Abstract This work investigates the use of physics-informed geometric operators in tandem with latent variable models to support surrogate learning, dimensionality reduction, and generative design of airfoils. A baseline dataset was constructed via a NURBS-based airfoil parametric model with physically interpretable design variables, and discretized using two schemes: a uniform parametric and a uniform arc-length sampling. A hybrid Variational AutoEncoder (VAE) with the addition of convolutional layers was developed and iteratively refined to evade shape invalidities. A systematic comparison of reconstruction accuracy, robustness, and diversity showed that a loss function based on the mean sum of squared distances had the best performance with sufficient stability during model optimization. However, this can be only established when the reconstruction and Kullback-Leibler terms in the β-VAE objective function are weighted via an appropriately selected β value. Additionally, augmenting geometry with physics-correlated high-level descriptors, such as geometric moments, further improves latent-space quality. Among the tested operators, third-order geometric moments yielded the most consistent robustness gains. Discretization and achieved diversity proved to be linked, with uniform arc-length spacing achieving the best reconstruction accuracy, but with many near-identical designs that degraded the resulting diversity. In contrast, uniform parametric spacing exhibited higher diversity without the need for any special treatment of design distributions and diversity quantification measures. This study consolidates practical guidelines on architecture, loss-function scaling, physics-informed features, and quantification protocols for reliable, data-efficient airfoil generative design with VAEs.

This paper proposes a spectrally optimized, multichannel filter-bank channelizer with time-multiplexed hardware reuse and block-based [Formula: see text] per-sample FFT complexity based on a unified framework for deep-space and satellite-onboard digital signal processing. This framework achieves fine-grained channelization using a single prototype filter with substantially reduced memory and computational overhead. The proposed approach derives all analysis and synthesis subband filters from a single prototype FIR filter using dyadic dilation and structured cascading. This eliminates the need for independently designed subfilters, achieves uniform channel spacing with consistent group delay, and reduces spectral distortion across all bands. A multi-objective cost-function-based prototype filter was obtained for the multilevel tree-structured uniform filter bank by minimizing transition-band energy, stop-band energy, and hardware complexity under QPSK recovery constraints. Strong channel isolation, a sharp roll-off, and reliable reconstruction at every level of the hierarchy are demonstrated by comprehensive simulation-based spectrum analysis. These channelizers are well-suited for onboard digital processors in next-generation satellite and deep-space missions. Simulation results demonstrate an average adjacent-channel isolation exceeding 98dB (with a worst-case value of 79.6dB) for a 64-channel 10kHz frequency grid, while maintaining the QPSK error vector magnitude (EVM) below 10%. Fixed-point simulations and FPGA synthesis results confirm that the architecture maintains communication-relevant isolation (>60 dB worst-case) under practical finite word-length constraints, demonstrating realistic hardware feasibility.

Motivation:Storing millions of sequence alignments from large-scale genomic comparisons requires efficient compression methods. While fixed-size alignment encodings offer uniform spacing and bounded reconstruction cost, they cannot adapt to variable alignment complexity across sequences, missing compression opportunities in conserved regions.Results:We present adaptive tracepoints, a complexity-aware alignment encoding that segments alignments using configurable complexity metrics (edit distance or diagonal distance) rather than fixed intervals. Segments are bounded by either the number of differences or the deviation from the main diagonal, adapting to local alignment characteristics. Reconstruction guarantees that alignments maintain identical or improved alignment scores. We validate the correctness of our method on simulated and real pangenomes with varying lengths and divergences. Diagonal-bounded tracepoints achieve 10.5–13.7× better compression than fixed-length encodings () on simulated long sequence alignments (100 Kb), while edit-bounded tracepoints provide a tunable trade-off between compression and reconstruction cost, approaching diagonal-bounded compression at higher thresholds with substantially lower memory and runtime. On real pangenomes (390M alignments), these methods compress alignments by 23–139× relative to uncompressed representations, with no score degradation and reconstruction time linear in alignment length.

