Search Results (2,140)

This paper addresses the team orienteering problem (TOP) with vehicles equipped with electric batteries under dynamic travel conditions influenced by weather and traffic, which impact travel times between nodes and hence might have a critical effect on the battery capacity to cover the planned route. The study incorporates a novel approach for solving the dynamic TOP, comparing two solution methodologies: a merging heuristic and a reinforcement learning (RL) algorithm. The heuristic combines routes using calculated savings and a biased-randomized strategy, while the RL model leverages a transformer-based encoder–decoder architecture to sequentially construct solutions. We perform computational experiments on 50 problem instances, each subjected to 200 dynamic conditions, for a total of 10,000 problems solved. The results demonstrate that while the deterministic heuristic provides an upper bound for rewards, the RL model consistently yields robust solutions with lower variability under dynamic conditions. However, the dynamic heuristic, with a 20 s time limit for solving each instance, outperformed the RL model by $3.35\%$ on average. The study highlights the trade-offs between solution quality, computational resources, and time when dealing with dynamic environments in the TOP. Full article

IoT sensors in oilfields gather real-time data sequences from oil wells. Accurate trend predictions of these data are crucial for production optimization and failure forecasting. However, oil well time series data exhibit strong nonlinearity, requiring not only precise trend prediction but also the estimation of uncertainty intervals. This paper first proposed a data denoising method based on Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) to reduce the noise present in oil well time series data. Subsequently, an SDMI loss function was introduced, combining the respective advantages of Soft Dynamic Time Warping and Mean Squared Error (MSE). The loss function additionally accepts the upper and lower bounds of the uncertainty prediction interval as input and is optimized with the prediction sequence. By predicting the data of the next 48 data points, the prediction results using the SDMI loss function and the existing three common loss functions are compared on multiple data sets. The prediction results before and after data denoising are compared and the results of predicting the uncertainty interval are shown. The experimental results demonstrate that the average coverage rate of the predicted uncertainty intervals across data from seven wells is 81.4%, and the prediction results accurately reflect the trends in real data. Full article

Wavelet analysis algorithms in combination with thresholding procedures are widely used in nonparametric regression problems when estimating a signal function from noisy data. The advantages of these methods lie in their computational efficiency and the ability to adapt to the local features of the estimated function. It is usually assumed that the signal function belongs to some special class. For example, it can be piecewise continuous or piecewise differentiable and have a compact support. These assumptions, as a rule, allow the signal function to be economically represented on some specially selected basis in such a way that the useful signal is concentrated in a relatively small number of large absolute value expansion coefficients. Then, thresholding is performed to remove the noise coefficients. Typically, the noise distribution is assumed to be additive and Gaussian. This model is well studied in the literature, and various types of thresholding and parameter selection strategies adapted for specific applications have been proposed. The risk analysis of thresholding methods is an important practical task, since it makes it possible to assess the quality of both the methods themselves and the equipment used for processing. Most of the studies in this area investigate the asymptotic order of the theoretical risk. In practical situations, the theoretical risk cannot be calculated because it depends explicitly on the unobserved, noise-free signal. However, a statistical risk estimate constructed on the basis of the observed data can also be used to assess the quality of noise reduction methods. In this paper, a model of a signal contaminated with additive Gaussian noise is considered, and the general formulation of the thresholding problem with threshold functions belonging to a special class is discussed. Lower bounds are obtained for the threshold values that minimize the unbiased risk estimate. Conditions are also given under which this risk estimate is asymptotically normal and strongly consistent. The results of these studies can provide the basis for further research in the field of constructing confidence intervals and obtaining estimates of the convergence rate, which, in turn, will make it possible to obtain specific values of errors in signal processing for a wide range of thresholding methods. Full article

The existing strawberry ripeness detection algorithm has the problems of a low precision and a high missing rate in real complex scenes. Therefore, we propose a novel model based on a hybrid attention mechanism. Firstly, a partial convolution-based compact inverted block is developed, which significantly enhances the feature extraction capability of the model. Secondly, an efficient partial hybrid attention mechanism is established, which realizes the remote dependence and accurate localization of strawberry fruit. Meanwhile, a multi-scale progressive feature pyramid network is constructed, and the fine-grained features of strawberry targets of different sizes are accurately extracted. Finally, a Focaler-shape-IoU loss function is proposed to effectively solve the problem of the difficulty imbalance between strawberry samples and the influence of the shape and size of the bounding box on the regression. The experimental results show that the model’s precision and mAP0.5 reach 92.1% and 92.7%, respectively, which are 2.0% and 1.7% higher than the baseline model. Additionally, our model is better in detection performance than most models with fewer parameters and lower FLOPs. In summary, the model can accurately identify the maturity of strawberry fruit under complex farmland environments and provide certain technical guidance for automated strawberry-picking robots. Full article

