Uniform Input Research Articles

A spatiotemporal model for the effects of toxicants on the competitive dynamics of aquatic species

In this paper, we develop a reaction–diffusion model with negative toxicant–taxis that incorporates spatiotemporally inhomogeneous toxicant input to investigate the impact of toxicants on the competitive dynamics of two species in a polluted aquatic environment. Here the negative toxicant–taxis models the evasive movement of avoiding toxicants by species. We establish the global well-posedness of the model, analyze the existence and stability of spatially homogeneous steady states, and derive sufficient conditions for species extinction and coexistence. Through linear stability analysis, we identify sufficient conditions on model parameters that destabilize spatially homogeneous steady states under spatiotemporally uniform toxicant input. Numerical experiments reveal the influence of key toxicant-related factors (input rate, taxis intensity, and diffusivity) on competition outcomes and species distributions. Notably, strong negative toxicant–taxis can induce spatial aggregation and segregation patterns between the species and the toxicant under uniform toxicant input. Our findings suggest that toxicant–taxis may promote population persistence and coexistence, particularly when the toxicant input is not uniform in space and time and the toxicant does not diffuse fast (i.e. weak diffusivity). However, strong toxicant diffusion can diminish the impact of taxis, adversely affecting population persistence and species coexistence.

Analytical generalized thermal resistance model for conductive heat transfer in pebble beds based on heat flux weighted temperature difference

A novel model for inter-particle contact thermal resistance based on analytical solutions is proposed. By applying the concept of heat flux weighted temperature difference in thermal resistance modeling for the first time, this model offers greater physical significance than traditional thermal resistance models. It effectively describes the dissipation in the heat transfer process and the influence of particle radius on thermal resistance, making it a generalized thermal resistance model suitable for multi-dimensional systems. The impact of applying different levels of uniform heat flux boundary conditions on thermal resistance is analyzed from the perspective of energy dissipation, revealing that a more uniform heat flux input results in lower thermal resistance. The effects of contact radius and particle size on the dimensionless generalized thermal resistance of inter-particle contact were studied, showing that the dimensionless resistance increases with larger particle contact radius and smaller particle size. Using the thermal discrete element method, the thermal resistance model with uniform heat flux density boundary conditions was applied to calculate the effective thermal conductivity of the pebble bed. Considering the distribution of contact radius, differences in effective thermal conductivity at various heights of the pebble bed due to different contact radii were examined and compared with the traditional fixed-coefficient contact resistance model. It was found that the difference between the two models decreases as the contact radius decreases. Finally, multiple sets of fixed contact radii were established under three different particle sizes, revealing that as the contact radius increases, the deviation between the new generalized resistance model and the traditional fixed-coefficient model rises from 8.18 % to 14.64 % for different particle radii.

Seismic Response Analysis of Hydraulic Tunnels Under the Combined Effects of Fault Dislocation and Non-Uniform Seismic Excitation

Hydraulic tunnels are prone to pass through faults and high-intensity earthquake areas, which will cause serious damage under fault dislocation and earthquake action. Fault dislocation and seismic excitation are often considered separately in previous studies. For tectonic earthquakes with higher frequency in seismic phenomena, fault dislocation and ground motion are often associated, and fault dislocation is usually the cause of earthquake occurrence, so it is limiting to consider the two separately. Moreover, strong earthquake records show that there will be significant differences in the mainland vibration within 50 m. The uniform ground motion inputs in previous studies are not suitable for long hydraulic tunnels. This paper begins with the simulation of non-uniform stochastic seismic excitations that consider spatial correlation. Based on stochastic vibration theory, multiple multi-point acceleration time-history curves that can reflect traveling wave effects, coherence effects, attenuation effects, and non-stationary characteristics are synthesized. Furthermore, a fault velocity function is introduced to account for the velocity effect of fault dislocation. Finally, numerical analyses of the response patterns of the tunnel lining under four different conditions are conducted based on an actual engineering project. The results indicate the following: (a) the maximum lining response values occur under the combined effects of fault dislocation and non-uniform seismic excitation, indicating its importance in the seismic resistance of the tunnel. (b) Compared to uniform seismic excitation, the peak displacement of the tunnel under non-uniform seismic excitation increases by up to 6.42%, and the peak maximum principal stress increases by up to 28%. Additionally, longer tunnels exhibit a noticeable delay effect in axial deformation during an earthquake. (c) Under non-uniform seismic excitation, the larger the fault dislocation magnitude, the greater the peak displacement and peak maximum principal stress at the monitoring points of the lining. The simulation results show that the extreme response values primarily occur at the crown and haunches of the tunnel, which require special attention. The research can provide valuable references for the seismic design of cross-fault tunnels.