We propose new goodness-of-fit tests for exponentiality based on progressively Type-II censored data. These tests utilize scale-invariant statistics obtained from the Mahalanobis norm of normalized order statistics, leading to three test statistics, corresponding to L 2 -, L 1 -, and L ∞ -norms of centered uniform spacings. Exact and asymptotic distributions of these statistics are presented. A power study evaluates the proposed tests against existing benchmarks across various alternative distributions and censoring plans, demonstrating superior performance in cases with small and moderate sample sizes. Furthermore, we extend the methodology to approximate goodness-of-fit tests for Weibull distributions via power transformation, ensuring robustness w.r.t. the approximated significance level under unknown shape parameters. An illustrative data example confirms the practical applicability of our tests. Our findings highlight the potential for further extending goodness-of-fit tests under progressive Type-II censoring to other null distributions.

The present investigation was carried out during 2024 at the Department of Horticulture, Babasaheb Bhimrao Ambedkar University, Lucknow, to evaluate the influence of different levels of nitrogen, phosphorus and potassium (NPK) on the vegetative growth of dragon fruit (Hylocereus costaricensis) under subtropical climatic conditions. The experiment was conducted in a Randomized Block Design (RBD) with 8 treatments viz. T1 (N-850, P-950, K-475), T2 (N-800, P-900, K-450), T3 (N-750, P-825, K-425), T4 (N-700, P-800, K-400), T5 (N-650, P-750, K-375), T6 (N-600, P-700, K-350), T7 (N-550, P-650, K-325) and T8 (Control) and 3 replications during the year 2024, focusing on the application of varying doses of Nitrogen, Phosphorus and Potassium via Urea, Single Super Phosphate, and Muriate of Potash, ensuring uniform plant health and spacing throughout. Observations on morphological and physiological parameters such as plant length, stem circumference, number of spines per areole, number of segments per plant, and chlorophyll content were recorded at regular intervals up to 225 days after treatment. Results revealed significant differences among treatments, with T2 (N-800, P-900, K-450 g/pole/year) exhibiting maximum improvement in plant length (70 cm), stem circumference (1.62 cm), number of segments per plant (2.33), and highest chlorophyll content (0.76 mg/g). The superior response under T2 treatment is attributed to balanced nutrient availability that enhanced photosynthetic efficiency and vegetative growth. The findings suggest that applying NPK at 800:900:450 g/pole/year in six split doses (April, May, June, August, September and November) through soil application optimally supports growth and physiological performance of dragon fruit under the subtropical conditions of Lucknow. This study emphasizes the importance of region-specific nutrient management to improve productivity and sustainability of dragon fruit cultivation in North India.

The pattern synthesis technique of antenna arrays has a wide range of applications in radar and communication systems, which is an important research direction in the field of smart antennas. In order to improve the optimization performance of pattern synthesis of linear antenna arrays, an optimization method based on the Modified RIME Optimization Algorithm (MRIME) is proposed. RIME is a new heuristic algorithm inspired by the condensation process of frost ice in nature, which has a unique update mechanism with strong convergence as well as randomization. In the present work, MRIME is used for optimal pattern synthesis of a linear antenna array (LAA). One part of the present study is to optimize the side lobe level by optimizing the antenna current amplitude while maintaining a uniform spacing; the other part is to optimize the antenna position while assuming a uniform excitation in the synthesis of a sparse LAA, where constraints are imposed on the spacing of the array elements and aperture length, and suppression of the side lobe level with the position of the zeros in the specified direction is also achieved. In this article, simulation experiments for uniform linear arrays as well as sparse linear arrays are presented in detail, and the results show that the MRIME optimization algorithm outperforms most of the existing evolutionary classes of optimization algorithms in optimizing the side lobe level. This illustrates the potential of utilizing the MRIME optimization algorithm for antenna arrays and various other electromagnetism-related challenges.

A high-power distributed feedback laser array suitable for optical I/O applications is demonstrated. The epitaxial structure incorporates a high-refractive-index layer in the N-doped region to improve single-transverse-mode characteristics and reduce internal loss, enabling single-mode operation at a ridge width of 5 µm with an internal loss of 3 cm-1. An asymmetric π-phase-shift, implemented using the reconstruction-equivalent chirp technique, ensures high single-mode yield. The fabricated array achieves output powers above 300 mW per channel at 25 °C with 800 mA injection current and exhibits uniform channel spacing with side mode suppression ratios exceeding 50 dB. Stable single-longitudinal-mode operation is maintained without mode hopping over a broad current and temperature tuning range. Under simultaneous operation at an injection current of 300 mA per channel, all channels of the laser array deliver output powers exceeding 100 mW, with an average wavelength deviation of 0.0692 nm. The devices further show a minimum Lorentzian linewidth of 300 kHz, and relative intensity noise of -154 dB/Hz, demonstrating strong potential for high-speed optical interconnects.