The status (or transmission) of a vertex in a connected graph is the sum of distances between the vertex and all other vertices. The minimum status (or minimum transmission) of a connected graph is the minimum of the statuses of all vertices in the graph. Previously, sharp lower and upper bounds have been obtained on the minimum status of connected graphs with a fixed maximum degree k and order n. Moreover, for $2\le k\le {\displaystyle \frac{n}{2}}$, the following theorem about graphs attaining the maximum on the minimum status has also been proposed without proof. The theorem is as follows: Let G be a connected graph of order n with $▵(G)=k$, where $2\le k\le {\displaystyle \frac{n}{2}}$. Then, the minimum status of G attains the maximum if and only if one of the following holds. (1) G is a path or a cycle, where $k=2$; (2) $G_{k,n}$ is a spanning subgraph of G and G is a spanning subgraph of $H_{k,n}$, where $3\le k<{\displaystyle \frac{n}{2}}$; and (3) either $G_{{\displaystyle \frac{n}{2}},n}$ is a spanning subgraph of G and G is a spanning subgraph of $H_{{\displaystyle \frac{n}{2}},n}$ or $G_{{\displaystyle \frac{n}{2}},n}$ is a spanning subgraph of G and G is a spanning subgraph of $H_n$, where $k={\displaystyle \frac{n}{2}}$ for even $n\ge 6$. For the integers $n,k$ with $2\le k\le n-1$, the graph $G_{k,n}$ has the vertex set $V(G_{k,n})=\{x_1,x_2,\cdots ,x_n\}$ and the edge set $E(G_{k,n})=\{x_i{x}_{i+1}:i=1,2,\cdots ,n-k\}\cup \{x_{n-k+1}{x}_j:j=n-k+2,n-k+3,\cdots ,n\}$; the graph $H_{k,n}$ is obtained from $G_{k,n}$ by adding all the edges $x_i{x}_j$, where $n-k+2\le i<j\le n$; and for even $n\ge 6$ the graph $H_n$ is obtained from $G_{{\displaystyle \frac{n}{2}},n}$ by adding the edge $x_{{\displaystyle \frac{n}{2}}-1}{x}_{{\displaystyle \frac{n}{2}}+2}$ and all the edges $x_i{x}_j$, where ${\displaystyle \frac{n}{2}}+3\le i<j\le n$. This study provides the proof to complete the above theorem. Full article

It is of great interest to utilize saline fields to promote rice production in China. It has still not been established how salinity stress affects grain-filling characteristics and the relationships with yield formation of rice in a saline field. This experiment was conducted with Ningjing 7 (salinity-tolerant rice variety) and Wuyunjing 30 (salinity-susceptible rice variety) in a non-saline field and a high-saline field in 2021 and 2022. The grain yields of Ningjing 7 and Wuyunjing 30 in a high-saline field were 37.7% and 49.8% lower (p < 0.05) than in a non-saline field across two years. Ningjing 7 exhibited a higher (p < 0.05) grain yield than Wuyunjing 30 in a high-saline field. The reductions in filled-grain percentage and grain weight in inferior grains were greater than in superior grains of Ningjing 7 and Wuyunjing 30. For Ningjing 7 and Wuyunjing 30, the total starch contents in superior and inferior grains at 15, 30, and 45 days after heading were reduced (p < 0.05) in a high-saline field compared to a non-saline field. The ADP–glucose pyrophosphorylase, granule-bound starch synthase, and starch synthase activities after heading in superior and inferior grains in a high-saline field were lower (p < 0.05) than those in a non-saline field, and the reductions were more pronounced for Wuyunjing 30. The maximum grain-filling rate and mean grain-filling rate were decreased, while the time to achieve the maximum grain-filling rate was increased in a high-saline field compared to a non-saline field, especially for Wuyunjing 30. The mean grain-filling rate and grain-filling amount in superior and inferior grains during the early, middle, and late stages were lower in a high-saline field than in a non-saline field. For Ningjing 7 and Wuyunjing 30, the reductions in the grain-filling amount in the inferior grains during the early, middle, and late stages in a high-saline field were greater than those in superior grains. Our results suggest that salinity stress inhibited the grain-filling rate, reduced the total starch content and affected key enzyme activities, which led to the poor sink-filling efficiency and yield performance of rice in a saline field, especially for the salinity-susceptible variety. Full article

