an amorphous alloy transformer is greatly reduced due to its core using iron-based amorphous metal material, which has been applied in many countries. However, due to the brittleness of its amorphous strip, an amorphous alloy transformer is prone to debris in the process of production, transportation and work. The charge and migration characteristics of these debris will reduce the insulation strength of the transformer oil and endanger the safe operation of the transformer. In this paper, a charge measurement platform of amorphous alloy debris is set up, and the charging characteristics of amorphous alloy core debris under different flow velocities, particle radius and plate electric field strength are obtained. The results show that with an increase in pipeline flow velocity, the charge-to-mass ratio of the debris increases first and then decreases. With an increase in electric field strength, the charge-to-mass ratio of the debris increases; with an increase in the number of debris, the charge-to-mass ratio of the debris decreases; with an increase in debris size, the charge-to-mass ratio of the debris increases. The debris with different charge-to-mass ratios and types obtained from the above experiments are added to the simulation model of an amorphous alloy transformer. The lattice Boltzmann method (LBM) coupled with the discrete element method (DEM) is used to simulate the migration process of metal particles in an amorphous alloy transformer under the combined action of gravity, buoyancy, electric field force and oil flow resistance under electrothermal excitation boundary. The results show that the trajectory of the debris is related to the initial position, electric field strength and oil flow velocity. The LBM–DEM calculation model and charge measurement platform proposed in this paper can provide a reference for studying the charge mechanism and migration characteristics of amorphous alloy core debris in insulating oil. Full article

When the quantities of interest remain underdetermined a posteriori, we would like to draw inferences for at least one particular solution. Can we do so in a Bayesian way? What is a probability distribution over an underdetermined quantity? How do we get a posterior for one particular solution from a posterior for infinitely many underdetermined solutions? Guided by discrete invariant underdetermined ill-posed inverse problems, we find that a probability distribution over an underdetermined quantity is non-absolutely continuous, partially improper with respect to the initial reference measure but proper with respect to its restriction to its support. Thus, it is necessary and sufficient to choose the prior restricted reference measure to assign partially improper priors using e.g., the principle of maximum entropy and the posterior restricted reference measure to obtain the proper posterior for one particular solution. We can then work with underdetermined models like Hoeffding–Sobol expansions seamlessly, especially to effectively counter the curse of dimensionality within discrete nonparametric inverse problems. We show Singular Bayesian Inference (SBI) at work in an advanced Bayesian optimization application: dynamic pricing. Such a nice generalization of Bayesian–maxentropic inference could motivate many theoretical and practical developments. Full article

Summer overheating has emerged as the primary comfort challenge in rural housing under a warming climate. Conventional retrofit measures are often infeasible due to high costs and limited technical capacity. This study investigates how spatial configuration influences summer thermal conditions while keeping envelope materials constant, focusing on rural dwellings in the Guanzhong region of China. Three representative prototypes are analyzed: the traditional courtyard type, the deep continuation type, and the progressive combined type. Thermal performance is evaluated using the Predicted Mean Vote (PMV) index through Ladybug and Honeybee simulations based on long-term meteorological data, and validated with multi-room field measurements. Two parametric analyses further test the effects of window opening rates (0.2–0.5) and room width-to-depth ratios (1:1–1:2.5). Results indicate that courtyards and galleries function as transitional zones, creating discrete yet connected thermal units and reducing PMV near edges. Second-floor rooms show a ventilation advantage with an average PMV reduction of 0.08. Enlarging window openings improves PMV only when cross-ventilation paths exist, while ratios wider than 1:2 raise PMV and slightly influence adjacent rooms. Field measurements confirm these simulated patterns. Cross-regional comparisons with Argentina, Brazil, and Japan further demonstrate that once the envelope is adequate, the spatial organization becomes the key driver of summer comfort. The study highlights practical, low-cost strategies such as reallocating high-use rooms to favorable zones, adding targeted shading, and ventilation, and introducing lightweight spatial interventions. These measures enhance summer comfort without invasive construction. Full article

The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM leads to higher throughput when the PAs are operating in their linear region, which is mostly the case for cell-interior users, whereas DFT-S-OFDM is more appealing when PAs are exhibiting non-linear behavior, which is associated with cell-edge users. Therefore, existing waveform selection solutions rely on predefined signal-to-noise ratio (SNR) thresholds that are computed offline. However, the varying user and channel dynamics, as well as their interactions with power control, require an adaptable threshold selection mechanism. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions. In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users’ service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. In addition, the solution accounts for the switching cost, which is related to the interruption of the communication after every switch due to implementation issues, which has not been considered in existing solutions. Results show that our proposed scheme achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput. Full article

This work introduces a unified Artificial Intelligence-based framework for the optimal tuning of gains in a neural discrete-time sliding mode controller (SMC) applied to a two-degree-of-freedom robotic manipulator. The novelty lies in combining surrogate-assisted optimization with normalized search spaces to enable a fair comparative analysis of three metaheuristic strategies: Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Genetic Algorithms (GAs). The manipulator dynamics are identified via a discrete-time recurrent high-order neural network (NN) trained online using an Extended Kalman Filter with adaptive noise covariance updates, allowing the model to accurately capture unmodeled dynamics, nonlinearities, parametric variations, and process/measurement noise. This neural representation serves as the predictive plant for the discrete-time SMC, enabling precise control of joint angular positions under sinusoidal phase-shifted references. To construct the optimization dataset, MATLAB^® simulations sweep the controller gains $(k_0^{*},k_1^{*})$ over a bounded physical domain, logging steady-state tracking errors. These are normalized to mitigate scaling effects and improve convergence stability. Optimization is executed in Python^® using integrated scikit-learn, DEAP, and scikit-optimize routines. Simulation results reveal that all three algorithms reach high-performance gain configurations. Here, the combined cost is the normalized aggregate objective $\tilde{J}$ constructed from the steady-state tracking errors of both joints. Under identical experimental conditions (shared data loading/normalization and a single Python pipeline), PSO attains the lowest error in Joint 1 ($7.36\times {10}^{-5}$ rad) with the shortest runtime (23.44 s); GA yields the lowest error in Joint 2 ($8.18\times {10}^{-3}$ rad) at higher computational expense (≈69.7 s including refinement); and BO is competitive in both joints ($7.81\times {10}^{-5}$ rad, $8.39\times {10}^{-3}$ rad) with a runtime comparable to PSO (23.65 s) while using only 50 evaluations. Full article

This study analyzes the determinants of impulsive buying behavior in low-cost retail stores in Metropolitan Lima, with particular emphasis on psychological, economic, social, and personal factors. The research draws on survey data collected from 380 consumers aged 18 to 39 belonging to socioeconomic levels B and C who had made recent purchases in discount stores. Data were gathered through a structured and validated instrument and examined using ordinal logistic regression and multinomial discrete choice models. The dependent variable, impulsive buying, was measured through three dimensions—remembered, suggested, and pure—while explanatory variables were classified into low, medium, and high categories. The empirical results demonstrate that psychological and economic dimensions exert a strong and positive influence on impulsive consumption, whereas social factors show no significant effect. Personal factors, though less consistent, also reveal a positive role. Diagnostic tests, including robustness checks, confirm the stability of the estimations. Beyond its marketing relevance, the findings contribute to the sustainability debate by highlighting how understanding impulsive behavior can guide the design of retail strategies that foster responsible consumption, reduce the risks of over-spending in vulnerable households, and support inclusive and resilient consumption practices. Thus, the study links the analysis of changing consumption patterns with broader sustainability goals in emerging urban contexts. Full article