This paper introduces a novel fixed-time control framework for simultaneous target tracking and circumnavigation in a multi-UAV system, using only bearing measurements. The proposed approach enables the UAV swarm to rapidly form and maintain a rigid circular formation around a moving target, with continuous tracking and uniform angular spacing between agents. A key innovation is the development of a distributed fixed-time estimator, which allows each UAV to localize the target within a fixed time using only local bearing information and limited inter-agent communication. Building on this estimator, a hierarchical control strategy is designed, where a leader UAV guides the formation while followers achieve and maintain uniform distribution along the orbit. The fixed-time stability of the overall closed-loop system is rigorously established through Lyapunov analysis. Numerical simulations confirm the fixed-time convergence of the algorithm. Compared to an existing asymptotic-convergence benchmark, the proposed approach achieves significantly faster and deterministic convergence, with improved formation accuracy.

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design.

Rice is a staple food for more than half of the global population and plays a vital role in sustaining millions worldwide. Precision planting requires accurate seed dispensing to achieve uniform intra-row spacing and minimize wastage under variable soil and operating conditions. This study developed a fuzzy logic controller optimized with Particle Swarm Optimization (PSO) to adaptively regulate seed metering. Soil moisture and forward speed were fuzzified into linguistic variables, while PSO tuned membership function parameters and output scaling to minimize spacing error, seed wastage, and energy use. Simulation results showed that the PSO–fuzzy controller reduced mean absolute error (MAE) by ~60% and spacing variability by ~65% compared to a fixed-rate baseline, while keeping energy consumption within ±5%. Bench tests with an Arduino prototype confirmed simulation trends, demonstrating significant improvements in uniformity without added energy cost. The findings validate PSO-optimized fuzzy control as a robust and practical strategy for precision rice planting

Visualization grammars from ggplot2 to Vega-Lite are based on the Grammar of Graphics (GoG), our most comprehensive formal theory of visualization. The GoG helped expand the expressive gamut of visualization by moving beyond fixed chart types and towards a design space of composable operators. Yet, the resultant design space has surprising limitations, inconsistencies, and cliffs - even seemingly simple charts like mosaics, waffles, and ribbons fall out of scope of most GoG implementations. To author such charts, visualization designers must either rely on overburdened grammar developers to implement purpose-built mark types (thus reintroducing the issues of typologies) or drop to lower-level frameworks. In response, we present GoFish: a declarative visualization grammar that formalizes Gestalt principles (e.g., uniform spacing, containment, and connection) that have heretofore been complected in GoG constructs. These graphical operators achieve greater expressive power than their predecessors by enabling recursive composition: they can be nested and overlapped arbitrarily. Through a diverse example gallery, we demonstrate how graphical operators free users to arrange shapes in many different ways while retaining the benefits of high-level grammars like scale resolution and coordinate transform management. Recursive composition naturally yields an infinite design space that blurs the boundary between an expressive, low-level grammar and a concise, high-level one. In doing so, we point towards an updated theory of visualization, one that is open to an innumerable space of graphic representations instead of limited to a fixed set of "good" designs.

Effect of cone-opponent modulation on visual discomfort from chromatic flicker.

The accuracy of seed placement is critical for achieving uniform plant spacing in the field. During seeding operations, the final seed placement is influenced by multiple factors. Irregular bouncing and rolling caused by seeds colliding with the walls of the seed guide tube during discharge is a key factor leading to low seeding uniformity. To investigate the optimal structural parameters and operational parameter ranges for the seed movement constraint device designed in this study, a kinematic analysis of its operational process was conducted. This analysis evaluated the device's operational effectiveness and the uniformity of seed placement. A multi-factor experiment was designed using central composite design and seed placement/posture detection technology. The experimental factors included buffer plate length, horizontal installation position of the guide plate, and working speed of the seed distributor. The experimental indicators were pass rate and coefficient of variation. Experimental results were processed and optimized using Design Expert-13 software. The most ideal combination was determined to be: a buffer plate length of 87.55 mm, and a guide plate horizontal installation position of 22.57 mm. At these settings, seed placement uniformity and stability were optimal, with a seed placement qualification index of 93.431% and a coefficient of variation of 9.324%. This confirms the rationality of the designed seed movement restraint device. 摘