This paper reports the development of a highly crosslinked hyper-branched polyglycerol (HPG) polymer bound to elastin-like proteins (ELPs) to create a membrane that undergoes a distinct closed-to-open permeation transition at 32 °C. The crosslinked HPG forms a robust, mesoporous structure (150–300 nm pores), suitable for selective filtration. The membranes were characterized by FTIR, UV–visible spectroscopy, SEM, and AFM, revealing their structural and morphological properties. Incorporating a synthetic polypeptide introduced thermo-responsive behavior, with the membrane transitioning from impermeable to permeable above the lower critical solution temperature (LCST) of 32 °C. Permeation studies using crystal violet (CV) demonstrated selective transport, where CV permeated only above 32 °C, while water permeated at all temperatures. This hybrid HPG-ELP membrane system, acting as a molecular switch, offers potential for applications in drug delivery, bioseparations, and smart filtration systems, where permeability can be controlled by temperature. Full article

The degree-constrained Steiner problem in graphs is well known in the literature. In an undirected graph, positive integer degree bounds are associated with nodes and positive costs with the edges. The goal is to find the minimum cost tree spanning a given node set while respecting the degree bounds. As it is known, finding a tree satisfying the constraints is not always possible. The problem differs when the nodes can participate multiple times in the coverage and the constraints represent a limited degree (a capacity) for each occurrence of the nodes. The optimum corresponds to a graph-related structure, i.e., to a hierarchy. Finding the solution to this particular Steiner problem is NP-hard. We investigate the conditions of its existence and its exact computation. The gain of the hierarchies is demonstrated by solving ILPs to compute hierarchies and trees. The advantages of the spanning hierarchies are conclusive: (1) spanning hierarchies can be found in some cases where spanning trees matching the degree constraints do not exist; (2) the cost of the hierarchy can be lower even if the Steiner tree satisfying the constraints exists. Full article

The recently introduced model in S. R. Kharel et al.’s study [Degree-preserving network growth. Nature Physics 2022, 18, 100–106] uses matchings to insert new vertices of prescribed degrees into the current graph of an ever-growing graph sequence. The process depends both on the size of the largest available matching, which is the focus of this paper, as well as on the actual choice of the matching. Here, we first show that the question of whether a graphic degree sequence, extended with a new degree $2\delta$, remains graphic is equivalent to the existence of a realization of the original degree sequence with a matching of size $\delta$. Secondly, we present lower bounds for the size of the maximum matchings in any realization of the degree sequence. We then study the bounds on the size of maximal matchings in some realizations of the sequence, known as the potential matching number. We also estimate the minimum size of both maximal and maximum matchings, as determined by the degree sequence, independently of graphical realizations. Along this line we answer a question raised by T. Biedl et al.: Tight bounds on maximal and maximum matchings. Discrete Mathematics 2004, 285, 7–15. Full article

A little-known thermomechanical relation between entropy and action, originally discovered by Boltzmann in the classical domain, was later reconsidered by de Broglie in relation to the wave–particle duality in the free propagation of single particles. In this paper, we present a version adapted to the phenomenological description of the hadronization process. The substantial difference with respect to the original de Broglie scheme is represented by the universality of the temperature at which the process occurs; this, in fact, coincides with the Hagedorn temperature. The main results are as follows: (1) a clear connection between the universality of the temperature and the existence of a confinement radius of the color forces; (2) a lower bound on the hadronic mass, represented by the universal temperature, in agreement with experimental data; and (3) a scale invariance, which allows the reproduction of the well-known hadronic mass spectrum solution of the statistical bootstrap model. The approach therefore presents a heuristic interest connected to the study of the strong interaction. Full article

This paper presents three approaches to estimating the battery parameters of the electrical equivalent circuit model (ECM) based on electrochemical impedance spectroscopy (EIS); these approaches are referred to as (a) least squares (LS), (b) exhaustive search (ES), and (c) nonlinear least squares (NLS). The ES approach is assisted by the LS method for the rough determination of the lower and upper bound of the ECM parameters, and the NLS approach is incorporated with the Monte Carlo run such that different initial guesses can be assigned to improve the goodness of EIS fitting. The proposed approaches are validated using both simulated and real EIS data. Compared to the LS approach, the ES and NLS approaches show better fitting accuracy at various noise levels, whereas in both the validation using simulated EIS data and actual EIS data collected from LG 18650 and Molicel 21700 batteries, the NLS approach shows better fitting accuracy than that of LS and ES approaches. In all cases, compared with the ES approach, the computational time of the NLS approach is significantly faster, and compared with the LS approach, the NLS approach shows a minimal difference in computational time and considerably better fitting performance. Full article