Competitive Data-Structure Dynamization

Data-structure dynamization is a general approach for making static data structures dynamic. It is used extensively in geometric settings and in the guise of so-called merge (or compaction) policies in big-data databases such as LevelDB and Google Bigtable. Previous theoretical work is based on worst-case analyses for uniform inputs—insertions of one item at a time and non-varying read rate. In practice, merge policies must not only handle batch insertions and varying read/write ratios, they can take advantage of such non-uniformity to reduce cost on a per-input basis. To model this, we initiate the study of data-structure dynamization through the lens of competitive analysis via two new online set-cover problems. For each, the input is a sequence of disjoint sets of weighted items. The sets are revealed one at a time. The algorithm must respond to each with a set cover that covers all items revealed so far. It obtains the cover incrementally from the previous cover by adding one or more sets and optionally removing existing sets. For each new set the algorithm incurs build cost equal to the weight of the items in the set. In the first problem the objective is to minimize total build cost plus total query cost , where the algorithm incurs a query cost at each time \(t\) equal to the current cover size. In the second problem, the objective is to minimize the build cost while keeping the query cost from exceeding \(k\) (a given parameter) at any time. We give deterministic online algorithms for both variants, with competitive ratios of \(\Theta(\log^{*}n)\) and \(k\) , respectively. The latter ratio is optimal for the second variant.

Equilibrium vertical structure with a common supplier

AbstractWe consider a vertically related market, in which each downstream firm produces a differentiated product by assembling a key input produced by a common supplier and another input produced by a dedicated upstream firm. On the one hand, vertical integration has the advantage of inducing the common supplier to set a lower input price, but the disadvantage of reducing downstream firms' competitiveness in the downstream market. On the other hand, vertical separation has the advantage of increasing downstream firms' competitiveness in the downstream market but the disadvantage of inducing the common supplier to set a higher input price. Contrary to results of previous studies, we find that the existence of a common supplier can lead to vertical integration under Cournot competition, which emerges as a unique equilibrium when a common supplier adopts input discrimination. Although vertical integration is better for the individual firms, it reduces the total welfare. Even when the common supplier uses uniform input pricing, vertical integration also emerges in equilibrium.

Repmono: a lightweight self-supervised monocular depth estimation architecture for high-speed inference

Self-supervised monocular depth estimation has always attracted attention because it does not require ground truth data. Designing a lightweight architecture capable of fast inference is crucial for deployment on mobile devices. The current network effectively integrates Convolutional Neural Networks (CNN) with Transformers, achieving significant improvements in accuracy. However, this advantage comes at the cost of an increase in model size and a significant reduction in inference speed. In this study, we propose a network named Repmono, which includes LCKT module with a large convolutional kernel and RepTM module based on the structural reparameterisation technique. With the combination of these two modules, our network achieves both local and global feature extraction with a smaller number of parameters and significantly enhances inference speed. Our network, with 2.31MB parameters, shows significant accuracy improvements over Monodepth2 in experiments on the KITTI dataset. With uniform input dimensions, our network’s inference speed is 53.7% faster than R-MSFM6, 60.1% faster than Monodepth2, and 81.1% faster than MonoVIT-small. Our code is available at https://github.com/txc320382/Repmono.

Gesture Recognition of Filipino Sign Language Using Convolutional and Long Short-Term Memory Deep Neural Networks

In response to the recent formalization of Filipino Sign Language (FSL) and the lack of comprehensive studies, this paper introduces a real-time FSL gesture recognition system. Unlike existing systems, which are often limited to static signs and asynchronous recognition, it offers dynamic gesture capturing and recognition of 10 common expressions and five transactional inquiries. To this end, the system sequentially employs cropping, contrast adjustment, grayscale conversion, resizing, and normalization of input image streams. These steps serve to extract the region of interest, reduce the computational load, ensure uniform input size, and maintain consistent pixel value distribution. Subsequently, a Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) model was employed to recognize nuances of real-time FSL gestures. The results demonstrate the superiority of the proposed technique over existing FSL recognition systems, achieving an impressive average accuracy, recall, and precision rate of 98%, marking an 11.3% improvement in accuracy. Furthermore, this article also explores lightweight conversion methods, including post-quantization and quantization-aware training, to facilitate the deployment of the model on resource-constrained platforms. The lightweight models show a significant reduction in model size and memory utilization with respect to the base model when executed in a Raspberry Pi minicomputer. Lastly, the lightweight model trained with the quantization-aware technique (99%) outperforms the post-quantization approach (97%), showing a notable 2% improvement in accuracy.