Cotton is a globally important economic crop and the foundational raw material for the textile industry, and the planting pattern plays a crucial role in determining both the yield and quality of cotton. The results demonstrated that compared with the use of the traditional wide–narrow row (66 + 10 cm) planting pattern, the use of uniform row spacing significantly increased cotton yield (pooled effect size = 0.09, p < 0.05; average yield increase of 9.41%) when interrow distances were homogenized to optimize the population canopy structure. Moreover, this approach comprehensively improved fiber quality, yielding an average increase of 2.02% in cotton fiber length (pooled effect size = 0.02, p < 0.001), an average increase of 8.32% in cotton breaking tenacity (pooled effect size = 0.08, p < 0.001), and an average decrease of 6.76% in the cotton micronaire value (pooled effect size = −0.07, p < 0.001). This study confirms that the use of a uniform row spacing planting pattern is a key agronomic measure for simultaneously achieving high yield and superior fiber quality in cotton, providing both theoretical and practical insights into the optimization of cotton cultivation patterns.

The Gaussian phase approximation (GPA) underlies many standard diffusion magnetic resonance (MR) signal models, yet its validity is rarely scrutinized. Here, we assess the validity of the GPA by analytically deriving the excess phase kurtosis , where is the cumulant of the accumulated phase distribution due to motion. We consider the signal behavior of the spin echo with constant gradient amplitude and echo time in several one-dimensional model systems: (1) a stationary Poisson pore-hopping model with uniform pore spacing and mean inter-hop time ; (2) a trapped-release model in which spin isochromats are initially immobilized and then released with diffusivity following an exponentially-distributed release time, ; and (3) restricted diffusion in a domain of length . To our knowledge, this is among the first systematic analytical treatments of spin echo phase kurtosis without assuming Gaussian compartments or infinitesimally short gradient pulses. In the pore-hopping system, , inversely proportional to the mean hop number, . In the trapped-release system, is positive and decreases roughly log-linearly with , where is the average release time. For restriction, vanishes at small and large , but has complicated intermediate behavior. There is a negative peak at and a positive peak at . Monte Carlo simulations are included to validate the analytical findings. Overall, we find that the GPA does not generally hold for these systems under moderate experimental conditions, i.e., . These results suggest that signal models reliant on the GPA must be carefully examined, particularly for strong, extended gradients and in media with complex kinetics, morphology, and/or microstructure.

Coal mining plays a crucial role in global energy production and regional development, including in Aceh, Indonesia, where operations such as PT. Mifa Bersaudara contribute to energy supply and employment. This study evaluated the revegetation success of three fast-growing species (Falcataria moluccana, Enterolobium cyclocarpum, and Gmelina arborea) in post-mining reclamation areas planted in 2017 and 2018. Planting was performed at a uniform spacing of 4 × 4 m across 27 plots (8 in 2017, 19 in 2018), each measuring 20 × 20 m and containing 25 trees per plot, with a sampling intensity of 3%. Analyzed using descriptive statistics, two-way ANOVA with Tukey HSD, t-test, and Wilcoxon rank-sum tests. Results showed F. moluccana achieved the highest height (17.53 ± 24.75 m in 2017; 10.73 ± 2.88 m in 2018) and diameter (22.25 ± 8.12 cm in 2017; 31.84 ± 11.59 cm in 2018), followed by E. cyclocarpum, while G. arborea grew more slowly. Survival rates of the planted trees remained relatively high in both planting years, with 80.0 ± 19.0% in 2017 and 70.2 ± 16.9% in 2018, and statistical analysis indicated no significant difference between the two years. Fast-growing species enhanced canopy cover, soil stabilization, and microhabitats, supporting slower-growing species, and their combination is recommended to optimize short-term revegetation success and long-term ecosystem resilience in degraded areas.