The limited surface area and structural constraints of small underwater communication devices necessitate a dense placement of transmitting and receiving array elements in optical multiple-input multiple-output (MIMO) systems. The compact layout leads to the formation of sub-channels that exhibit notable spatial correlation and a tendency toward homogeneity. Although sub-channel spatial homogeneity (SSH) may diminish the communication capacity of MIMO systems, it provides a significant advantage by reducing the pilot overhead. In this study, we exploit the inherent SSH and the natural time-domain sparsity of channel impulse response (CIR) in the underwater optical densely arrayed MIMO (UODA-MIMO) system to propose an innovative SSH-based channel estimation (SSH-CE) method. We model the underwater optical CIR at Gbaud rates and integrate it with SSH characteristics. This approach transforms the reconstruction targets of compressive sensing (CS) from conventional CIR samples to prior CIR model parameters and the fitting residuals of the homogeneous sub-channels, reducing the pilot overhead. The simulation results of photon tracing for UODA-MIMO sub-channels in turbid harbor water indicate a monotonic, exponential decay in CIR at Gbaud rates, with transmission delays exceeding 5 nanoseconds for distances over 8 m. Moreover, the correlation coefficients among sub-channels reach a minimum of 0.975, confirming the presence of SSH in UODA-MIMO systems. In comparison to existing CS methods that rely on known sparsity, sparsity adaptation, and the structural sparsity of MIMO channels, the SSH-CE method achieves a lower degree of sparsity in reconstruction targets and a reduced lower bound for pilot requirements under the SPARK criterion. Specifically, the SSH-CE method achieves a reduction in the pilot overhead for reconstructing $N_t$ sub-channels of $\mathcal{K}$-sparse to 2$N_t$ irrespective of CIR residual compensation. Full article

The Doppler effect caused by the rapid movement of high-speed rail services has a great impact on the accuracy of train positioning and speed measurement. Existing train positioning algorithms require a large number of trackside equipment and sensors, resulting in high construction and maintenance costs. Aiming to solve the above two problems, this article proposes a train positioning algorithm based on orthogonal time–frequency space (OTFS) modulation and integrated sensing and communication (ISAC). Firstly, based on the OTFS, the positioning and speed measurement architecture of communication awareness integration is constructed. Secondly, a two-stage estimation (TSE) algorithm is proposed to estimate the delay Doppler parameters of HST. In the first stage, a low-complexity coarse grid search is used, and in the second stage, a refined off-grid search is used to obtain the delay Doppler parameters. Then, the time difference of arrival/frequency difference of arrival (TDOA/FDOA) algorithm based on multiple base stations is used to locate the target, the weighted least square method is used to calculate the location, and the Cramér–Rao lower bound (CRLB) for positioning and speed measurement is derived. The simulation results demonstrate that, compared to GNSS/INS and OFDM radars, the algorithm exhibits enhanced positioning and speed measurement accuracy. Full article

This research studies the potential of frozen low lunar orbits to be used in the design of constellations for global and regional communication or navigation. We introduce a robust two-stage approach to the frozen low lunar orbit design based on the successive application of non-gradient techniques, the Bayesian optimization and the Nelder–Mead method. The developed methodology has a number of advantages over existing numerical design techniques and allows revealing orbits with the periodic behavior of the eccentricity vector over long propagation intervals in the full dynamical model. By leveraging a convenient nomogram with constellation visibility parameters and lower bound coverage curves, we have identified most suitable low-altitude orbital configurations of Walker type and then adjust them to be frozen. The frozenness condition can be achieved without changing the orientation of orbital planes. Visibility and coverage metrics (multiplicity of continuous coverage for specified sites, polar regions, or the whole lunar surface; position dilution of precision) of candidate constellations are analyzed. Several promising designs of frozen constellations in near-circular low lunar orbits are singled out. The frozen orbit stability and the station-keeping cost are discussed. Full article

In a real-world implementation, machine learning models frequently experience concept drift when forecasting the electricity load. This is due to seasonal changes influencing the scale, mean, and median values found in the input data, changing their distribution. Several methods have been proposed to solve this, such as implementing automated model retraining, feature engineering, and ensemble learning. The biggest drawback, however, is that they are too complex for simple implementation in existing projects. Since the drifted data follow the same pattern as the training dataset in terms of having different scale, mean, and median values, radian scaling was proposed as a new way to scale without relying on these values. It works by converting the difference between the two sequential values into a radian for the model to compute, removing the bounding, and allowing the model to forecast beyond the training dataset scale. In the experiment, not only does the constrained gated recurrent unit model with radian scaling have shorter average training epochs, but it also lowers the average root mean square error from 158.63 to 43.375, outperforming the best existing normalization method by 72.657%. Full article