Mutual Information Upper Bounds for Uniform Inputs Through the Deletion Channel

Mutual Information Upper Bounds for Uniform Inputs Through the Deletion Channel

An Experimental Study of a Grooved Aluminum-Ammonia Heat Pipe: The Effect of Tilting and Non-uniform Heat Transfer on Thermal Behavior

Much research has been done on aluminum/Ammonia heat pipes in recent years due to their wide applications in aerospace. Although lab-scale research regularly focuses on uniform heat input, heat generation is actually non-uniform in reality. Also, the circumferential temperature distribution and heat transfer coefficient (HTC) have not been reported yet. In this study, an axial grooved heat pipe, while the condenser state is kept constant, was experimentally tested to investigate the uniform and non-uniform heat modes, and the effect of tilted angle along with horizontal configurations were also examined in terms of circumferential temperature distribution and HTCs. Yet, a detailed explanation of the measured results is provided. For the same total heat load, it was found that the decreasing heat mode showed superior performance; the HTC at the evaporator is 72 % and 35 % higher than the increasing mode (the least performance heat mode) at 77 and 165 W, respectively. Upon inclination, the HTC in the evaporator is relatively independent of inclination. However, a sharp difference occurs in the condenser section where the HTC of the tilted configuration is more than twice higher than that of the horizontal configuration.

Analytical Model of Natural Circulation with a Sinusoidal Heat Input in a PWR

This research presents the development of a one-dimensional analytical model to investigate the impact of pressure variations in the primary loop on natural circulation (NC). The model takes into account a sinusoidal input heat distribution and derives equations for the parameters of NC. The model covers a broad spectrum of NC patterns, spanning from fully single-phase to fully two-phase flow. The research demonstrates a smooth and continuous transition between various kinds of NC. Moreover, the research demonstrates that NC is capable of efficiently dissipating the decay heat generated inside the core of a pressurized water reactor, encompassing a range from 100% to 60% of the total inventory present within the primary loop. The findings of this study are compared to prior research outcomes and demonstrate a reasonable level of consistency. Additionally, comparisons are made with uniform input power distribution to demonstrate that there are no significant differences in the NC parameters when using sinusoidal heat input.

Effective FE simulation methods for fillet welding process by continuous distributed heat input and element combination

This study proposed effective finite element methods for simulating the fillet welding process on T-shaped joints. The T-shaped fillet welded joints were fabricated, and the process was simulated by a Continuous Distributed heat input method (CD method) and combining shell and solid elements. This study compared the models by the Discontinuous Uniform heat input method (DU method) and the CD method. The results showed that the CD method not only simplified the modeling process but also reduced the calculation time compared to the DU method. Furthermore, combining solid and shell elements reduced the number of nodal points in the models. The calculation time of the models by the element combination was significantly reduced. By comparing with experiments, it was shown that the CD heat input method and the combination of solid and shell elements could save calculation time while keeping the analysis accuracy.

Light-Triggered Inflation of Microdroplets.

Driven systems composed largely of droplets and fuel make up a significant portion of microbiological function. At the micrometer scale, fully synthetic systems that perform an array of tasks within a uniform bulk are much more rare. In this work, we introduce an innovative design for solid-in-oil composite microdroplets. These microdroplets are engineered to nucleate an internal phase, undergo inflation, and eventually burst, all powered by a steady and uniform energy input. We show that by altering the background input, volumetric change and burst time can be tuned. When the inflated droplets release the inner contents, colloidal particles are shown to transiently attract to the release point. Lastly, we show that the system has the ability to perform multiple inflation-burst cycles. We anticipate that our conceptual design of internally powered microdroplets will catalyze further research into autonomous systems capable of intricate communication as well as inspire the development of advanced, responsive materials.