ABSTRACT This paper addresses the vision‐based leader–follower formation control problem for wheeled mobile robots, considering explicit visibility constraints imposed by the limited field‐of‐view of onboard cameras. The system relies on vision‐based sensing for direct inter‐robot detection and localisation and models each robot as a nonlinear kinematic agent. To ensure that each follower remains within the leader's visible region, the follower's state is rigorously constrained within the visibility set. A Lyapunov‐based nonlinear feedback controller is designed to ensure that formation tracking errors converge within a prescribed time. This also guarantees that the system's state remains within the intersection of all visibility‐constrained sets throughout the motion. The control law maintains a circular formation with uniform spacing between robots, even as the leader moves along a predefined trajectory at constant velocity. Rigorous stability analysis confirms that the proposed controller meets both safety (persistent visibility) and formation‐keeping objectives, despite bounded actuation and sensing limitations. The framework is validated through extensive hardware experiments using the Quanser QBot platform, demonstrating real‐time convergence, robustness to environmental disturbances and scalability to larger robot teams.

Abstract In contact resonance atomic force microscopy (CR-AFM), the CR frequency and quality factor, which exhibit intrinsic correlations with the sample material’s elastic modulus and loss modulus, constitute key observation parameters for nanomechanical characterization. However, conventional imaging methods such as pixel-by-pixel CR spectrum sweeping, band excitation, uniform frequency spacing sequential excitation imaging, and dual AC resonance tracking are fundamentally constrained by the trade-off between imaging efficiency and accuracy. To overcome this limitation, we propose a Gaussian process (GP)-based adaptive excitation frequency selection strategy that accelerates sequential imaging while maintaining high reconstruction accuracy for CR frequency and quality factor images. At the single-pixel level, the optimal excitation frequency is determined through GP-based spectrum prediction, incorporating both mean and variance estimates. To account for spatial heterogeneity in the resonance responses, K-clustering algorithms are implemented to optimize subsequent excitation frequency selection across disparate sampling pixels. Simulations and experimental results demonstrate that this strategy achieves approximately fourfold efficiency improvement compared to dense equidistant frequency excitation while preserving the accuracy of evaluated CR parameters. The proposed strategy exhibits potential for extensive applications in the analysis of high-dimensional spectral data cubes and holds promise for rapid nanomechanical mapping, as well as subsurface imaging via CR-AFM.

By grafting polymer chains to nanoparticles, one can create inorganic-organic hybrid materials whose structure and properties can be tuned by controlling graft density, chain length, and other molecular features. These polymer-grafted nanoparticles (PGNs) are usually synthesized and processed in solution, and then are often deposited and dried to create films or bulk materials with uniform spacings of particles in a mechanically robust matrix. Understanding interparticle interactions in solvated PGN systems is crucial to controlling the structure and properties of PGNs during and after deposition. Here, we use molecular dynamics simulations with a generic coarse-grained model to study polymer conformations and effective interparticle interactions of PGN systems in implicit solvent, for systems at two graft densities and a range of solvent strengths. As expected, higher graft density and good solvent correspond to more extended graft chain conformations, which are analyzed via mean-squared internal distances. We calculate the potentials of mean force between pairs of PGNs and find a relatively sudden onset of a deep attractive well with increasing solvent strength, which occurs at nearly the same solvent strength regardless of graft density. The implications of our results for solution phase behavior are discussed.

ABSTRACT Terahertz (THz) dual‐comb spectroscopy has been revolutionized by adaptive sampling, which enhances measurement precision and relaxes the requirement for ultra‐stable lasers. However, conventional adaptive clock schemes, reliant on analog electronics, are limited by bandwidth and inherent electronic/thermal noise. To overcome these limitations, we introduce a fully digital adaptive clock implemented on a field‐programmable gate array platform. Following time correction in sampling, we achieved a THz dual‐comb spectroscopy system spanning 1.6 THz with 24,191 resolved comb lines and a uniform spacing of 66.14 MHz. Our system demonstrates remarkable pulse‐period stability (1.15 × 10 −10 at 32 s integration time) and a comb‐line resolution at the kHz level. This all‐digital solution replaces complicated analog circuitry with a compact and reconfigurable digital architecture, enabling portable, high‐precision THz spectrometers capable of real‐time molecular fingerprints in field applications while maintaining laboratory‐grade accuracy.