Multi-source precipitation estimation using machine learning: Clarification and benchmarking

Multi-source precipitation estimation techniques, including interpolation, bias correction, and merging, are essential for acquiring high-precision precipitation datasets. Machine learning (ML) methods have achieved inspiring success in this field, but current studies utilizing ML often blur the boundaries of interpolation, bias correction, and merging because of the utilization of uniform input–output frameworks. The ambiguity in classification (interpolation, bias correction and merging) results in inconsistent and inappropriate comparative benchmarking among studies. A widespread example is the tendency to select easy-to-beat benchmarks, which can magnify the relative performance of ML methods. We propose to reclarify ML-based precipitation estimation by conducting a series of experiments with designed feature combinations to unveil the essence of ML-based methods. Our findings suggest that with different feature combinations, there are no essential distinctions between models for merging, bias correction and interpolation. Ground measurements, as the reference in supervised learning, play a more vital role than different features in precipitation estimation. This means that the ML-based multi-source merging or bias correction should be treated as gauge data-based interpolation with various auxiliary predictors. Moreover, the improvements of ML estimates are more significant compared to low-standard benchmarks like individual precipitation datasets and simple merging methods, but less preeminent compared to high-standard benchmarks such as a sophisticated interpolation method (e.g., Geographically Weighted Regression). Therefore, we call for positioning such ML applications as interpolation and adhering to high-standard benchmarks whenever possible, which can illustrate the true potential and promote further advancements of ML in geospatial estimation.

Modelling and numerical analysis for rotatory friction welding of U75V steel rails

This article presents a rotatory friction welding (RFW) method for U75V steel rail, aiming to mitigate challenges related to property discrepancies between the as-welded joints and the rail base metal (BM) and to narrow the heat-affected zone (HAZ) in conventional flash-butt welding (FBW) joints. A rotational intermediate plate is designed for rails with non-axisymmetric cross-sections, necessitating stationary during RFW. Advantages include achieving a relatively uniform welding heat input and maintaining the peak temperature of the contact interface near A1. To implement these concepts, a 2D finite element (FE) model for the RFW process of U75V rail steel rods was established and validated through experiments with identical process parameters. Microstructure predictions derived from continuous cooling transformation diagram confirm that ferrite microstructure is formed near A1 through rail steel RFW. Subsequently, a 3D FE model for intermediate plate RFW steel rails is developed to explore appropriate process parameter combinations. A suitable process parameters combination was identified, ensuring the peak temperature of the majority model contact interface does not exceed A1, resulting in a 76.7% reduction in HAZ (from ∼50 to 11.66 mm), and axial shortening of 8.10 mm, a significant decrease compared to the usual burn-off (30–40 mm) during FBW. These findings underscore the efficacy of this innovative welding solution and emphasize the significance of simulation technology in process optimization.

Theoretical efficiency limit of diffractive input couplers in augmented reality waveguides.

Considerable efforts have been devoted to augmented reality (AR) displays to enable the immersive user experience in the wearable glasses form factor. Transparent waveguide combiners offer a compact solution to guide light from the microdisplay to the front of eyes while maintaining the see-through optical path to view the real world simultaneously. To deliver a realistic virtual image with low power consumption, the waveguide combiners need to have high efficiency and good image quality. One important limiting factor for the efficiency of diffractive waveguide combiners is the out-coupling problem in the input couplers, where the guided light interacts with the input gratings again and get partially out-coupled. In this study, we introduce a theoretical model to deterministically find the upper bound of the input efficiency of a uniform input grating, constrained only by Lorentz reciprocity and energy conservation. Our model considers the polarization management at the input coupler and can work for arbitrary input polarization state ensemble. Our model also provides the corresponding characteristics of the input coupler, such as the grating diffraction efficiencies and the Jones matrix of the polarization management components, to achieve the optimal input efficiency. Equipped with this theoretical model, we investigate how the upper bound of input efficiency varies with geometric parameters including the waveguide thickness, the projector pupil size, and the projector pupil relief distance. Our study shines light on the fundamental efficiency limit of input couplers in diffractive waveguide combiners and highlights the benefits of polarization control in improving the input efficiency.

Implications of variable synaptic weights for rate and temporal coding of cerebellar outputs.

Purkinje cell (PC) synapses onto cerebellar nuclei (CbN) neurons allow signals from the cerebellar cortex to influence the rest of the brain. PCs are inhibitory neurons that spontaneously fire at high rates, and many PC inputs are thought to converge onto each CbN neuron to suppress its firing. It has been proposed that PCs convey information using a rate code, a synchrony and timing code, or both. The influence of PCs on CbN neuron firing was primarily examined for the combined effects of many PC inputs with comparable strengths, and the influence of individual PC inputs has not been extensively studied. Here, we find that single PC to CbN synapses are highly variable in size, and using dynamic clamp and modeling we reveal that this has important implications for PC-CbN transmission. Individual PC inputs regulate both the rate and timing of CbN firing. Large PC inputs strongly influence CbN firing rates and transiently eliminate CbN firing for several milliseconds. Remarkably, the refractory period of PCs leads to a brief elevation of CbN firing prior to suppression. Thus, individual PC-CbN synapses are suited to concurrently convey rate codes and generate precisely timed responses in CbN neurons. Either synchronous firing or synchronous pauses of PCs promote CbN neuron firing on rapid time scales for nonuniform inputs, but less effectively than for uniform inputs. This is a secondary consequence of variable input sizes elevating the baseline firing rates of CbN neurons by increasing the variability of the inhibitory conductance. These findings may generalize to other brain regions with highly variable inhibitory synapse sizes.

Uniform asymptotic and input to state stability by indefinite Lyapunov functions

In this work, we study uniform, uniform asymptotic, and input-to-state stability conditions for nonlinear time-varying systems. We introduce an easily verifiable condition for uniform attractivity by utilizing an indefinite sign upper bound for the derivative of the Lyapunov function. With this bounding structure, we propose novel conditions that enable us to test uniform stability, uniform asymptotic stability, and ISS, easily. As a result, the constraints on the coefficients of the bound that identify uniformity for stability and attractivity, and many of the available conditions have been relaxed. The results are also used for the perturbation problem of uniformly stable and uniformly asymptotically stable linear time-varying systems. Consequently, we demonstrate that uniform asymptotic stability of nonlinear time-varying systems can be robust for perturbations, but with special time-varying coefficients.

Mathematical Modeling Approach to the Optimization of Biomass Storage Park Management

This paper addresses the critical issue of managing biomass parks, a key component in the shift towards sustainable energy sources. The research problem centers on optimizing the management of these parks to enhance production and economic viability. Our aim was to bridge the gap in current research by developing and applying mathematical models tailored for biomass park management. The study commenced by constructing a basic model based on assumptions such as uniform biomass and steady input rates. Progressing from this initial model, we explored sophisticated control strategies, including Pontryagin’s maximum principle and dynamic programming, and employed numerical methods to tackle the nonlinearities and complexities inherent in biomass management. Our approach’s scope extended to predicting and managing biomass flow, highlighting each method’s distinct advantages. The simple model laid the groundwork for understanding, while optimal control techniques revealed the system’s intricate dynamics. The numerical methods provided practical solutions to complex equations. We found that while each method is beneficial on its own, their combined use can significantly improve decision-making in biomass park management. This research emphasizes the importance of aligning the chosen method with specific operational challenges and desired outcomes for optimal efficacy, offering both theoretical insights and practical applications in the field of renewable energy management.

Output feedback consensus for input-delayed nonlinear multi-agent systems via event-triggered communication

This paper designs two periodic event-triggered control protocols to achieve the output feedback leaderless consensus for nonlinear multi-agent systems with uniform input delays, and leader-follower consensus with nonuniform input delays. The involved system permits uncertainties in input delays and inherent nonlinearities, which brings the challenge on the design of observers. To overcome this challenge, a compensator is constructed independent of nonlinearities and input delays for every agent. To guarantee the event-triggered communication among different agents, two new periodic triggering mechanisms are designed based on the discrete output information. Moreover, these triggering mechanisms naturally avoid constantly monitoring and the Zeno behavior since triggering conditions are only checked on sampling points. Specifically, the designed event-triggered output feedback control protocols achieve convergence not boundedness of consensus errors. Finally, two examples are listed to demonstrate the validity of the two event-triggered strategies.

The influence of the shape of the end faces on the efficiency of a cycloidal rotor

The paper presents the results of an experimental and numerical study of the aerodynamic and thrust-energy parameters of a cycloidal rotor depending on the geometric features of the end faces. It has been shown experimentally and by numerical methods that the use of end disks with sharp edges leads to the formation of a separated flow and a decrease in the effective surface area of the wing. Various design options for end fairings are considered. The expediency of using inward-rounded fairings forming a uniform input flow to the entire plane of the rotor blades is shown. The use of fairings leads to an increase in aerodynamic efficiency of up to 8%. Changing the rotor design by placing blade control mechanisms inside a profiled end faces leads to an increase in efficiency of more than 12%.